SPEAKER_00: Today on This Week in Startups, Jason is joined by Stephen Wolfram of Wolfram Research. The two have an incredible conversation about AI, including Wolfram launching one of the first ChatGPT plugins, the history of neural nets, how exactly ChatGPT works, how this technology is going to shape jobs in the future, and so much more. Stick with us. This Week in Startups is brought to you by CastAI. If you run software in the cloud and it's been a significant cost driver, listen up. CastAI automates cloud cost reduction with clients saving an average of over 60%. Twist listeners can get a cloud cost audit with a personal consultation free of charge. Visit cast.ai slash twist to get started. Vanta. Compliance and security shouldn't be a deal breaker for startups to win new business. Vanta makes it easy for companies to get a SOC 2 report fast. Twist listeners can get $1,000 off for a limited time at vanta.com slash twist. And Clumio. When you're building a company, don't let backups and compliance requirements distract you. Let the data protection experts at Clumio help with immutable air gap backups that put compliance on autopilot. Visit them at Clumio.com slash twist to start a free backup or sign up for a demo.

SPEAKER_02: All right, I'm really excited for our next guest today. Stephen Wolfram is here. He's the founder and CEO of Wolfram Research. You might have used Wolfram Alpha, which I guess some people call a search engine, but it's obviously much more than that. And he's a prolific author. I really don't need to introduce him all that much. I guess a great place to start would be maybe to talk about what we've seen with chat GPT. And how impressive is it to you? Watching three 3.5 and four come out over the past year, and

SPEAKER_04: then we'll get into the plugins and how Wolfram Alpha is sort of plugging into it.

SPEAKER_06: Well, you know, I've been paying attention to neural nets, since about 1980. That was when I

SPEAKER_07: first programmed up a neural net, didn't do anything terribly interesting. So it's, you know, and then 2012 comes around and deep learning neural nets started doing interesting things. We started putting them into language and so on. And I've been sort of tracking large language models for a while, and they didn't seem that exciting. And then chat GPT came out, and suddenly it was exciting, and it was able

SPEAKER_08: to do really useful things. And I think we still don't completely understand what allowed that jump to

SPEAKER_09: occur. But I think we kind of get some idea now, now that that jump has occurred, we can go back and

SPEAKER_07: look at, you know, why does this work? What's what's really happening, and so on.

SPEAKER_13: Yeah, so for a layperson, when you type a question into chat GPT, or I guess Google's bard is out, we see Poe from Cora, so many different language models are being released. What is actually happening under the hood? When we ask it, Hey, I have some salmon. And how should I prepare it? What are my options? What is it doing actually behind the scenes?

SPEAKER_07: Well, I mean, it's doing something incredibly mundane, that's very surprising that it can be as human like in its output as it actually is. Because, you know, in the end, what it's doing is it's saying, you've typed some

SPEAKER_08: text, I'm going to continue that text, the way that the statistics of text on the web and in other places that it's been trained from works. So it's kind of like, if you if you just do it with letters, if you typed a cue, then, you know, there's an overwhelming probability that you comes next in English, at least. And it's got a much more elaborate version of that. And the thing that, you know, you might think, well, you just count, you know, if you've if you've got some some phrase, you just say, how many times does that occur on the web? And what's the typical next word, when that occurs on the web, that in and of itself doesn't work, because there just isn't enough text on the web, there might be a trillion words that you could find, you know, between the web and books and things, but that's not enough to be able to give you sort of statistics on what's the next word after, you know, the best thing about AI is or something, there aren't enough occurrences of that you can sort of statistically work it out. So you have to have a model. And the thing which is interesting, surprising, is that this particular model, that's the idea of a neural net, turns out

SPEAKER_07: to give you results that are very human like, that, you know, when it when it has to work out sort

SPEAKER_08: of, how will it extrapolate from just the pure statistics of what's on the web, it extrapolates in a way that somehow similar to the way humans do it. And, you know, I think that in the end, that's because neural nets actually work very much the same way as the sort of wiring in our brains works. And, you know,

SPEAKER_07: the history of this is, you know, back in the 1940s, people knew that, you know, brains had neurons, and there were, you know, what we know, though, there are about 100 billion neurons in our brains, and they've all got, you know, these little electrical devices, basically, where each one is connected to maybe 1000 10,000, whatever other ones, it's a big complicated mass of sort of wiring neural wiring. And that was what people started doing was thinking about, well, what's the kind of formal representation of that, what's the kind of mathematical way to represent that, that was invented in 1943. And at that time, and in the 1950s, 1960s, people were like, well, what does it do if you have five neurons, if you

SPEAKER_09: have 10 neurons, you know, if you have, you know, 30 connections between neurons, and so on, and didn't do anything, a few things that were somewhat interesting, we didn't do anything terribly exciting. Turns out when you have 100 billion neurons, 100 billion connections between neurons, a few million neurons, that turns out that you can capture a lot more of what actual brains do. And it wasn't obvious what that number would be, it wasn't obvious how big the, you know,

SPEAKER_08: how much data you would have to train with how big the number of neurons would have to be, to get sort of human like behavior. I mean, the thing that is the other other critical point is, there is enough text now available on the web, that you can kind of figure out the statistics, you can train the neural net kind of well enough from that text, that it can produce things which are a good match to what would be sort of the human like way to continue that sentence, so to speak.

SPEAKER_09: That's kind of, it is sort of remarkable that these systems are basically just writing one word

SPEAKER_08: roughly at a time. And yet, just by the way, the sort of statistics works out, the whole,

SPEAKER_19: the whole essay or whatever, ends up being coherent.

SPEAKER_13: And so three things had to come together. One, the corpus that I was trained on, and who knew it, but that wound up being the world wide web, and all of these different, you know, data sets could have been Reddit, Wikipedia, the obvious one. So the data sets had to grow to a certain size, then we had to have enough compute and enough storage to process it fast enough. And then the language model had to be written or built by somebody. Those were, those are the three components

SPEAKER_06: that have been actually to make this happen? You know, the language model, there are some clever

SPEAKER_09: ideas. But actually, between 1940. And now, there were a lot more, you know, very clever ideas that

SPEAKER_08: didn't work out. And what we actually have now the structure of the neural nets, with a few extra pieces that are kind of important, but, but they are kind of they seem minor relative to the things that were tried in the intervening years, the neural net is really close to what people imagine

SPEAKER_26: neural nets will be like back in the 1940s. You know, it turns out that the simple thing kind of worked.

SPEAKER_32: Yeah. Explain to a layman what is a neural net and how this comes up with these connections. And it is pretty amazing that it is exactly what we thought it was. And we just had to wait for compute and

SPEAKER_35: corpus of data to training data to kind of reach critical mass, I guess, or some tipping point.

SPEAKER_09: So, okay, so, what is a neural net? So, so, in brains, and in neural nets, there are neurons,

SPEAKER_08: and neurons have this feature that will explain the case for brains, it's rather similar for artificial neural nets, when a neuron has all these so called dendrites, all these incoming connections,

SPEAKER_07: that are just, you know, pieces of the nerve cells, so to speak. And a nerve cell is basically an electrical device. And when the nerve cell kind of fires, it produces an electrical pulse, which it sends out to its outgoing wires, so to speak, neural wires, so to speak. So,

SPEAKER_09: what's happening is, when, roughly, when there are kind of, when, in the first approximation,

SPEAKER_07: in the original way this was set up, when there kind of are enough incoming wires that have signals on them, then the neuron says, okay, I'm going to fire, and then it produces a signal that gets sent out to the sort of next neurons that are connected to it. Now, the thing, this idea of weights, which is a big thing that people talk about in neural nets, that has to do with the fact that if the incoming, the sort of the, if there are incoming signals on all these various wires, it's not just there's a

SPEAKER_09: signal, and every signal is treated the same, these, each of these incoming wires has a certain weight, it might be a positive number, might be a negative number, you know, it's like a weight of 0.72,

SPEAKER_08: a weight of minus 0.34. And roughly all those different weights get the, when there's a signal,

SPEAKER_07: you multiply by the weight, you add all those things up, then there's kind of a thresholding function. And, and that determines whether the neuron fires and sends data on to the next neurons

SPEAKER_38: down, down the line. That's, that's how it seems to work in brains. And that's pretty much how it

SPEAKER_40: works in artificial neural nets. Listen, if you run software on AWS, GCP, or Azure,

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SPEAKER_38: today. Okay, so first question is, you've got this prompt, you wrote out the prompt, you're saying,

SPEAKER_09: you know, the best thing about AI is or something that has to turn into a bunch of numbers that

SPEAKER_08: represent kind of the, the intensities of firing of these different of a collection of neurons. And so there are, there's, there's a certain amount of, well, there's this whole idea of embeddings,

SPEAKER_09: these are ways to sort of turn, turn words into numbers. And the idea is that if you have a sort of a good embedding, then words that are similar in meaning will correspond to collections of numbers that are nearby. So, you know, something like, I don't know, elephant and rhinoceros might have a sequence of what might be, let's say 1000 numbers, that the 1000 numbers that represent elephant are fairly similar to the 1000 numbers that represent rhinoceros. But they're completely

SPEAKER_08: different from the 1000 numbers that represent, you know, Jupiter or something like that. And so, so the first thing is, you've got to, you've got to grind the words up, turn them into numbers,

SPEAKER_09: then those numbers are used to determine kind of the, the intensities of this first layer of neurons.

