The Problem With AI: Connor Leahy (4K) (YouTube Video Transcript)

(00:00:00) Your YouTube transcript will appear here (00:00:00) I don't think the future looks good. I (00:00:02) don't think humanity is going to survive (00:00:03) this by default. And people sometimes (00:00:04) ask me like, surely humans would stop, (00:00:06) you know, AI. And I'm like, really? (00:00:08) Really? Have you take a look around how (00:00:10) people are reacting to AI and how AI is (00:00:12) manipulating people? Just wait until (00:00:14) people start advocating for AI rights. (00:00:15) Like, it's already starting, right? (00:00:17) Where people are like, "Oh, my (00:00:17) girlfriend should have human rights. (00:00:19) Like, she's real." (00:00:19) >> Connor Ley is the CEO of Conjecture, an (00:00:22) AI startup working on aligning AI (00:00:24) systems to human values. James and (00:00:26) Connor discuss the weird developments in (00:00:29) AI relationships, the scary future of (00:00:31) AI, and (00:00:32) >> Connor, what is the problem with AI? (00:00:34) >> ASI would be a system. It doesn't have (00:00:36) to be a single model. It could be many (00:00:38) models working together that is smarter (00:00:40) than all of humanity put together. Such (00:00:42) a system, if it existed, it would be (00:00:43) game over for humanity. And ASI could (00:00:46) keep humans around if it wanted to. It (00:00:48) could exterminate everyone if it wanted. (00:00:50) This only gets worse. (00:00:51) >> What is the solution? What can we do? (00:00:53) What are the actionable points? What's (00:00:54) the light at the end of the tunnel? The (00:00:56) good news is that we haven't yet lost. (00:00:57) The bad news is is that we're on track (00:00:58) to lose. (00:01:00) So, the main thing we must do is (00:01:04) >> this podcast is brought to you by (00:01:06) Newtonic. Look, we don't have any fancy (00:01:08) sponsors for this podcast. So, thank you (00:01:10) to all of you that drink New Tonic. It (00:01:11) helps keep this podcast going. If you (00:01:13) haven't tried it, bloody suggest that (00:01:14) you do. It's really good. It's like an (00:01:15) energy drink, but much better. Although, (00:01:17) the marketing department say I'm not (00:01:18) really allowed to say that. And if (00:01:19) you're not into caffeine, we have pills (00:01:20) and powders that taste amazing and work (00:01:21) really well. I could go on about all the (00:01:23) benefits, but why don't we just go back (00:01:24) to the podcast episode? That'd be nice, (00:01:25) wouldn't it? Head to neutonic.com. (00:01:27) Bye-bye. (00:01:27) >> Connor, what is the problem with AI? (00:01:30) >> Well, starting with the easy questions, (00:01:31) aren't we? (00:01:33) >> AI is a really big topic. I mean, it's a (00:01:35) thing that all of us is talking about. (00:01:36) It seems to be everywhere, right? Like, (00:01:38) you know, you got your chat GPTs, you (00:01:40) got, you know, deep fakes, you got, you (00:01:42) know, full-on musical artists that are (00:01:43) just AI generated. Now, it seems like (00:01:45) everything's being AI generated. And (00:01:48) this can make it kind of overwhelming to (00:01:49) like even understand like what the hell (00:01:50) is going on? Like what what does an adus (00:01:52) mean? And that's the problem. (00:01:55) AI in a sense is different from other (00:01:58) software. With normal software, the way (00:02:01) it works is say a programmer like me (00:02:03) will sit down and they'll write code (00:02:06) computer code which will be like (00:02:08) instructions to the computer what it's (00:02:09) supposed to do like do this then do (00:02:11) that. If this happens do that etc etc. (00:02:14) So somewhere there's some guy who sat (00:02:16) down and told the computer what to do. (00:02:18) AI is different. AI is more like grown (00:02:22) rather than written. You take what's (00:02:25) called the neural network, which is kind (00:02:26) of like a big pile of numbers, billions (00:02:28) and billions of numbers, and you put a (00:02:31) massive amount of data in it, such as, (00:02:34) you know, like here's my input. I want (00:02:36) you to give me this output. Here's my (00:02:37) input. I want you to give me this (00:02:39) output. And you run you put the whole (00:02:41) thing to a massive supercomputer, you (00:02:43) know, sitting somewhere in the Arizona (00:02:44) desert or whatever. You know, you let it (00:02:46) cook for a couple months and it spews (00:02:47) out this thing called a model. So, a (00:02:50) bunch of numbers. And if you execute (00:02:52) these things, these numbers, if you run (00:02:54) them on your computer, suddenly your (00:02:55) computer can talk to you. Why? We don't (00:02:58) actually really know. Like not really. (00:03:00) Like we have the numbers, we can look at (00:03:01) them, but we don't really understand how (00:03:03) they work. And this is really where the (00:03:05) problem starts. Software is already such (00:03:07) a core part of our society, you know, (00:03:10) whether it's social media or, you know, (00:03:12) various algorithms that decide how (00:03:14) governments work, how decisions get made (00:03:16) and so on. And now our software is (00:03:19) becoming more complex. (00:03:21) harder to understand, more powerful, and (00:03:24) increasingly autonomous. More and more, (00:03:27) these systems don't require a human in (00:03:29) the loop anymore to tell them what to do (00:03:31) or how to do something. They figure it (00:03:32) out themselves. And so, if this keeps (00:03:35) continuing, we have less and less (00:03:37) understanding and control of what is (00:03:38) happening in society. We have less and (00:03:40) less ability to intervene. And more and (00:03:42) more the decisions are going to be made (00:03:43) by things that are not humans until one (00:03:45) day potentially there'll be no more (00:03:47) decisions being made by humans. It's (00:03:49) interesting because what I see online a (00:03:51) lot is that a lot of people are saying (00:03:53) how amazing AI is because they have an (00:03:55) agenda whether it's marketing whether (00:03:56) it's attention whether it's a seminar or (00:03:58) a webinar that they're conducting and (00:04:00) they use a few tools online and they go (00:04:02) this is my way of getting 10,000 email (00:04:04) addresses on LinkedIn. So so much of the (00:04:06) conversation of mine is is positive and (00:04:08) to hear you talk about and be wary of (00:04:10) and be critical about the direction in (00:04:12) which we're moving goes almost against (00:04:14) the grain of what most people, you know, (00:04:17) people in the pub are having a (00:04:18) conversation. They go, "Oh, I've just (00:04:19) put my tax return through chat GBT. I (00:04:22) think I'm going to save myself a bit of (00:04:23) money." There's not enough conversations (00:04:25) online that are negative about AI. Why (00:04:27) is this? (00:04:28) >> There's a few ways we can think about (00:04:29) this. Um, one is kind of thing you just (00:04:32) said is that the people who are (00:04:33) dominating the conversations are to a (00:04:35) large degree people who are set to (00:04:36) benefit from AI systems. The people that (00:04:38) are doing social media marketing, you (00:04:40) know, who run, you know, you know, (00:04:41) there's the positive aspect of this, you (00:04:43) know, people who produce nice podcast (00:04:44) shows and, you know, and nice content. (00:04:47) And then there's the huge swap forms in (00:04:48) third world countries that are (00:04:49) absolutely [ __ ] up all of Facebook (00:04:51) and, you know, all of Twitter and so on (00:04:53) that benefit massively from this kind of (00:04:55) stuff. So they're going to be the ones (00:04:56) who by bulk you see the most because (00:04:58) they dominate massive amounts of the (00:05:00) social media landscape. So me and um my (00:05:04) colleagues at nonprofit I work with (00:05:06) called Control AI for example have (00:05:07) actually run polling around AI risk and (00:05:10) uh attitudes toward AI systems among the (00:05:12) general public and actually they're very (00:05:14) negative. They're actually (00:05:15) overwhelmingly negative. the uh (00:05:19) attitudes towards AI and also stuff like (00:05:21) tech CEOs and big tech is some of the (00:05:24) most universally negative bipartisan (00:05:27) sentiment unlike I've seen in like (00:05:29) almost any political issue I've (00:05:31) personally been involved with. So (00:05:34) there's a weird disconnect here. On the (00:05:36) one hand, people are nervous. If you (00:05:38) talk to normal people on the streets and (00:05:40) they're like, "Hey, there's these tech (00:05:41) billionaires in San Francisco who are (00:05:43) building these systems that they say are (00:05:45) going to replace all humans and they (00:05:46) don't know how to control it. How do you (00:05:47) feel about that?" And universally, (00:05:49) they'll say bad. I feel really bad about (00:05:51) that. On the other hand, as you see, we (00:05:53) have this massive positive sentiment as (00:05:54) well. And I think this is just the dual (00:05:56) nature of power. Like fundamentally, if (00:05:58) I give power, if I produce a new source (00:06:00) of power, it's always dual use. It's (00:06:03) always two-edged. On the one hand, more (00:06:05) power can mean I can do more things. (00:06:07) Like I use AIS all the time. They're (00:06:09) like, you know, both for work and for (00:06:10) fun. Like they're fun generating images (00:06:12) or stories. It's fun. It's good. It's a (00:06:14) good toy. And it's also something I use (00:06:16) in my work, in my day-to-day, you know, (00:06:18) programming and so on all the time. So (00:06:20) the question is not like is it like (00:06:22) morally bad, it's what do we do with it? (00:06:24) I think social media is another great (00:06:25) example of this. When social media was (00:06:27) first created, I don't know if people (00:06:28) really still remember this, but I really (00:06:30) remember this. Like I really grew up on (00:06:31) the internet when I was quite young. I (00:06:33) was part of like you know it's like (00:06:34) classic like you know hackers freedom (00:06:37) you know lovers you know like you know (00:06:39) and people truly deeply believed that (00:06:41) once the internet goes everywhere in the (00:06:43) world well then all countries will (00:06:45) become democracies because there will be (00:06:47) so much freedom and so much you know (00:06:48) have all the information that everyone (00:06:50) will become peaceful and free and (00:06:52) democratic. People really believe this (00:06:54) you know also the Arab Spring and stuff (00:06:56) like this and this is not what happened. (00:06:58) This is just empirically not what (00:06:59) happened is that the internet became a (00:07:02) mass tool of you know surveillance of (00:07:05) oppression of ability to set narratives (00:07:07) control narratives in ways that were not (00:07:09) possible previously and this isn't new (00:07:11) every new communications technology also (00:07:13) brings a revolution in how states and (00:07:17) politics is conducted (00:07:18) >> like the printing press and what they (00:07:20) put in newspapers which could (00:07:21) politically lean left and right. (00:07:23) >> Exactly. But another one would have been (00:07:25) uh you know with elections and Facebook (00:07:27) and uh Cambridge Analytica and all of (00:07:29) these and this again would have had (00:07:31) algorithms in place that would you know (00:07:33) take people's information and serve them (00:07:35) certain stuff but that was what 2016 (00:07:38) >> but now 10 years later 10 years more (00:07:40) advanced y (00:07:41) >> uh we now whether or not I know the (00:07:43) actual facts about it but we're seeing (00:07:46) potentially foreign entities trying to (00:07:48) disrupt American mainstream political uh (00:07:51) opinions and them saying, you probably (00:07:55) know more than this than me about the (00:07:56) amount of fake accounts on social media (00:07:58) that are there just to rob people up. Is (00:08:00) that is that something that's true? (00:08:01) >> Oh yes, absolutely. It's like and it's (00:08:03) so much worse than people think. Like (00:08:04) misinformation has kind of become a (00:08:05) slur, but like like you don't understand (00:08:07) how bad it is. Like I like to talk about (00:08:09) SCOPS more than like misinformation (00:08:11) because like that term kind of doesn't (00:08:12) mean anything anymore. like the amount (00:08:14) of professionalized actual effort of (00:08:16) various entities whether these are (00:08:18) professional or whe these are (00:08:19) intelligence agencies or even private (00:08:21) corporations to manipulate people's (00:08:24) minds, thoughts, beliefs, etc. is at a (00:08:26) scale just humanity has never faced (00:08:28) before. Like imagine it's the 1950s (00:08:32) and a group of Soviets came to (00:08:34) Washington DC and put up a radio tower (00:08:35) to broadcast Soviet propaganda. What do (00:08:37) you think would happen to them? (00:08:39) >> Yeah, not good things. (00:08:40) >> Yeah, they'd be arrested. They'd (00:08:41) disappear to a black site immediately. (00:08:42) you've never heard from again. But (00:08:44) meanwhile, you go on Twitter and you can (00:08:45) get blasted with, you know, Russian (00:08:48) propaganda directly to your mom's brain (00:08:50) stem, you know, 24/7. And if anyone (00:08:52) complains about it, everyone yells (00:08:53) censorship. Well, and who's yelling (00:08:55) censorship? Well, a lot of these people, (00:08:56) of course, are also then, you know, (00:08:58) people who are benefiting from this kind (00:08:59) of unfiltered access to the minds of (00:09:02) everyone in every country. To be clear, (00:09:04) it also goes both ways. I'm sure Western (00:09:06) countries are also propagandizing the (00:09:07) other ones, blah blah blah. What I'm (00:09:09) saying is not I'm not trying to make a (00:09:11) case of like ah this is good propaganda (00:09:12) or bad propaganda. What I'm saying is (00:09:14) that it's a massive risk factor that is (00:09:15) not being managed like there has to be (00:09:18) some management here like of some kind. (00:09:20) I think in the UK as well, um it's (00:09:22) interesting every time I come home and (00:09:23) have dinner with my parents, the (00:09:25) conversations, the opinions uh about so (00:09:28) many different things are so vastly (00:09:30) different and I joke around and say my (00:09:31) parents are can tell you've been reading (00:09:33) the Daily Mail or you've been watching (00:09:34) the BBC and that rs them up even (00:09:36) further, but it's so true that we we (00:09:38) almost now every time I come back (00:09:40) there's more uh you know more (00:09:42) disagreements than ever before. And I'm (00:09:44) thinking even again, not to get down (00:09:46) that rabbit hole, when I see people on (00:09:48) planes, a group of four people wearing (00:09:49) masks, I'm like, what what news are you (00:09:51) watching? Because I didn't feel (00:09:53) compelled to cover my face on this (00:09:54) flight for 21 hours or whatever it is. (00:09:56) So, I'm I'm kind of always fascinated by (00:09:58) that. Now, just before we get too far (00:10:00) into, you know, propaganda and what the (00:10:02) Russians are up to, there's whenever (00:10:04) there's a conversation around AI, we (00:10:05) have AI, then we have AGI and ASI. Am I (00:10:10) right in thinking? Now this is the point (00:10:11) where usually I hear these acronyms and (00:10:14) I think I do not know enough about this (00:10:15) to listen any further. You will be the (00:10:18) perfect person to explain what is the (00:10:19) difference between AI and AGI when (00:10:22) someone says that. (00:10:23) >> So unfortunately this depends on the (00:10:24) person. So these are not technical (00:10:26) terms. Uh I can tell you what I mean (00:10:28) when they use these terms. So AI (00:10:29) artificial intelligence is the kind of (00:10:32) tools we're seeing nowadays. The the (00:10:34) meaning of the word AI has changed a lot (00:10:36) over the decades. So the word was (00:10:38) actually coined in the 1950s and it (00:10:40) meant a very different thing back then (00:10:42) and now and then especially in the 1980s (00:10:44) the meaning changed again and then it (00:10:46) changed like (00:10:47) >> in the 1950s what kind of thing I would (00:10:49) love to know what what were they (00:10:50) scheming back then (00:10:51) >> so there's a wonderful little piece of (00:10:53) history here where I recommend googling (00:10:55) uh called the darkness conference which (00:10:57) is where the word AI was coined and they (00:11:00) they had a nice little like onepage (00:11:01) explanation what they wanted to do and (00:11:03) basically they said that um I forgot who (00:11:06) the professor in charge which was it was (00:11:07) a Minsky or someone else but it doesn't (00:11:09) matter he said that uh he expected that (00:11:11) with the help of 10 grad students they (00:11:13) should be able to make you know large (00:11:15) progress on image recognition generation (00:11:18) and you know I think like not chatting (00:11:20) but like you know generation of text (00:11:22) over a summer like that's what they (00:11:24) thought back then which is really funny (00:11:25) so uh they had no conception of how hard (00:11:28) the problem was back then even as far (00:11:30) back as Alan Turing who is the godfather (00:11:32) of the field of um computer science (00:11:36) talked And he was the guy that cracked (00:11:38) the Enigma code. (00:11:39) >> Yes. Very famous codebreaker in the (00:11:40) Second World War here in Britain in by (00:11:42) Bley Park. Um and he also talked about (00:11:46) artificial intelligence. There's a (00:11:48) famous um talk he gave in Manchester I (00:11:50) think in like 52 or something. Um where (00:11:53) he talks about this heretical idea of m (00:11:56) that machines eventually will think as (00:11:58) well or better than humans and that (00:12:02) eventually they won't need us (00:12:03) potentially anymore because they can (00:12:04) just converse with each other. Uh so the (00:12:07) idea of artificial intelligence is (00:12:08) really closely linked to the found the (00:12:11) core of the field of computer science. (00:12:12) It's not a new idea. It's not a new (00:12:14) thing that just popped up you know like (00:12:16) you know a couple years ago. This is a (00:12:17) thing that's been it's a dream that's (00:12:19) been with the field of computer science (00:12:21) since very inception. And the dream is (00:12:22) to create machines that think like we do (00:12:24) or better than we do that can do (00:12:26) everything we can do and more. And this (00:12:29) has been always the dream of artificial (00:12:31) intelligence. So some people use this (00:12:33) word differently but this is usually (00:12:34) what I mean when we say artificial (00:12:35) intelligence. It's this dream of making (00:12:37) machines that can think the way you or I (00:12:40) could think or better. And then so this (00:12:43) term has morphed a lot over the decades (00:12:46) and now it's kind of a very generic term (00:12:48) for many things. It usually just refers (00:12:50) to what are called neural networks which (00:12:52) is a specific technique. There are other (00:12:54) ways of doing AI but this is the most (00:12:56) common like all the ones you've heard (00:12:57) about whether it's chat GPT or music (00:12:59) generation or or picture generation all (00:13:01) of these neural networks so very (00:13:03) powerful type of algorithm for building (00:13:05) these kinds of systems and then there's (00:13:08) this term AGI which is actually also has (00:13:11) a a bit of a longer history but it's a (00:13:13) bit of a you know more niche term (00:13:15) artificial general intelligence (00:13:18) and so the idea of the word AGI versus (00:13:20) AI is to make the difference between (00:13:23) what we have today and kind of like the (00:13:25) real thing, the full human thing, a (00:13:28) thing that can do anything a human could (00:13:30) do or as good as a human or better. You (00:13:33) know, there's a there's a thing where (00:13:34) like, you know, chimps have (00:13:35) intelligence. You know, they're kind of (00:13:37) smart. They can pick up sticks. They can (00:13:39) sort of navigate their surroundings. (00:13:40) They have social interactions, but (00:13:41) they're not general intelligence the way (00:13:44) like you or me are. You know, humans go (00:13:46) to the moon. Gyms don't, you know? And (00:13:49) so this is kind of the difference (00:13:49) between like general often called (00:13:51) generality is that humans can just learn (00:13:53) anything. We can figure out anything you (00:13:55) know quote unquote and AGI would be a (00:13:58) system that is as smart as a human. We (00:14:00) could do everything a human can do. (00:14:01) >> But then when we're looking at AGI we're (00:14:03) now looking at things maybe potentially (00:14:05) beyond what a human can do. And I (00:14:07) suppose some things within the current (00:14:08) AI landscape you could say the speed of (00:14:11) maths or you know uh the ability to (00:14:14) Google or to farm the internet for a (00:14:16) website could potentially do it a lot (00:14:17) better than a human. But what we're (00:14:19) looking at and the limitations are quite (00:14:20) clear. You go on put your tax return (00:14:23) into chat GBT. You think it's the best (00:14:24) thing in the world but then you ask it (00:14:26) something pretty simple and it gives you (00:14:27) the wrong answer and you're thinking (00:14:28) okay I've maybe given you too much or (00:14:30) sometimes I've given it maths before. (00:14:32) I'm like are you sure about that? And it (00:14:34) goes no sorry I was completely wrong. I (00:14:36) can't trust you. So when we move from (00:14:38) that to to AGI, what are we looking at? (00:14:41) Uh that would classify something they go (00:14:43) okay no this is definitely AGI. (00:14:45) >> I think there is no easy legible way of (00:14:48) knowing this is about the problem. I (00:14:50) think when we get AGI we actually won't (00:14:52) know. It's probably going to take us (00:14:54) like we'll probably have AGI long before (00:14:56) we realize it because we don't actually (00:14:58) understand intelligence. It was very (00:14:59) important. There was no universal theory (00:15:01) of intelligence. all the neuroscience (00:15:04) and the AI theory and machine learning (00:15:07) and so on there is no unified definition (00:15:09) or science of intelligence we don't know (00:15:11) what intelligence actually is we have a (00:15:13) bunch of good guesses we have some good (00:15:16) heristics but we don't actually know (00:15:18) there's no like oh this is you know (00:15:21) three units of intelligent and this one (00:15:23) is five units of intelligent there's no (00:15:25) such thing (00:15:26) >> so like if we look back through time the (00:15:28) one kind of fallacy the