Speaker 1: Cancer Straight Talk is a podcast for Memorial Sloan Kettering
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Speaker 1: Straight Talk. You'll learn so much. Hi everyone, I'm Kitty
Speaker 1: Kuric and this is next question. So I have to
Speaker 1: confess friends that if I sound weird, it's because I
Speaker 1: have a terrible cold. So I apologize in advance. Luckily,
Speaker 1: my plus one isn't sitting next to me catching my germs,
Speaker 1: but she is at a remote location. Where are you, Vivian.
Speaker 2: I'm in Bethesta, Maryland, in my home.
Speaker 1: Oh nice, Well, Vivian Schiller, as you probably heard, is
Speaker 1: my plus one today. And Vivian, I thought i'd start
Speaker 1: by telling folks how we know each other? Do you
Speaker 1: want to start?
Speaker 2: Well? Actually, I think we knew each other when you
Speaker 2: were at CNN, but you would not remember me. Well,
Speaker 2: you knew my husband?
Speaker 1: Yes? Was this in Atlanta?
Speaker 2: This is in Atlanta? You worked with my husband.
Speaker 1: In the early days of CNN. Was that at Take two.
Speaker 2: In the mid eighties, So I think that's how we
Speaker 2: initially met.
Speaker 1: Vivian and I have cross paths at various times in
Speaker 1: our lives. I think Vivian is the only person I
Speaker 1: know who has worked at more news organizations than I have.
Speaker 1: Vivian give us The Rundown.
Speaker 2: CNN, New York Times, NPR, NBC, The Guardian well as
Speaker 2: a board member, and also at Twitter doing a news
Speaker 2: role there, and at Discovery running a news documentary channel.
Speaker 1: So basically, like me, Vivian cannot hold a job. So
Speaker 1: we are going to have a conversation today that actually
Speaker 1: was prompted by a conversation I heard at an Aspen
Speaker 1: Institute board meeting. Vivian and I are both very involved
Speaker 1: in the Aspen Institute. In fact, she's got a paying
Speaker 1: job there. Vivian, what exactly is your role at Aspen?
Speaker 2: I run a program at the Aspens too, called Aspen Digital,
Speaker 2: and our focus is on all things technology and media
Speaker 2: and their impact on society, so exactly all the stuff
Speaker 2: we're talking about today.
Speaker 1: And Vivian and I got to know each other even
Speaker 1: better when we both served on the Aspen Institute Commission
Speaker 1: on Disinformation, which has a more formal title, which is
Speaker 1: go Ahead.
Speaker 2: Vivian Commission on Information Disorder.
Speaker 1: Thank you, Yes, And we got to know each other
Speaker 1: well during that time, but we've known each other for
Speaker 1: a long time, so we are excited to have this
Speaker 1: conversation for all of you. I learned a great deal
Speaker 1: and we have two incredible experts who are coming on
Speaker 1: to talk about not all what AI is, but obviously
Speaker 1: the promise and the perils of this new technology. And
Speaker 1: of course we're going to touch on Sam Altman's auster
Speaker 1: and then reinstatement by the board at Open AI, which
Speaker 1: is full of all sorts of intrigue. And this is
Speaker 1: hopefully a podcast that is AI for dummies, but I
Speaker 1: fear the only dummy in the conversation is yours truly.
Speaker 1: So without further ado, let's invite in our guests, Chris
Speaker 1: Wiggins and the Last Star. Welcome to the podcast. Thank
Speaker 1: you so much for being here. I should note that
Speaker 1: November thirtieth today is the one year anniversary of chat GPT,
Speaker 1: so we actually have a newspeg for this podcast. And
Speaker 1: before we dig in, I thought i'd ask you briefly
Speaker 1: about what you all do and why you're qualified to
Speaker 1: have this conversation Chris, Why don't we start with you?
Speaker 3: Sure?
Speaker 4: Fair question. So for the last twenty two years have
Speaker 4: been on the fact ficulty. At Columbia, I teach applied mathematics.
Speaker 4: My research is in machine learning, mostly apply to biology.
Speaker 4: For the last ten years, I've also been the Chief
Speaker 4: Data Scientist to The New York Times, which means I
Speaker 4: lead a team that develops and deployees machine learning.