SPEAKER_08: And then you go through a sequence of layers. So for ChatGPT, I think it's a few hundred, maybe 400 layers. And so what's happening is the sort of the data from the thing that the, you know, the initial numbers are kind of, they go into the first layer neurons, then they go through these

SPEAKER_09: weights, they will get multiplied, things fire, you go to the next layer, go to the next layer, and so on. And when you've gone through those, you know, I think it's about 400 layers, you get to

SPEAKER_08: another collection of numbers. And that other collection of numbers, then gives you essentially

SPEAKER_09: the probabilities for a set of possible words that might follow. And then you have to decide, well, which word are you going to pick? Are you going to pick the word that was most probable, according to the statistics of the web, so to speak, you're going to pick the word that was second most probable or whatever. And one of the kind of, there are many pieces of kind of slightly black magic that go into making one of these systems really work well. There's sort of the, if you always pick the most probable word, then at least for writing like English essays, that tends to be, it seems rather monotonous. Sometimes it just repeats itself, all kinds of bad things like that. But as soon as you pick, sometimes the not top probability word and the sort of a parameter, the temperature parameter that determines kind of which, how far down the ranking words you'll pick, that seems to lead to a more lively result. I should mention one other thing that's sort of a critical piece of what's worked in something like ChatGPT is this idea of transformers. And so the question is, when you have the words that it's already written, what do you do with those words? How do you feed them into the neural net? And the question is,

SPEAKER_08: the one thing you kind of know about those words is they're in a sequence. They're not just, oh, there's different words in different places. And so what happens is, the neural net kind of learns, it knows, given that we're going to add the next word, it says, well, the word three back

SPEAKER_07: has this level of importance, the one five back has this level of importance, and so on. And then it combines in a very kind of sort of bizarre way, it combines multiple different sort of patterns of how it pays attention to previous words, and does the whole thing multiple times. And out of all of this comes, comes the results from once. Now, one question is, okay, so that's sort of the setup of

SPEAKER_09: how, given that you are feeding in a prompt, you're feeding in text, how it will determine what text to write next. Next question is, well, you've got this whole neural net, and it's got all these weights, and, you know, in ChatGBT, it right now has 175 billion weights. How do you determine those weights? You know, any collection of weights will have the property that you can feed words in, and some words will come out. Problem is, if those weights are picked at random, the words that come out will just be complete nonsense. The question is, how do we pick weights so that the thing kind of conforms to the statistics of the web? And so that's this process

SPEAKER_08: of neural net training. And essentially, what you do is you kind of you say, well, here's some text from the web. And we know what the next word is, but the neural net doesn't know what the next word is. So have the neural net sort of guess what the next word is. And then it might get it right, it might get it wrong. But typically, it will start off getting it wrong. And then you say, okay, how would you have to change the weights in the neural net to make the word that comes out be closer to right than the one that actually came out? And so you iteratively do this. That's the training

SPEAKER_07: process is just kind of tweaking all those weights. And there's a mechanism called backpropagation that

SPEAKER_08: kind of helps you make it not be an absurdly mathematically difficult problem to figure out how to tweak the weights, so that you'll actually get the thing that so you're kind of training it on you got a piece of text, you're kind of masking out the words at the end of the text, you're trying to training it so that the weights are such that the words that are at the end of the text will be

SPEAKER_46: the ones that when you took the mask off will really be the ones that were there.

SPEAKER_04: So so you find some high quality piece of text, here is the Wikipedia page, let's assume it's high

SPEAKER_13: quality, and it's been vetted on China, and it just starts reading it, it gets the word wrong, you don't punish it, but you tell it, hey, you got it wrong. And then until it gets the words, right, it gets a cookie, or he gets punished in some way.

SPEAKER_16: Well, the trick, the trick is that you're kind of it's like an evolution process, like biological

SPEAKER_08: evolution or something, you're kind of gradually adapting it to get closer and closer to the right

SPEAKER_07: answer. And there's a systematic way to do that. And that's what that's what the training process ends

SPEAKER_08: up being. And, you know, the fact is, it's trained on a trillion words. So it's trained on, you know,

SPEAKER_09: this whole process, if you only trained it on a million words, well, it would be able to learn some things like, you know, you follows q and things like that. But it wouldn't learn the things that make it seem like a meaningful, you know, essay or something of this kind. And that seems to require,

SPEAKER_08: you know, an amount of, of, of text, that is, you know, reasonably, that's, that's about what we

SPEAKER_09: humans have produced and put and put out in, in kind of publicly accessible form. And it's also, the number of weights that you need is sort of roughly comparable to the kind of number of words that you read in your in your training set, nobody really quite knows why that is. But that's

SPEAKER_56: another sort of a random fact, so to speak. Listen, it's 2023, the macro picture is a little shaky, it's uneasy out there, and tech is getting hit super hard. As such, you cannot afford to lose sales for silly stuff like not having your SOC 2 right now. If you are unsure about your SOC 2, you need to check out Vanta. Vanta makes it incredibly easy to get and renew your SOC 2. On average, Vanta customers are SOC 2 compliant in just two to four weeks, compare that to three to five months without Vanta, huh? And they partner with over two dozen audit firms who have been trained to file SOC 2 reports directly within Vanta. This is a total no brainer. A bunch of my portfolio founders have used Vanta, and they've had amazing experiences. And if you don't have SOC 2 compliance, you can't close major customers. One major customer that can be the difference between your startup thriving or going away. So get it done right now. Vanta is going to give you $1,000 off because you listen to this podcast. Think about it. $1,000 off. Vanta.com slash twist. You got to

SPEAKER_21: write that down. Put it in your notes. V-A-N-T-A.com slash twist for $1,000 off your SOC 2. When we compare it to what's happening in a human's brand, and I know we actually don't have the answer to this

SPEAKER_32: yet. We don't understand what consciousness is exactly. We have theories and ideas. But when a human is

SPEAKER_13: asked, hey, what are the most popular desserts in America? And when the neural net is asked and chat GPT is asked, whichever version of it is asked, hey, what are the most, you know, popular desserts in America? What, when you look at what happens in a human, and then we look at what's, what we know is happening in the software, how close are they? Or do we actually think we're emulating what happens in a human brain, or that we've developed a new process that is similar, but maybe not exactly? And what is

SPEAKER_64: that overlap? If there are two circles of consciousness and answering questions versus a computer answering it? How much do they actually overlap? Well, I mean, I think what's happening

SPEAKER_06: in LLMs is fairly close to what happens in brains. There are some things missing in current LLMs. You know,

SPEAKER_09: brains have this in a computer that's typically, you know, the CPU, GPU, it's processing a lot of data, and then there's memory, and most of the time, the memory of a computer sits doing nothing, just sits there storing what it's storing. In human brains, every neuron both computes and stores things. So we have a little

SPEAKER_08: bit of an advantage, at least for right now, in that regard. Also, the way that something like chat GPT

SPEAKER_09: works is it just feeds forward, you know, you feed it in the prompt, and it'll, you know, the kind of ripple through the neural nets, it'll say, okay, the next answer is this. In our brains, we're pretty sure that there's some kind of feedback loop. And maybe the feedback loop is similar to the one that chat GPT effectively has, or an LLM in fact, effectively has, where it sees the prompt it's got so far, it adds a word that becomes the new prompt, and it can kind of kind of feedback that way.

SPEAKER_13: So when we asked this question, Hey, what are the most popular desserts in America? And I, the first thing that comes to mind is ice cream. And then ice cream triggers me to think, well,

SPEAKER_32: apple pie, of course, and then apple pie, and ice cream trigger whatever the next thing is, Yeah, maybe. And we kind of, yeah.