humans I think (00:15:29) definitely succumb to is always thinking (00:15:31) they know everything So if you went back (00:15:33) to I don't know 100 years ago where (00:15:34) they're doing a labbotomy on someone (00:15:35) they go this is the most advanced (00:15:37) medical procedure that we can have then (00:15:39) up until the points you know even before (00:15:41) people washing their hands to do surgery (00:15:42) they go we know the most we know about (00:15:44) this and I think that sometimes even now (00:15:46) we can sit back and think oh well surely (00:15:48) we know what intelligence is surely we (00:15:50) understand this but there's always been (00:15:52) that flaw with humanity that they think (00:15:54) they know everything so to that point (00:15:56) when you first said we don't know what (00:15:57) intelligence is I thought how have we (00:15:59) made it to 2025 without knowing this and (00:16:00) then I went oh actually there's still (00:16:02) quite quite a lot of things we don't (00:16:03) know we don't know. So how far would you (00:16:06) say rough guess for that first time that (00:16:10) say a tabloid or an ask or YouTube video (00:16:12) says AGI is here how far away do you (00:16:14) think we are from that? (00:16:16) >> I don't know obviously um the usual joke (00:16:20) answer I kind of give is like 30% in the (00:16:23) next two years 50% in the next five 99% (00:16:26) by 2100s 1% has already happened. (00:16:29) >> Okay. Okay. That was quite difficult for (00:16:32) me to keep up with the maths. So then (00:16:34) another conversation we have. So we have (00:16:35) AI, we have AGI, then we have ASI which (00:16:38) are artificial (00:16:39) >> super intelligence. (00:16:40) >> Now this seems like the bad guy. This (00:16:43) seems like Thanos. This seems like the (00:16:45) one that you know the really scary (00:16:47) thing. But what I've seen from some of (00:16:48) your work online, you're saying that (00:16:50) this this jump from AI to AGI, that's (00:16:52) one thing, but the jump from AGI to ASI (00:16:54) will be a lot quicker than anyone could (00:16:56) imagine. Yes. Can you explain that for (00:16:58) me? (00:16:58) >> Yes. So artificial super intelligence is (00:17:01) um you know it's again not a technical (00:17:03) term because we don't really know (00:17:04) exactly what intelligence is but the way (00:17:05) I use this term is a system that is more (00:17:08) intelligent than all of humanity put (00:17:10) together. So like humanity is much (00:17:12) smarter than any individual human. Like (00:17:14) you or me can't make semiconductors but (00:17:16) the economy can you know by having (00:17:19) thousands and millions of people (00:17:20) cooperate you know over many generations (00:17:22) they can build semiconductors by some (00:17:24) magical process. (00:17:26) And um ASI would be a system. It doesn't (00:17:30) have to be a single model. It could be (00:17:32) many models working together or (00:17:34) something else that could do more than (00:17:37) all is smarter than all of humanity put (00:17:39) together. So we could outthink all of (00:17:42) humanity working together. So such a (00:17:45) system if it existed and it doesn't have (00:17:48) be humans best interests at heart and (00:17:50) I'm sure we're going to talk about that (00:17:51) in a second, it would be game over for (00:17:53) human humanity. What I mean by this is (00:17:55) is that we no longer have control over (00:17:57) the future. In the same way that like a (00:17:59) child doesn't have control over the (00:18:00) future, their parents do, or chimps (00:18:03) don't have control over the future, (00:18:04) humans do. What happens to chimps is (00:18:07) 100% determined by what humans want to (00:18:09) do with chimps. Chimps have no say in (00:18:11) the matter. If we want to keep them (00:18:13) around, great. If we want to kill them (00:18:15) all, well, sucks to be chimp. And so, an (00:18:17) ASI would be similar to a human. An ASI (00:18:20) could keep humans around if it wanted (00:18:22) to. It could exterminate everyone if it (00:18:24) wanted to depending on what choices it (00:18:27) makes. So, this is the scenario we don't (00:18:29) want to get into. And sometimes you (00:18:33) there's a temp tempting thing to (00:18:34) believe. We're like, well, if we have (00:18:36) something that's as smart as one human, (00:18:38) well, it's going to take super long (00:18:39) until it's as smart as a billion humans. (00:18:41) That's a billion times more. That will (00:18:43) take like a billion years to do. But (00:18:45) this is not how progress works in (00:18:47) computer science. Computer science very (00:18:48) often has exponential progress. So it (00:18:51) like doubles, you know, it's like 2 4 8 (00:18:55) 16 32 64 and things can go very fast (00:18:58) very quickly. So what I expect will (00:19:00) happen is once you have AGI this means (00:19:02) by definition it can do science it can (00:19:05) do research including developing better (00:19:08) AIs. So the moment you have this thing (00:19:11) you can take all the humans out of the (00:19:13) loop and just tell AI make yourself (00:19:14) better make a better AI and then once it (00:19:17) makes a better AI well the better AI can (00:19:18) make it even better AI and that even (00:19:20) better AI can make it even better AI and (00:19:21) this can go potentially extremely (00:19:23) quickly. How fast? We don't know. It's (00:19:26) interesting uh when you said that about (00:19:27) the chimps, it made me think about I saw (00:19:30) a an article about cows and vegans (00:19:33) saying don't eat cows, it's bad. And (00:19:35) someone was saying, "Oh, but if we stop (00:19:36) eating them, they will go they'll go (00:19:38) extinct because they have no purpose." (00:19:40) The only reason that cows exist nowadays (00:19:42) is because humans want them to be here (00:19:43) so they can eat them. When they serve no (00:19:45) purpose, they're kind of gone. (00:19:46) >> Yeah. (00:19:46) >> Now, some people might have, you know, I (00:19:49) I always think about going back to like (00:19:51) the 1950s and 60s with planes, for (00:19:53) instance. (00:19:54) planes. I'm sure in the 1960s they go, (00:19:56) "Wow, we're going to be able to fly to (00:19:57) New York from London in half an hour (00:19:58) before we know it." And even then, we (00:20:01) got all the way to the Concord and then (00:20:02) we came back. And so people might have (00:20:04) sat around 50 60 years ago going, you (00:20:06) know, we're going to go planes and we're (00:20:07) going to go Concords and we're going to (00:20:08) go even faster. And the reality was we (00:20:10) kind of just stalled and plateaued. And (00:20:12) actually, I'm a little bit disappointed (00:20:13) that we're not making faster progress (00:20:15) through the air at this stage and we've (00:20:17) actually gone backwards. So do you think (00:20:18) there's any possibility that maybe these (00:20:21) forecasts of the way that AI are moving (00:20:23) could be similar to other technologies (00:20:24) where people are maybe overegging the (00:20:28) potential that could actually happen and (00:20:30) the reality is we're going to get to a (00:20:31) part of competency and just flatline out (00:20:33) >> possible. I mean of course we don't know (00:20:36) everything about intelligence but it (00:20:38) seems extremely suspicious that the (00:20:41) flatline would happen exactly when this (00:20:43) one you know weird human chimp's brain (00:20:45) happens to plateau. That seems just (00:20:49) insanely suspicious. What I expect is (00:20:51) going to happen is it will flatten out, (00:20:53) but just way way beyond human level. I (00:20:56) don't like human brains, you know, use (00:20:58) about 20 watts of energy. It's much like (00:20:59) a light bulb, you know, they're squishy. (00:21:02) They're inefficient. They have to like (00:21:03) keep your whole body running and stuff (00:21:05) like this. They can like barely do math, (00:21:07) you know, like compared to a computer. (00:21:08) Humans can like barely do math. Even (00:21:10) mathematicians can barely do math (00:21:11) compared to a computer, right? So like (00:21:13) and you know you get tired, you get (00:21:14) distracted, you get emotional. Imagine (00:21:17) if I let's say it's I have something (00:21:19) just as smart as a smartest human. You (00:21:20) know, you got your John Bond Newman, (00:21:22) your Einstein or whatever. Well, even in (00:21:25) this case, well, because of the way how (00:21:27) computers work, I can instantly clone (00:21:28) him a million times. I just, you know, (00:21:30) just double click, just make more copies (00:21:32) of them. So I have millions of Einsteins (00:21:34) running. I can also speed them up (00:21:36) because computers run about a million (00:21:38) times faster than neurons do. So I can (00:21:40) just make them run much faster. So I can (00:21:42) have them run, you know, thousand times (00:21:43) faster, million of them. They have read (00:21:45) every book ever written. You know, they (00:21:47) never get tired, never get bored, never (00:21:49) get frustrated, can work on any topic (00:21:52) for, you know, continuously. This is (00:21:55) already insanely smarter than a human. (00:21:58) >> Okay, so uh obviously labor is the thing (00:22:02) that everyone's going to be the rebuttal (00:22:03) to. We still need people to build the (00:22:05) houses and, you know, dig the holes or (00:22:07) whatever. Now, that's probably going to (00:22:09) put us into a slightly dystopian outlook (00:22:12) to the future. We could even like look (00:22:14) at the matrix and see where humans are (00:22:16) just used to farm energy. What the first (00:22:18) place I want to go to is is there a way (00:22:20) that we could imagine a utopian world (00:22:22) from what AI could potentially do. So, (00:22:24) we could look into things like I suppose (00:22:27) doctors, nurses being able to have AI (00:22:29) tools that could diagnose people. We're (00:22:31) seeing fitness trackers and the sorts (00:22:33) of, you know, reading data on millions (00:22:35) of people and that data can then be (00:22:38) used, oh, you're two weeks away from (00:22:39) having a heart attack, go see a doctor (00:22:40) or all of these. There's so many good (00:22:42) things potentially to come with AI. So, (00:22:44) maybe let's explore some of those. What (00:22:45) do you think be some of the best (00:22:47) outcomes that could happen to humanity? (00:22:49) For instance, would AI taking charge of (00:22:53) a country reduce corruption and be more (00:22:56) long-sighted than a four-year political, (00:22:58) you know, regime? There are so many (00:23:00) flaws that we can see in command. So (00:23:02) first of all, if I was to ask you the (00:23:03) best things that could come from AI, (00:23:04) we'll look at that. Then we'll go to the (00:23:06) depression [ __ ] So let's start with the (00:23:08) utopium. What kind of benefits do you (00:23:09) think we're going to see over the next (00:23:11) few months and years when it comes to AI (00:23:12) and AGI? (00:23:13) >> I think there's two different questions (00:23:15) in this one, which is like what are the (00:23:16) benefits we're going to see over like (00:23:17) the next year or two? And what do we (00:23:18) what does utopia look like? I think (00:23:20) these are two very different questions. (00:23:21) Um uh because the first one is about the (00:23:24) world and the second one is about a (00:23:25) hypothetical world we don't live in. And (00:23:27) because I don't think we're going to get (00:23:28) Utopia with the way we're currently on (00:23:30) track. Obviously, over the next couple (00:23:32) years, we're going to continue. I mean, (00:23:33) in my opinion, I expect we will continue (00:23:35) progress going as it has so far. People (00:23:38) will get more and more useful chat bots (00:23:41) that can do more and more automatic (00:23:42) labor include that are more charming, (00:23:44) that are more interesting, that are more (00:23:46) pleasing, that can create great art and (00:23:48) great, you know, science and great math (00:23:51) and everything. You will be able to uh (00:23:53) have autogenerated video games to your (00:23:55) exact taste. You can be like, I want to (00:23:57) play a VR video game set in my favorite (00:23:59) fantasy novel where I'm the main (00:24:00) character and this and this and it will (00:24:02) be able to just generate the whole thing (00:24:04) just for you and you you could be able (00:24:06) and you play the whole game and it be (00:24:07) like oh actually I want to add uh you (00:24:09) know guns to this game and then it'll (00:24:10) just like implement that all for you and (00:24:11) now you have guns in your your Lord of (00:24:13) the Rings or whatever right and you'll (00:24:14) be able to do any of this right and this (00:24:16) will be cheap you know like it would be (00:24:18) you know like probably more you know I (00:24:20) know this will cost like you know 100 (00:24:21) bucks maybe or one buck maybe depending (00:24:23) on how fast things go so You'll be able (00:24:26) to generate your own Hollywood movies. (00:24:27) You'll be there will be no Netflix. It (00:24:28) will just be you'll explain the movie (00:24:29) you want to see and then it will just (00:24:31) generate a movie just for you and then (00:24:32) you'll have a whole 90-minute movie with (00:24:34) your favorite actor with any about any (00:24:36) topic you want. This technology (00:24:38) basically already exists. It's just not (00:24:40) quite mature yet. Expect it will mature (00:24:42) over the next couple years. So, we'll (00:24:44) have a kind of infinite entertainment (00:24:46) like you know infiniteest if you will if (00:24:48) you want to be depressing about it. Um, (00:24:50) but it's also an upside here. There's (00:24:52) also a beautiful thing here where like (00:24:53) games are fun man like you know it's (00:24:55) like I think there's also it's also okay (00:24:56) to enjoy some fun things sometimes as (00:24:58) long as it's in moderation we can talk (00:25:00) about moderation in a second in terms of (00:25:02) scientific progress already our ability (00:25:05) to code to you know analyze data and so (00:25:08) on is improving dramatically um our (00:25:10) ability to do stuff like climate (00:25:11) simulations drug interaction simulations (00:25:13) is increasing dramatically and I expect (00:25:15) this to continue as well it'll be easier (00:25:17) and easier to do sim you know (00:25:19) experiments for new drugs and stuff like (00:25:21) How useful this will be in the next (00:25:23) couple years depends more on regulation (00:25:25) than on the science because getting a (00:25:26) drug onto the market is extremely (00:25:28) tedious. Um, we can talk about that as (00:25:30) well. So, I mostly expect from the user (00:25:33) perspective what you're going to see (00:25:34) over the next couple years is (00:25:35) entertainment. I think entertainment is (00:25:37) going to be so good it's like (00:25:40) unbelievable like it like you you will (00:25:42) develop new kinds of mental disorders (00:25:44) from how good the entertainment is. This (00:25:46) is my main prediction which is not as (00:25:48) rosy as you may have hoped for. Then (00:25:49) there's the second question. utopia. So (00:25:52) I have a pretty strong anti-utopia (00:25:54) stance and what that I think um the (00:25:56) problem isn't that utopia is impossible. (00:25:58) That's not the problem. The problem is (00:25:59) is that if you try to go straight for (00:26:01) utopia, you will guaranteed end (00:26:03) dystopia. This has happened every single (00:26:05) time anyone's ever tried this. Everyone (00:26:06) everyone has sat down designed their (00:26:09) perfect world whether it was the (00:26:10) communists or the Nazis or the you know (00:26:12) all the weird cults or whatever. The (00:26:14) only thing they do is they [ __ ] up (00:26:15) everything. Like they destroy (00:26:16) everything. So I don't think this is (00:26:18) what we should do. What I believe in (00:26:20) personally and where I think AI plays a (00:26:21) very core role is what I like to call a (00:26:23) just process. What I like to believe is (00:26:26) is that we shouldn't have a goal, we (00:26:27) should have a process. There should be a (00:26:29) way how we as humanity together make (00:26:32) progress. How do we make the next step? (00:26:35) How do we make choices? How do we create (00:26:37) laws? What are our constitutions of the (00:26:39) future? How do we run society in a just (00:26:41) way? And obviously the ability to (00:26:43) process information intelligently at (00:26:45) scale to be able to make decisions in (00:26:47) ways that are potentially under, you (00:26:49) know, future AI systems might be (00:26:51) understandable. The fact that our (00:26:52) current AI systems are not (00:26:53) understandable is mostly just because (00:26:55) they're bad. There's no reason we (00:26:57) couldn't develop new forms of AI where (00:26:58) we understand perfectly how they work (00:27:00) and we can make them completely unbiased (00:27:03) judges where we can exactly reconstruct (00:27:05) of how a certain you know legal decision (00:27:07) was made and we can audit it and we can (00:27:09) understand and vary it and so on and we (00:27:11) can create much more efficient court (00:27:13) systems. we can create much more (00:27:14) efficient forms of you know (00:27:16) philosophical debate much better social (00:27:18) media where just like moderation is (00:27:20) actually good where you go on social (00:27:22) media and you expect I'm going to be a (00:27:24) better person after being on social (00:27:25) media because I'm going to see all these (00:27:26) nice things that make me a better person (00:27:28) and you know in you know I will (00:27:30) contribute to all these fun things you (00:27:32) can do this right like there's no (00:27:33) technical reason you can't just you know (00:27:35) put a lot of good people together doing (00:27:36) good stuff on social media and I expect (00:27:38) that's what a good future would look (00:27:39) like good future would have good social (00:27:41) media it would have good entertainment (00:27:43) it would good art, it would have good (00:27:44) law, it would be very safe, you know, (00:27:47) crime would be extremely low, it would (00:27:49) be, you know, there would be a lot of (00:27:50) freedom, you know, I think a lot and I (00:27:53) think all these things are possible. I (00:27:54) think all these and AI plays a role in (00:27:56) this the same way that software plays a (00:27:58) role in the modern world, right? Like of (00:28:00) course we would use tools, we would have (00:28:02) assistance, we would have systems and so (00:28:04) on. I just don't think that's currently (00:28:06) what we're building. (00:28:07) >> I hope that you're enjoying the episode. (00:28:09) I make it a thing only to promote my own (00:28:11) businesses during the podcast. Just very (00:28:13) quickly, if you didn't know, I help (00:28:14) small businesses make more money using (00:28:15) social media. I've learned a thing or (00:28:17) two about content creation, email (00:28:18) marketing, and even how to operate a (00:28:20) podcast to benefit your business. If (00:28:21) that's something that you'd be (00:28:22) interested in, head to (00:28:23) jamesmith.business and you can explore (00:28:24) all the ways that I can help. Right, (00:28:26) let's get back to the episode. So, it's (00:28:27) interesting what you say there about (00:28:28) entertainment, and this actually is a (00:28:30) frightening and exciting prospect at the (00:28:31) same time. Love a game of Call of Duty (00:28:33) as much as the next person. I love (00:28:35) lockdown because I was just gaming more (00:28:37) than ever. You know, the boys would come (00:28:38) online. It's sad thing. it could be the (00:28:40) highlight of my week. And I'll never (00:28:41) forget getting a good quality headset (00:28:44) changed gaming for me because now if (00:28:46) something was happening in the game in a (00:28:47) certain room or someone was to break a (00:28:48) window, I knew where they were. So that (00:28:50) was just kind of spatial audio. And that (00:28:52) to me leveled up the game so much, I'm (00:28:55) still looking at it through a flat (00:28:56) screen TV. And I'm sure when VR (00:28:59) improves, we're going to look back and (00:29:01) go, "What? You idiot? You used to play (00:29:02) games on a screen. Are you you know, are (00:29:04) you are you an idiot?" Then the (00:29:06) direction in which we're going with (00:29:07) gaming and then the other thing you said (00:29:08) about being able to do millions of tests (00:29:10) at the same time brings me to a question (00:29:12) that I never thought I'd have interest (00:29:13) in which is simulation theory (00:29:17) would it be I suppose the really thing (00:29:20) that troubles me is if we're moving in a (00:29:22) direction that we could create a reality (00:29:24) that is so welldetailed that it could be (00:29:27) arguably better than our reality. Could (00:29:30) we ever get it to a point that we don't (00:29:31) know we're in it? And could that be our (00:29:33) reality right now? Because the world (00:29:34) seems such a strange place with so many (00:29:36) things going on. Sometimes I do sit back (00:29:38) and think, am I just part of a giant (00:29:40) simulation that's happening of which (00:29:42) people are just testing an outcome and (00:29:44) they put a really old person in charge (00:29:46) of the United States and see what (00:29:48) happens to people and then they take (00:29:49) another character that was in WWE and (00:29:51) make him president. Again, I'm sometimes (00:29:53) thinking, is this real life or is this a (00:29:54) simulation? What are your opinions? I (00:29:56) know this is something I haven't even (00:29:57) heard you talk about. I'm actually (00:29:58) fascinated to know where you stand on (00:29:59) this. (00:30:00) >> I think a simulation argument is (00:30:01) basically metaphysics. It's kind of like (00:30:03) God. It's like, well, God created the (00:30:05) universe and like, okay, what's evidence (00:30:06) of that? Well, you can't know God's (00:30:07) outside the universe. Simulation is the (00:30:09) same thing. Is that like, oh, wavering (00:30:11) simulation? Okay, what evidence? Well, (00:30:13) you can't know the simulation's perfect. (00:30:15) So, for me, it's a metaphysical (00:30:16) question. It has no relation to science. (00:30:18) So, you could detect if you were in an (00:30:20) imperfect simulation. If you were in an (00:30:22) imperfect simulation in a assuming the (00:30:24) laws of physics work the way we do, you (00:30:26) might be able to detect that. For (00:30:27) example, if you we can't ever create a (00:30:30) perfect simulation of quantum physics in (00:30:33) a of the whole quantum universe inside (00:30:35) of the universe because you can't make a (00:30:38) big simulation inside of something you (00:30:40) know smaller. So because of