Speaker 1: Bloomberg Audio Studios, podcasts, radio news.
Speaker 2: You're listening to Bloomberg Business Week with Carol Messer and
Speaker 2: Tim Stenebeck on Bloomberg Radio. Look, I said we're going
Speaker 2: all in on AI. The author comes to our four
Speaker 2: o'clock hour today. Alphabet just reported we got that news
Speaker 2: just now, Caroline Hyde and man Deep Singh joining us
Speaker 2: Microsoft's GitHub agreeing to bank artificial intelligence models from Anthropic
Speaker 2: and Google into a coding assistant used by millions of
Speaker 2: software developers. And then there was that Wall Street Journal
Speaker 2: report earlier today Carol elon Musk's XAI is in talks
Speaker 2: to raise funding at a value of forty billion dollars.
Speaker 1: Yeah, that's real money. So much of the reason we
Speaker 1: are talking about AI is because of Jeffrey Hinton, Nobel
Speaker 1: Prize winner in physics for work in AI, known as
Speaker 1: the godfather of AI. He was on the most recent
Speaker 1: episode of Wall Street Week with David weston.
Speaker 3: These big eye systems, great sub goals. Now, the problem
Speaker 3: with that is if you give something the ability to
Speaker 3: create subcoals, it will quickly realize this one particular subcoal
Speaker 3: that's almost always useful. So if I have the goal
Speaker 3: of just getting more control over the world. That will
Speaker 3: help me achieve everything I want to achieve. So these
Speaker 3: things will realize that very quickly. Even if they've got
Speaker 3: no self interest. Then I understand that if I get
Speaker 3: more control, I'm it'll be better at doing what they
Speaker 3: want me to do, and so they will try and
Speaker 3: get more control. That's the beginning of a very slippery slope.
Speaker 1: A slippery slope. Indeed, perhaps our next guest work with
Speaker 1: Jeffrey Hinton developing the Boltzmann machine. It is the first
Speaker 1: learning algorithm for Mueller neural networks. And if you're scratching
Speaker 1: your et duh, you're going to get an explainer in
Speaker 1: just a moment.
Speaker 2: We're very pleased to have a doctor Terrence Sanowski with
Speaker 2: us today's Francis Crick Chair at the Salk Institute for
Speaker 2: Biological Studies, Distinguished Professor at the University of California at
Speaker 2: San Diego. He's also president of the Neural Information Processing
Speaker 2: Systems Foundation. He joined us from San Diego. His new
Speaker 2: book out. It's called chat GPT and the Future of AI,
Speaker 2: The Deep Language Revolution. Terry, good to have you with us.
Speaker 2: Congratulations on the book. You have been thinking about this
Speaker 2: stuff for more than three decades. At this point, I
Speaker 2: think a lot of people have been talking about it
Speaker 2: and thinking about investing in it for two years. At
Speaker 2: this point, are you just saying every day, finally people
Speaker 2: are taking seriously what I've been looking at for a generation.
Speaker 4: Well, it's great to be here to have this opportunity.
Speaker 4: My book launched today, and I'm really excited about it.
Speaker 4: I think that what's happening right now is really unbelievable
Speaker 4: in terms of the breath and the depth and the excitement.
Speaker 4: And so I was there at the beginning. Jeffrey Hinton
Speaker 4: and I collaborated in the nineteen eighties. All the learning
Speaker 4: algorithms that are being used today for these large languages
Speaker 4: models and deep learning were developed by us back in
Speaker 4: that era. And of course what we didn't have back
Speaker 4: then were computers that were fast enough that could scale
Speaker 4: up these models to be you know, be able to
Speaker 4: solve these very difficult problems and artificial intelligence. But this
Speaker 4: you mentioned, the Neural Information Processing Systems Foundation on the
Speaker 4: President reorganized the biggest AI meeting and in December, we're
Speaker 4: expecting sixteen thousand researchers to descend on Vancouver. After by
Speaker 4: the way Taylor Swift, she's the big headliner on Sunday,
Speaker 4: but we have the rest of the week.
Speaker 1: So I want to ask you, how did you think
Speaker 1: about neural networks in large language models in the nineteen eighties.
Speaker 1: How do you think about them today.
Speaker 4: Well, we actually had a premonition that these large language models,
Speaker 4: I should say not the large they were small language models.