SPEAKER_69: Right. Look, I think that the, the thing to understand is, when it comes to sort of a

SPEAKER_09: computational process, like how brains work, it's, there's a lot of detail that in the end doesn't matter. And it's just like saying, if you want to fly, do you need feathers, do you need flap, you know, or do you just need wings? It turns out you need wings, pretty much, unless you're a drone with, with rotors, but those are little wings that happen to go around. Yeah. That's, you know, things like the, you know, the glucose that's, that's supplying energy to the

SPEAKER_08: neurons and things like this, which is obviously different from the electronic case. But at a sort

SPEAKER_09: of computational architecture level, I think it's, it's surprisingly close. And what's sort of remarkable is people have kind of known roughly what this is like for, you know, what is it, you know, 80 years or something. I mean, this is, that part is sort of unsurprising. Now, I would say there's, there's, well, there's a lot more to say about how, how a computer works, as compared to how a neural net, the sort of brain like neural net works. The thing about computers, and computation in general, is it can sort of, it kind of goes much deeper than what a neural net can do. Because what happens is, you have some computer, it can, for example, it can go in a very sort of tight loop, figuring out what's, what's, you know, the result of a computation, none of that stuff is happening, in something like, you know, an LLM like neural net, it's just rippling through saying, what's the

SPEAKER_08: next word, and you know, it just ripples through and so on. And there's this kind of concept that sort of a concept that I kind of invented in the 1980s, called computational irreducibility,

SPEAKER_09: is kind of the feature that of sort of deep computation. Because, you know, you say, okay, what is the computation, you have certain rules, and you're going to just apply these rules over and over again, and you see what the results are, those rules might be the sort of the way the CPU of a computer is set up, they might just be some rules about black and white squares, or whatever else. But the way it works is sort of the essence of the computation is, just keep applying these rules over and over again, you see what see what comes out. So the question then is, you're applying these rules, there's a certain number of times you have to apply the rules to get a certain result. The question is, can you jump ahead and see what the result will be more quickly than just following all those rules. And what turns out to be the case is there are many situations where you can't jump ahead, that means you have to do this computation, if you want to get the result, you have to actually go through the steps of the computation. And when you have computational

SPEAKER_08: irreducibility, you basically the neural nets, it's too shallow to be able to deal with that,

SPEAKER_09: you know, it can go a certain distance, like if you ask ChatGPT right now, you know, match parentheses, you've got open parens, open, open, open, close, close, close, open, close, whatever, you're trying to make it just make sure that the number of close parens, you know, the close parens match the open parens, it can do it up to a certain point. And then it sort of says, well, it doesn't say this as

SPEAKER_08: such, actually, I haven't asked it, but maybe I should. Well, why it fails, it might have something

SPEAKER_09: interesting to say. But basically, it's just sort of run out of layers of neural nets, it just can't represent that deeper computation. And so there's this sort of in there's this world of computation, which include irreducible computations, computations that you just can't shortcut, you just have to do the computational work. And then there are these shallower things that are what we humans are using most of the time when we're generating language, probably a lot of the thinking that we do works that way. And so there's there's sort of a difference between how how you can do things in principle with computers, and how things work in something like an LLM. And, you know, you might say, you know, do you care about irreducible computations? Well, the answer is, for example, in nature, many things that go on and sort of the physical world, if you want to work out how they work, you kind of have to do irreducible computations, those things weren't sort of made for humans, so to speak, I mean, our language and things like that is sort of made for humans in

SPEAKER_08: some sense. But nature just is what it is. And it can to work out what it's going to do can involve

SPEAKER_74: these irreducible computations. And then it's up to us to try to simulate them in some way or or to try to

SPEAKER_13: figure them out here in this. Yeah, let's talk about emergent behavior here. Like, are we projecting into it? That it's learning in some way or evolving in some way at this point? Or is it truly, with so many people using it now and the reinforcement learning that's happening, and then all these plugins putting in? Do we get the sense that the model is learning at some faster pace now, and that this concept of hey, maybe we're not in control of it? Do you believe that that's kind of the moment we're in right now? Because as you were saying earlier, like, this thing is kind of surprising us right now. So I'm kind of wondering what the next surprise will be. Let's pull that apart a little

SPEAKER_08: bit. So first of all, sort of emergent behavior. One of the things that that typically means is you put certain rules for how a system works. And what the system does is much more complicated than the rules

SPEAKER_09: that you put in. That that's and that's what happens in irreducible computations all the time. That's kind of that there. That's that's kind of the thing that probably makes nature seem so complex to us is it's full of these irreducible computations where the rules are quite simple, where the actual behavior is is seems to us very complicated. So that's a now the question of what LLMs are doing, and to what extent that sort of an emergent thing. First of all, just to clarify one thing. So in the present state of things, the actual little chat sessions that people are having with these LLMs, yes, they're being stored, they will be used for training, but it's not an immediate loop. That's not that's not something that's that's been done technologically as an immediate thing. It's it's more of a sort of a long term process. So it's not like, you know, every person who types into it is getting smarter, and it's going to, you know, it's going to take over the world as a result.

SPEAKER_80: My chat with it is independent of your chat with it. It has threaded chats together. So it's

SPEAKER_13: learned, it's applying the model in each of our individual threads. But if we both started asking it

SPEAKER_32: about desserts, it wouldn't suddenly be like, Oh, wow, two people on two different coasts of the United States are talking about dessert. And let's pull that all into our knowledge. But that is

SPEAKER_83: coming, obviously. Yeah, yeah. But that just doesn't happen to be here yet. I mean, that's

SPEAKER_08: just a technological that's a that's a technological privacy, you know, policies, etc, etc, etc, kind of

SPEAKER_86: issue. But that's an interesting one, actually, I just I've never heard anybody actually have this

SPEAKER_13: conversation. But what is the ethical right thing to do if 100 people right now are talking about, you know, this new pandemic that they're seeing, and it's trying to put together that information to maybe warn us a pandemic is actually happening, there's 100 people talking about it in this region, and they're spaced out at this distance. And this is the qualifier of, of a pandemic starting.

SPEAKER_09: Right? Well, I mean, this is something, obviously, one's already seen, you know, from things trending on various, you know, social media, and, you know, search search queries, and things like that, that's already the thing of it like that. I mean, the question of, you know, how how private is your particular chat session and your particular, you know, I don't know, psychological counseling session with the with the chat bot or whatever else versus that, right? Well, versus what I mean, there's the same thing that happens in your medical stuff all the time, which is, you know,

SPEAKER_08: to know in aggregate, what the results what what happens medically to lots of people is a huge societal value, let yet you want to keep the individual records of individual people private.

SPEAKER_09: So this kind of, you know, you want the aggregate to be something that can be mined, but you don't want the individual things to be separately minable. And that's a whole, whole tech and technical can of worms about how you can do that, and to what extent you can do that, and so on. I think the same thing will, will probably happen here. I mean, coming back to this question of, of sort of what, why does the LLM work? What is it really doing? In what sense is it emergent? What's, you know, what's going on? I think the thing that is probably the, for me, sort of the, the biggest kind of aha feature of what we've seen with ChatGPT is, is the fact that probably language is not as complicated as we thought it was. I mean, language is kind of the pinnacle of our species is sort of collective achievement in some sense. And so we think it's a very sophisticated, complicated thing. But we already know there are certain rules of language, like we know, you know, syntactic grammar, we know, you know, a typical sentence has a noun, a verb, and, you know, a noun might be an adjective and a noun, things like that. We know this kind of these kind of structural regularities to language. Well, I think what's happened is that that in these LLMs, what's been discovered is that there are many more regularities in language than we had sort of classified before. So there are many, it's kind of like language, we know from sort of the structure of sentences about nouns and verbs, and so on, we know this sort of a construction kit, the sort of puzzle pieces that you can put together, you can't go, you know, verb, verb, verb, that's not a possible sentence. You know, it's got to be a noun, verb, noun type thing, or something like that. So we know that there are these sort of puzzle pieces you put together. And I think what's happened is that there are sort of what's been discovered by LLMs, in fact, is that there are a whole collection of other puzzle pieces that don't just deal with parts of speech, but they deal with little fragments of meaning and language. And there are things that can be put together meaningfully, and there are ones that can't be put together meaningfully. And you know, we have, we have one example historically, of where this kind of thing was discovered was discovered 2000 years ago, which is the idea of logic, which was presumably discovered by Aristotle. And you know, in a sense, Aristotle was doing a humanized version of sort of machine learning, because what he did, presumably, is he took all these arguments that people made all these pieces of rhetoric and so on. And he said, what's the pattern of how arguments work? You know, if you say, all men are mortal, Socrates is a man, therefore Socrates is mortal, that's a certain pattern, you don't have to be talking about Socrates, you don't have to be

SPEAKER_08: talking about mortality, you could be that you could substitute in any kind of any kind of thing there.