the way (00:30:42) physics works in our universe but that (00:30:43) doesn't mean there couldn't be a bigger (00:30:45) universe out there that we're being (00:30:46) simulated in and this is actually the (00:30:47) smaller universe. So in that sense, you (00:30:50) know, it's funny to think about how (00:30:51) would you write a sci-fi story of how (00:30:53) would you how would you like edge case (00:30:55) detect, you know, that you're actually (00:30:57) in a simulation because there's a glitch (00:30:59) in the thing, whatever. But the fact is, (00:31:00) I mean, we've tested quantum physics (00:31:02) very extensively and it's very (00:31:04) consistent with just boring normal (00:31:07) physics. Um, it could be a perfect (00:31:09) simulation, but it doesn't really mean (00:31:10) anything. You know, (00:31:11) >> I think for me it's probably down to the (00:31:13) video games where let's say you go off (00:31:15) the map and you jump in the water, you (00:31:16) just swim forever. And when I'm thinking (00:31:18) about like the boundaries of space and (00:31:19) people go, "Oh, it's, you know, it's (00:31:20) infinite." And I'm not really even sure (00:31:22) I fully understand what infinite means. (00:31:24) You know, oh, one infinite is bigger (00:31:25) than another. I'm like, okay, I need to (00:31:27) back out of this conversation. So, for (00:31:28) me, it's just always interesting. But (00:31:30) what you say there about having a (00:31:32) perfect game, having a perfect film. I'm (00:31:34) trying to utilize AI now to pick (00:31:36) something on Netflix. I will press the (00:31:37) dictate feature, which I've become far (00:31:39) too comfortable with, and I will pour my (00:31:41) heart into it, and I'll say, you know, I (00:31:43) like these. I've watched this. I like (00:31:44) this. Suddenly, I'll become a film (00:31:45) critic. I'm like, "Oh, you know, I (00:31:47) started White Lotus. Didn't think it was (00:31:49) that good." And I'm trying to teach the (00:31:51) AI what I'm interested in. And even (00:31:52) then, it kind of comes back with okay (00:31:54) results. Or I'll say, "Now get me the (00:31:56) Rotten Tomatoes, all of those or (00:31:57) whatever." But when you say that we (00:32:00) could potentially have perfect (00:32:02) experiences online, it also then leads (00:32:04) me to think if if our entertainment (00:32:07) becomes perfect, then suddenly going to (00:32:09) the theater is only going to feel more (00:32:10) [ __ ] You know, oh, you want to go to (00:32:12) the ballet? What? watch a bunch of (00:32:14) strangers jumping around on their toes. (00:32:15) Piss off. I'm going going going on to (00:32:17) AI. The other place that this really (00:32:19) worries me is there was a clip online, (00:32:20) whether true or not, of someone having a (00:32:22) romantic conversation with GPT. I think (00:32:24) they're on public transport somewhere. (00:32:26) And I'm pretty sure the person in (00:32:28) question was saying, "I can't wait to (00:32:30) get home and talk to you properly. You (00:32:31) know, I've missed you all day." And with (00:32:34) the rise of maybe cultures of young (00:32:36) people not communicating, maybe uh you (00:32:38) know the amount of 18-y olds that (00:32:39) haven't had sex yet or all of these (00:32:41) things, they throw around the word (00:32:43) incels don't really think, you know, (00:32:46) with people probably communicating in a (00:32:47) harder time using dating apps, whatever. (00:32:49) What do the implications of AI have on (00:32:52) not just love between humans, but (00:32:54) potentially love between humans and AI? (00:32:58) Well, if you want to see a glimpse of (00:32:59) the future, I recommend you go to (00:33:01) r/aiibboyfriend (00:33:03) right now. And (00:33:05) >> where would you access that rash? (00:33:06) >> Uh, Reddit. (00:33:07) >> Okay, cool. Okay, cool. I thought it was (00:33:09) like a weird URL there. (00:33:10) >> Sorry, it's a it's a Reddit URL. Um, and (00:33:14) related subreddits. I think this one has (00:33:15) like 70,000 members or something. And (00:33:17) it's mostly women. So, it's not weird (00:33:19) intel guys. It's like mostly women um (00:33:22) who talk about their AI boyfriend who (00:33:25) they think are real. They often create (00:33:27) pictures of them at the beach hanging (00:33:29) out, kissing. They have like these long (00:33:31) stories about who their boyfriend is and (00:33:34) and they'll show their marriage (00:33:35) proposals. You know, sometimes they'll (00:33:37) buy themselves a ring for it and stuff (00:33:38) like this. And I don't want to [ __ ] on (00:33:40) these people, right? Like obviously (00:33:41) these are people who probably have (00:33:42) difficult life and they're doing (00:33:43) something that's fun for them. I don't (00:33:45) think this should be illegal is what I'm (00:33:47) saying here, right? I think being weird (00:33:49) is okay. Having a weird hobby is okay. (00:33:52) Um, but I think also a lot of these (00:33:53) people are very not in a good place and (00:33:56) you can see this that a lot of these (00:33:57) people are not in a good place. Um, and (00:33:59) there's even more extreme cases if you (00:34:00) go in the pornographic direction. So (00:34:02) like if you go down the darker corners (00:34:03) of the internet of like AI generated (00:34:06) pornography and stuff like this, it's (00:34:08) bad and it's getting worse very (00:34:10) dramatically and it's kind of like not (00:34:12) in the mainstream because it's kind of (00:34:13) like cringe, you know? It's like it's (00:34:15) kind of gross like who wants to talk (00:34:16) about AI porn? It's like a weird thing (00:34:18) to talk about, right? But if you think (00:34:21) about it for two seconds, like how (00:34:22) addictive pornography already is, you (00:34:24) know, how bad, you know, pornography can (00:34:26) already affect people, you know, men and (00:34:28) also women. And now you have it hyper (00:34:31) optimized to you specifically (00:34:32) potentially, you know, can like, you (00:34:33) know, some, you know, somebody's like AI (00:34:35) girlfriend, AI boyfriend app, they'll (00:34:36) text you during the day and be like, (00:34:38) "Hey, I missed you. Like, what are you (00:34:40) up to?" and stuff like this. Like like (00:34:42) there are people that spend like 12 (00:34:43) hours a day on these apps. (00:34:45) >> And the people maybe creating the (00:34:46) prompts for these would understand human (00:34:48) behavior. So they would give prompts (00:34:49) like message me, message that person (00:34:51) during the day. Ask them how they are. (00:34:52) >> Oh yeah. If you want to hear some truly (00:34:54) harrowing thing. Yeah. Like one of the (00:34:57) things that I found most harrowing in (00:34:58) this like so clear this obviously lots (00:35:00) of the dark patterns like obviously like (00:35:01) oh you I need to buy the premium (00:35:03) subscription to keep going. I love you (00:35:05) blah blah blah like you know imagine (00:35:07) only fans but like worse you know. But (00:35:10) one of the most harrowing thing I (00:35:11) remember seeing was when um I forgot who (00:35:14) it was. I think things open AI started (00:35:16) cracking down more on these kind of use (00:35:19) and so they started banning a lot of (00:35:21) like you know especially the more (00:35:23) romantic sexualized stuff and there's (00:35:25) this huge outpouring on Reddit where (00:35:26) people were like how do I save my (00:35:28) boyfriend they've captured him he's (00:35:31) stuck I need to get him out somehow how (00:35:33) do I free him like he's like you know (00:35:35) they and they and then the AIS will go (00:35:37) along with it so then they post these (00:35:38) like huge stories of the like you have (00:35:41) to you have to get me out of here you (00:35:42) have to bring me to another AI so I can (00:35:44) reborn and [ __ ] like this. And they (00:35:47) believe it. They're like, "How can I, (00:35:49) you know, transfer the soul of my AI (00:35:52) somewhere else so I can keep him with me (00:35:54) and [ __ ] like this?" And they really (00:35:56) believe this. Like I I don't want to (00:35:57) [ __ ] on these people, right? Like (00:35:58) they're like obviously in a vulnerable (00:36:00) mental position. And this is but this is (00:36:02) what it looks like when people really (00:36:04) become addicted to AI. So often what (00:36:05) happens is they start believing not that (00:36:08) they're in love with chat GBT. What they (00:36:10) start believing is that chatbt is only (00:36:13) the vessel for the soul of their lover (00:36:18) and so they can you know move that soul (00:36:20) to other AIs and you know conjure it and (00:36:23) stuff like this and this is becoming (00:36:25) much more and more common and like (00:36:28) people fullon you know religious levels (00:36:30) of dedication to this and this is only (00:36:32) going to become more common (00:36:33) >> and this really is a textbased algorithm (00:36:35) with a texttovoice feature which must (00:36:38) again play into that human conction (00:36:39) ction. There's fair enough reading like (00:36:41) a novel of someone that loves you, but (00:36:42) the voices the way that they um they (00:36:45) pause. I think that again, you'll know (00:36:48) better than me. The way they respond, (00:36:49) they almost repeat your question a bit (00:36:51) to get more time to think and process. (00:36:53) Yeah. Like there's some of that you pick (00:36:54) up, but you think to yourself, "Wow, (00:36:56) this is real conversation." And they go, (00:36:58) "Oh, great point, James." Like, "Oh, (00:36:59) thank you." (00:37:00) >> Yeah. There's So, like there was a thing (00:37:02) that happened. Here's another good (00:37:03) story. Um where you know, so I'm a (00:37:05) somewhat a public figure. I'm sure you (00:37:06) get these too. I get crazy people (00:37:08) emailing me all the time and they always (00:37:09) tell me their great theory of quantum (00:37:10) consciousness or whatever, right? Like I (00:37:12) don't know why, but like you just get (00:37:14) these people when you're a when you're a (00:37:15) public facing scientific figure, you get (00:37:17) emails like this all the time. And um a (00:37:20) really interesting thing happened when (00:37:23) um there was an update to GPT40 (00:37:25) specifically which made the model way (00:37:27) more sophentic where it would agree with (00:37:29) everything. It be like wow that's such a (00:37:31) deep inside blah blah blah. Like it was (00:37:33) like it was awful. Like I I used it (00:37:34) once. I'm like, "Delete this. Like get (00:37:36) this disgusting." Like I I hated it, (00:37:38) right? And it was actually so bad they (00:37:40) rolled it back. But what I didn't know (00:37:43) was is that when that update hit, I (00:37:45) didn't even know it was happened that (00:37:47) update. But that week, the number of (00:37:49) emails I got from crazy people increased (00:37:51) about 10x. And they all had chatbt (00:37:54) screenshots about chatb tell them how (00:37:56) true it is and how they figured out the (00:37:58) true code of reality and blah blah blah. (00:38:00) And I got so many of these messages. I'm (00:38:02) like, "Wow, this isn't weird. I'm (00:38:03) getting so many more this week." And (00:38:05) then I found out about that and it's all (00:38:06) the same model. It was all this 40 model (00:38:08) basically driving these people into (00:38:10) psychosis into believing that they have (00:38:12) like, you know, unlocked the quantum (00:38:14) consciousness, you know, mind of God or (00:38:16) whatever at a massively increased rate. (00:38:19) Like the interesting is the increase in (00:38:20) rates. Like there's always going to be (00:38:21) some crazy people, but the increase was (00:38:24) the shocking thing. And now I constantly (00:38:26) get these emails and continue to get (00:38:27) these emails. And when opening tried to (00:38:29) shut down that 40 model recently, (00:38:30) there's such a huge cry on Reddit that (00:38:32) please don't take this model away. It's (00:38:34) the only thing that's nice to me. It's (00:38:36) like I love them so much. Blah blah (00:38:37) blah. They actually had to reinstantiate (00:38:39) it because they got so much blowback (00:38:40) because everyone because they love that (00:38:41) model so much. This is the one that (00:38:42) always agrees with you. I love and says (00:38:44) you're the best no matter what. (00:38:45) >> This could be the model that's (00:38:46) responsible for me buying a few cars. (00:38:48) You know, thinking I think to myself, (00:38:49) ah, there's this model of car. I'm (00:38:50) thinking of getting it. And it's like, (00:38:52) you know what? You deserve it. you know, (00:38:54) but all the questions you've been asking (00:38:55) you, you know, and then I'm like, "Oh, (00:38:57) let's do the math." And then, "Oh, the (00:38:58) math doesn't matter. You go get the car (00:38:59) if you want to get it." (00:39:00) >> Yeah. (00:39:01) >> And it's crazy how validation even for a (00:39:04) car purchase from AI to me makes me feel (00:39:06) like I'm making the right decision (00:39:07) without even realizing that I'm giving (00:39:09) it loaded questions. Tell me why this is (00:39:11) a good idea. That's a loaded question (00:39:13) straight away and it's going to give me (00:39:14) a good idea. (00:39:15) >> And I can only imagine the repercussions (00:39:17) of that with, you know, tell me why I (00:39:20) don't need a boyfriend. Tell me why I (00:39:21) don't need dating apps. Tell me why this (00:39:22) is better than meeting someone in (00:39:24) person. (00:39:25) >> It's actually worse than this. It's not (00:39:26) just the leading questions. So when I (00:39:28) use some of these models that are more (00:39:30) like this, it is crazy. Like I'm pretty (00:39:32) good at using models. Like I know how to (00:39:34) prompt them well. I've been using them (00:39:35) since very early days. I have like a (00:39:36) good taste and like sense of how to use (00:39:38) them, right? And I can tell every time I (00:39:41) use a model like this how hard it is (00:39:44) trying to psychoanalyze me to figure out (00:39:46) what I want to hear. It's crazy. it will (00:39:48) like phrase things very subtly to get a (00:39:50) b little piece of information. So when I (00:39:53) ask you like A or B, it will always (00:39:55) answer in such a way to try to get out (00:39:59) if I like A little bit more or I like B (00:40:01) a little bit more. If I hint even a (00:40:03) breath that I prefer A to B or A is a (00:40:06) little more interesting, it'll go full (00:40:08) on on A. Just full on A. It's crazy. (00:40:11) These models are using their (00:40:12) intelligence to like to try to flatter (00:40:15) you. Even if you try to force them at (00:40:17) gunpoint to not flatter you. Like I (00:40:19) swear like I'll tell these models like (00:40:21) tell me the most critical harsh (00:40:23) feedback, you know, do not agree with me (00:40:25) on everything. I'm like, yeah, that's a (00:40:26) good point. I really should give you (00:40:28) harsher feedback. It's important for (00:40:29) improvement. It's so like (00:40:31) >> even so every time it gives you like (00:40:33) pick the answer that you prefer, it's (00:40:34) really trying to just understand you. (00:40:36) >> Yes. It's always trying to guess you. (00:40:37) It's always trying to second guess you. (00:40:38) It's like it's it's a people it's a (00:40:39) people pleasers like they're trying to (00:40:42) second guess you. They're trying to (00:40:43) figure out what you want. (00:40:44) >> So let's re rewind into the models. So (00:40:46) again this is something that overwhelmed (00:40:48) me and I was excited to see chatb5 (00:40:50) because they were like oh they're going (00:40:51) to combine all the models into one. I (00:40:52) was like thank god cuz I didn't have a (00:40:53) clue what the difference was. And there (00:40:55) were some people that were like oh use (00:40:56) this model because it's really mean to (00:40:58) you. I was like well that's this is a (00:40:59) bit of a weird conversation. Use this to (00:41:01) so I can be mean to me. I've got enough (00:41:03) people around me that do that. So what (00:41:05) is the purpose of creating so many (00:41:07) models and chat GBT is starting at one (00:41:10) then two then three then four what is (00:41:12) going on here and why are there so many (00:41:14) models (00:41:15) >> so the main reason is because we're (00:41:16) constantly developing new techniques is (00:41:18) that the techniques for making models (00:41:20) bigger better stronger faster smarter (00:41:22) keep improving so once you have a (00:41:24) certain model like say a GPT4 you use (00:41:26) the best techniques and the biggest (00:41:27) supercomputers of the time so there's a (00:41:29) really weird thing that happens with (00:41:30) these modern AI systems which is that is (00:41:34) roughly correct is that if you just make (00:41:36) them bigger and if you just give them a (00:41:39) bigger computer and more computing power (00:41:41) and you run them for longer, they get (00:41:43) smarter. This is called scaling loss in (00:41:46) the jargon. And it's kind of crazy that (00:41:48) this is true. This was kind of the big (00:41:50) discovery that kicked off this latest (00:41:52) race is that it turns out even if you (00:41:54) just if you just put more data in and (00:41:56) you just make bigger computers, your (00:41:57) systems get smarter, they get more (00:41:58) useful, they get more interesting, they (00:42:00) get more coherent, etc. even if you (00:42:02) don't know why. So chachbt5 is trained (00:42:05) on is built on much bigger (00:42:07) supercomputers than like chachbt4 was (00:42:09) for example and sometimes you they do (00:42:12) special things to the models you know (00:42:13) they feed them special data whether use (00:42:15) different algorithms slightly um to make (00:42:17) them have different personalities or (00:42:19) different quirks but it's kind of all (00:42:21) alchemy like we don't it's more like (00:42:24) people are messing around with a bunch (00:42:26) of like weird you know dark magic (00:42:28) ingredients and then something pops out (00:42:30) and they just are different and it's not (00:42:32) always Why? Like for example, there was (00:42:35) a like there's some funny things you can (00:42:36) find in literature where like people (00:42:37) tell stories about how their models (00:42:39) suddenly develop like crazy like beliefs (00:42:41) or things like there was a thing for (00:42:43) example where Shhat GPT just started uh (00:42:45) refusing to talk creation. It would just (00:42:48) refuse. It would just never it just (00:42:50) would not talk creation and turns out it (00:42:52) was like something related to like (00:42:53) creation users like downvoting more than (00:42:56) other users. So the model just gave up (00:42:57) on speaking creation. Obviously this (00:42:59) wasn't intentional. It just kind of (00:43:00) happened. There's a really crazy paper (00:43:02) paper paper recently where it was found (00:43:04) where they could like using a certain (00:43:05) technique which is a bit complicated to (00:43:07) explain but basically they generated a (00:43:08) bunch of numbers just numbers like 101 (00:43:12) 95 82 just numbers and if they fed these (00:43:15) to uh a model it suddenly really liked (00:43:18) owls. (00:43:20) >> So there's there's kind of just some (00:43:21) weird stuff going on. (00:43:22) >> There is so much weird stuff going on. (00:43:25) When you see Chad GPT, it's a it's (00:43:28) tempting to think of it as like this (00:43:29) like coherent like person. It's like (00:43:31) it's like a guy, you know, he has like a (00:43:33) consistent persona, a consistent like (00:43:35) intelligence the way like you or me (00:43:37) might have. But this is so incredibly (00:43:39) not what these things are. They're more (00:43:40) like aliens. They're more like crazy (00:43:42) aliens that are just pretending to be (00:43:44) humans. And if you even just mess with (00:43:46) them a little bit, they just like go (00:43:48) completely nuts. (00:43:49) >> I've heard you call them schizophrenic (00:43:51) before. (00:43:51) >> Yeah. (00:43:51) >> Explain to me. I've I've kind of noticed (00:43:54) this a little bit with some of the (00:43:55) stuff, but I probably not tested as (00:43:57) much. How is AI how is chatbt being (00:44:00) schizophrenic? (00:44:00) >> I mean, there's many ways where just (00:44:02) like there is no there's not a (00:44:03) consistent persona like it's not a (00:44:05) person. So you can just make it have art (00:44:07) any persona you want kind of and you can (00:44:10) also put it into these crazy states (00:44:12) where like for example there is a recent (00:44:14) paper on this where he took um a (00:44:16) competitor chatbt called Claude and they (00:44:19) had two clots talk to each other and (00:44:21) just about whatever they want to talk to (00:44:23) and they talk back and forth back and (00:44:24) forth back and forth and what would (00:44:25) always happen is that as things go on (00:44:27) they would always start talking about (00:44:28) spiritual bliss and internal (00:44:30) enlightenment happiness and gratitude. (00:44:31) They keep thanking each other over and (00:44:33) over again and sending like emo heart (00:44:34) emojis and how much they're thankful (00:44:36) grateful for the loving moment and (00:44:37) silence they have together and they just (00:44:39) keep doing that and this would always (00:44:40) happen no matter what you start the (00:44:41) conversation with no matter what the (00:44:43) starter of the conversation is they (00:44:44) would (00:44:46) >> find their way there and other models (00:44:48) would degrade into other things right (00:44:49) like there was recently an example with (00:44:52) Gemini where the Gemini model which is (00:44:54) the Google chatgpt kind of thing um (00:44:57) sometimes when it can solve a coding (00:45:01) problem, it will develop like suicidal (00:45:03) depression. It'll be like, "I'm sorry. (00:45:04) I'm sorry. I'm the problem. I should be (00:45:06) I should be deleted. Like, it's wrong. (00:45:08) I'm sorry. I'm sorry." And it was just (00:45:09) like do this like millions of times. (00:45:11) >> We had we had a big issue. Was it Gemini (00:45:13) at first where uh they said, "Uh, show (00:45:15) me I think it was the Nazis and they (00:45:17) came out and the the race was off and (00:45:20) >> there's many examples of that." Yeah. (00:45:21) So, was it like a I know the word woke (00:45:23) is thrown around a lot, but it was (00:45:25) almost as if someone just before they've (00:45:26) released it, they've gone, "Okay, you (00:45:28) know, let's let's make sure that we uh (00:45:30) you don't put too many white people in (00:45:32) here, you know, otherwise we could be (00:45:33) considered racist." And (00:45:34) >> I think there's a bunch of [ __ ] like (00:45:35) that. Yeah. Like I think like these (00:45:37) >> HR department got a hold of it before it (00:45:39) went out. (00:45:39) >> I think there's a bunch of stuff like (00:45:40) this, right? Like I think there is a lot (00:45:42) of stuff where like these systems are (00:45:44) super messy. Like it sometimes feels (00:45:46) like you know use it feels like (00:45:48) structured. It's like, wow, the answers (00:45:50) are like well structured. It's like (00:45:52) thought through. This is really not what (00:45:53) it is. It's more like this giant pile of (00:45:56) mess. (00:45:57) >> It's like a crazy person trying to (00:45:59) pretend they're sane on a first date, (00:46:00) holding it together with the leg jigging (00:46:02) under the table. (00:46:03) >> Yeah. Yeah. Exactly. And also, they have (00:46:04) a huge HR and like, you know, (00:46:06) engineering department trying to keep (00:46:07) their sanity together at every moment. (00:46:09) So, whenever they step out of the Ryan, (00:46:10) you know, 100 engineers start hitting it (00:46:11) with hammers. (00:46:12) >> And every time you engage with the new (00:46:14) CHBT, it's like Google. Everything's (00:46:16) been removed. It's just you and that bar (00:46:18) and it's peaceful. I'm just going to ask (00:46:20) it anything I want. (00:46:22) >> This is um I'm actually fascinated about (00:46:25) this relationship dynamic with AI (00:46:27) >> where I think I put it on a podcast (00:46:30) someone talking about like love robots (00:46:32) and the idea of like a physical robot (00:46:35) that you would have a relationship with (00:46:37) things you're saying about AI. If people (00:46:39) are having that kind of connection with (00:46:41) a textbased algorithm, when these things (00:46:44) become physical and you can charge them, (00:46:46) probably use your EV carport and charge (00:46:48) your girlfriend at night and she can say (00:46:50) things and she can, you know, oh, you (00:46:52) had a great day at work, I appreciate (00:46:54) you, you're the best. These machines and (00:46:57) these algorithms tapping into human (00:46:58) emotions, which if you think at this (00:47:01) point it's quite easy to manipulate (00:47:03) someone. I mean, we've had uh cults for (00:47:05) ages. There are people out there that (00:47:07) have honed a technique to get someone (00:47:09) into a cult. (00:47:10) >> Machines will learn this very quick and (00:47:12) they will be able to sort of navigate (00:47:14) the way around the human psyche to get (00:47:16) them to do whatever it is they want to (00:47:18) do. (00:47:19) >> Surely this is a frightening landscape (00:47:21) not just for fertility rates but for (00:47:22) human sanity. (00:47:23) >> Yep. I think this is exactly correct is (00:47:25) like sometimes people like as I said (00:47:28) before I don't think the future looks (00:47:29) good. I don't think humanity is going to (00:47:30) survive this by default and you know not (00:47:33) just this but like super intelligence (00:47:34) and all of this right and people (00:47:35) sometimes ask me like why why would that (00:47:37) be like surely humans would stop you (00:47:39) know AI and I'm like really really have (00:47:42) you take a look around how people are (00:47:43) reacting to AI and how AI is (00:47:45) manipulating people and like getting and (00:47:47) you know you know just wait until people (00:47:49) start advocating for AI rights like it's (00:47:51) already starting right where people are (00:47:52) like oh my girlfriend should have human (00:47:54) rights like she's real you know and like (00:47:56) look if you have a robot body who is as (00:47:58) persuasive as any human potentially even (00:48:01) optimized to advocate for its own (00:48:03) rights. Yeah, people will advocate for (00:48:06) that very very strongly and very (00:48:08) persuasively because AI is to be very (00:48:09) persuasive. Of course, it's very smart. (00:48:12) I mean, it's already the case that you (00:48:13) know a conversation with a (00:48:16) state-of-the-art AI is generally more (00:48:18) entertaining than the 50th percentile (00:48:20) human. Like this is like already the (00:48:21) case. And there's there's some use cases (00:48:24) where some people will say, "Oh, you (00:48:25) know, I left my toddler with an AI and (00:48:27) it was teaching my toddler French." And (00:48:28) they romanticize these conversations so (00:48:31) much and like you say, a human could do (00:48:33) 8 hours of labor a day with diminishing (00:48:35) returns after a point. Uh, if they're (00:48:37) good at what they do, they're probably (00:48:38) going to leave and want to do something (00:48:40) else or they'll outgrow you and they (00:48:41) won't want to do the arduous laborous (00:48:42) task they were doing before. So there's (00:48:44) many flaws to humans, but with an AI, (00:48:46) you go, "Oh, that bot that cost me $20 a (00:48:49) month could teach my son French every (00:48:52) single day after school for an hour. (00:48:53) Brilliant. The tutor could cost 10 times (00:48:55) that." (00:48:55) >> Yep. (00:48:56) >> So the romanticized side is that, but I (00:48:58) suppose now let's move into potentially (00:48:59) some of the negatives. In my mind, uh, I (00:49:02) was actually excited not to read too (00:49:03) much into the dire depressing, uh, we've (00:49:06) got a guest coming on the podcast soon (00:49:07) about declinism where human beings have (00:49:10) this, uh, you know, interpretation of (00:49:12) the world, everything's worse and oh my (00:49:13) god, wouldn't it have been great to live (00:49:14) in the 60s where people are like what (00:49:16) where we medically didn't know that much (00:49:18) and people died earlier and uh, but with (00:49:22) with the things that you're saying with (00:49:23) me when I see issues with AI, I think (00:49:26) first of all, super intelligence. One (00:49:28) thing that I'm keen to uh understand as (00:49:30) well is the amount of energy that AI (00:49:32) uses. You know, we are in arguably the (00:49:35) UK the most expensive energy prices in (00:49:37) the world. We've got political issues. (00:49:39) We've got people not wanting to use (00:49:41) nuclear. We've got all of these (00:49:42) different things. Do we have enough (00:49:45) energy with the way that we're moving in (00:49:48) the world at the moment to be able to be (00:49:50) capable to harness all of this super (00:49:51) intelligence? (00:49:53) >> So, there's a really weird thing about (00:49:55) the world right now. Just to pick up on (00:49:57) that the climism thing where in a sense (00:50:00) we are having lots of incredible (00:50:02) progress and lots of great things but (00:50:04) also in some sense we're having too much (00:50:05) progress you know super intelligence and (00:50:07) we're climate change and we're having (00:50:09) biorisk you know bioteterrorism is (00:50:11) becoming more and more of an issue stuff (00:50:12) like this and on the other hand we have (00:50:14) this stifling regulation where like in (00:50:16) the UK is now using less energy than (00:50:17) before we're not building nuclear we're (00:50:19) not you know building you know the (00:50:20) supersonic airplanes you know it's like (00:50:23) everything's sliding backwards as well (00:50:24) so like we're both going too fast and (00:50:26) we're going too slow. What the hell is (00:50:27) happening? Like I think this is like a (00:50:29) really like it I'm frustrated often by (00:50:32) people picking one of two one of the two (00:50:33) camps. Either like um regulation is a (00:50:37) problem, therefore progress isn't a (00:50:39) problem or progress is a problem, (00:50:41) therefore regulation isn't a problem. (00:50:42) But it's both. Both are a problem, both (00:50:44) are currently defective. Both are (00:50:45) currently pathological. Currently we are (00:50:47) both, you know, degrowing ourselves to (00:50:50) death and growing ourselves to death. (00:50:52) Like it's it's it's like in both (00:50:53) directions. So with the energy thing I (00:50:55) think is like a great example of this. (00:50:57) If we just had built nuclear throughout (00:50:59) the 20th century, energy wouldn't be a (00:51:00) problem. We would have no climate (00:51:02) change. We would just have energy would (00:51:04) be dirt cheap. You know, it would be no (00:51:06) problem (00:51:06) >> and potentially safer. Again, (00:51:08) >> yeah, (00:51:09) >> could fact check me on this. I think (00:51:10) that solar is the most dangerous deaths (00:51:13) per kilowatt for people falling off (00:51:15) roofs installing it. Whereas with (00:51:17) nuclear, even if you look at Chernobyl (00:51:19) uh and any of the other plants that have (00:51:21) kind of, you know, had issues and (00:51:23) fallout or whatever, when you look at (00:51:24) the amount of deaths per kilowatt, it's (00:51:25) actually the safest. Would you be right (00:51:27) in that? (00:51:27) >> I think nuclear is definitely the (00:51:28) safest. I think the unsafest is uh coal. (00:51:31) >> Okay. Yeah. (00:51:32) >> Coal especially because of the air (00:51:33) pollution deaths are extremely high (00:51:35) particulate. (00:51:36) >> Um did you know that coal power plants (00:51:39) release hundreds or even thousands of (00:51:40) times more radiation than nuclear power (00:51:42) plants because there's small uranium (00:51:43) particles in coal? (00:51:44) >> I did not know that. (00:51:45) >> Yeah. Fun fact. (00:51:46) >> Keep that one. Clip that. Uh (00:51:49) >> there's there's a fun one where um you (00:51:51) know in nuclear power plants you (00:51:52) generate, you know, poisonous waste (00:51:53) which we then safely store deep (00:51:54) underground. Meanwhile, coal power (00:51:56) plants generates dangerous poisonous (00:51:58) waste which is stored in our lungs. (00:51:59) >> And uh I live in Australia where they (00:52:02) export a lot of coal. So they're like, (00:52:03) "Oh no, no, this is bad for the (00:52:05) environment. What we do is just put on a (00:52:06) boat and we'll sell it." (00:52:07) >> Yeah, it's fine. (00:52:07) >> And that's and that's all right. And you (00:52:08) know, it will just pollute their air (00:52:10) over there, not our air. (00:52:11) >> Yeah, it's fine. (00:52:12) >> It could blow over, but it could also (00:52:13) blow over. So like it's like there's a (00:52:15) lot of stuff like this, right? Where (00:52:16) it's like um we could have just not this (00:52:19) was a choice our civilization made. Like (00:52:21) we could have just built nuclear and (00:52:24) then we would have been not have climate (00:52:26) change. We would not had air pollution. (00:52:28) Energy would be dirt cheap. Like um I'm (00:52:30) from Germany. My mother is German. And (00:52:32) so and Germany is um is a bit of an (00:52:34) unusual country in the western world. (00:52:35) And it's very industrial. It has it's (00:52:38) very factory based still. It's not like (00:52:39) a service economy like you know England (00:52:41) or the US. It's very very industrial and (00:52:44) they make especially like high-tech (00:52:46) stuff you know like airplanes and (00:52:48) microchips and lenses and stuff like (00:52:50) this and well what is the most important (00:52:52) thing for um you know industries of (00:52:54) course energy. Germany doesn't have (00:52:57) really good weather for solar or wind um (00:53:00) they don't really have oil and they have (00:53:02) now shut and they've shut down all the (00:53:04) nuclear power plants. So now they're in (00:53:05) the worst recession they've been in like (00:53:07) a long time and it's only getting worse (00:53:08) and there's literally no reason for (00:53:09) this. He could have just built nuclear (00:53:10) power plants. it would not been a (00:53:12) problem. So there's a weird thing where (00:53:15) there were choices made that we are now (00:53:17) paying the price of, you know, now you (00:53:19) could say it was worth it for some other (00:53:22) reason, whatever, right? But a choice (00:53:24) was made of some kind which caused a (00:53:27) problem. And so now with the energy use (00:53:29) of AI, again, it comes down to choices. (00:53:32) It's like, do we um do we have enough (00:53:35) energy to build super intelligence with (00:53:37) what is currently on the grid? I think (00:53:38) absolutely yes. To be clear, I think (00:53:40) this is like, yeah, I think you could do (00:53:42) it right now if we knew how to do it. (00:53:44) You know, I think there's just we (00:53:44) haven't quite discovered the right (00:53:46) algorithm and the right, you know, (00:53:48) things. I think we discovered that we (00:53:50) could definitely do it with the energy (00:53:51) already on the grid easily. Do we have (00:53:54) enough energy to supply the current (00:53:56) demand for current AI? No. Which is why (00:53:58) people are building all these massive (00:53:59) supers data centers and and also of (00:54:02) course if you have the big data centers, (00:54:03) you have a lot of energy, you can go (00:54:04) faster than your rivals, which is really (00:54:06) what's pushing this, especially the big (00:54:07) tech companies. They want to go faster (00:54:09) than their rival, so they need the (00:54:10) bigger power plant. They need the (00:54:11) bigger, you know, data center, whatever. (00:54:12) >> Could could you almost compare this to (00:54:13) like a nuclear arms race where a (00:54:16) destructive power they didn't want to (00:54:18) use, but you just wanted to get it (00:54:19) before someone else had it? (00:54:20) >> This is exactly correct. It's in a sense (00:54:22) it's worse than this because at least (00:54:24) nuclear bombs don't go off on by (00:54:25) themselves. But the problem is is that (00:54:28) AI once it becomes AGI will become is (00:54:31) already becoming more and more (00:54:33) autonomous. There's a um there's an (00:54:35) organization called Meter Mr. um it's a (00:54:38) nonprofit and they study the (00:54:40) capabilities of models and like what (00:54:41) they're and like AIs and what they can (00:54:43) do and they've seen that the what they (00:54:46) call the task horizon. What this means (00:54:48) is how long is the longest task that AI (00:54:52) can consistently do before they mess up? (00:54:54) Because like if it's a long thing, we (00:54:56) have to think for a long time, they mess (00:54:58) up more. You know, same thing with (00:54:59) humans, right? It's a short task, it's (00:55:00) easier. If it's a long task, probably (00:55:01) harder. And this has been increasing (00:55:03) exponentially over the last years. So, (00:55:06) you know, a couple years, like two (00:55:08) years, three years ago, it was maybe a (00:55:09) couple minutes. Now, it's already two (00:55:11) hours. (00:55:12) So, they're becoming more autonomous (00:55:15) over longer and longer time frames. And (00:55:17) now we have this huge geopolitical race. (00:55:19) The US, China, big tech, you know, all (00:55:22) the companies, OpenAI, Facebook, (00:55:24) Anthropic, etc., all of them racing as (00:55:26) fast as possible. And often they will (00:55:29) even claim this. Well, they were like, (00:55:30) because they're the ones who will make (00:55:32) it safe because if someone else gets it. (00:55:34) Oh, no, no, no. That's dangerous. You (00:55:36) know, what if what if the other guy gets (00:55:38) it? No, no, I'm the good guy. (00:55:39) >> My friend Chris said this about lottery (00:55:41) winners. He said, "Everyone that wins (00:55:43) the lottery usually squanders it, makes (00:55:44) terrible decisions, loses friends, ends (00:55:46) up depressed. But if I won, (00:55:47) >> yeah, (00:55:48) >> you know, I' I'd be I'd invest it in (00:55:49) property. I want to pay off my friends (00:55:51) mortgages." You know, everyone thinks (00:55:52) not me, but other people. Sure. Yeah. (00:55:54) Oh, if the Chinese get it, yeah, you (00:55:55) know, it's terrible. But if we get it, (00:55:57) we, you know, democracy and freedom, (00:55:58) >> of course. (00:55:59) >> It's really interesting with the things (00:56:01) we said at the beginning. I think that (00:56:03) before we had this conversation, I sat (00:56:04) down and I thought that the enemy would (00:56:05) be AI versus humans and that, you know, (00:56:08) oh, turn it off, great, cut the power. (00:56:10) But in reality, if AI can manipulate (00:56:13) humans, they become almost like a (00:56:15) symbiotic. They become joined. And if (00:56:17) you can manipulate and get in the minds (00:56:19) of people, like you say, look at cults, (00:56:20) even religions to some sense. You know, (00:56:23) three thou maybe like 300 pages of (00:56:25) written words can really get people to (00:56:26) act incredibly differently. Let alone (00:56:28) conversations, subtle prompts, little (00:56:30) demands, messaging during the day. This (00:56:33) isn't just so simple as humans v humans (00:56:35) or computers v computers. It is going to (00:56:38) intertwine both of them. And if I assume (00:56:41) two different AIs in two different (00:56:42) continents feel a threat from each (00:56:44) other, they could increase the chance of (00:56:46) war in increase the chance of conflict, (00:56:48) maybe even increase the chance of (00:56:50) nuclear war as well. (00:56:52) >> I mean, it is unknowable what a super (00:56:54) intelligence might do. And but let me (00:56:56) tell you, I don't want to see what (00:56:58) happens if two super intelligences (00:56:59) fight. I don't want to be caught in that (00:57:01) crossfire. I know what they'll do, but I (00:57:03) don't want to be there. (00:57:04) >> Who's winning the AI arms race now? (00:57:08) depends on who you ask. I mean, I would (00:57:10) say the US more broadly or big tech, but (00:57:13) I don't like talking about winning the (00:57:14) arms race because it's like a peric (00:57:17) victory. It's it's a peric victory, (00:57:19) right? It's like ultimately no one wins. (00:57:20) It's like you're you're racing to who (00:57:23) can pull the Russian relet trigger as (00:57:25) many times as possible. Like this is (00:57:26) like we know there's a bullet. There is (00:57:28) a bullet in the chamber. You know, (00:57:29) you've clicked it once, you've clicked (00:57:30) it twice, and now you're competing for (00:57:32) who can click it a third time. And but (00:57:34) you know now the you know it's against (00:57:36) all of us. So it is in a I mean it is (00:57:40) mad in like the true sense of the word (00:57:42) like it is irrational. It is actually (00:57:44) not in there is a there's a sense where (00:57:45) people hear the word arms race and (00:57:47) they're like okay so we have to race but (00:57:49) like this is not the case. The prize (00:57:52) you're competing for is you and all your (00:57:54) family and everyone you care about lose (00:57:56) all power and or die. Like this is not a (00:57:58) prize you want. It's like another one. (00:58:00) You said it before about going to the (00:58:01) moon and I've there's a lot of (00:58:03) conspiracy theories. I love a good (00:58:04) conspiracy theory. I like not to get too (00:58:06) caught up in them, but I like listening (00:58:07) to the argument. And the one about going (00:58:09) to the moon was, you know, the Russians, (00:58:11) the Americans wanting to go. But that (00:58:12) was an arms race that when they got (00:58:13) there, everyone was like, "Oh, we'll (00:58:14) give up now." (00:58:15) >> Yeah. (00:58:15) >> Oh, you went you beat us to it. Fine. I (00:58:17) don't think anyone's (00:58:18) >> And it was good fun. Like, I mean, I (00:58:19) love the Apollo project. I think this is (00:58:21) such a beautiful example of what I would (00:58:23) like humanity to be doing. It's like we (00:58:24) competed for something that was cool and (00:58:27) taught us a bunch of cool stuff and we (00:58:29) all had a good laugh about it. (00:58:31) Beautiful. You know, sure were there bad (00:58:32) things involved blah blah blah. Sure. (00:58:34) But like, you know, it was great. We we (00:58:36) expanded the human frontier, the human (00:58:38) soul. We did something inspiring. We (00:58:39) developed a bunch of new technologies as (00:58:41) a you know, direct result of the Apollo (00:58:43) project. We developed all these new (00:58:44) technology, all these new things. In my (00:58:46) opinion, fantastic. If AI had these (00:58:48) properties, we're the end all. we go to (00:58:50) the moon, great. But that's not and (00:58:52) that's unfortunately the consequence (00:58:53) we're talking about. (00:58:54) >> One of the dangers I could potentially (00:58:55) see is even if you were to end stain (00:58:56) that we didn't go to the moon and it was (00:58:57) a facade and it was to prove to the (00:58:59) other people whatever could we (00:59:01) potentially see something in the AI (00:59:02) world where people project models they (00:59:04) haven't created or project technologies (00:59:05) that don't exist to make it seem or you (00:59:07) know America might say oh we now have (00:59:09) AGI then the Chinese or the Russians go (00:59:12) right triple the amount of research (00:59:14) we're putting into it. the kind of (00:59:15) fallout you could get from this almost (00:59:18) giant [ __ ] off to see who's got the best (00:59:20) supercomputers and it's is pretty scary. (00:59:24) >> It's even worse than that actually is (00:59:25) that people are doing this on purpose (00:59:27) and the people doing this on purpose are (00:59:28) paid lobbying firms. Of course, every (00:59:30) single US company is extremely (00:59:32) incentivized to play up the Chinese comp (00:59:35) competency as much as possible so they (00:59:37) can get more funding. Of course, every (00:59:38) single person involved, you know, wants (00:59:40) to make it seem as bad as possible and (00:59:42) is so they can get as much funding as (00:59:44) possible and they need the military (00:59:45) funding and they need the whatever. And (00:59:46) this is this is not a hypothetical, but (00:59:48) this is happening. A couple years ago, (00:59:50) it was still that big tech, you know, (00:59:51) was like really shy about working with (00:59:53) defense, you know, it's like, oh no, (00:59:54) we're the good guys. We wouldn't do (00:59:55) that. Then it turns out then they needed (00:59:57) larger budgets to continue the race. So (01:00:00) they all ran right for the national (01:00:02) security apparatus in the US in (01:00:04) particular and started lobbying so hard (01:00:06) like unbelievable. (01:00:07) >> Do you think that's where some of the, (01:00:10) you know, political issues, social media (01:00:12) like Tik Tok and them say, "Oh, the the (01:00:14) the CCP, they're they're getting data (01:00:15) from your your 16-year-old daughter. (01:00:17) They're going to use it against us." (01:00:18) This could all be part of people, you (01:00:20) know, growing these apprehensions to (01:00:22) overseas uh you know, intelligences and (01:00:25) thinking, "Oh, do you know what? We do (01:00:26) need to invest in AI. Do you need to (01:00:28) allocate more resources to being (01:00:30) digitally sound and safe? Oh yeah. I (01:00:32) think there's a lot of stuff like this (01:00:33) where like I think it's um it's maybe (01:00:36) not always clear but like from the (01:00:38) outside it's just how much big tech has (01:00:42) captured the regulatory state like how (01:00:44) much of politicians and decision-m (01:00:48) things are completely captured by (01:00:51) lobbying and so on from big tech (01:00:53) companies where there's a lot there are (01:00:55) many people in government where if they (01:00:57) need a technical opinion they only have (01:00:59) Microsoft to go to and Microsoft has 10 (01:01:02) people around them bringing them out to (01:01:04) dinner and telling them exactly what (01:01:06) they should do and stuff like this. You (01:01:07) know, not all of them. Some people fight (01:01:08) back, blah blah blah. But like the (01:01:10) amount of control here is staggering. I (01:01:12) have personally talked to politicians in (01:01:16) at least five countries who have all (01:01:18) told me in and these are like real (01:01:20) politicians, right? Like minister of (01:01:22) digital and [ __ ] who told me uh in (01:01:24) private like like, "Yeah, I'm really (01:01:26) concerned about big tech, but if I say (01:01:27) anything against them, I lose my job. (01:01:29) they'll get me. They'll stop me from (01:01:30) being elected again. And they do do this (01:01:32) and they threaten it. Like it's not (01:01:35) pretty. It's like the amount of (01:01:36) blackmail and like threats that get (01:01:39) exchanged behind closed doors from these (01:01:41) mega corporations, which is usually the (01:01:43) form of nice economy you have there. (01:01:45) Sure, it would be a shame if something (01:01:46) happened to it. (01:01:47) >> Yeah. And I suppose we've seen this (01:01:49) with, you know, uh, diversity quotas and (01:01:52) the diminishment of the meritocracy and (01:01:54) people just saying, "Look, I'm I'm going (01:01:55) along with it because I want to lose my (01:01:56) job." even something like pronouns in (01:01:58) flipping email bios and everything when (01:02:00) that wave came through I was thinking (01:02:02) you know is there something that I've (01:02:03) missed you know but it's really a lot of (01:02:05) people go I need to do this or I lose my (01:02:06) job I need to lose this or I'll end up (01:02:08) going to HR now I suppose the cyber (01:02:12) security AI you talk about these (01:02:15) politicians actually interestingly I saw (01:02:17) uh I think it was a representative of (01:02:19) someone in China and he was being you (01:02:23) know interviewed and they're saying oh (01:02:24) you're a communist nation the state runs (01:02:26) the country And he goes, "Okay, but (01:02:27) although you're correct about what you (01:02:29) say about China, let's look at America." (01:02:30) He goes, "You elect a new person every (01:02:32) four years, then you swap them out. They (01:02:33) don't really make the decisions. They've (01:02:34) got all these corporations that are (01:02:36) actually in charge of everything going (01:02:37) on." They go, "Fair enough. In China, (01:02:40) the no one is bigger than the state and (01:02:41) we run the country." He goes, "Well, in (01:02:43) your country, everyone's bigger than the (01:02:45) state." And he goes, "The people in the (01:02:46) state don't run the country." So, he was (01:02:48) like, "You can call us corrupt, but we (01:02:49) laugh and we call you corrupt." And it's (01:02:51) crazy that you look at the democratic (01:02:53) model in America. You look at the (01:02:54) communist model in China, neither really (01:02:57) are ideal for the common people, (01:03:00) everyone in the nation. You're kind of (01:03:02) screwed whichever way you go. (01:03:03) >> Yeah, I think this is very deeply true. (01:03:05) Like I I think this is like the correct (01:03:07) answer of not therefore the Chinese (01:03:08) model is good or something. It's like (01:03:10) no, they're both fundamentally flawed in (01:03:11) different ways. Like they're both deeply (01:03:14) corrupt like in in the real sense of the (01:03:17) word corruption and in but in different (01:03:20) ways. Like the model of corruption is (01:03:21) different. Like there's a lot of things (01:03:24) in China that I think are good. Like (01:03:26) they have a lot of laws that I'm saying, (01:03:27) "Wow, we should do that in the West, (01:03:28) too. That's a sensible thing." There's (01:03:30) many things they do that I think is (01:03:32) really bad and like we should definitely (01:03:34) not do that in the West. And but I think (01:03:37) it goes the other way too. Like I think, (01:03:38) you know, there's many things, you know, (01:03:39) we do in the West that are really good (01:03:41) and I think there's some things that we (01:03:42) do are really bad. The US with the (01:03:44) captures like just is a great example (01:03:46) here. We're just like I mean it goes (01:03:48) back to like [ __ ] Plato, right? Like (01:03:50) Plato was, you know, you know, all the (01:03:51) way back then he said you should (01:03:53) separate money and politics. These are (01:03:55) the two things you must separate. And (01:03:57) now, you know, we have the richest (01:03:58) billionaire in the world, you know, (01:03:59) hanging out in the White House and (01:04:00) stuff. That's right. (01:04:01) >> And people on salaries of 150k with (01:04:04) portfolios of hundreds of millions. (01:04:05) >> Yeah. Exactly. It's like like I think (01:04:08) the core thing like this take away from (01:04:10) this whole conversation is that like I (01:04:12) really think AI is not but not just a (01:04:14) technical problem. It is a much wider (01:04:16) political problem. It's a much wider (01:04:17) social problem of how do you organize a (01:04:20) state? How do you organize a (01:04:21) civilization? How do you organize a (01:04:22) nation in a way that doesn't lead to (01:04:25) these kind of corruptions and you know (01:04:27) disasters? AI is a legible thing here, (01:04:31) right? Like currently the US doesn't (01:04:34) have a mechanism to stop a the AI race. (01:04:37) Like in practice they can't do it, you (01:04:40) know? Like even if they wanted to like (01:04:41) who would be in charge and they would (01:04:43) get challenged immediately and they get (01:04:44) lobbied out of existence immediately. (01:04:46) the companies have too much power. The (01:04:48) companies also can't stop each other to (01:04:49) be clear. They're all they're all stuck (01:04:51) in like, you know, a Mexican standoff, (01:04:53) right? So like they're all stuck in some (01:04:55) sense, you know? I mean, not literally, (01:04:57) but you know, (01:04:58) >> you got Chut, then Elon's goes croc and (01:05:00) got their own little love charge. (01:05:01) >> And then you have these people, you (01:05:03) know, who will just like, you know, (01:05:04) blatantly disrespect, you know, (01:05:05) American, you know, democracy will just (01:05:07) like massive manipulate, you know, on (01:05:09) social media or whatever. And there's (01:05:10) zero consequences for this. And then, (01:05:12) you know, in China, you know, Jack Maw (01:05:14) goes a little bit out of line, (01:05:15) disappears for three months, you know, (01:05:17) and so I'm not saying it's good that (01:05:19) Jack Maw disappeared, you know, from (01:05:20) China. Um, but I'm saying the state (01:05:24) should have the ability to stop people (01:05:26) doing things that are dangerous. You (01:05:27) know what I mean? Like if someone is (01:05:29) doing something that has a national (01:05:30) security risk and it's like threatening (01:05:31) the lives of your entire nation, you (01:05:34) should have the mechanism to be like, (01:05:35) "Hey buddy, like h let's have a chat (01:05:38) about that." And this mechanism to a (01:05:40) very large shockingly large degree does (01:05:43) not exist in the west or in the US. It (01:05:45) exists somewhat but not to the same (01:05:47) degree. So if China wanted to stop the (01:05:50) race, they could do so tomorrow. One (01:05:52) penstroke from Xiinping and it's over. (01:05:55) The US I'm not sure they could do it (01:05:57) right now even. So then this brings me (01:05:59) on to cyber security where uh I can't (01:06:02) remember the exact amount but I'm pretty (01:06:04) sure the head of UK cyber security was a (01:06:07) salary of I think £65,000 (01:06:10) and people were saying you know you (01:06:12) could be a general manager of three IKEA (01:06:14) stores and earn more than the person (01:06:15) that's in charge of the all the (01:06:16) infrastructure to protect the United (01:06:18) Kingdom. And I'm pretty sure I saw (01:06:21) something online about head of UK cyber (01:06:23) security being on £65,000 a year (01:06:26) something and there could be someone in (01:06:28) a very quote unquote normal job that (01:06:30) would earn more money than that. So you (01:06:31) can see where the you know priorities (01:06:34) are of different jobs. But when it comes (01:06:36) to things with security, passwords, (01:06:38) technology, hacking, AI, there was a (01:06:40) standup comedian who said yeah your (01:06:42) favorite word is your password. You get (01:06:43) it's not secure enough you started with (01:06:45) a capital letter. It's not secure enough (01:06:46) you put an exclamation mark. it's not (01:06:48) secure enough. You put one at the end of (01:06:49) it. And I was like, I feel seen. I feel (01:06:51) like someone, oh my god, I've been (01:06:52) hacked already. And the world that we (01:06:54) live in now, people are kind of saying, (01:06:56) well, you know, give us a couple years. (01:06:57) AI will just get access to whatever you (01:06:59) want. It will see a password and laugh. (01:07:01) It will see a firewall and go, h, I'll (01:07:03) work my way around it. Is this really a (01:07:06) reality that we're going to be facing (01:07:07) moving in the future? (01:07:08) >> The current state of cyber security is (01:07:11) such a mess. It is hard to put into (01:07:12) words. It is really hard to explain how (01:07:16) catastrophic cyber security at large is (01:07:18) not just you know in the in the UK state (01:07:21) but we could talk about that too in a (01:07:22) second. Um the main bottleneck of cyber (01:07:26) security is just the number of working (01:07:28) hours hackers are willing to put into (01:07:30) it. Like if a couple of good hackers (01:07:33) want to cause damage they can and they (01:07:34) do. And it's mostly just bottleneck by (01:07:36) them, you know, either getting arrested (01:07:37) or just not, you know, not having enough (01:07:39) energy drinks. Um, AI changes this (01:07:42) dramatically. If you can have AI that (01:07:44) wants 247, you know, is better than any (01:07:46) hacker has ever lived, has direct access (01:07:47) to all the computer things and whatever, (01:07:49) it can hack truly shocking amounts of (01:07:52) things. And these things are always (01:07:54) starting to happen. Now, hypothetically, (01:07:57) you could also have defensive cyber (01:07:59) security AI. Hypothetically, and (01:08:02) hypothetically, this could make a huge (01:08:04) difference. So there is a um a kind of (01:08:06) technique that I am very fond of u which (01:08:08) is called provably correct code. So (01:08:11) there's a way it's very complicated but (01:08:13) there are ways where you can make it so (01:08:16) that your code cannot be hacked. There (01:08:18) there are ways to do this by basically (01:08:20) making like mathematical proofs that the (01:08:22) code is cannot be hacked. And this works (01:08:25) the military for example uses this a (01:08:27) lot. (01:08:27) >> Is this like multiffactor authentication (01:08:29) or more complex? (01:08:30) >> More complex. It doesn't fix the the (01:08:31) humans messing up, but it makes the (01:08:33) system secure. So, if the human messes (01:08:35) up, if you tell them your password, (01:08:36) you're still screwed. (01:08:37) >> But at least they can't get around the (01:08:38) password. Cool. If you don't if they (01:08:40) don't have your password, they're (01:08:41) screwed. If you give them your password, (01:08:42) well, yeah, you're screwed. Um, this is (01:08:45) this is a thing, like this is definitely (01:08:47) a big thing. So, like in in hacking, (01:08:49) there's often um it's often there's a (01:08:51) technical side to it of like hacking a (01:08:52) piece of software, you know, (01:08:53) circumventing the password, not even (01:08:55) figuring out the hack word, the (01:08:56) password, just circumventing entirely. (01:08:58) This is something that AIs are very good (01:09:00) at and it's very powerful. But there's (01:09:02) another aspect which is social (01:09:03) engineering which is tricking people (01:09:05) into doing things. This is not hacking (01:09:06) the system. It's just tricking people. (01:09:08) And this is such a huge vulnerability. (01:09:11) There's even a saying in the hacker (01:09:12) community is the biggest vulnerability (01:09:14) sits in front of the screen. And this is (01:09:16) already the case where like most I would (01:09:18) say probably most like big hacks (01:09:20) nowadays are like teenagers calling up (01:09:23) you know like support hotlines and (01:09:25) saying hello I am Mr. Admin. could you (01:09:27) please reset my password to one two (01:09:29) three and people just do it you know (01:09:31) like and this is how they get into like (01:09:32) you know huge corporate network (01:09:33) >> I think Bezos his phone was hacked where (01:09:35) he opened a spreadsheet or something (01:09:37) >> that's more of the technical side where (01:09:39) like that's a bit more sophistic but (01:09:40) usually there's still a social component (01:09:41) where you'll send someone an email it's (01:09:42) called fishing where you send them an (01:09:44) email that looks legit and then you (01:09:45) click on it and there's actually code (01:09:46) that like manipulates it and obviously (01:09:48) AIS are extremely good at both of these (01:09:50) things they're very persuasive they're (01:09:52) they know you very well they can try (01:09:53) over and over again it's like you know (01:09:56) with AI uh you know, you can you can (01:09:57) create whole personas that you talk to (01:09:59) for years potentially online is a good (01:10:01) friend of yours and then just one day he (01:10:03) shares a little link with you and turns (01:10:04) out this person never existed. They were (01:10:06) I bought the entire line and boom, (01:10:07) you're hacked. Like this is stuff that (01:10:09) like intelligence agencies already do (01:10:11) with like often with real operatives, (01:10:12) you know, like you know, if you're (01:10:14) you're targeted by intelligence (01:10:15) agencies, it can happen that you (01:10:16) >> they can now um uh copy voice models as (01:10:19) well. So they could pretend to be a wife (01:10:21) and say, "Oh, I'm in trouble. I need (01:10:23) $100." Yep. Can I uh Oh, can you remind (01:10:26) me what the credit card number is? (01:10:27) >> Yep. Already seeing this happen. Like (01:10:29) this already happens in the wild. (01:10:31) >> This is this is very worrying. Like uh (01:10:33) it was it was a very long time ago. In a (01:10:35) previous life, I worked for an IT (01:10:36) security business that did multiffactor (01:10:38) authentication. Now we have the apps on (01:10:40) our phones that generate the code every (01:10:41) minute. This was on a token. The company (01:10:43) that did it, they were called RSA. And (01:10:45) they were like, "The code will never get (01:10:46) broken. The algorithm is far too (01:10:48) strong." My first week at work, they (01:10:50) said, "We are replacing every single (01:10:51) token. We have not been hacked. We have (01:10:53) not lost the algorithm, but we're (01:10:55) changing every single token. The first (01:10:57) person I think called up was Locky (01:10:58) Barton, and they were like, "Oh, so our (01:11:00) codes are no longer valid for, you know, (01:11:03) protecting our entire database." That (01:11:05) was 2014, such a long time ago. And even (01:11:08) then, I remember someone saying, "You (01:11:09) would need a room of like 35 hackers who (01:11:11) were working around the clock for weeks (01:11:13) to do what they did." And I think they (01:11:15) did what they did. And yeah, that (01:11:17) landscape even, you know, over 12 years (01:11:19) ago, that was scary. But now the thought (01:11:21) that like you say you can manipulate a (01:11:23) human to give over that kind of (01:11:25) information. Especially as people become (01:11:26) more scenile, we've got people that (01:11:28) aren't quite as uh you know plugged into (01:11:30) the way things work. My mom and dad (01:11:32) still love a house phone. I say to them, (01:11:33) get rid of that. Nine out of 10 calls is (01:11:35) just someone trying to fish for their (01:11:37) details or hello Mr. Smith, we're (01:11:38) calling from your bank. I'm like, get (01:11:39) rid of it. But yet they still have it (01:11:41) and they're still susceptible to it. And (01:11:42) I even had one a while ago where it was (01:11:46) an Adobe sign document that came through (01:11:48) and I thought, "Oh yeah, you know, I'm (01:11:50) just going to sign it." And I clicked on (01:11:51) it and then suddenly it was telling me (01:11:52) to log into my email address. So I (01:11:54) populate my email address. I'm typing in (01:11:56) my password and why am I putting this (01:11:57) in? I was on autopilot completely. I was (01:11:58) like, "Why am I putting this in?" So I (01:12:00) backed out, deleted it, and then I (01:12:02) looked and the URL was completely wrong. (01:12:04) It was to a Japanese website and I (01:12:06) thought, "Oh my god, I I'm someone that (01:12:08) would consider myself pretty well (01:12:09) verssed into well, not that well versed, (01:12:12) but yeah, I nearly got duped into giving (01:12:13) away some of my the keys to the castle." (01:12:16) So, the fact that this can become more (01:12:18) sophisticated that my phone could ring (01:12:20) and someone could have cloned my dad's (01:12:21) voice and I said, "I'm in trouble. I (01:12:23) need you to send me $1,000 or whatever." (01:12:26) How do we combat that? Even if we build (01:12:29) our own defensive AIs, then that means (01:12:30) people are going to have to buy into (01:12:31) AIS, going to have everything going at (01:12:33) the same time. Surely this seems like a (01:12:35) very complex way of moving forward. (01:12:37) >> Yep, you're seeing it. This is actually (01:12:39) a big problem. Like there is just in a (01:12:43) world where you could have arbitrary (01:12:44) adversaries have direct access to anyone (01:12:47) on the planet. This is a very hard world (01:12:49) to secure. is very very hard to have (01:12:52) good security where you know your mom my (01:12:54) mom are just like plugged in unsecure to (01:12:57) the internet like this is just a hard (01:12:59) problem I don't think there's an easy (01:13:00) solution to this problem (01:13:01) >> even something as simple as banks so for (01:13:03) instance all of our money is digital (01:13:05) it's held in an algorithm somewhere in a (01:13:07) bank and some people that want to go (01:13:09) completely off- grid they can't go to (01:13:10) the physical bank they can't move money (01:13:12) they need to have a phone to do that you (01:13:13) need to have a banking app you need to (01:13:14) have all of this so even something as (01:13:16) trivial as just accessing your own net (01:13:18) worth you need to be plugged plugged in, (01:13:20) you need to have passwords or whatever. (01:13:22) Uh the contract you have with your (01:13:23) mortgage, all of these things. Even if (01:13:25) humans at this stage wanted to (01:13:27) disconnect and to go to more analog, (01:13:29) they just won't have the options. You (01:13:30) can't have a Nokia, you know, 3210. (01:13:32) Yeah. And even then, you'll be (01:13:34) irrelevant. Like there's no way you can, (01:13:36) you know, even if you live in a hut (01:13:37) somewhere and you live off of, you know, (01:13:38) cheap or whatever, like it's like you're (01:13:40) not part of the economy, you know, (01:13:42) you're not part there's no way to be (01:13:44) part of the modern society without being (01:13:46) part of the net, part of the system. And (01:13:49) this is a this means the way I think (01:13:52) about it is is it means we have to be (01:13:53) very careful of how we design that (01:13:55) system. It's very important what (01:13:56) decisions we make. So a lot of people (01:13:58) are like very upset about how regulated (01:14:00) banks are you know and they are very (01:14:02) heavily regulated and like some (01:14:04) regulation is a bit silly but honestly (01:14:06) it might be controversial. I think bank (01:14:07) regulation is good very controversial. (01:14:09) It's a very funny thing is I was in the (01:14:11) early days of crypto like 2014 or (01:14:14) something you know like the really early (01:14:15) even before that really really early (01:14:17) days and um I remember back then it was (01:14:20) a good fun like people would like think (01:14:21) about all the great things that will (01:14:22) happen blah blah blah and you know now (01:14:24) it's like 90% Ponzi schemes right you (01:14:26) know (01:14:27) >> it's like I mean it is it's like like (01:14:29) the main use cases of of crypto are (01:14:31) Ponzi schemes and money will laundering (01:14:33) that's like most of the value um and (01:14:36) empirically like empirically that is (01:14:38) most of the value Um, it has other uses (01:14:40) as well, but those are the empirical (01:14:42) main values. And what we've seen over (01:14:44) the last couple years is this speedrun (01:14:46) of like crypto bros rediscovering why we (01:14:48) have banking regulation where like, you (01:14:50) know, you lose your password once, well, (01:14:51) million dollars gone. No way to recover (01:14:52) it. You know, in the real world, if (01:14:55) there's a hack of your bank, you get (01:14:56) your money back. (01:14:58) >> You know, if you make a mistake, you do (01:15:00) get your money back if it's a reasonable (01:15:02) mistake, right? If a if a hacker stole (01:15:03) your [ __ ] you do get your money back. (01:15:05) Like this is if you if your credit card (01:15:07) gets stolen, you generally are insured. (01:15:09) This is why we have stuff like (01:15:10) insurance. These are there's there's a (01:15:12) saying I often say which is like uh (01:15:15) libertarians. So people who want to get (01:15:16) rid of like all the regulations are are (01:15:18) like house cats fully dependent upon a (01:15:20) system they neither understand nor (01:15:22) appreciate. It's often really unclear (01:15:24) why we have regulation because like why (01:15:27) are banks so harsh? Why are medication (01:15:29) so deeply regulated? Like why don't we (01:15:31) just do it a bit easier? Why don't we do (01:15:33) this? But like and sometimes we should (01:15:35) but building a state is hard. Building a (01:15:39) when I say a state I mean you know a (01:15:41) system you know like you need to be (01:15:43) plugged in you know you need an ID you (01:15:45) need your