Speaker 3: And I'm a lost star. Twenty odd years ago, I
Speaker 3: started my career as a computer science researcher, working on
Speaker 3: what we called artificial intuligence back then. Since then, I've
Speaker 3: spent my career building private and public organizations that focus
Speaker 3: on using tech as a way to advance justice and equity,
Speaker 3: and now lead probably one of the largest film propic
Speaker 3: organizations focused on funding AI that makes the world a
Speaker 3: better place.
Speaker 1: Well, I'm very excited to have you both, as well
Speaker 1: as my friend Vivian, who you both know well. And
Speaker 1: I thought i'd start with a very basic question, what
Speaker 1: is AI? Who wants to take a shot at that?
Speaker 4: I could try a historical view. AI is one of
Speaker 4: my favorite drifting targets meeting It's a term that means
Speaker 4: different things to different people in different decades, in different communities.
Speaker 4: So when the term was coined in nineteen fifty five
Speaker 4: by John McCarthy, it was a proposal that the idea
Speaker 4: that any feature of intelligence can be, in principle, be
Speaker 4: so precisely described that a machine can be made to
Speaker 4: simulate it. So the conception of what artificial intelligence meant
Speaker 4: in nineteen fifty five is so different than what it's
Speaker 4: come to me now even in twenty twenty one, let
Speaker 4: alone a year ago today when chat TPT was launched,
Speaker 4: And now everybody when they think of AI, they're thinking
Speaker 4: of a chatbot, which is really chatbot is a small
Speaker 4: example of machine learning, which is a small example of
Speaker 4: artificial intelligence. So the term has come to mean different
Speaker 4: things in different times, which is why the term never
Speaker 4: feels like you're standing on solid ground when you're saying it,
Speaker 4: because different audiences can mean very different things. When you
Speaker 4: say those two letters.
Speaker 3: You know, I'll agree with that. I agree with everything
Speaker 3: Chris said. I mean, on one side, AI is a
Speaker 3: technology conversation. It's a new set of tools that let
Speaker 3: computers do what people have tradisally thought only we could do.
Speaker 3: But it's also something much bigger. It's a social phenomenon.
Speaker 3: It's a moment now where we get to test and
Speaker 3: examine some pretty basic assumptions about what it means to
Speaker 3: have an economy, a political society, about what it means
Speaker 3: to be human. And that's why we're seeing this amazing
Speaker 3: grounds full of interest in what AI is.
Speaker 1: When you think about AI, can you explain in very
Speaker 1: sort of eighth grade terms, how it works, how these
Speaker 1: large language models are assembled, and how machine learning enables
Speaker 1: technology to spew out things that make sense. Chris, can
Speaker 1: you help me with that?
Speaker 4: Sure? I think again. History is really useful here. One
Speaker 4: example of how you might build a chatbot statistically was
Speaker 4: Claude Shannon in probably nineteen forty four was thinking about
Speaker 4: this model where you generate words at random. Imagine that
Speaker 4: you're reading a book and you find some word, and
Speaker 4: then you keep reading in that book until you find
Speaker 4: that word again, and then write down the word that
Speaker 4: follows it. Then keep reading the book, wait until you
Speaker 4: find that word again and write down the word that
Speaker 4: that's the basic nexus of a small language model. So
Speaker 4: you're predicting the next word based on the previous word
Speaker 4: you can think about what's being done today as a
Speaker 4: very large version of that same small language model. It's
Speaker 4: a statistical prediction model. And an important part there is
Speaker 4: that it really matters what book you're training it on,
Speaker 4: and so you need a very large corpus of training data.
Speaker 4: In this case. One of the things that makes large
Speaker 4: language models possible is the vast amount of information that's
Speaker 4: available online, and so computer programs automatically ingest all of
Speaker 4: the text on the web could be from Reddit, newsgroups
Speaker 4: or Wikipedia, or hey, they.
Speaker 1: Use my book the best advice I ever got for
Speaker 1: chat GPT, and nobody asked my permission. By the way,
Speaker 1: that's right.
Speaker 2: That's a whole other issue.
Speaker 4: That's exactly right. That's all other issues is how this
Speaker 4: relate to the rights of the authors. But the statistical
Speaker 4: problem is one of training from data. So the data
Speaker 4: are central and it's counterintuitive, I think to many people
Speaker 4: who think that computers are about writing down rules, and
Speaker 4: when you write down the rules about how we think
Speaker 4: we think, then you'll get something that acts like how
Speaker 4: we think we think. And in fact, for most of
Speaker 4: artificial intelligence as a field, for the last seventy years,
Speaker 4: that's how people thought we were going to achieve artificial
Speaker 4: intelligence was by understanding how we think we think, and
Speaker 4: then you would just simulate it or just program it.