SPEAKER_09: But that structure is a meaningful structure that you can put into something that you say. And he lifted from that this kind of idea of logic, of, you know, ands and ors and nots. And you know, this implies that and so on. And that becomes one of these kind of semantic regularities of language. That's one that we know. There are a bunch of others, I think. And the LLM has basically found them. And we've been a bit negligent in the last couple of 1000 years not looking for these things. I mean, there was a little burst of interest in the 1600s, but it kind of died off. And then people had, I think people kind of thought it was too hard. And they were a little bit proud of the fact in the 1950s, it became clear that this kind of grammatical structure of nouns and verbs and things, how that works in lots of different languages, and people kind of excited about the way that it had been figured out that that worked. And so they didn't really look for these other things in in a serious way, I think. So that's kind of the, you know, I think that's what's sort of a science fact that was discovered. And in the end, once you know that that's the science fact, it's sort of puzzle pieces being fit together, it all seems a bit less miraculous, so to speak.

SPEAKER_32: And so we're figuring out or if the models figured things about language that we just

SPEAKER_13: maybe haven't been looking for. And we as humans with language as the pinnacle of our existence, whether it's poetry, or science, or, you know, any number of arts or debates, it's kind of how we mitigate the entire world. It's how we make decisions, these debates that occur, presidential debates, Congress, Senate, at your dinner table, who are you going to vote for? How are you going to raise your kids? We maybe have valued this as something super magical, but with the corpus being actually kept somewhere, the internet, and then the ability to process it so quickly with these new GPUs, you may have just figured out,

SPEAKER_101: hey, this actually isn't all that complicated.

SPEAKER_08: But I think what we learn is that sort of the essence of meaning is something that is,

SPEAKER_09: which is the thing that we represent with language, there's sort of a calculus in a sense, a formal structure of how meaning works. Now, the fact is, some aspects of that, well, somebody like me, or perhaps me in particular, you know, my lifelong project, basically, has been sort of figuring out how to make things computational. And one of the things that, you know, as my kind of long term project, is to make a computational language, a language that can represent things in the world in a sort of precise formal computational way. And that's what the thing we call Wolfram language is, it's kind of started off as Mathematica and kind of evolved into Wolfram language over the last 35 years. But the kind of the idea there is to take things in the world, like, I don't know, two cities, and you're asking, you know, what's the distance between them, or these kinds of things, and have a precise kind of formal representation of those things, that is sort of both writable by humans, readable by humans, readable by computers, executable by computers. And the fact that I mean, it's been my kind of last 40 years, basically, I've spent building up this kind of language to represent things computationally. And in a sense that the language represents a lot of kinds of things that are very useful to talk about in the world, it doesn't happen to represent kind of everyday chit chat type conversation. But, you know,

SPEAKER_08: and ChatGPT is sort of added that as another element of kind of something that we can see how it fits

SPEAKER_107: together with language. But but you're gonna say no, you're gonna say no, I mean, the thing that,

SPEAKER_09: you know, people ask, for example, does ChatGPT understand what it's talking about? Well, it just has these rules that say how the next word goes in, it doesn't, you could, I mean, that's how we work to probably. And you can ask, do we understand what we're talking about, so to speak? And there isn't this, but but it is in a sense, doing a very shallow computation, kind of the idea of computational language, is once you have something represented in computational language, you can kind of go all the way and compute whatever you want with it, you can do irreducible computations, you do all sorts of things. And so, you know, the thing we did a dozen years ago with Wolfram Alpha was natural language understanding where you go from small fragments of human language to computational language. And once you can do that, that that's a sense in which you have true understanding, you've got natural language, you turn it into computational language, once it's computational language, you can compute anything you want from it. So that, in a sense, is true computational understanding, so to speak. And that's a different thing from what sort of a raw LLM feels with. And that's, by the way, what, what the, you know, the plugin that we just worked on with OpenAI, you know, the Wolfram plugin for ChatGPT, that's what it's achieving, is being able to connect this kind of LLM layer to this sort of what we might think of as kind of computational bedrock

SPEAKER_109: of what one can compute from. And, you know, that's, there's all sorts of implications.

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SPEAKER_74: If you were asking, hey, what's the distance between these two cities? Or what are the similarities of this elephant and rhinoceros? Oh, both mammals, both gray, skin color, whatever, both formidable,

SPEAKER_13: whatever the words are that are coming up. ChatGPT actually seems to be getting things wrong. If you ask it for numbers or equations, doesn't seem to get it right very often. And so is the idea

SPEAKER_04: here, ChatGPT can start discussing and maybe summarizing what's the difference between these two things or the distance between these two cities or the difference between these two cities. But then Wolfram could actually give the correct answer computation.

SPEAKER_09: You know, we didn't know how well this would work. This actually works rather well. I mean, we have very conveniently, we have, I mean, in the technicalities of the plugin, it has two different endpoints inside it. One of them is going to Wolfram Alpha. Wolfram Alpha takes natural language input, takes small fragments of natural language. And Wolfram language is a precise computational language. And sometimes what ChatGPT is doing is taking this big lump of text that somebody might have given as a prompt or the thing it's trying to write. And it does surprisingly well at crispening that up to the point where it's either a small fragment of natural language that can be sent to Wolfram Alpha, or it's a piece of Wolfram language code that can be sent to the Wolfram language interpreter. A tricky thing that happens is in both those cases, particularly the Wolfram language case, sometimes it gets it roughly right, but it isn't exactly right. But then we actually run the code, and we can see what happens. And then we tell ChatGPT, well, it didn't quite work. Why don't you rewrite it? And it does. And so it goes through several

SPEAKER_04: Oh, fascinating. So just a simple thing, like what's the distance? If you ask Wolfram Alpha,

SPEAKER_13: everybody probably knows us who's listening. You asked the distance between Los Angeles and London, it's going to give you a really broken down tight answer. But if somebody were asking that in a less precise way, Wolfram, the plugin could then mitigate kind of the wordiness or how it's buried.

SPEAKER_09: Yeah. If you wrote a very poetic description of what you wanted, you know, Wolfram Alpha was built for people who kind of have a question to ask, they write it in a natural language way, but they're kind of direct, they just ask the question. They don't say, you know, I'm having a whole thought about, you know, going from here to there on an elephant. And I'm wondering, you know, how many, you know, steps that the elephant have to take and this and that and the other. What chat GPT does pretty well is to boil that down into something which turns into distance between this and that divided by stride length of an elephant. That type of thing. I haven't tried that particular thing. I'm not sure that that particular thing with elephants, I'm not sure about that. But, you know, the other part, definitely. But so, you know, the other things that can be done once you're computing, you can do things like have chat GPT produce, you know, call the Wolfram plugin, generate graphics, you know, do we have

SPEAKER_49: lots of real time feeds of data. So do a histogram, do a chart, whatever it happens to be, right. And then

SPEAKER_09: it could be a little weather in some particular place where it's the current weather and, you know, or the current stock prices or whatever else. So it's, you know, it's able to, and it has a sort of precise computational way to kind of figure out what to say about the world, so to speak. You know, what the LLM does very well is to take this complicated mass of natural language, boil it down into something that becomes a sort of a precise thing. And then it takes back the results. And sometimes it'll just generate a picture that comes straight from us. But sometimes it'll knit back the results

SPEAKER_07: into the essay that it's writing. There's another workflow that's really quite interesting right now, which is, you know, we only learned this workflow in the last two weeks. So it's, it's very fresh.

SPEAKER_13: The pace is crazy right now, right? I mean, it's amazing how when everybody in the world becomes enamored by something and says, Oh, let me try to break it. Let me try to fix it. Let me try to, you know, stress test it. It's really incredible what the hive mind of just consumers and scientists, developers and everybody in between trying to break this thing or jail.

SPEAKER_09: I think that the big thing right now is to, I think one big thing is just understand workflows, understand use cases, and understand kind of how to think about what to do. So like, for example, you might, you know, here's a thing that people have discovered, you could say it gives some results, and you can say, you know, do you think that answer is right? And it turns out, then it will, it will, that question turns out, it's better at answering that question, probably, than generating the answer in the first place. So nobody knew that was going to be the case. The other thing that's just totally bizarre, is the whole business of prompt engineering, of being able to say, you know, things like if you look at the, the prompt for the Wolfram plugin, it's just, it's, it's, you know, we've been, we've been steadily adapting it. But it's full of, you know, we put please into the sentence, and that makes a difference. We put wild, you know, don't do this, do that, don't do this, here are examples of what you should do. The fact that any of this stuff works is, is really remarkable. And I think we're, you know, the sort of theoretical description of how the neural net works, we're pretty far away from being able to say, given that theoretical description, this is how you should put commas into your prompt or whatever. That's a, that's a, that's a big distance at this point. At this point, prompt engineering is, it's kind of a bit like animal wrangling, I think it's kind of like, you don't really know, is this animal that's flapping around? And it turns out, if you pull on its ear, it will do this.