Speaker 4: Back we're really good at language. And that was a
Speaker 4: particular project, a summer project for a gratitude in my
Speaker 4: lap called net talk, which was trained on a dictionary
Speaker 4: to be able to pronounce English text. You know, if
Speaker 4: you give it an article from the Wall Street Journal
Speaker 4: and they would pronounce it in an understandable way. And
Speaker 4: this in linguistics is a very difficult problem because English
Speaker 4: is so irregular. There are a lot of regularities, but
Speaker 4: you also have irregularities, and then you have rules for
Speaker 4: the irregularities. But it really was amazing that a small,
Speaker 4: tiny network with just a few hundred units and tens
Speaker 4: of thousands of weights, the parameters, the connections between the
Speaker 4: units could do that. It was like an amazing compression
Speaker 4: of complexity. And now we know that these large language models,
Speaker 4: the deep learning networks, they love language, and they are
Speaker 4: capable of things that we never could have imagined.
Speaker 2: Well, we're gonna we have a few minutes with you now,
Speaker 2: and then we're going to come back and have even
Speaker 2: more time with you. But that's really what I wanted
Speaker 2: to talk about the idea of super intelligent AI. What
Speaker 2: are we not thinking about? What's the threat out there?
Speaker 4: So you know, my good friend Jeff is very concerned,
Speaker 4: and I think he's one of the smartest people I've
Speaker 4: ever met. And if he's worried about it, then there's
Speaker 4: some as a concern. However, I think that even if
Speaker 4: you're concerned, it's very difficult to know when that's going
Speaker 4: to happen, if it ever will happen. And there are
Speaker 4: super forecasters out there, and this is from the Economist magazine,
Speaker 4: who are much better at people who are experts at
Speaker 4: predicting you know, if and when there may be a
Speaker 4: catastrophic or existential threat, and it turns out that in fact,
Speaker 4: they're not as a concerned as the experts in AI.
Speaker 4: I'm happy that someone is thinking about the worst case
Speaker 4: outcome because if not, then if it ever happens, we're
Speaker 4: in trouble. But right now, I'm more concerned about trying
Speaker 4: to understand how they work mathematically and also to learn
Speaker 4: more from the brain. After all these were designed. Back then,
Speaker 4: Jeff and I looked at the brain the only existence
Speaker 4: proof you could solve any problem in AI. And you know,
Speaker 4: we tried to build something that was based on similar principles.
Speaker 4: So now we can continue. There's a lot more in
Speaker 4: the brain we can learn from.
Speaker 2: But paint that picture for us, because I think a
Speaker 2: lot of people are worried about doomsday scenarios here, and
Speaker 2: if Jeffrey Hinton is worried about that stuff, I mean,
Speaker 2: should we we should be worried about it.
Speaker 4: You're saying, I think that we should be cautious, that
Speaker 4: is to say, we should be uh constantly thinking along
Speaker 4: the lines that Jeff is in terms of what could
Speaker 4: possibly happen, and you know, be cautious and put in
Speaker 4: precautions so that it can't happen.
Speaker 2: What sorry, I just yeah, go ahead.
Speaker 4: What I'm really concerned about are the unintended consequences, things
Speaker 4: that you cannot predict. Something may happen that you know,
Speaker 4: no one thought of, even Jeff.
Speaker 1: Yeah, and like you know, we have learned certainly right
Speaker 1: great financial crisis pandemic like the un the unthinkable can
Speaker 1: happen and you throw technology into it and you just
Speaker 1: kind of don't know where it's gonna go exactly. Okay,
Speaker 1: so now I'm terrified. Okay, Terrence Stoke go anywhere. We're
Speaker 1: gonna do some news. I do wonder what.
Speaker 2: Yeah, I think we have time for one more question
Speaker 2: before we go.
Speaker 1: Well, so you know, okay, we have a minute and
Speaker 1: then we're gonna take a break and come back. But
Speaker 1: I just do wonder. You know, when you talk to people,
Speaker 1: do you say, wait, this is really going to be
Speaker 1: net net a good thing?
Speaker 4: Look, all new technologies have good and bad consequences, and
Speaker 4: you try to mitigate the bad, and you you have
Speaker 4: to balance them, you know. Yeah, and right now it
Speaker 4: looks like the good is way way ahead of the
Speaker 4: bad in terms of the impact it may have on
Speaker 4: us and society and businesses. But you know, like I say,
Speaker 4: we have to be careful because we don't really know
Speaker 4: where it's heading.
Speaker 1: You know what worries me too, And we will talk
Speaker 1: about this maye when we come back. I feel like
Speaker 1: we throw around a lot of words, not you, but
Speaker 1: all of us in the general like you know, whether
Speaker 1: it's you know, AI, generative AI, and like you know,
Speaker 1: and I don't know that we really understand what's going on,
Speaker 1: and so it's hard to know what it could possibly become.