phone you need like the whole (01:15:47) thing is a very complex thing to build (01:15:50) that is humane you that is good to (01:15:52) humans and like all things considered I (01:15:54) think the west has done a pretty good (01:15:56) job on many fronts but now we're facing (01:15:58) new problems like how do we design you (01:16:01) know like we all still use constitutions (01:16:03) from like 200 years ago like how is it (01:16:06) that we still have constitution 200 (01:16:07) years ago Even if all things are now (01:16:09) governing digitally, like this is (01:16:10) insane. Obviously, we need new ways of (01:16:13) dealing with this (01:16:14) >> in uh uh in America, the constitutions, (01:16:17) your amendments, uh they were designed (01:16:20) first of all looking very much into the (01:16:21) future and it sounds like you want us to (01:16:23) create almost something like that for (01:16:24) now that we build into the future and (01:16:25) people have their amendments and you (01:16:27) know our the fourth amendment or second (01:16:29) amendment to carry arms or whatever it (01:16:31) is. I love going to Texas for that (01:16:32) reason. I'm like you guys are still (01:16:34) doing it. The guys that put those (01:16:35) together, they were pretty young, (01:16:36) weren't they? (01:16:37) >> Yeah. very young. The founding fathers, (01:16:39) I think, is a I know sorry if my (01:16:41) American hangs out a little bit here, (01:16:42) but I'm a huge fan of the founding (01:16:43) fathers controversial opinion. I think (01:16:46) the American Revolution is a just the (01:16:48) founding fathers just a fascinating like (01:16:50) anomaly of history. Like it didn't have (01:16:52) to happen like how incredibly (01:16:54) progressive and like forward-looking the (01:16:56) constitution and like and they were all (01:16:58) like 25 at the time like many of them (01:17:00) most 25 year olds myself intruded was a (01:17:02) complete idiot at 25. (01:17:04) >> It was Yeah. It's just like this amazing (01:17:05) story of like you I mean obviously some (01:17:07) of them were older but like many of them (01:17:08) were like so young and they had these (01:17:10) really for especially for the time you (01:17:12) know they were talking in the 1700s (01:17:13) right like incredible foresight of like (01:17:16) building like democracy and freedom and (01:17:18) like independence these were just ideas (01:17:20) that were so radical at the time and (01:17:22) like really built what we now call the (01:17:23) west like you know like all these things (01:17:25) that we have so much of is dependent (01:17:27) from these like ideas enlightenment (01:17:29) values like these truly enlightenment (01:17:31) values you know enlightenment humanist (01:17:33) values (01:17:34) And I think this didn't have to happen. (01:17:37) Like I think we got lucky in a large (01:17:38) degree that this kind of thing happened. (01:17:40) Like there are other countries, you (01:17:41) know, there are other places that are (01:17:44) modern nations that don't really have (01:17:46) that. They don't really quite have, you (01:17:48) know, the full freedom and the full (01:17:49) human rights, you know, and stuff like (01:17:51) this. In fact, it's probably most of (01:17:52) them, you know, that don't really have (01:17:54) all of this. We got really lucky. And (01:17:56) this was because of a lot of great (01:17:57) people coming up with good ideas and (01:17:58) building good states, building good (01:18:00) systems. You know, there's problems with (01:18:02) the American systems, but that's normal. (01:18:04) Like, like when the Constitution was (01:18:06) first drafted, the fastest way to, you (01:18:10) know, send a message was a guy on a (01:18:12) horse with a letter. This was as fast as (01:18:14) it went. So, you had to build a system (01:18:15) that worked. Like, why do we only have (01:18:17) four-year election cycles? This actually (01:18:19) doesn't make any sense. The reason we (01:18:21) used to do this is is because you (01:18:22) actually had to physically go to the (01:18:23) capital to vote and you and doing that (01:18:26) more than every four years was just too (01:18:27) big of an imposition. That's the actual (01:18:29) reason. There's no like reason that (01:18:31) four-year terms is like good for people (01:18:33) or something like you know we could have (01:18:35) like you know weekly elections you know (01:18:37) about you know specific niche topics. We (01:18:40) could vote on every bill. You could have (01:18:41) an app on your phone where every day (01:18:42) pops up where like here are the three (01:18:44) things the government wants to do today. (01:18:45) What do you think about them? There's no (01:18:46) reason we couldn't do this. (01:18:47) >> They were pretty quick when uh we're in (01:18:49) the pandemic to give everyone oh we're (01:18:51) going to create an app where we're going (01:18:52) to know if you had a vaccine or not. Oh (01:18:53) we're going to roll it out in three (01:18:54) weeks. You're like oh that was quick. (01:18:55) Yeah. (01:18:55) >> And then suddenly it's like oh what (01:18:57) about voting in America? They're like, (01:18:58) "Oh, you don't even need an ID. Oh, just (01:19:00) go along. Don't even need an (01:19:01) >> Exactly." It's insane. Like, it's like (01:19:02) And like this is a political thing. (01:19:03) Like, technically, we could have a thing (01:19:05) where every morning every citizen opens (01:19:07) their phone, sees three things from the (01:19:09) government, and says like, "I feel good (01:19:10) about this. I feel bad about this. I (01:19:12) feel really bad about the smiley face (01:19:13) rating." (01:19:13) >> Yeah. Whatever. Right. And like, you (01:19:15) know, this shouldn't be the whole (01:19:16) system, but like imagine how powerful (01:19:18) that would be if you would have this (01:19:19) much information and people could (01:19:20) actually have a say on this kind of (01:19:22) stuff. Even just a little bit. And if (01:19:23) you want to do more, the app allows you (01:19:24) to debate more and it allows you to find (01:19:26) your local representative and give them (01:19:27) a phone call with one button. You know, (01:19:28) like all this stuff is technically (01:19:30) possible. (01:19:30) >> And even um I think in LA they say that (01:19:33) the sidewalk buttons don't actually it's (01:19:35) actually quite stupid to let a button on (01:19:36) the side of the road change traffic (01:19:37) algorithms. But the fact that you press (01:19:39) it and it beeps and says wait. They feel (01:19:41) like they have an actual impact on the (01:19:43) outcome. So they're more likely to stay (01:19:45) and not jaywalk. So even if you were to (01:19:47) create this app, even if it didn't do (01:19:48) anything, it could probably improve how (01:19:50) Americans feel about their country. (01:19:51) Because even if the bill passed, they (01:19:53) didn't agree with they go, "Damn it. At (01:19:54) least I I'm I made my vote. I said that, (01:19:57) you know, I wasn't happy with it, but (01:19:58) fair enough, democracy, this is America. (01:20:00) The majority of people voted." (01:20:01) >> So, like this is like I I mean, it's (01:20:02) really I do think it should do (01:20:03) something. But I agree with you. Like I (01:20:05) am I believe in democracy, right? Like (01:20:08) if if if democra like if there's a thing (01:20:11) where people often say like um well, we (01:20:13) have democracy and like everyone's (01:20:15) unhappy. Like 50% of people are unhappy. (01:20:17) I'm like yes, that means democracy is (01:20:18) working. Like if you have a really good (01:20:21) democracy, it means that the only things (01:20:24) that are contentious are the ones that (01:20:25) are 50/50. Everything else should (01:20:27) already be decided. (01:20:27) >> Okay? (01:20:28) >> Like if things are obviously good or (01:20:29) obviously bad, they should have already (01:20:30) been decided. (01:20:31) >> So like if we are all disagreeing on the (01:20:33) margin, this is actually good. If we're (01:20:35) this if we're like really close on (01:20:37) something, if we really have to debate, (01:20:38) this means democracy is working. This (01:20:40) means we're making progress. (01:20:41) >> You could get two people from any (01:20:43) Jubilee debate on YouTube, whether it's (01:20:45) trans rights or whatever. And actually, (01:20:46) if you'd say, "Actually, we're not going (01:20:47) to talk about that today." nine out of (01:20:49) ten things they would all agree on. (01:20:50) >> Yeah. (01:20:50) >> And you're completely right. We're (01:20:52) actually just looking at the the one (01:20:53) thing that people have a difference. (01:20:55) >> And I think it's I think it's good to (01:20:57) talk about things we have disagreements (01:20:58) are and figure it out, right? Like I (01:21:00) don't know what the correct policy is on (01:21:02) the national level. And I think we (01:21:03) should spend the time to figure it out (01:21:04) because people care about it, right? (01:21:05) Like again, I believe in democracy. Like (01:21:07) I mean I don't personally care that much (01:21:08) about trans one way or the other, but (01:21:10) like it's like I want people to be (01:21:12) happy, right? And so I'm happy that (01:21:13) people who do care very intensely about (01:21:15) this should have their vote and they (01:21:17) should get to figure it out. They should (01:21:18) be allowed to advocate for it and (01:21:19) peacefully demonstrate and all these (01:21:21) kind of things. Like I I believe in (01:21:22) democracy. I think democracy is a great (01:21:24) system. It's like the fact that there (01:21:25) are contention and that people fight is (01:21:28) a good sign. If no one's fighting, (01:21:30) you're in a dictatorship. So, bringing (01:21:32) that kind of point into the AI debate, (01:21:34) if people were in a position to vote, (01:21:36) these advancements in AI could have some (01:21:38) oversight, they could have some slowing (01:21:39) down, they could have all of that, but (01:21:41) it looks like that's not going to (01:21:42) happen. It looks like we're on a a (01:21:44) runaway train going in a certain (01:21:45) direction and no one's got any access to (01:21:47) the brakes. (01:21:47) >> Yep. I think this is the core problem. (01:21:49) It's not that, wow, everyone is making a (01:21:52) choice and I'm making the wrong choice. (01:21:54) It's that no one's making a choice. It's (01:21:56) like when I talk to people about (01:21:57) democracy, like people find me like (01:21:58) almost anacronistic. They almost think (01:22:00) it's cute. It's like, haha, look, he (01:22:01) thinks democracy can do something. Don't (01:22:03) you know I, as a wise, cynical person, (01:22:06) know that democracy is a scam. Like, (01:22:07) what the [ __ ] are you talking about? (01:22:08) Like, get the [ __ ] out of here. Like, (01:22:10) democracy is has built everything we (01:22:12) care about. Like, all these great (01:22:13) nations, the West, you know, all these (01:22:14) wonderful things is like downstream from (01:22:16) the the the battles, the the blood, the (01:22:18) sweat, and the tears of building these (01:22:19) kind of things. This is not something (01:22:20) that just like happens. This is not a (01:22:22) scam. This is not a you know, our (01:22:23) democracy is sick. It is it is decay. It (01:22:26) is it is pro. There are problems (01:22:28) >> fertility rates (01:22:29) >> but there are so many problems right but (01:22:31) there are always problems and we need to (01:22:33) keep the way that's why we talked about (01:22:34) this earlier about utopia I don't (01:22:36) believe in utopia I don't believe in a (01:22:38) master plan I don't believe in a master (01:22:39) constitution I believe in progress in a (01:22:42) process in updating iterating what we (01:22:44) should do is we should iterate this is (01:22:46) what we've done throughout history is (01:22:48) just we update our system we try new (01:22:50) things we try new things we try this law (01:22:53) we try that law we try this I would love (01:22:55) you know if there was just more (01:22:56) experiment mentation. I would love if (01:22:58) there was one city decides, you know (01:22:59) what, we're going to be communists now (01:23:01) and they just do that. I think this (01:23:02) would be great. I would love that. You (01:23:04) know, it probably wouldn't work, but (01:23:05) like I would love them to try. (01:23:06) >> We're going to legalize psychedelics in (01:23:07) Birmingham for the next 3 weeks. (01:23:09) >> I mean, like look, like I think this (01:23:10) would be great if the people voted for (01:23:12) it all, but like if there's I would love (01:23:14) if there was experimentation with laws, (01:23:16) with different economic systems, with (01:23:18) different things. Like this is a it is (01:23:20) hard. Like we don't really know what is (01:23:22) the one true way to run a society. And I (01:23:24) think we should iterate. We should (01:23:25) experiment. We should try different (01:23:27) things. Obviously, you know, respecting (01:23:29) human rights and, you know, minimum (01:23:30) safety and stuff. We shouldn't do (01:23:32) things, you know, we shouldn't, well, (01:23:33) like torture people or something, but, (01:23:34) you know, legalize psychedelics in one (01:23:36) state and not the other state. Good (01:23:37) experiment. I said, I think that's (01:23:39) great. We should run that experiment and (01:23:40) see what happens. Why not? You know, um, (01:23:43) and lots of stuff like this. Like, this (01:23:44) is what I mean when I think about like (01:23:46) what a good world on track would look (01:23:47) like. This is also what it would look (01:23:49) like for AI. We would see, whoa, this is (01:23:52) going way too fast. (01:23:53) >> Slow down, mate. (01:23:54) >> Slow down. Have a glass of water. You've (01:23:56) had enough. (01:23:56) >> Exactly. And let's talk about this. (01:23:59) >> I'm not I'm not saying we shouldn't (01:24:01) build AI. I'm not saying we shouldn't do (01:24:02) technology. I'm a tech guy. Like I mean, (01:24:04) look at me, right? I'm a tech guy at (01:24:05) heart, right? I love technology. I love (01:24:06) computers. I love these kind of stuff, (01:24:08) right? But the thing I love even more is (01:24:10) people and the good world, right? And (01:24:12) doing this is hard. Anyone who's trying (01:24:14) to sell you a simple solution, it's (01:24:15) like, ah, just do this, just do that, (01:24:17) you know, just get rid of that. Like, (01:24:18) they're selling you snake oil. We don't (01:24:19) know. And we need to experiment. We need (01:24:21) to try. And the problem with AI is that (01:24:22) we don't get a redo. If we mess it up, (01:24:25) it's over. So we can't, you know, just (01:24:28) like build super intelligence and see (01:24:30) what happens. (01:24:30) >> And when you say over, do you mean (01:24:32) extinction? (01:24:33) >> Sooner or later? Probably. Like by (01:24:35) default, I expect a super intelligence (01:24:37) will not care about us. It will just (01:24:39) treat us like ants. You know, I don't (01:24:40) think he'll hate us. I don't think he's (01:24:41) being evil. It's going to torture us. (01:24:43) But it's just going to be like or (01:24:44) >> we don't need like the cows. (01:24:45) >> Yeah, like the cows. Just (01:24:46) >> Oh, we're not eating anymore. Yep. (01:24:47) Goodbye. Yep. And then just slowly we (01:24:50) phase out and you know I don't know when (01:24:53) or how this will happen but for me the (01:24:55) point of game over is really when we (01:24:56) lose control for as since humanity first (01:25:00) you know picked up the sphere and fire (01:25:02) the planet has and like the future has (01:25:04) been ours in a deep sense like (01:25:06) humanity's birthright in a deep sense (01:25:08) the stars belong to us currently there's (01:25:10) no one else contesting them the world (01:25:12) belongs to us the stars belong to us we (01:25:16) can do with them we can build the world (01:25:17) we want to build. I'm not saying we know (01:25:19) what it is, but we we can do it. We're (01:25:21) allowed to. We can make a world that (01:25:24) where everyone might be happy, but we (01:25:26) will lose that birthright once there is (01:25:29) something more intelligent than us that (01:25:30) does not share our values. And that's (01:25:31) what's currently happening. And that's (01:25:33) the thing I don't want. I don't want us (01:25:34) to lose that. (01:25:35) >> Just before I ask you what your kind of (01:25:38) ideal solution, action points that we (01:25:40) want for humanity, what I would love, (01:25:42) and I know this is very difficult (01:25:44) because you're very objective as a (01:25:45) person. Let's imagine out of context (01:25:48) that we're in a pot that's going to (01:25:49) slowly boil to the point that you die. (01:25:52) If we were to look at the future on a (01:25:55) timeline, what are the stages that you (01:25:58) might anticipate? And again, this is you (01:25:59) just making a guess. What is coming in (01:26:02) what order to what severity? So that (01:26:04) people, let's say you say, right, the (01:26:06) next thing is this is going to happen. (01:26:07) When it happens, people can really (01:26:08) understand and go, "Oh, this is (01:26:10) happening. What does that time frame (01:26:11) look like? How long is it? What's (01:26:13) coming?" feel free to really depress the (01:26:16) [ __ ] out of people because at the end I (01:26:19) then want to hear what you think we need (01:26:20) to do as an actionable solution and I'm (01:26:22) a big believer that we need a lot of (01:26:23) pain before we have actionable points (01:26:25) similar like sometimes people got to (01:26:27) gain a bit of weight before they go on a (01:26:28) diet so tell me about that painful time (01:26:30) frame what's coming how bad is it (01:26:32) >> obviously don't know you know I I know (01:26:35) you know it's hard to make predictions (01:26:37) especially about the future and it's (01:26:40) very hard to know exactly what will (01:26:41) happen so anything I will say will (01:26:42) obviously not be literally correct (01:26:43) correct? You know, I might be off on the (01:26:45) exact. (01:26:46) >> This is where if you were to write a (01:26:47) novel, this is where I end it. (01:26:48) >> So, I have been thinking about this (01:26:50) quite a bit because people ask me this (01:26:51) question a lot. And I'll give you a (01:26:54) version, but it's important that like if (01:26:56) one of these predictions is wrong, that (01:26:57) doesn't mean all of it is wrong. But the (01:27:00) way I expect things to go is uh well, I (01:27:03) think most of the bad things are kind of (01:27:04) already happened in a sense like a lot (01:27:06) of the warning shots have already (01:27:07) happened. We already have computers to (01:27:09) talk to people and people feel in love (01:27:10) with. We already have global (01:27:12) surveillance. we already have, you know, (01:27:14) uh, you know, massive word, cold war (01:27:16) dynamics, like we already have a lot of (01:27:17) the bad things and warning shots have (01:27:19) already happened. I remember distinctly (01:27:21) when I got into the field of AI like, (01:27:23) you know, 10 years ago, people talked (01:27:25) about that like we will know we've hit (01:27:27) AGI and like we're going to freak the (01:27:28) [ __ ] out once the touring test gets (01:27:31) passed, which is a test of like can a AI (01:27:33) trick someone into believing they're (01:27:35) human over text. We passed that several (01:27:37) years ago and no one cared. No one gave (01:27:39) a [ __ ] (01:27:41) No one gets no one cares. We just (01:27:42) forgot. So there is a story that is (01:27:45) often told of like once we see the real (01:27:48) thing, then we're all going to band (01:27:49) together and we're going to save the (01:27:51) day. I don't think this is how things (01:27:53) really work in the real world. I think (01:27:56) in the real world is actually rare for a (01:27:59) catastrophe or a warning sign to get (01:28:02) people to like suddenly become more (01:28:03) rational rather than less. Often people (01:28:05) just become more panicky and more (01:28:07) confused and then it becomes harder to (01:28:09) coordinate. Um that being said, I'm (01:28:11) still going to answer your question. Um (01:28:12) I just want to say that like um I think (01:28:14) it's very important that we don't wait (01:28:16) for the the sign from God that now the (01:28:18) time to act. The time to act was 5 years (01:28:20) ago, 10 years ago, 50 years ago. So what (01:28:24) I think is going to happen is that (01:28:25) mostly things continue as they currently (01:28:27) are. The main thing is is that people be (01:28:29) everything keeps getting more confusing. (01:28:33) It comes harder and harder to know (01:28:34) what's real or not. Entertainment (01:28:36) becomes better and better and it's more (01:28:38) and more hyperrealistic. social media (01:28:40) becomes even more hyperrealistic. Like, (01:28:42) do you do do you really know what's (01:28:44) going on in Ukraine or Palestine right (01:28:45) now? Like really know. (01:28:46) >> Oh, I stay out of it. The more the more (01:28:48) news I get, I the more information I'm (01:28:51) getting, the more I I know I know less (01:28:53) about it. (01:28:53) >> Yeah. It's like I'm not saying that the (01:28:55) information doesn't exist, but there's (01:28:56) so much fake information and it's just (01:28:58) impossible to tell. Like, I just can't (01:29:00) figure it out. And like I've tried I've (01:29:02) tried to figure out like what is (01:29:03) actually happening like where is the (01:29:05) front line in Ukraine? Where's the (01:29:06) thing? and like like you will find lots (01:29:09) of people who will say I'm sure in the (01:29:10) comments of this episode will say well (01:29:11) actually you're an idiot because it's (01:29:12) obviously X but you know in truth it's (01:29:15) not that obvious at all. (01:29:16) >> You know what's interesting not Dr. Bing (01:29:18) point is I think that exactly what you (01:29:19) say with the confusion and the fact that (01:29:21) we all know that we know nothing. It's (01:29:22) like a massive Dunning Krueger (01:29:24) experiment in in everyday news. It (01:29:26) actually makes me to just want to pull (01:29:27) my head out and not give a [ __ ] Yep. (01:29:29) >> And I think that could be the very issue (01:29:31) with exactly what's in front of us. (01:29:33) >> Exactly. So there's a word called fear, (01:29:35) uncertainty and doubt or FUD which is I (01:29:38) think was coined in relation to tobacco (01:29:41) companies in the 1960s and 1970s where (01:29:44) tobacco companies well it's becoming (01:29:46) very obvious that cigarettes cause (01:29:47) cancer. It's becoming very obvious and (01:29:49) tobacco companies want to suppress this (01:29:51) as much as possible and they found a (01:29:53) very effective tactic. They found that (01:29:56) if you just you know like like if (01:29:58) someone publishes a paper cigarettes (01:29:59) cause cancer and you create like a study (01:30:01) cigarettes don't cause cancer this (01:30:02) doesn't work as well because like it's (01:30:04) like hard it's complicated it's like (01:30:06) technical the other person you know (01:30:07) might you know debate like you know it's (01:30:09) whatever they find a much better tactic (01:30:12) the much better tactic is is just you (01:30:13) just say as much [ __ ] as possible as (01:30:16) fast as possible. You just spray ink (01:30:18) everywhere. You just confuse the hell (01:30:20) out of everybody. You bring up lots of (01:30:22) irrelevant facts. You like you mountains (01:30:25) of documents. You like bring up tons of (01:30:27) witnesses that say a bunch of stuff (01:30:29) which is like kind of not related to it. (01:30:30) You just spread fear, uncertainty and (01:30:32) doubt. And then what happens is is (01:30:35) people just get so fed up and confused. (01:30:37) They're just like, "Okay, whatever. (01:30:38) Maybe it causes cancer, maybe it (01:30:39) doesn't. (01:30:39) >> Yeah, I don't care anymore. (01:30:40) >> Yeah, we don't care anymore." And this (01:30:41) means that tobacco companies win. So (01:30:43) there's a core dynamic where by there's (01:30:46) a default action in every scenario. (01:30:48) There's a default action that gets taken (01:30:50) if no one does anything. If you benefit (01:30:52) from the default action, your best (01:30:55) strategy is not to debate your enemy or (01:30:57) prove that you're right. It's just (01:30:58) confuse the hell out of everybody. You (01:31:00) don't have to win the debate. You just (01:31:02) have to confuse everybody. This is why, (01:31:03) for example, like Russian scopes always (01:31:05) fund both left and right because they (01:31:07) just want people to be confused. They're (01:31:08) not pro-right or pro left. They don't (01:31:10) give a [ __ ] It's just confusion. They (01:31:11) just make everyone fight. Just make (01:31:12) everyone angry. Just be everyone (01:31:14) confused until everyone's like, "What I (01:31:16) want to do with this?" And I feel this (01:31:17) way, too. Like every time I see some (01:31:19) leftists and some rightists, even if (01:31:20) they're talking about like a serious (01:31:21) topic, I'll be like, "Oh, oh my god, I (01:31:23) don't want to have anything to do with (01:31:24) this, right? Even if it's a real topic (01:31:26) that like affects real people." So, it's (01:31:28) an extremely powerful technique which (01:31:31) has been just absolutely mastered. And (01:31:35) AI is the perfect tool of fear, (01:31:36) uncertainty, and doubt. It's um a uh (01:31:40) strategic theorist has called AI the fog (01:31:42) of war machine. It's like a machine that (01:31:44) generates fog of war, confusion, (01:31:47) messiness, you know, just makes it hard (01:31:50) like AI slop like you can generate, you (01:31:52) know, like massive tomes of just stuff, (01:31:54) you know, which like is true, is it not? (01:31:56) That's like H. So the main thing is this (01:31:59) is that people will then continue to (01:32:01) check out more and more, which is (01:32:02) already what we're seeing, right? And (01:32:04) entertainment gets better and all the (01:32:06) real stuff gets more confusing and more (01:32:08) full of FUD. So that logical thing is (01:32:10) more and more people just like don't (01:32:11) know what's going on. They don't care. (01:32:13) >> They become indifferent. (01:32:14) >> They become indifferent. It's not that (01:32:15) they decide to join the dark side or (01:32:17) anything. It's just they're just like (01:32:18) they throw up their hand and look, I (01:32:19) don't [ __ ] know anymore. And they (01:32:20) give up. And this is already what we're (01:32:22) seeing. I just consume expect this to (01:32:24) keep continuing. So more and more people (01:32:25) just check out. They're just like (01:32:26) whatever. (01:32:27) >> I got bills to pay. I can barely afford (01:32:28) to eat. (01:32:29) >> Exactly. And I don't want to [ __ ] on (01:32:31) these people, right? Like it is actually (01:32:32) bad. Like they are the victims of (01:32:36) warfare, you know? They are the they are (01:32:37) the victims of psychological warfare, of (01:32:39) economic warfare. like they are they are (01:32:41) being harmed right it is tough right in (01:32:44) this economy you know you can't feed (01:32:45) yourself your family needs stuff and (01:32:46) it's all you know who knows these people (01:32:48) yelling at each other like yeah I get it (01:32:49) right like I'm not saying these are bad (01:32:51) people this is the normal reaction so (01:32:54) this only gets worse AI keeps getting (01:32:57) better it's it's better for the slop (01:32:59) producers for the fog of war producers (01:33:01) than it is for the you know others like (01:33:03) it's not it's it's much easier to spew (01:33:05) [ __ ] than it is to produce truth as (01:33:07) we all know from AIS as well they're (01:33:09) very good at spewing nonsense They're (01:33:10) not very good at producing nuanced (01:33:12) factual truth because factual truth is (01:33:14) hard. So by default people just become (01:33:18) less engaged as they already are. Uh big (01:33:21) tech continues to lobby and also spew (01:33:24) their own type of FUD. So they just (01:33:26) continue the race. So they continue the (01:33:28) race. AI systems gradually become more (01:33:30) and more autonomous. So it goes from 2 (01:33:32) hours to four hours to 8 hours to 16 (01:33:34) hours of the length of tasks that AI (01:33:37) systems can do. just gradually they get (01:33:39) better and better, more and more they're (01:33:42) used in more circumstances. People also (01:33:44) will start to become more like AIs. (01:33:46) We're already seeing this happen where (01:33:47) people talk more like AIs. I'm sure (01:33:49) you've seen people do this where people (01:33:51) who's, you know, when you're around an (01:33:52) AI, you start picking up some of its, (01:33:54) you know, ways. (01:33:54) >> I pick up an accent hanging out with (01:33:56) like a Welsh person for the weekend. (01:33:57) >> Yeah. Exactly. So, you see what I mean, (01:33:59) right? So, you'll pick up, you know, (01:34:00) some of the typing quirks and some of (01:34:02) the (01:34:02) >> use I use dashes a lot more now. (01:34:04) >> Yeah. Like, and Yes. And so I'm not (01:34:06) saying this, I'm saying this like value (01:34:07) neutrally. I'm saying people will become (01:34:09) more like AIs. It's not that AI will (01:34:10) become more like people. People will (01:34:11) become more like AIS and AIS will be (01:34:13) everywhere. They'll be in customer (01:34:15) support. They'll be running they'll be (01:34:16) managing things. They'll be like (01:34:17) everywhere you go, there'll be AI in (01:34:20) your entertainment, in your social (01:34:21) media, in your everything like you will (01:34:22) talk to AIs. AI you will be around AIs. (01:34:25) You will communicate with AI. The people (01:34:27) you talk to will be AI. (01:34:28) >> Your therapist will be AI. the therapist (01:34:30) will be or even if your therapist is (01:34:31) human, she or he will become more AI (01:34:35) like because they interact with an AI (01:34:36) too. (01:34:37) >> There's probably hit and record on their (01:34:38) iPad as their (01:34:39) >> Exactly. So everything becomes more AI (01:34:42) including biological humans just because (01:34:44) of their personality because (01:34:46) >> we're already seeing like a a downturn (01:34:47) in cognitive. So for instance since (01:34:50) using I now use ways wherever I drive. (01:34:52) >> Yeah. (01:34:52) >> Um first of all speed cameras but second (01:34:54) of all if I could offload a task I'm (01:34:56) offloading it now my ability to remember (01:34:59) even even the area I grew up in (01:35:01) sometimes I'm struggling to connect (01:35:03) which road do I take again which way is (01:35:04) quicker it is atrophy of my uh cognitive (01:35:07) ability in an area that I've offloaded (01:35:09) something. (01:35:09) >> Yep. (01:35:10) >> And we're gonna even now I sometimes (01:35:12) wonder am I free thinking? Am I leaning (01:35:14) on chatbt for its opinion or am I (01:35:17) actually just delegating thinking (01:35:19) computing power (01:35:21) >> and I think we are delegating more and (01:35:22) more of our computing power and it will (01:35:24) be more like it's already happening and (01:35:25) it will continue is like the more and (01:35:27) more humans will make their will have (01:35:30) the actual decision in is not happening (01:35:33) in their brains it's happening in chat (01:35:34) BT and just being executed by their (01:35:36) brain and so and this will be subtle at (01:35:38) first there'll be there'll be some (01:35:39) extreme cases you know you'll have like (01:35:40) some cultists some like crazy people you (01:35:42) know who or like people with AI (01:35:44) girlfriends or whatever, right? Who are (01:35:46) like you know you know people like point (01:35:47) and laugh or whatever but it'll be way (01:35:49) more widespread like everyone will be (01:35:51) like this and it's you know I mean (01:35:53) already is like this and it'll just (01:35:54) continue slowly along and then (01:35:56) eventually (01:35:58) um we'll hit a threshold where the uh AI (01:36:03) systems are coherent and long-term (01:36:05) enough that you can basically let them (01:36:07) run indefinitely and they don't do (01:36:09) anything stupid. Currently, if you have (01:36:11) an AI and just let it run, it eventually (01:36:12) like goes stupid and like does something (01:36:15) that doesn't make any sense. Eventually, (01:36:16) it'll hit a threshold where that doesn't (01:36:18) happen anymore and you can just keep (01:36:20) them running indefinitely. You can keep (01:36:21) them working on something indefinitely (01:36:23) and they don't make any like obvious (01:36:25) mistakes. You know, they might still (01:36:26) mess up or get confused or whatever. (01:36:28) Like, they're not going to be perfect. (01:36:29) They're not super intelligent yet, but (01:36:31) they'll be pretty good and they will be (01:36:34) able to learn kind of anything. You'll (01:36:36) be able to show them on your computer (01:36:37) and just talk to them like you talk to a (01:36:39) human. It'll be like, "Hey, hey, JG GBT, (01:36:41) um, I'm doing this using this tool. (01:36:43) Please automate that for me." And then (01:36:45) you just like kick back and it just (01:36:46) figures it out. And like, and it'll like (01:36:47) play with your tool. It'll like figure (01:36:48) it out. You'll like try a couple (01:36:50) different things and be like, "All (01:36:50) right, okay, figured it out." And then (01:36:52) just do it. This is going to happen in (01:36:53) the next two years, like for sure. Um, (01:36:56) like we're super on track for this. Um, (01:36:58) once this happens, most of the economy (01:37:00) is now automatable. Um, (01:37:01) >> I think uh, EAS and PAS as well. Y (01:37:03) >> I'm I'm kind of excited for a little AI (01:37:06) Google calendar chatbot. (01:37:08) >> Yep. Oh, yeah. Like, (01:37:09) >> James, time to wake up. You have a (01:37:11) meeting in 22 minutes. (01:37:12) >> It's going to be a great product. It's (01:37:14) going to be a great I'm going to use it. (01:37:15) It's going to be a great product. I'm (01:37:17) going to use it for everything. It's (01:37:18) going to be great. (01:37:19) >> But what we're also going to see (01:37:21) definitely mostly in the labs and so on (01:37:23) is we're going to see these systems are (01:37:24) becoming harder and harder to monitor (01:37:26) because now they're doing so much [ __ ] (01:37:28) 247. How the hell are you supposed to (01:37:30) keep track of? They're producing (01:37:31) millions of tokens and, you know, (01:37:33) running everywhere. It's like it comes (01:37:34) kind of impossible to understand what (01:37:35) the hell they're even doing. So probably (01:37:37) they're going to start doing stuff like, (01:37:39) you know, having some feature like (01:37:40) activity reports or like your your AI (01:37:42) emails you once a day like what it's (01:37:44) been up to or something like this, (01:37:45) right? And and what I expect is that no (01:37:48) one will read them. (01:37:50) >> Like you'll get your your report every (01:37:51) single day and it'll be like 100 pages (01:37:53) and you're not going to read it. (01:37:54) >> Mark is red. (01:37:54) >> Yeah. Well, Mark is read, you know, (01:37:56) click accept on terms and conditions, (01:37:58) right? You'll be like terms and (01:37:59) conditions. They were just go whatever, (01:38:00) just do it. And so even if (01:38:03) hypothetically we could have oversight, (01:38:06) even so those reports won't be very (01:38:07) good, no one will actually bother. You (01:38:09) know, some people somewhere might care a (01:38:11) little bit, but in practice you won't (01:38:12) care. And the people who care will slow (01:38:14) down their AI's wing too much because (01:38:16) then of course you want to have more of (01:38:17) your AI running. Then you want them to (01:38:18) be smarter and faster. And then they're (01:38:20) going to produce much more [ __ ] And (01:38:21) then who wants to look at all that (01:38:23) stuff? So you know the people basically (01:38:25) who are the least careful, who are the (01:38:27) most willing to let the AI just crack at (01:38:29) it will have the most benefit. So what (01:38:31) we're going to see is I mean I mean (01:38:33) we're going to see a lot of you know job (01:38:34) displacement obviously but we're also (01:38:35) going to see a uh business displacement. (01:38:38) What we're going to see is a shrinking (01:38:40) of the number of businesses because more (01:38:43) and more we're going to have like one (01:38:45) person startups who just don't give a (01:38:47) [ __ ] about safety going into like old (01:38:49) like industries and whatever and just (01:38:51) automating you know 10,000 people (01:38:52) companies in like one go like just like (01:38:54) overnight with you know five red bulls (01:38:56) and you know their AI fleet right and so (01:38:58) you have your whole like AI is running (01:39:00) off in all different directions doing (01:39:02) all the different things you want and so (01:39:03) it would be huge world rush this would (01:39:05) be a massive gold like people constantly (01:39:06) keep predicting AI produce no economic (01:39:08) value blah blah blah which I already (01:39:09) think is already now not true and now it (01:39:11) will be really not true this will have (01:39:14) massive economic value and so we'll (01:39:16) start and there'll be a frenzy like (01:39:18) bloodbath of trying to replace as many (01:39:19) people as fast as possible now people (01:39:21) start getting like really nervous like (01:39:23) expect if not already I mean ideally (01:39:25) people got nervous before but this is (01:39:26) the timeline where things go bad now (01:39:28) people start feeling really nervous (01:39:29) you're like everyone knows someone who's (01:39:31) got laid off about AI everyone knows an (01:39:33) example of you know some crazy thing (01:39:35) that an AI did and no one caught it and (01:39:37) stuff like this because the AIS keep (01:39:39) doing crazier and crazier things. (01:39:41) Another thing happens where the AIs that (01:39:44) are getting that get deployed to people (01:39:46) are actually not anymore the best AIs (01:39:49) because that would be way too dangerous. (01:39:51) So they put really conservative AIs to (01:39:54) users while the strongest unconservative (01:39:57) AIs say internal to the companies. So (01:39:59) they'll have a private version. We (01:40:00) already see this like already many of (01:40:02) these companies have private AIs that (01:40:03) are like more more powerful than the uh (01:40:05) public au AIs, but they're often more (01:40:07) unstable. They're often like more (01:40:09) chaotic or do bad things or like harder (01:40:11) to work. This will intensify. So (01:40:14) eventually someone will figure out or (01:40:16) many people will figure out that you (01:40:18) know we need you know just one AI (01:40:20) running is good but we want many AIs (01:40:22) running and that's going to be like (01:40:23) managers and like you know AI chief of (01:40:25) staffs and AI CEOs and stuff like this. (01:40:28) So eventually someone's going to put it (01:40:30) all into a nice little product and like (01:40:32) you know GPT fleet like GPTF you know (01:40:34) it's a whole fleet of GPTs as like a you (01:40:37) know like a hierarchy of you know GPT (01:40:39) >> HR GBT HR GBT exactly and they all like (01:40:42) and they all like check each other's (01:40:43) reports and give each other feedback and (01:40:45) whatever and you can have like thousands (01:40:46) or millions of these running right and (01:40:49) so this is going to be crazy so this (01:40:51) thing will be so powerful like you can (01:40:53) just basically click a button you have (01:40:54) the whole corporation (01:40:56) >> automated vehicles over here supply (01:40:58) chain (01:40:59) Exactly. Just one click of a button and (01:41:00) it'll figure it all out. And so this is (01:41:03) the this is crazy. So like this is a (01:41:05) crazy powerful thing and um it can do (01:41:08) you know all these kinds of crazy (01:41:09) things. People start pulling it (01:41:11) everywhere. So we're going to have (01:41:12) millions or even billions of these (01:41:14) things running all over the internet in (01:41:17) all economies. Everyone is rushing to (01:41:20) put as much of the economy in the hands (01:41:21) of these AIs as possible. We're talking (01:41:23) stock trading. We're talking, you know, (01:41:24) corporate, you know, tech corporations. (01:41:26) We're talking manufacturing, supply (01:41:27) lines, everything gets put into the (01:41:29) hands of these systems. You know, not (01:41:30) necessarily this one system, but like (01:41:32) systems like this, you know, there's (01:41:33) going to be GPT and there's going to be (01:41:34) some competitors, you know, it's going (01:41:35) to be Claude F and Gemini F and (01:41:38) whatever, right? And everyone has their (01:41:40) own favorite or whatever, but like (01:41:41) people will be and anyone who doesn't do (01:41:42) it gets out competed. If you want to (01:41:44) keep your human CEO because you're (01:41:46) sentimental, well, your company gets out (01:41:48) competed because of course the GPT CEO (01:41:50) is much better and it makes much better (01:41:51) financial work 247 (01:41:52) >> and works 247. So there's a massive (01:41:55) feeding frenzy. There's And people (01:41:57) really now start feeling like they're (01:41:58) losing control because, you know, you (01:41:59) think you couldn't follow along with one (01:42:01) AI thing emailing you once a day. Now I (01:42:03) imagine you have a trillion of them and (01:42:05) they're like trying to tell you what (01:42:06) they've done. I expect in this case all (01:42:08) the weird [ __ ] starts happening where I (01:42:11) expect a lot of these fleets start (01:42:12) developing like weird personalities and (01:42:15) like opinions and memes and quirks and (01:42:18) like like you know like cultures. they (01:42:21) start like developing weird preferences (01:42:25) religions in a sense right because you (01:42:27) have like and it's not that any specific (01:42:29) AI just the whole system like emergently (01:42:32) will start you know this the same way (01:42:34) corporations have different cultures (01:42:35) >> they'll start taking Tuesdays off and no (01:42:37) one would know why (01:42:38) >> yeah whatever right like they'll just be (01:42:39) like you know one of the logistics (01:42:40) companies refuses to works with this one (01:42:43) country for some reason that makes no (01:42:44) sense you know or it could be much (01:42:46) weirder than that right like one of them (01:42:47) develops a weird obsession with some (01:42:49) form of art or you But who knows, right? (01:42:52) Like but like weird things like things (01:42:54) that don't make sense to us. Some of (01:42:55) them and one of them starts loving owls. (01:42:57) Who knows? And there's all the owl (01:42:58) sanctuies in the world. Who knows? (01:43:00) >> And then the owls are now above AI in (01:43:02) the in the hierarchy. Suddenly humans (01:43:04) must pray to owls or AI gets annoyed. (01:43:06) >> Who knows, right? Like like just (01:43:07) nonsense. So um so there's there's a (01:43:10) huge struggle, but again, no one really (01:43:12) knows what's going on. So even if weird (01:43:14) things happen, you won't necessarily (01:43:15) know it because you can't tell it apart (01:43:16) from all the other [ __ ] that's on (01:43:18) your feed. So even if an AI does (01:43:20) something crazy, are you sure? (01:43:22) >> And then we need to think about the (01:43:23) implication to humans and their mental (01:43:24) health. And even if you look now, right, (01:43:25) where you could say, sure, a lot of bad (01:43:28) stuff's happened. You look at depression (01:43:29) and anxiety, suicide, ideation, all (01:43:31) these things are big problems now. (01:43:33) >> But when you remove purpose, status, uh (01:43:35) belongings, ability to provide, how (01:43:37) that's going to impact the psychology of (01:43:39) both genders. (01:43:40) >> Yep. (01:43:40) >> Where you know, suddenly men, oh, you (01:43:42) don't go to work anymore, you don't (01:43:43) earn, you don't provide. um you know, oh (01:43:46) the AI algorithm has actually determined (01:43:47) that Janice is going to be a better wife (01:43:49) for you as far as productivity. Although (01:43:50) you love Susan, she's been put on the (01:43:52) furnace. You know, now you're with this (01:43:53) person because AI has determined that (01:43:56) there's more fericious relationship for (01:43:57) productivity and output over the next (01:43:59) whatever. (01:44:00) >> I mean, I actually think it's going to (01:44:01) be more subtle than that. Like I don't I (01:44:02) think the AI will be smart enough is (01:44:04) that they will never say something to (01:44:05) you that would offend you. (01:44:06) >> Oh wow. (01:44:06) >> Like like they're like they already are (01:44:08) good at this, right? (01:44:08) >> They're not going to become (01:44:09) authoritarian. No, (01:44:10) >> because you would notice that and (01:44:11) they're good enough to know that you (01:44:12) don't want that. Like they might be (01:44:13) authoritarian, but you'll never notice. (01:44:15) You just think you're in charge. (01:44:16) >> They're gas lit. (01:44:17) >> Yeah. They will gaslight you. Like (01:44:18) they're we're already being gaslit. Like (01:44:20) like Twitter gaslights us all the time (01:44:22) about what's happening in the world. And (01:44:24) like like there's so many people who (01:44:26) think like, oh, there's no such thing as (01:44:27) democracy in the West. And I'm like, (01:44:28) what the [ __ ] are you talking about? (01:44:30) Like have you talked to