Speaker 4: And the truth is, it's been a realization in the
Speaker 4: last two decades that the way that we are able
Speaker 4: to achieve such exciting results is from taking really large
Speaker 4: data sets and learning from the data how to build
Speaker 4: a computer that emulates, really imitates what we sound like
Speaker 4: when we are intelligent.
Speaker 1: In words, sometimes when I'm writing emails, these words like
Speaker 1: so much. I must use that all the time, thank
Speaker 1: you so much. It shows up in my email if
Speaker 1: I want to just kind of press a button and
Speaker 1: not write anymore. Is that an example a rudimentary example
Speaker 1: of AI.
Speaker 4: Yes, very much.
Speaker 2: So.
Speaker 4: There's the math behind it, which is how are you
Speaker 4: going to predict the next word? But the other thing
Speaker 4: about it is the product and sort of the user interface.
Speaker 4: People like to talk about how in the late fifties
Speaker 4: night at Stanford there was John McCarthy who was working
Speaker 4: on the mathematics of AI, and then there were people
Speaker 4: like Doug Engelbart who were working on the product of AI.
Speaker 4: How are we going to make an interface that allows
Speaker 4: people to interact with the computer. Well, so when you
Speaker 4: just saw it, there was a good example of good math.
Speaker 4: And the math could be as simple as counting the
Speaker 4: number of times that the word what follows the word,
Speaker 4: So it's very simple math. But as well as the
Speaker 4: product idea, which is, how do I make a suggestion
Speaker 4: to you in a way that's useful to you while
Speaker 4: you use that digital product and not creepy and not intrusive.
Speaker 4: So yeah, that's another thing that we're seeing with chatchept
Speaker 4: is a good coming together of technology and mathematics and
Speaker 4: statistical models, but also just a nice product that people
Speaker 4: are enjoying musing.
Speaker 2: What you're describing, Chris, is a predictive model. But so
Speaker 2: many people, particularly since a year ago today when chatchipt
Speaker 2: came out and sort of collectively blew the world's minds,
Speaker 2: it feels like we're talking to a machine that is
Speaker 2: actually thinking, that is actually sentient, and it's in fact
Speaker 2: design that way, and that has societal implications, some of
Speaker 2: the societal implications that you were referring to earlier.
Speaker 3: VELAs know, you're pointing out the critical kind of missing
Speaker 3: element in what Chris described as what AI is today.
Speaker 3: At the end of the day, every version of AI
Speaker 3: that we have today. It's a mathematical model that predicts
Speaker 3: what happens next based on what's happened before. It doesn't reason,
Speaker 3: it doesn't think, it doesn't have agency, it doesn't have preferences,
Speaker 3: all of the things that people now try to scare
Speaker 3: us with that. I imagine we'll talk a little bit
Speaker 3: about that crazy term AGI. Today's AI is none of that.
Speaker 3: I often like to say, do you all remember that
Speaker 3: movie Honey, I Stroked the Kids?
Speaker 1: Yeah, Rick Moranez, Yeah.
Speaker 3: Great movie. Right, Today's AI. All it is is this,
Speaker 3: take everything that's ever been written, put it in a
Speaker 3: giant library, beam a rate gun at that library, and
Speaker 3: enough power to power a small city for months and months,
Speaker 3: and say, how do we compress that entire library down?
Speaker 3: And give us one little map? And all the math
Speaker 3: does is it says this. If before, when people said
Speaker 3: a word, they often followed it with another word, that's
Speaker 3: all we're going to do right now. Now, what that does.
Speaker 3: It's an amazing magic trick. It's a great illusion. It
Speaker 3: makes you think you're talking to somebody who wants to
Speaker 3: talk back to you. But at the end of the day,
Speaker 3: all the machine is doing is predicting what the next
Speaker 3: word and the answer should be. This is so critical
Speaker 3: because it reframes how we engage with these tools, and
Speaker 3: that's really all they are. They're just tools. They're not,
Speaker 3: you know, all knowing entities. They're not partners, they're not
Speaker 3: conversational and sparring buddies. They're just tools that help us
Speaker 3: maybe be better.
Speaker 1: Let me ask you this because I thought this was interesting.