SPEAKER_149: And we don't really know. If you're trying to get this Mustang, and you're trying to

SPEAKER_13: tame it. Yeah, be careful. But yeah, walking up to it quietly, and like, we're taking steps and just trying to get the Mustang and corral it and get it to put a saddle on it. Maybe it's gonna work, maybe it's not, it's pretty amazing. I always think, and then I'd love to get your thoughts, since you've, you know, basically helped create this category here, on the impact on society, humans, and how quickly that happens. Because this feels qualitatively different, the pace that this is happening, then, I don't know, automation of software, there was this concept, Oh, my God, you know, TV comes out, everybody's going to get a PhD, because you can just turn your TV on, you have all this free time, you just turn it on, you're gonna learn, or, oh, Wikipedia came out, the internet's out, and oh, MIT is putting every course online, Coursera, you know, this one, Stardex, everything, okay, everybody's gonna be able to go to MIT or Harvard, it turns out, well, human motivation is such that maybe everybody doesn't want to take the time to take all these courses that are freely available on YouTube today, which is just mind blowing for a Gen Xer who thought, wow, whatever they're teaching at MIT and Harvard, that's like, locked up in this ivory tower. Now it's literally available for free. And it has 300 views on YouTube right now, instead of 3 billion. So what happens now in society, realistically, when a whole swath of things that people are getting paid for copywriting is one example, journalism is another example, certain aspects of journalism, research, and then design, I was reading a Reddit thread recently, somebody said they went from spending three or four weeks to make a character in a video game, and now it takes them two or three days, but they kind of feel bad about it, because it's not as artistic, but they're going to be able to make characters in games, you know, already 10% of the work effort, which means like, you're not going to need as many designers, logos, whatever it happens to be. Is this does this concern you? Or are you in the camp that humans always find more work to do? Because this seems to be moving at a faster pace

SPEAKER_09: than anything we've ever seen. You know, I actually just sort of was curious. So I kind of studied what happened to jobs in the US over the last 150 years. And, you know, things happen that are fairly dramatic and, you know, in technology. But actually, it takes a generation before it fully works its way through the system, and you fully see the effects. But I think here, the things that are happening is, there's, there's a lot of kind of cases where there's sort of semi boilerplate text that people generate, or the selling semi or this text, there's so semi boilerplate that people have to understand. There's a bunch of people who do that. And this is a, you know, this is a really way good way to do it. So how will it actually work? I mean, so let's say, you are filing some, you know, you're writing some proposal, you're filing some, you know, compliance type thing, whatever else, you have certain points that you know, you have to make. But dressing that in a whole giant essay, is something that you used to have to do, it used to take a lot of human effort to do that. Yeah, you just say, here are the main points I want to make, go make an essay out of this, using sort of background foundational facts, and it'll make an essay, and then goes over to the other, you know, whoever's going to read it.

SPEAKER_08: And well, they might actually read it as a human, or they might feed it to their LLM, which will sort of grind it down. And they will have given their LLM a prompt that says,

SPEAKER_09: look for these kinds of things. And so they will extract the information that they want from that, which again, might turn into, you know, three bullet points. And so it's kind of like, so what this is, is it's like an interface. It's like we had, you know, graphical user interfaces, I just started calling these Louie's linguistic user interfaces. Yeah, I like Louie's, it's works. Because I mean, it's kind of like, the, you know, it is a convenient transport medium, you know, an essay is a convenient transport medium for information, particularly when the two sides aren't really quite aligned. I mean, it's like, fill out this form, check this box, check that box, then then you can easily sort of transfer it from one side to the other. But when, when you know, each side doesn't really quite know what the other side is looking for, this is a convenient way to transfer information. Now that that means there are, there are people and professions that have been that are quite kind of knowledge worker type professions, but people have assumed are like, Oh, nobody's going to automate these knowledge worker type professions.

SPEAKER_163: Yeah, it's not possible. Right, right. But that's human judgment. Yeah, right. Turns out turns out

SPEAKER_09: that that's not true. And it turns out. And so, you know, and one of those areas is well, for example, one area of programming, where, you know, I have to say, if people had paid attention to stuff we've been doing for the last 40 years, they wouldn't be in this particular pickle. Because you know, the whole idea of the computational language that we've been building, is that all of that boilerplate stuff that exists in low level programming languages, we already automated that, you know, when you say, you know, geo distance or something between two cities, we've already automated all that stuff about, you know, pulling lat long from databases and figuring out, you know, the, you know, spherical geometry of blah, blah, blah, blah, blah. All that stuff, which, you know, you write it in, I don't know, Java, Python, whatever else, it's a big slab of code. Or maybe you pull it out of some library here that doesn't, you know, interface with some library there. This is, this has been kind of the low level kind of manual labor type programming that now, you know, it's not to say that there aren't millions of people who use our computational language. So I, this is, and none of this applies to them, because they already know how to kind of do things at this higher level. But there's an awful lot of programming that has been done using programming languages. And, you know, one thing to make clear is that, you know, what is a programming language, it's a way of kind of letting a human telecomputer in the computer's terms, what the computer should do, you know, the computer has a memory, you can make an array, you can have variables, you can do this. But those are things that are sort of in the computer's terms, kind of that the whole idea that, that, you know, I've pursued for the last 40 years or so, is to have a language which is kind of a bridge between how we humans think about things, and what can be done computationally, so that we're kind of representing things at a human level, rather than at the level that happens to be convenient for the computer. There's a lot more work for the people who build the language to do that. But that's what I just spent doing.

SPEAKER_74: When we look at it, is this going to be a slow change? Like, I remember when I got my first loft

SPEAKER_171: in New York, and it was the 90s, and they had manual elevator operators, they would take you to your floor in this old building. And I remember the, you know, 10 years later, they, yeah, they got

SPEAKER_32: rid of them, and they put in automated elevators, elevator operators as a concept took 50 years to

SPEAKER_13: kind of deprecate over time. I think there's like a couple left in America, the Hotel Del Coronado

SPEAKER_64: in San Diego famously kept their old elevator and their elevator operator because it's charming or

SPEAKER_172: whatever ice cutters. I think I've been in that hotel. Yeah, maybe I even know it's like an old guy

SPEAKER_32: who's in there. It's the one from some like it hot, the famous film, and it's, it's quite charming. But

SPEAKER_13: ice cutters, refrigerators, switchboard operators, you know, operators generally, lamp lighters, all this stuff has gone away. But it took time. So when we look at this, does this feel like programmers are going to become 10 times better? And yeah, we'll just get more accomplished in the

SPEAKER_04: world? Or does it feel like this is going to wipe out swaths of jobs really fast? And then what do

SPEAKER_09: you think that does societally? I think some things will go fairly quickly. In this particular case, not only because the technology exists to do it, but also because the sort of societal attitudes and, and sort of, oh, this is going away. So we'll make it go away even quicker, because we can kind of already see the future. My guess is that some things will happen reasonably quickly. But, you know, it's always the case that things I don't know, in my life, I've, I've had the good or bad fortune or something to invent a bunch of things that end up being many, many decades ahead of the current time, so to speak. And so then it's maddeningly slow, how quickly, you know, maddeningly slowly, things actually get absorbed. I think this one because of the kind of momentum that exists right now, I think, I think some of it will go quite quickly. Now, you know, what does that mean? You look at the pattern of what's happened in all previous cases, let's say telephone switchboard operators, you know, the fact that telephone switchboard operators existed was a consequence of the fact that telephones existed, which was a technological advance. But then automated switching came in, and you didn't need a manual telephone switchboard operator. But what did automated switching do? Well, it enabled basically the telecommunications industry. And that has generated just an immense range of jobs. I think one of the things you see seems to be the case is that, you know, look at America, you know, in around even 1900, was still 1850, it was it was more than half agricultural work. Yeah. And you know, the pie chart of what people did was very, you know, it was a big wedge of agriculture, and then a few other wedges, and they were all quite big. If you look at, you know, today, it's much more sliced up, that you know, the pie is in much smaller pieces. And I think that's a thing that one can expect to see, as sort of more automation happens, more things become possible, there are more niches that people can fill, so to speak. And I kind of think that what tends to happen is, when one of these sort of steps of automation happens, it enables things that and then enables more diversity and what people can do. It isn't because people aren't all just pushing, you know, pushing the plow or whatever. So for agriculture, it's like, okay, now we've we've got that done. So now let's look at what's possible. And I think the thing to realize about the fascinating, the, you know, kind of the interplay between, you know, AI automation, humans, you know, you've got a raw AI, it does its neural net thing, whatever else, but if you say to the AI, you know, what is your goal in existence, so to speak, it, you know, it has no intrinsic answer to that question. We humans think we have an intrinsic answer, where does that answer come from, it comes from the whole sort of web of history, it comes from our biology, etc, etc, etc. But we are pretty convinced that we have, you know, we have definite goals, we want to do this, we want to do that. Those goals tend to be things that are intrinsically coming from humans, the how the goals get achieved, that's where the AI is an automation and so on come in. So, you know, you're, you're, and what I think you see happening is that when there's a big sort of enablement of things, what becomes important is what can you do with that enablement? I mean, we were talking before about kind of use cases for LLMs, it's like, okay, now we have LLMs, now we've got to figure out which use cases do we care about. And that's sort of an intrinsically human activity, because there might be lots of you know, an LLM could just go spinning, you know, random words out and so on. And it might, it might, in some weird sort of anthropomorphizing of the thing, it might have a very happy time just spinning random words out, humans look at it and say,