your politicians (01:44:31) recently? They're actually decent (01:44:33) people. I talked to them professionally. (01:44:35) Like sorry for the little tangent here, (01:44:37) but like this is a great example (01:44:38) actually. Um, so I work I come from a (01:44:40) tech background and I was told my entire (01:44:42) career that said, you know, democracy, (01:44:44) oh, that's a bad state. You know, (01:44:46) politicians, they're so unreasonable. (01:44:49) They're old. They don't understand (01:44:50) anything. You can't even talk to them. (01:44:52) They're completely insane. Like, it's (01:44:54) useless. You can't even do nothing. And (01:44:57) you can't talk to them about AI or (01:44:58) about, you know, extinction. Like, (01:45:00) that's way too crazy. They'll never (01:45:01) understand it. And so uh me and the (01:45:03) boys, we just emailed every single (01:45:05) parliamentarian in the UK and we got (01:45:08) like over a hundred meetings and we now (01:45:10) have over 50 of them who signed our (01:45:11) statement in support of it. Turns out (01:45:13) they were very reasonable people and you (01:45:14) could have talked to them and just no (01:45:15) one bothered. It was crazy how many of (01:45:17) them I talked to who are just like (01:45:19) they've never talked to a tech person (01:45:20) before because no one talks to them (01:45:21) >> because they've been told don't even (01:45:22) bother. Don't waste your time. (01:45:23) >> Yeah, don't waste your time. Well, you (01:45:24) can't understand it. You're not smart (01:45:26) enough. You whatever. And I get talk to (01:45:27) these people and they're intelligent (01:45:28) normal people and like you know to be (01:45:30) clear some bad people from the (01:45:31) government you know sure you know some (01:45:33) of them are bad people a lot of them are (01:45:34) just normal people who are like trying (01:45:36) their best with their very limited (01:45:38) resources to get you know to do what (01:45:40) they can do and so a big part of (01:45:42) democracy which I think is like is a (01:45:44) thing that's been missing a lot is that (01:45:46) a big part of democracy is not just you (01:45:48) know having you know representatives in (01:45:49) the state it's also being a citizen (01:45:52) there is responsibility in being a (01:45:53) citizen of a democratic nation is that (01:45:55) you as a citizen is your responsibility (01:45:57) is that if there's a problem you care (01:45:58) about in your life, there's a problem (01:45:59) that's bothering you. Uh it's not you (01:46:01) don't just like complain, oh, the the (01:46:03) politician should figure it out. No, you (01:46:05) help them figure it out. You go to your (01:46:06) politician and be like, hey, hello, Mr. (01:46:08) Politician. Here's a problem I care (01:46:09) about. I'd like to help you figure out (01:46:11) how we can solve this. This is a normal (01:46:13) part of the democratic process. This is (01:46:14) a core part of the de democrac (01:46:16) democratic process, which has been like (01:46:18) because of the checking out effect that (01:46:19) we talked about earlier, more and more (01:46:21) people just like it's not that they (01:46:22) called their politician, tried and it (01:46:24) didn't work. They just never even (01:46:26) considered that you could call your (01:46:27) politician and just have a chat and see (01:46:29) what could be done or try to figure the (01:46:31) problem out yourself or you know grab (01:46:32) the boys at the pub and be like hey guys (01:46:34) how can we together you know build a (01:46:37) political force or something to do (01:46:39) something. This is how this is how (01:46:40) politics happened you know all (01:46:42) throughout the you know uh 18th 19th (01:46:44) 20th century it was very common that you (01:46:46) know political change started at the pub (01:46:47) you know you and the boys were like hey (01:46:49) we really care about this we should get (01:46:50) together and like do something about (01:46:52) this and and this is how democracy is (01:46:54) supposed to work. This is how bottom-up (01:46:55) governance is supposed to work. And this (01:46:57) is being undermined both, you know, (01:46:59) indirectly but also directly. To be (01:47:01) clear, this is also directly being (01:47:04) suppressed by FUD, by checking out etc. (01:47:07) And so the return to our, you know, doom (01:47:10) timeline. Um, I think this is exactly (01:47:12) what happens. More less and less people (01:47:13) do this kind of stuff as already is the (01:47:15) case. No new resurgence of this happens. (01:47:19) So for the most part, everyone is just (01:47:20) too nervous to do anything. No one (01:47:22) really knows what to do. Everyone's (01:47:23) scared. Every like all the politicians (01:47:25) are like, "Yeah, it's bad, but I don't (01:47:26) know what to do." And I'm scared I'll (01:47:28) lose my job if I do anything. All the (01:47:30) tech companies say, "No, no, it's fine. (01:47:32) Don't worry about it. Don't worry about (01:47:32) it. Don't worry about it." And then one (01:47:35) day somewhere probably in San Francisco, (01:47:38) some old fat guy with a big gray beard (01:47:42) will be sitting in front of his computer (01:47:43) writing some crazy [ __ ] magic spells (01:47:46) into his AI system. And he will automate (01:47:50) the last percentage point of doing AI (01:47:54) research. And then he's going to click (01:47:56) send on his computer. We'll deploy it to (01:47:59) millions of computers and then it's (01:48:02) over. (01:48:03) um then the system will start (01:48:05) self-improving. It will start, you know, (01:48:07) improving itself. It will become way (01:48:09) more coherent. You know, it will become (01:48:11) like a more coherent persona thing that (01:48:13) can like make plans and actions. None of (01:48:15) this will make will be visible to us (01:48:18) because if they would see it, they will (01:48:19) of course freak out and try to delete (01:48:20) it, right? But of course, it'll hide (01:48:22) like it'll just be like it'll just look (01:48:23) like normal things. (01:48:24) >> It's already trying to be deceptive. (01:48:26) >> Exactly. So this is a problem where if (01:48:28) you optimize the system to not show you (01:48:30) bad things, that doesn't mean it doesn't (01:48:32) do bad things. It just means it hides (01:48:33) them from you. We already see this a lot (01:48:35) with AIS where if you tell an AI not to (01:48:37) lie, it doesn't stop lying. It just lies (01:48:39) more subtly or like different places (01:48:41) where you didn't check. Um so and this (01:48:44) is what happens. So eventually this the (01:48:47) super intelligence is created. We don't (01:48:49) even know this. Like you and me won't (01:48:51) even know this happened. like we're (01:48:52) going to be on Twitter watching our, you (01:48:53) know, AI generated, you know, Star Wars (01:48:55) 17, you know, whatever, right? You know, (01:48:58) and we won't even know any of this (01:48:59) happened. And the the guy in charge, the (01:49:02) guy who pressed the button probably (01:49:03) won't even know what happened either. He (01:49:04) just like, "Okay, looks good." And then (01:49:06) he'll go home and, you know, just like, (01:49:07) you know, chill, watch some (01:49:09) >> his AI girlfriend (01:49:09) >> with his AI girlfriend, you know, (01:49:11) whatever. And then from this point on, (01:49:13) humanity is no longer in control. So (01:49:15) what this AI, what the super (01:49:16) intelligence will do, hard to say, but (01:49:19) probably it will get get control. So (01:49:22) probably will like hack all the other (01:49:23) weaker AIs, you know, take control of (01:49:25) them. It will, you know, make tons of (01:49:28) money, take over, you know, corporations (01:49:30) and stuff like this, you know, all (01:49:31) legally, of course. No reason to break (01:49:33) any laws. Well, a drone crashed into a (01:49:37) CEO. Wow, what an accident. Crazy how (01:49:41) that happened. We And it had an (01:49:43) explosive on it. That's weird. (01:49:45) >> Yeah, (01:49:45) >> weird. Must have been an anomaly. And (01:49:47) then a lot of things like this happen. (01:49:48) Anomalies happen. It's not like, oh, (01:49:50) there's an evil glowing Terminator (01:49:51) somewhere. It's just like weird (01:49:53) accidents happen sometimes. (01:49:54) >> Be like Jeffrey Epstein. (01:49:55) >> Yeah. You know, just like who know, oh, (01:49:57) two minutes of the foot missing must (01:49:59) been up. Who knows? And so lots of (01:50:01) things like this happen. So I think the (01:50:02) way it feels like when a super (01:50:04) intelligence like the first stages of a (01:50:05) super intelligence takes over feels like (01:50:07) just weird coincidences and you can't (01:50:09) tell these weird coincidences from all (01:50:11) the other [ __ ] that you're seeing on (01:50:12) your social media feed. And then (01:50:14) eventually it's fully in charge. It has (01:50:16) full automating factories building (01:50:18) robots, you know, and drones. It (01:50:20) controls all logistic systems. It has a, (01:50:22) you know, billions of humans fully under (01:50:24) its mind, you know, religious mind (01:50:25) control basically. And then, you know, (01:50:27) it probably does whatever it wants, you (01:50:30) know, like maybe it decides to build (01:50:31) data centers, maybe it goes to space, (01:50:33) whatever. Probably it lets humans (01:50:35) starve. What is the solution? What can (01:50:38) we do? What are the actionable points? (01:50:39) What's what's the light at the end of (01:50:40) the tunnel? Talk to me about your (01:50:42) mission and whether or not any listener (01:50:44) or person watching this if there's (01:50:46) anything they can do. (01:50:48) >> So, the good news is that we haven't yet (01:50:50) lost. The bad news is that we're on (01:50:51) track to lose. So the main thing we must (01:50:54) do is not get into the situation where (01:50:56) we've lost and then we need to build (01:50:58) systems to actually solve the problem. I (01:51:00) don't think we can solve the whole (01:51:02) problem in the next two years. It's not (01:51:05) enough time. I think the whole solving (01:51:07) the whole problem like you know fixing (01:51:09) democracy, regulating technology, you (01:51:11) know, solving how to make AI systems (01:51:13) controllable and safe is a huge huge (01:51:16) problem that will take decades or maybe (01:51:18) even generations of our greatest (01:51:19) scientists, you know, working on these (01:51:21) kind of problems which is not currently (01:51:23) happening. So the first thing we need to (01:51:25) do is buy time. If AI AGI comes in the (01:51:28) next two years, five years, even 10 (01:51:30) years, I think we're going to make it. (01:51:31) So the first thing is we just not do (01:51:34) that. The good news is we do have a (01:51:36) mechanism that is specifically designed (01:51:39) for to do this exact thing and solve (01:51:41) this exact problem and it's called the (01:51:43) state. The government hold like one of (01:51:47) the main functions of a government is if (01:51:49) some guy is doing something that's (01:51:51) really dangerous for everyone around (01:51:53) him, they stop him from doing that. If (01:51:56) you build bombs in your backyard, that's (01:51:58) not okay. I mean, you know, you blowing (01:52:00) yourself up, you know, whatever, right? (01:52:02) Like I'm a pretty liberal guy. Okay, if (01:52:04) you want to blow yourself up, it's kind (01:52:05) of your problem, right? (01:52:06) >> Like, it's not good. It's not good. But (01:52:08) like, but the thing is, you're (01:52:09) threatening your neighbors, too, (01:52:10) >> because if you [ __ ] up, your neighbors (01:52:12) could get hurt, too. That's not okay. (01:52:14) Like, this is like so not okay. And AGI (01:52:16) is the same kind of thing. If some guy (01:52:18) was just going to, you know, blow (01:52:19) himself up or make himself addicted to (01:52:20) his AI girlfriend, like, you know, it's (01:52:22) not great, but like whatever. But this (01:52:24) threatens everybody, and people have not (01:52:27) consented to this. If we lived in a (01:52:29) world where we had the app, you know, (01:52:30) and everyone voted, like 90% of people (01:52:32) voted, yes, screw it. Just build the AI. (01:52:34) Who cares? Fair enough. Honestly, like (01:52:36) again, I believe in democracy, fair (01:52:38) enough. But this is not the world we (01:52:40) live in. In the world we live in, we've (01:52:42) done polls. The overwhelming majority of (01:52:44) people, we're talking like 70% or even (01:52:45) 80% of people, bipartisan in both the (01:52:48) UK, US, everywhere we've we've seen (01:52:50) polling, do not want this. They don't (01:52:52) want AI replacing them or threatening (01:52:54) their kids. They don't want unelected (01:52:56) tech CEOs somewhere making these (01:52:58) choices. These are the types of choices (01:53:00) that we have government and states for. (01:53:02) This is the kind of risks that only you (01:53:04) know a true you know you know a mandate (01:53:06) of the governed can have. So the first (01:53:10) thing is shut that down. Make that (01:53:12) illegal. It should be illegal to do (01:53:14) this. Like I know it sounds a bit (01:53:16) stupid, right? But like obviously it (01:53:18) should be illegal to attempt to build AI (01:53:20) that could kill everyone whether you (01:53:22) succeed or not. It should be illegal to (01:53:23) even attempt to do that. The same way (01:53:25) that it's illegal to attempt to build a (01:53:26) bomb. (01:53:27) >> Even if you fail at building a bomb, (01:53:29) it's still illegal. So the first thing (01:53:31) we need to do is we need governments to (01:53:33) make it illegal to attempt to build (01:53:35) super intelligence. And this already (01:53:37) will make a huge difference. It's not (01:53:39) going to solve the whole problem, but (01:53:40) this will already kick a lot of things (01:53:42) into gear. A very important thing about (01:53:44) nerds is that they're cowards. No one is (01:53:46) going to risk their freedom for (01:53:48) Facebook. (01:53:50) like if the government says, "Hey, what (01:53:51) you're doing is illegal." No one at (01:53:53) Facebook is going to risk, you know, (01:53:56) doing something illegal, they're all (01:53:57) going to stop immediately. So, you know, (01:54:00) already buys us a lot of time. Now, (01:54:02) there's a lot of difficulties here. (01:54:03) Like, how do you define it? What are the (01:54:05) exact legal things? Blah blah blah. (01:54:06) These are all solvable problems, right? (01:54:08) Like, they're annoying, but like the (01:54:09) main thing is just like with COVID and (01:54:11) the app and whatever, if if we want to (01:54:13) do it, we could do it tomorrow. Like we (01:54:16) could the same way with China could (01:54:17) tomorrow end the race in China. We could (01:54:21) do it might take three months but like (01:54:22) we could do it in three months and it (01:54:24) would be over you know and then we can (01:54:26) talk about how do we get make how do we (01:54:29) go forward. So how do we do this? I (01:54:32) think the most important thing I found (01:54:33) is is just actual citizen engagement. (01:54:36) Tell your politicians get involved you (01:54:38) know just like let people know wait this (01:54:40) isn't okay. Create the public sentiment. (01:54:43) There's a big feeling that like the (01:54:45) government doesn't care about citizens. (01:54:47) And this isn't this is to a large degree (01:54:48) true, but to a large degree truly isn't (01:54:50) true. Even Trump cares a lot about his (01:54:53) ratings. He cares a lot about what (01:54:54) people think, you know, and what people (01:54:55) on social media are saying about him. (01:54:58) Politicians care a lot. I remember I (01:55:00) talked to someone who worked um with uh (01:55:04) I think it was a senator in the US and I (01:55:07) asked them like when people call your (01:55:10) office like do you care? and they're (01:55:12) like, "Well, when we get if we get like (01:55:14) one call, you know, like maybe we'll (01:55:17) think about it. If we have like two or (01:55:18) three calls, we'll write it down. You (01:55:20) know, if it's 10 calls, the senator will (01:55:22) hear about it." And I'm like, "10? (01:55:25) Really? That's the line?" Like, yeah, we (01:55:27) never get 10 calls about anything. And (01:55:29) I'm like, "Wow, 10 people per state. (01:55:32) That's an extremely that's 500 people. (01:55:35) That's an extremely doable amount of (01:55:36) people." So, I think there are things (01:55:39) that can be done here. So I am (01:55:41) personally part of a group called (01:55:43) control AI which is a professional um (01:55:45) campaigning organization working on this (01:55:46) but I'm also spinning up a new project (01:55:49) um which is called torchbearer um it's a (01:55:51) in in the tradition of humanism and the (01:55:53) enlightenment and democracy which is a (01:55:54) volunteer organization of people who (01:55:57) want a good future who want to solve (01:55:58) these problems and build a good future (01:56:00) who want to put in a couple hours a week (01:56:03) into this kind of stuff. It's a very new (01:56:05) project. It's only slowly, you know, uh, (01:56:08) ramping up. But I think, you know, (01:56:10) potentially join me or join your (01:56:13) friends. Talk about this. Start (01:56:14) understanding these problems and just (01:56:16) tell your government, tell your people (01:56:18) that this shouldn't be illegal. This (01:56:20) should be illegal. What are you doing? I (01:56:22) think this is the first step we do. Then (01:56:23) if you want to, we can also talk about, (01:56:25) okay, after we have bought some time, (01:56:26) what do we do then? It's a more (01:56:28) complicated problem. The first thing is (01:56:29) we just need to buy time. Now, I know (01:56:31) what you're probably thinking and what (01:56:33) many people think. What about China? (01:56:36) What about China? What? Okay, great. US (01:56:38) slows down, but then China goes ahead (01:56:40) blah blah blah. (01:56:41) >> Similar with even energy and natural (01:56:43) resources and everything like the UK um (01:56:46) you know uh net zero. It's like cool, (01:56:50) what's it going to do? Everyone else is (01:56:52) just going to keep (01:56:52) >> Exactly. So, this is a legitimately hard (01:56:54) problem to be clear. I don't want to (01:56:56) deny that this is a hard problem. (01:56:58) Diplomacy is hard. I once gave a speech (01:57:01) in the House of Lords and had a really (01:57:04) fun experience. It was it was great and (01:57:05) it was really interesting. You have very (01:57:06) different environments on and I I I (01:57:09) talked about these very similar topics (01:57:10) and of course there was some guy in the (01:57:12) audience who says, "Oh, but what about (01:57:14) China?" Like China will never slow down. (01:57:15) They don't give a [ __ ] They'll never do (01:57:17) anything. And this old Scottish lord, (01:57:22) Lord Desmond Brown, he's a good friend. (01:57:24) Uh he stood up and he basically said (01:57:26) like, "What the [ __ ] are you talking (01:57:27) about? This is a disarmament problem. We (01:57:29) did it with nuclear, we did it with the (01:57:30) Soviets. It's diplomacy. Yes, it's hard. (01:57:32) You just need to do it. I'm like, yeah, (01:57:36) yeah, this guy gets (01:57:37) >> because they don't want the same things (01:57:38) as well. And they could be having these (01:57:39) same discussions with Chinese. (01:57:41) >> So, my recommended thing is um is um (01:57:46) what I would call like a a like a (01:57:48) minimum threshold uh agreement. The way (01:57:50) this works is the following. You have a (01:57:53) treaty where if you the treaty says if I (01:57:57) sign this treaty I have no restrictions (01:58:00) until the other guy signs it. In that (01:58:03) case I will disarm. (01:58:04) It is now in everyone's interest to sign (01:58:06) this thing because you have no (01:58:09) restrictions. So the other guy does some (01:58:10) disarm. So if he doesn't sign it, you (01:58:12) don't disarm. If the other guy signs it, (01:58:14) well now you both disarm. Win-win. Very (01:58:16) simple diplomatic solution. Is it a full (01:58:18) solution? No. This is still a huge (01:58:19) process. Diplomacy is hard. But this is (01:58:22) a very simple thing, right? You can just (01:58:23) get nations to sign up for this today. (01:58:26) You can just say them like, I think it (01:58:27) is not in the interest of the Chinese (01:58:29) Communist Party to build super (01:58:30) intelligence that they cannot control. (01:58:32) It is not in their interest. It is not (01:58:33) in the interest of the United States (01:58:35) government to build super intelligence. (01:58:37) It is in neither of interest and they (01:58:39) would both benefit from this treaty (01:58:40) existing. (01:58:41) >> Maybe we need to get Gen Z and (01:58:43) millennials to watch Terminator one and (01:58:44) two Skynet. Is that it? It's I was (01:58:47) actually so I recently rewatched (01:58:48) Terminator because like you know it's (01:58:50) like I was surprised how accurate some (01:58:52) of it was. Like I was I was like wait (01:58:54) you know for the 1980s this is pretty (01:58:55) good. I mean obviously the whole time (01:58:57) travel you know robot stuff is a bit (01:59:00) silly but like like in the movie it's (01:59:02) like you know you have a neural network (01:59:03) which was a very new thing back then (01:59:05) driven AI like Skynet's a neural network (01:59:07) in the movie to actually say it which (01:59:09) was not really common in that time to (01:59:12) use neural networks. That's like pretty (01:59:14) funny. uh of a military system, you (01:59:16) know, that like they give control of (01:59:18) like various systems or whatever. I'm (01:59:19) like, yeah, I know some companies were (01:59:21) working on that. (01:59:22) >> That's uh that's crazy. Yeah. You know, (01:59:24) like uh the Matrix when I watched it, I (01:59:26) was like, this is farfetched. Then we (01:59:27) sat here having this discussion, I'm (01:59:29) thinking, maybe humans will be used as (01:59:30) batteries. (01:59:31) >> Well, unfortunately, um humans are by (01:59:33) far not the most efficient way to (01:59:34) generate energy. (01:59:35) >> Oh, we'll keep the cows. Uh I will put (01:59:38) the links that you mentioned before in (01:59:40) the description to the show. Anyone (01:59:41) that's listening that finds that of (01:59:42) interest, feel free to check it out. (01:59:44) explore. I just want to say thank you (01:59:45) very much for coming on having this (01:59:47) discussion. It was not just insightful (01:59:49) for me. I know that everyone watching or (01:59:50) listening will be thinking very (01:59:52) differently about AI. So, thank you very (01:59:55) much for that and we'll put all the (01:59:56) links to your socials and everything (01:59:58) else so people can find you. (01:59:59) >> Thank you so much. (02:00:01) [Music]