Speaker 1: So we're going to pick your brain a little bit
Speaker 1: more Terry when we come back.
Speaker 2: It's Terry Sanowski. He's a Francis Kirk Charity Sauk Institute
Speaker 2: for Biological Studies, Distinguished Professor at the University of California
Speaker 2: at San Diego, President of the Neural Information Processing Systems Foundation.
Speaker 2: The new book out now, Chat GPT in the Future
Speaker 2: of AI The Deep Language Revolution More with doctor Sanowski.
Speaker 1: Right after this, I want to get back to our guests.
Speaker 1: We're talking with doctor Terrence Sanowski. He is Francis Crick
Speaker 1: Chair at the Salk Institute for Biological Studies, Distinguished Professor
Speaker 1: at the University of California at San Diego. He's also
Speaker 1: president of the Neural Information Processing Systems Foundation, and he's
Speaker 1: joining us from San Diego on this Tuesday. His new
Speaker 1: book Chat CHEPT in the Future of AI, The Deep
Speaker 1: Language Revolution. I got to ask you because I am
Speaker 1: still trying to understand and I get worried that we
Speaker 1: throw these words around. Certainly not you, but we as
Speaker 1: we try to understand this with now, you know, not
Speaker 1: having full comprehension of what art artificial intelligence, the large
Speaker 1: language models that we're talking about today, where it takes us.
Speaker 1: Is it as subtle at evolving in life changing as
Speaker 1: the Internet was for us.
Speaker 4: So this is something that is emerging. And I have
Speaker 4: since the book was sent to press in the summer,
Speaker 4: I have a sub stack where I have tried to
Speaker 4: fill in with, you know, the new things that are happening.
Speaker 4: And I'm preparing something a new twelfth version the blog
Speaker 4: on the question of whether AI is overhyped or under hyped,
Speaker 4: and and you know, I've thought a lot about this,
Speaker 4: and you know, I think that it depends on the timescale.
Speaker 4: I think that on the short timescale it is overhyped.
Speaker 4: There's no doubt about it. There's just so much out there.
Speaker 4: I mean, every day the newspapers are filled with AI
Speaker 4: and your program. But I think in the long run
Speaker 4: it's actually under hyped. I think the real change in
Speaker 4: the Internet, for example, didn't occur within the first ten years.
Speaker 4: It was much later. Again, unintended applications that merge that
Speaker 4: you know, have enormous impact on our lives, like social media,
Speaker 4: So I think the same thing's going to happen with AI.
Speaker 1: But is it is it different? Like I guess, I'm like,
Speaker 1: what do you mean, Like, the Internet is not I
Speaker 1: wanted to say comfortable, but it's not because there's some
Speaker 1: really bad things that happen and we know that, right,
Speaker 1: and that's the battle we have with social media. And
Speaker 1: we want to talk to you about kind of regulatory
Speaker 1: oversight of AI in a moment. But I just I'm
Speaker 1: just trying to understand. You know, it does feel so
Speaker 1: seamless and just such a part of everything we do.
Speaker 1: But it hasn't necessarily replaced a ton of jobs. It's
Speaker 1: created jobs, It's replaced some jobs. I guess you could say,
Speaker 1: I'm just trying to understand, Like on what scale do
Speaker 1: you put it? You mentioned the internet, So is it
Speaker 1: apples to apples or is it something else?
Speaker 4: No, Well, first of all, it'll it uses the internet,
Speaker 4: So I mean that's like the the machinery that you
Speaker 4: need to reach to scale up and reach a large population.
Speaker 4: But it's more intimate than the Internet in the following
Speaker 4: sense that it talks to us, right, I mean It's
Speaker 4: as if an alien landed on the Earth and could
Speaker 4: talk to us in English, and it knew everything about
Speaker 4: you know, what, humans, history, everything, and the only thing
Speaker 4: we can be sure if it's not human. But it's
Speaker 4: really quite remarkable. Let me give you one example of
Speaker 4: something that I was really surprised at when they did
Speaker 4: a study of whether people who needed cognitive therapy preferred
Speaker 4: real humans or AI. They preferred AI, which was really
Speaker 4: quite remarkable. I didn't expect that. And part of the
Speaker 4: reason is that the AI is not judgmental like humans.
Speaker 1: Well wait, but isn't it depends on the data, Like
Speaker 1: we talk about, it wasn't.
Speaker 2: Getting trained on judgmental data.
Speaker 4: It was you know, Actually, it's a good question. What
Speaker 4: was it trained on. I think that it was fine
Speaker 4: tuned with you know, data from real subjects that we're
Speaker 4: that we're talking with a doctor. But even without that,
Speaker 4: I'll tell you something again, it's shocking is that it
Speaker 4: is actually empathy. These large language models can empathize with humans.