SPEAKER_163: what the heck is that? We don't care about that. Yeah, because need a jockey, gonna need a pilot,

SPEAKER_09: right? Right. I mean, you kind of, yes, you need to, you need to kind of define what the, what direction what the objective is. So I kind of think that that's, I'm sort of, you know, what you see over and over again, is something gets automated, that enables a lot of other opportunities. Sometimes, and, you know, that's, that's been the pattern. Now, you know, it's kind of like the question of, well, will that come to an end? Kind of like, will everything that could be invented eventually have been invented? Well, we actually know, from sort of theoretical science considerations, actually related to computational irreducibility, we know that in sort of a formal sense, it will never be the case that there's no more to be invented. There'll always be unexpected things that you can figure out that you can invent. So, so in principle, there's no limits to what could be invented. So the question is, could be the case that we humans will say, hey, we're done now, you know, everything that we care about, right, the, you know, everything we care about, it's been invented, right? You know, we're good from here on out. Actually, that wouldn't work, because it turns out the world, the natural world, and so on, will continually throw up unexpected things that we'll have to respond to. So it won't, we won't be able to get into that kind of, oh, we're done now. But you know, in the situation where we could say, we're done now, then yes, it could be the case that everything that we care about has been automated by AI, other forms of automation, and so on. And sort of then, then we could be in, oh, there's nothing for humans to do anymore. But, you know, I think that for both theoretical science reasons and practical reasons, I don't

SPEAKER_193: think that's what's going to happen. Yeah, what would you, when we look at this paradigm shift

SPEAKER_13: that occurred, we had agriculture, factories, knowledge work. Now, knowledge work seems like it's going to be automated. So, you know, we, we put robots into factories, we put robots and automation into the field. So agriculture and factories, you know, we don't need as many humans involved in those things. And knowledge work, we probably won't need as many humans involved in it. So then what if this is a true paradigm shift, what is the post knowledge work era going to be? Is it going to be prompt engineering? What is it? What do we call this new era, where anybody can talk to a chat interface and create a product or service in the world that maybe accomplishes or solve some really important or pressing problem?

SPEAKER_09: Right? Look, I think that it really reflects on, you know, what we humans do and are on a special about doing. And that might be, it might be thinking, you know, one of the things about knowledge work is it turns out and, you know, the education sort of directs people this way, there's procedures for doing lots of kinds of knowledge work. Yes, it requires sort of analytical steps. But if you say big picture, think about stuff. That's not what the typical knowledge worker is trained to do. And, you know, I think that's a, that's a great sort of intrinsic human thing is just globally think about stuff. And that's something where I think the value of that is going to go way up. I think the value of the kind of super specialized siloed knowledge is going to go down. Because that stuff, you know, you can drill pretty deep into a silo using automation, if you know, the kind of the overall way to think, getting deep into that silo is something that is is now much easier than it than it once was. So I, you know, I kind of tend to think that the, you know, other things that are kind of, oh, I don't know whether it's other in a sense, more creative, more kind of things that are in a sense, more arbitrary, more, more human chosen, like thinking we could go in this direction, rather than that direction, we could come up with this, you know, cool, you know, sort of routine or whatever, that's that, you know, that entertains people or whatever, these are things which are sort of much more arbitrary, they're not things where we say, you know, here's the endpoint, now just go fill in that endpoint in the best way. And I think, you know, quite a bit of knowledge work has, has ended up being something that is kind of we know what the end result is, more or less, we know where we're

SPEAKER_07: going, now just fill in the details, so to speak. And I think like a journalism job, or a legal job,

SPEAKER_13: it's, it's kind of road, it's like, Okay, who, what, when, where, why? Okay, talk to a couple of people, they got one side of the story, so you can get the other side hit publish. Okay, lawyer, what do you want this agreement to say? What do you want to happen if people break the agreement? Okay, we're done. And what you're proposing is maybe this next era,

SPEAKER_35: would be the creative era of humanity. I don't know, maybe it's the judgment base,

SPEAKER_151: I'm trying to come up with the right word. But it seems like an era where human judgment,

SPEAKER_74: and creativity is the driving force, not the rote knowledge work.

SPEAKER_207: I think that's, that's a good possibility. I mean, I think that the,

SPEAKER_09: you know, I tend to be, I suppose, generically an optimist, and I kind of look at the pie chart, getting more and more fragmented. And I think about all sorts of different people who have all sorts of different skills. And I think about the fact that, you know, for example, in my own case, right, I've spent my time doing science and computation and some technology and so on, and, and companies and things like that. And, you know, if I'd lived at a different time in history, the things that I've really had a good time doing just wouldn't have been available to do. And you know, that wasn't, that wasn't part of the pie chart. Back in 1850, you know, computation and science around computation wasn't part of the pie chart of things you could do. And so I think, you know, in my kind of optimistic view of things, it's kind of that, you know, there's, there's more pieces of the pie, there's more different things that can be done. And there's more, you know, for different people who have different interests and skills, and so on, there's, there's more that can be, can be sort of explored. Now, you know, I think that there are, when you ask, kind of,

SPEAKER_38: by the way, I mean, there, there are, there are just, there are sort of, there will be lots and lots of new job categories. I mean, we just got prompt engineer, we're going to have

SPEAKER_13: podcast or having conversations professionally in a vertical of something you're passionate about. And then the fact that I get to do that for a living, just, I mean, there was Charlie Rose that, you know, there was Oprah, but the idea that now that there are probably 100,000 people making a living and just doing podcasts, and then hundreds of millions of people listening to them,

SPEAKER_203: is mind blowing, it's like a little slice of the pie that nobody ever considered.

SPEAKER_09: Yeah, that's right. And we just got, you know, we just got prompt engineers, love we're going to have AI Wranglers, we're going to have AI psychologists. You know, you're gonna have a whole bunch of new categories. That, and I think that is,

SPEAKER_211: that is just incredibly typical of what you see happening with, with innovations,

SPEAKER_74: particularly automations. How close is, uh, because we're watching this all happen in a chat interface, not scary at all. Um, but I guess people just wrote a, signed a petition, hey, maybe we should pause this. I think that was largely, I don't know if you saw it, but this, you know, future of life petition, um, I think it was largely ceremonial, like just probably worth us considering is I don't see anybody stopping their work for six months. I don't, I don't see

SPEAKER_109: anybody stopping. I think it's a, it, it, the cynic would say it's a list of, of, uh, people in places that feel like they're getting left behind and want everybody else to stop for a while while

SPEAKER_13: they catch up. Yeah. So there is, that would be a cynical approach or just, hey, I, I know that

SPEAKER_32: this isn't going to happen, but I just want to have it on record that I said, this might have been a good time to be more thoughtful. Uh, but let's talk about being more thoughtful. Um, do you think we are getting to a point where unintended consequences are a possibility? Again, the pace

SPEAKER_07: you and I haven't seen it, always unintended consequences. I mean, of, of almost anything, you know, who thought that, you know, doing research on virology that did this or that or the other

SPEAKER_09: would lead to this or that or the other thing, you know, good or bad, but, uh, pandemics. Yeah.

SPEAKER_89: For example, don't, don't say or this podcast is going to get flagged. If we actually speculate

SPEAKER_228: that a human created COVID seems probable, right? But, but, um, well, maybe it was an AI. No,

SPEAKER_230: I don't think so. It wasn't quite at that level. The AI wasn't quite ready to do that. Now it would

SPEAKER_74: probably be an AI. I mean, let's talk about that for a second open-mindedly here. If you were to put,

SPEAKER_13: uh, some prompts in and you put in the sequence of COVID, which isn't really a difficult thing to sequence and said, come up with things more deadly or come up with things that, you know, instead of affecting old people affect young people that have a longer incubation period. So they're harder to recognize or stop. AI could do that today. And I, you know, it's a little complicated

SPEAKER_233: because it turned out one of the things that's totally bizarre is that large language models are

SPEAKER_09: actually useful for understanding the structure of proteins. It's just something that has nothing to do with human language. It is, however, you know, what happens with proteins, you know, proteins are these long strings of amino acids, which is these kind of collections of atoms. And, you know, every, every protein is specified by some piece of our DNA, our genome. And it's, you know, a protein is a string of thousands to millions of, of, uh, of amino acids. And actually, they don't usually get as far as a million amino acids, but, but, um, uh, you know, it's a long string of these things and then they fold up in certain complicated ways. It's been a

SPEAKER_08: long time problem to figure out given the sequence, how does the protein fold up? It matters a lot how

SPEAKER_09: the protein folds up because the, the way that proteins actually have, uh, significance for biology has to do with their shape. And so, you know, is there a particular hole in the protein that where some, some particular, you know, other molecule can fit in that hole or not? Does the protein kind of, uh, you know, knit itself together to make a muscle, you know, all these kinds of things. So it matters what the shape is. And so the, the question is given the sequence, can you predict the shape? So then the, uh, that's been a long time problem. That was, uh, a lot of progress was made on that using, well, initially not quite large language models, but now large language models. But really what's happening there is you take the protein where you take the sequence, you want to figure out what kind of shape its protein is. You, you then say, well, this piece matches this protein that we've already studied. This piece matches another protein. This piece matches another protein. Now let's figure out how to knit those pieces together. And the knitting those pieces together is something that's a little bit like this kind of puzzle piece thing that I mentioned for human language. It seems that knitting together is something that LLMs seem to be quite good at. And so then you can do the, the more extreme thing that people have started to do, which is to kind of use generative AI to say, you know, given a bunch of words, make a protein that does such and such. So, yes, you know, the thing you're describing, I don't know, there are lots of issues and there are lots of computational irreducibility questions actually, but in, in broad outline, yes, it will be possible for sure to say, you know, take this and, you know, with just a linguistic type prompt, you know, find something that does, you know, that works a little bit differently and so on. And, you know, it pulls in perhaps something from some other, you know, genome database or whatever else. So yeah, I'm sure that will, that will be a thing that unfortunately, perhaps that will be about maybe fortunately, in some cases, and maybe unfortunately, in others. And that's,

SPEAKER_238: it depends on the prompt engineer and what their goal is, right?

SPEAKER_09: Right, right. But I mean, that's so typical of progress of all kinds, you know, you can,

SPEAKER_228: you can use it to, you know, cure a terrible disease, you can use it to make a terrible disease.

SPEAKER_240: Right. You can make a nuclear reactor, you can make a nuclear bomb.

SPEAKER_74: And yeah, right.

SPEAKER_13: Just seemed like that, you know, in those examples, people didn't have as much access to a tool. And this tool feels like it's going to have everybody's going to have access to it.

SPEAKER_74: Put a couple billion people on this thing that is qualitatively different than

SPEAKER_245: the number of people who know how to operate, you know, and do nuclear science.

SPEAKER_09: Yeah, well, right. It's also the materials you need to make nuclear stuff are not in such easy supply, right?

SPEAKER_16: There's a long supply chain to produce them. Yeah. So this is certainly much more accessible.

SPEAKER_245: I think we just talked ourselves into signing the six month ban.

SPEAKER_13: So many scary possibilities here. It's almost like talking about them is, I don't want to accept them in the world. But, you know,

SPEAKER_09: Well, I think the thing to understand is when one thinks about, okay, so what are the AI is going to do? First question is, what do we want the AI is to do? You know, if we were going to define a system of ethics, let's say for the AI, what would we want that to be? So, you know, one thing people would say is, well, you know, let's have the AI is just mimic what humans do. Most people would say that's a bad idea. You know, humans do all kinds of things that humans, we don't think humans should be doing. Yeah, get drunk and beat each other up. Yeah. Yeah. Which, you know, in most cases, people think is a bad thing, but sometimes people don't think that's a bad thing. And it's complicated. Yeah. And, you know, I think then what it ends up being is, let's make the AIs sort of be the way that humans aspire to be. But that's a much more fuzzy, complicated thing, because it's like, whose aspirations? You know, you pick some, you know, sacred book, you pick some self-help book. Be careful there, yeah. Right. And, you know, you end up with, so, but then in the end, it's kind of like, well, maybe some group of people would agree, this is how we want the AIs to generally behave. You know, we could invent a sort of AI constitution that defines how we generally want the AIs to behave. And that's, that's probably not, that's probably a sensible thing to do. It also happens to be comparatively hard, I think, to come up with what you want to say there. And, you know, one of the things that we get to define, and perhaps computational language, perhaps better than in prompts, is kind of a sort of definition of what we want, you know, what we want the AIs to do, so to speak. And then we have to figure out, how do we do that? You know, are we going to have a worldwide, this is what we want the AIs to do? Probably not a very good idea. You know, if you have, if you have a sort of mono, you know, mono-cultured sort of AI world, it becomes rather brittle. I mean, if something's, if it's like, yeah, something wrong with the, you know, with the, with the, with the code, so to speak, there's something wrong with a legal code effectively for the AIs,

SPEAKER_12: oops, you know, we just made the whole world follow this legal code, you know, it's going to blow everything up. So, you know, the thing that has...

SPEAKER_254: I'm curious how you feel about the fact that this started, at least, you know,

SPEAKER_74: OpenAI as an open source nonprofit, that somehow flipped into a for-profit, and then flipped from, everybody should have access to this code, to suddenly, Sam, and the team saying, you know what, this code is a little too dangerous for everybody to see, so now nobody can see it except us. Do you think it should be open sourced, and people should see this stuff, and it should be more out in the open? Or do you think it's

SPEAKER_193: fine for it to be, you know, programmed and a small number of people have access to the source code?

SPEAKER_07: I don't think it matters, because I think that this whole idea of LLMs is now kind of,

SPEAKER_09: the genie is out of the bottle, and, you know, one can make LLMs. You know, OpenAI did a great engineering job, and they have a, you know, they seem to have, you know, they've achieved a bunch of things other people haven't yet achieved. And that's, you know, from a business point of view, that has lots of significance, there's lots of timing, and lots of, you know, what will ramp up, how quickly, and so on. And, you know, I don't think that it's a question of, you know, I don't think in the big picture, I don't think that's an important thing. Honestly, the ability for, you know, innovation is hard, and you have to kind of have, you know, you have to have a certain, I mean, I know, in our own case, you know, I have a fairly small company that I've been running for 36 years now. I mean, 800 people or something fairly small, by many standards. But, you know, and the fact that we are able to innovate and go on innovating is a consequence of the fact that we have a viable business model for the things that we do. If we didn't have that, if we just said, Oh, we're going to give everything away. And, you know, then, okay, how do we how do we feed the 800 people? So yeah, exactly. We have to have some business model. And I personally, you know, I have to say, for myself, I prefer business models, which have a directness where the people getting value, other people, you know, paying for the thing, so to speak, rather than sort of more indirect models, because it gives it a better alignment of kind of what one's building with what with what customers actually want.

SPEAKER_264: Seeing what's happened here, you're just going to build your own language model and compete

SPEAKER_09: against chat. Well, I mean, it's, it's something where, where, you know, obviously, a company like

SPEAKER_264: ours is capable of doing stuff like that. Easily, yeah, sounds like it would be a no brainer for you.

SPEAKER_09: So, okay. I mean, it's one of these things where I don't think, you know, there will be many of these things. Um, and, you know, I think that I mean, the thing I was was saying is that I, I don't think, you know, it's kind of this thing where if you say, Well, let's, let's pull everybody down. So that sort of nobody has the, the either the sort of the war chest, or the, or kind of the, the motivation to be a leader, you know, that's not really very good for the world. If you want innovation to happen, you know, you have to have a situation where, where that, you know, where, for example, some, you know, organization can decide for itself what it's going to do up to a point, because if it if the whole world is going to vote on, you know, what should we do next? Well, you can you can kind of, you know, it's, it's, it's very implausible, that creative innovation is going to happen in that situation. So I think, you know, I, I, I, I'm not, again, I think in the big picture, it doesn't really matter what these particular details are. But I think that the, you know, having having the ability in the runway to, to actually have the motivation to, to kind of independently innovators is kind of important. And I think that's, that's borne out by the fact that, you know, a year ago, we didn't have ChatGPT. Yeah. And, you know, it was, you know, it's particular people who had to, you know, who were in a situation where they could do and were motivated to do the kind of innovation that

SPEAKER_245: was needed to create ChatGPT. Yeah, and it's a small number of people, a couple of hundred, I guess, got them to here. So it's not a, it's not like it took Google to do it. And obviously,

SPEAKER_32: Google has their own, but it didn't take a Facebook Google size effort. It took a relatively modest size group of people to achieve this, and they should get all the credit in the world. As we wrap here, I guess I have two questions at the end. How close are we as possible question to answer? I know, but very interested in hearing your thoughts on it to AGI. And, you know, if you had to put a

SPEAKER_171: year or set up a betting line over under, and then how do we how do you know, like, in your mind,

SPEAKER_32: when will you know that we have something that is an AGI? You know, about that, you know,