Speaker 4: And why is it? How is that? Well, it actually
Speaker 4: absorbed a lot of text out there, novels, you know, letters,
Speaker 4: and read it and so forth, and where empathy was
Speaker 4: being part of the discussion, and so it absorbed that
Speaker 4: too well.
Speaker 2: I wanted to hear a little bit of your thoughts
Speaker 2: on what we heard from Elon Musk a little earlier today.
Speaker 2: He actually participated in a surprise conversation at the Future
Speaker 2: Investment Initiative to discuss the future of AI.
Speaker 5: It's most likely going to be great, and there's this
Speaker 5: some chance which could be tense, that it goes bad.
Speaker 5: The chances on zero that it goes bad. But overall,
Speaker 5: at one point you said that covers eighty percent full
Speaker 5: is one positive way to look at it.
Speaker 2: Maybe ninety percent, okay, eighty or ninety percent positive. The
Speaker 2: question I have for you, professor, is do we need
Speaker 2: an international regulatory body? Do we need the largest, most
Speaker 2: powerful governments around the world to create some sort of
Speaker 2: standard to ensure or help ensure that this goes the
Speaker 2: right way.
Speaker 4: Well, as you know, in the UK they passed an
Speaker 4: AI Act which is like one hundred pages long and
Speaker 4: you know, incredibly detailed, and it's already absolute. I just
Speaker 4: moving blasting forward and you know you're trying to catch
Speaker 4: up with it. But I do believe that it's absolutely
Speaker 4: essential that it be regulated, and it should be regulated
Speaker 4: by people who a building it. The government, okay, is
Speaker 4: the business of protecting people. And we'll see how that
Speaker 4: plays out. But in for example, genetics, this happened, you know,
Speaker 4: back in the sixties seventies. They had a meeting where
Speaker 4: they came together at a solomar and they came up
Speaker 4: with a containment rules and regulations for doing experiments under
Speaker 4: the careful protection so that nothing leaks out, nothing gets out.
Speaker 4: And I think we need to do the same.
Speaker 1: Okay, So when does as you said, ten years for
Speaker 1: the internet to really kind of make its impact and
Speaker 1: presence really known and maybe you know, integrated into our lives.
Speaker 1: So is it a decade before we see LMS and
Speaker 1: AI at this level integrated into our lives.
Speaker 4: We are at a stage that aviation was at when
Speaker 4: at Kitty Hawk the Wright brothers made the first flight.
Speaker 4: It was ten feet up and one hundred feet long,
Speaker 4: and that really was the you know, something that then
Speaker 4: took decades and decades to build. And the most difficult thing,
Speaker 4: by the way that airplanes, you know, design of airplanes
Speaker 4: had to solve was control. How do you how do
Speaker 4: you make it go where you want to go without
Speaker 4: crashing and that's something that again it's like we're going
Speaker 4: through right now with AI. And yes, it will take decades.
Speaker 4: It's not going to happen overnight.
Speaker 3: I know.
Speaker 1: It's just it's kind of fascinating. We have a million
Speaker 1: more questions. Is it going to take all the jobs?
Speaker 1: Is it going to is it going to create jobs?
Speaker 1: Is it going to take jobs? Just got thirty seconds
Speaker 1: and yes.
Speaker 4: Yes, you know, I get asked that question whenever I
Speaker 4: give a public talk, and my answer is that you
Speaker 4: won't lose your job, but your job's going to change
Speaker 4: and you're going to need new skills, and you know
Speaker 4: it will morph over time. Now, you know these are
Speaker 4: people who are in the workforce now, but you know
Speaker 4: young people coming up, they'll have no trouble whatsoever finding
Speaker 4: new jobs in this new industry.
Speaker 2: We gotta get you back on the show, doctor Sanowski.
Speaker 2: Really appreciate you.
Speaker 1: Just say, Terry, what we always want to know is,
Speaker 1: like you know, people like us, you know anchors.
Speaker 2: We don't have time for him to answer that question.
Speaker 2: We don't have time for him to answer that question. Carol,
Speaker 2: I'm sorry, I got to give the book a play.
Speaker 1: Well, come back hopefully.
Speaker 2: The new book Chat GPT in the future of AI,
Speaker 2: the Deep Language Revolution, Doctor Terry Sanowski. He was there
Speaker 2: at the beginning. He knows it all. He's Francis Krickchair
Speaker 2: at the Salk Constitute for Biological Studies, among many other things.
Speaker 2: This is Bloomberg BusinessWeek.