SPEAKER_09: last 50 years, I've been paying attention to kind of what happens with computers and all this kind of thing. And people saying, when we can do X, then we'll know we have true, you know, artificial intelligence. You know, I've personally built a few of those Xs that people have said, you know, when we have this, and then when you actually have it, people say, Oh, it's just a piece of engineering. That's not true, you know, intelligence, and so on. I think that the thing, I mean, it's, it's almost the thing that you'll when you'll know you have true human intelligence, is when you basically have a copy of a human. And you can always say, Oh, well, it doesn't have this attribute, you know, because it isn't mortal, it can't think this way, or because it doesn't have, you know, five fingers, it can't do this, you know, the only way you'll have something which is just like a human is to have something really just like a human. Now, I think that the question of kind of when the, you know, it's, it's sort of an incremental thing, it's, it's kind of a, a, you know, this was a big shock, chat GPT, people didn't expect, sort of this level of, of humanity, so to speak, in an automated system. I think that I would say that, well, in, in terms of, you know, what else do you want kind of thing, you know, you'll be, you know, there was the Turing test that Alan Turing made up in 1950, which I think pretty firmly, you know, as of 2023, we can do that, that one is done. Nobody knew when that was going to happen. And, and, you know, that was, that was one of the last of the kind of standard, this is a test for whether you have true artificial intelligence. So, you know, when we can ask questions like, you know, for the things, for this set of people, when we'll be able, we'll be able to automate the main thing that they do, it's worth understanding that when we say, when we talk about automating things, it's kind of like, you know, back in the day, people would handwrite this or that thing, and then printing came along, and there was just a standardized, you know, font for A and B and C. And some people would say, well, it's much more efficient, we can, you know, much more automated. And some people will say, well, you know, you kind of lost the human touch of the of the calligraphic stuff, and the same will happen here. Now, there'll be plenty, you know, like, it's like, you know, when I read a chat GPT written essay, it's very perfect. It's very kind of anodyne in some ways. And, you know, it doesn't have it, it's kind of like the, the rug that was made by a machine, rather than by a person with those little errors in it type thing. And, you know, I think people will, people will continue to say, oh, well, if the rug doesn't have the little errors, then it isn't really, you know,

SPEAKER_278: you know, an AGR, an automated general rug or something.

Chamath Palihapitiya: Yeah. Uh, who should, uh, when, when these machines are, uh, you know, the other thing is,

SPEAKER_74: I, I was thinking when you talked about how this surprised everybody, it reminds me of when Boston Dynamics made that first robot that could kind of run and do flips. And you're like,

SPEAKER_171: how I wasn't expecting that. When did these two things combine the Boston Dynamics, you know, parkour robot and chat GPT? Uh, and what is that going to look?

SPEAKER_09: You know, one of the things that, so, I mean, I think some of the things about sort of, uh, being able to create geometrical kinds of things in a kind of large language model ish way, you know, those things are very much coming. Some of those things already here. Um, you know, that's important for, you know, if you're making 3d objects, you're doing animation, you know, those kinds of things. That's, that's very, you know, incipient. That's very, very, you know, very, very close, I would say. Um, you know, I think that the question of, you know, using machine learning to figure out how do you grasp, you know, how do you pick up, you know, how do you pick up a cell phone or something that's proved comparatively difficult. My guess is that will be cracked, but it has proved comparatively difficult. Um, I think that, uh, uh, the whole question of sort of, um, how robotics advances my own, you know, one of the things that's surprising about robotics is it's fairly non-general purpose. Like with computers, the big thing that was the big sort of advance and that really made computers possible was the idea of universal computation. The idea you could have a fixed piece of hardware, put different programs into it, and it would do different computations. Um, that's, you know, that was an idea originally from 1920s and 1930s. It sort of became real in the 1950s and so on. Um, and that's what made software possible. That's what basically

SPEAKER_35: made computers useful. A work processor, a video game, or an Excel spreadsheet could all be done

SPEAKER_09: on the same computer. Right. Exactly. So for, for, for robots, that hasn't really been the case. It's not the case that you can have a general purpose robotic system, you know, people, uh, and I think that's something I've even thought a bit about how to do that. Um, you know, I think that's something that is conceivable. Um, it is tricky because the physical world is, is nasty to deal with relative to the informational world, so to speak. Yeah. But, uh, you know, if that happened, and I think it will eventually happen, uh, by the way, I should say at a molecular scale, biology has solved that problem. Biology has basically made with these proteins we were talking about earlier. Biology is, you know, you just have a sequence of amino acids and it curls itself up and, you know, sometimes it can be muscle. Sometimes it can be a brain cell. Sometimes it can be, you know, the things, the critical things in those, in those different kinds of, uh, biological devices, so to speak. So biology at a molecular scale has sort of solved the universal robotics problem, but on a large scale, we haven't solved it yet. Yeah, probably we will. And when that happens, you know, for example, in terms of the, oh my gosh, what jobs are going to be automated, you know, another, you know, another set of chunks of the pie will, you know, the things that are being done now will sort of zero out and there'll be the sort of a new collection of things that become possible. And then that's, you know, something about manipulating the physical world, which, which hasn't yet been, uh, you know, and then what will happen is, you know, the main thing that will happen is sort of manipulating the physical world will be a, become a problem of software, so to speak, rather than a problem of how you put different, you know, uh, sort of pieces on the,

SPEAKER_32: on the hand of the robot and so on. Now, you know, that's going to be wild when you can say to this audit, this sort of general robot, uh, take, you know, uh, you know, Jason's, uh,

SPEAKER_13: you know, bags to his room and it's like, okay, bags. I know what those are. There's Jason. I know who he is. He's a guest. And now I need to know what room is in. Let me go query what room is in. And I'm going to carry them upstairs. The chat GPT interface or the language model would actually be able to figure out what you meant. And then you just need a physical specimen that can actually

SPEAKER_09: pick up bags and do this. Right. You know, the thing to understand about that and a little bit more generality is this, this whole question about, you know, that the, the chat interface can take sort of your whole, you know, speech about what you want the robot to do. And the question is, how are you actually sure that robots can do what you thought it was going to do? Because you just had this language thing. And this is where, you know, one of the things that I've been excited about very recently is this is where, you know, our computational language initiative is really important because once you have that, that, you know, you've got the thing you say in natural language, if you can generate from that a piece of computational language, that's something that is intended for humans to read. And, you know, a few million people know how to read it now, and probably a lot more will learn how to read it. And they'll say, Oh, yeah, that's what I wanted. You know, these two lines of computational language, I can read them. And yes, you know, yep, that's what I wanted, you know, go do it now. Without that, it's, it can be a bit challenging, you know, you can watch the robot because they know, no, no, don't do that. Don't pick up the,

Chamath Palihapitiya: you know, don't. Yeah, baggage doesn't mean the spouse, or the kids, like, that's not the baggage we're talking about. Yeah, right, right. So I mean, you know,

SPEAKER_09: and you can obviously reprompt it and so on. But there are plenty of situations in which, particularly when you're building up a bigger system, and you when you want to do, you know, a whole collection of things where having this intermediate layer of the sort of precise computational notation is really important. But yeah, I think that, you know, we can expect, well, one of the things that's also funky about something like robotics, is that the world, the sort of the built environment that we have was built for humans. So you know, we have doors we can open with, you know, with hands that are at a certain height, et cetera, et cetera, et cetera. So, you know, there's sort of a certain pressure to have humanoid like robots, just because we built an environment that is suitable for humanoid robots. Now, there are plenty of environments in the world, you know, which are, you know, thrown up by the natural world that are quite unsuitable to humans. Yeah. And where we don't tend to hang out. Yeah, right. Yeah. And where, you know, something quite different would be appropriate. But, but, you know, that that that tends to make it, you know, that that's kind of the analog of you got an LLM, and it's learning actual human language, and it could learn all kinds of other things, but it actually aren't human language to sort of fit into the human world of the human linguistic world in that case, as opposed to sort of the human built world, so to speak.

SPEAKER_295: It's crazy how fast this is moving. And it's just great to have people like you working on it.

SPEAKER_193: All right. Everybody can just check out Wolfram Alpha, check out the plugin, start playing with it, and share whatever you're building on Twitter. And I really appreciate you taking the time.

SPEAKER_298: Are you Dr. Wolfram? Should I be calling you Dr.? I feel like I should.

SPEAKER_274: You know, I've noticed, here's a basic rule. If one's doing business, if somebody calls me professor,

SPEAKER_09: that's really deadly. Doctor is sort of okay. But, you know, for business, it's Mr.

SPEAKER_137: I really appreciate you taking the time. I know you're very busy, especially at this moment in

SPEAKER_193: time when everybody's really excited about the work you've done and continue to do. So, thank you so much, and we'll see you all next time. Bye-bye.