Speaker 1: Bill Gates recently noted that AI as it exists today
Speaker 1: is quote still pretty dumb. Chris. Do you agree with that?
Speaker 2: Yeah?
Speaker 4: Absolutely so. I think what VELAs is saying is apt,
Speaker 4: which is language, and the ability to produce language is
Speaker 4: a great imitation of what thinking. And in fact, I
Speaker 4: use the word imitation because in Hellan Turing's original nineteen
Speaker 4: fifty paper on can MA Machines think he basically set
Speaker 4: out this problem. Imagine a computer that could imitate what
Speaker 4: it's like to talk to somebody. That's sort of an
Speaker 4: operaginalization of what it means to think. But there's still
Speaker 4: many things a I can't do. As often described, planning
Speaker 4: is difficult, Compositional thinking is difficult, Working with multiple modes
Speaker 4: at once is difficult. Meaning like words and images together.
Speaker 4: So I think you're right that it's uncanny. Right, we're
Speaker 4: in the uncanny valley of conversations right now with chatbots.
Speaker 4: But it's very difficult for people not to impose this
Speaker 4: belief that it is intelligent or thoughtful. And the truth
Speaker 4: is people have been having that experience for as long
Speaker 4: as they've been building chatbots. Even in the nineteen sixties,
Speaker 4: people were building chatbots based on simple rules, and users
Speaker 4: using that chatbot had the same experience of feeling like
Speaker 4: even though they knew it was just a very simple
Speaker 4: computer program, there was the emotional resonance was one as
Speaker 4: though you were talking to an intelligent agent.
Speaker 1: Hear this word sentient a lot, which of course is
Speaker 1: capable of sensing or feeling conscious of, or responsive to
Speaker 1: the sensations of seeing, hearing, feeling, tasting, or smelling sentient beings,
Speaker 1: which is really what it means to be a human.
Speaker 1: Does AI have the capacity to be sentient?
Speaker 4: I think what we've shown is that it does a
Speaker 4: great imitation of it. But I think it's important for
Speaker 4: us all to remember that it is, as Vela said,
Speaker 4: just math right. It is a mathematical model that spits
Speaker 4: out words and it's optimized for generating words that sound
Speaker 4: like what a human being would say and given the
Speaker 4: same prompt. But we should remember that it is a
Speaker 4: machine and it's executing a mathematical act that we trained
Speaker 4: it on.
Speaker 2: But yet there's a lot of experts out there and
Speaker 2: researchers and some pretty serious people who are trying to
Speaker 2: warn us that these machines may become sentient. Now, is
Speaker 2: that just a matter of seeing too many sci fi
Speaker 2: movies or is that something that is possible obviously not today,
Speaker 2: but on the horizon.
Speaker 4: I think there's a couple of reasons why people are
Speaker 4: warning us of that possibility. I mean, again, part of
Speaker 4: it is wordplay, and I think that's what Alan during
Speaker 4: was getting at in nineteen fifty when he said, can
Speaker 4: machines think? Is an ill posed problem, so let's try
Speaker 4: to operationalize it. But I think the warnings are often
Speaker 4: distractions from real problems that automated inequality and other downsides
Speaker 4: of using algorithms today are causing. In the here and now.
Speaker 4: It's sometimes very difficult to think about our existing challenges
Speaker 4: in sociotechnical systems, and somehow more pleasant to think about
Speaker 4: this terminator doomsday future which is not cured yet. I
Speaker 4: think also there's.
Speaker 1: A yet yet that's scary, that's right.
Speaker 4: I think there's also a concern that people are putting
Speaker 4: forward this idea of a doomsday because there's only a
Speaker 4: small number of companies at present which are able to
Speaker 4: afford amassing lots of data and producing really good products.
Speaker 4: And often these companies are saying, we are the ones
Speaker 4: who can tell you how to regulate this. So there's
Speaker 4: concern that some of the doomsday scenario might be coupled
Speaker 4: to re trey capture or getting ahead of potential regulation
Speaker 4: both domestically and internationally.
Speaker 3: Let's zoom out a little bit. I agree with what
Speaker 3: Chris said, but maybe and you spoke to experts and
Speaker 3: serious people who are trying to scare us all, and
Speaker 3: I just want to put us in context. There are,
Speaker 3: you know, eight billion people on the planet. There are
Speaker 3: maybe a few hundred thousand that really understand how AI works.
Speaker 3: And there's maybe on the order of a few thousand
Speaker 3: people who have decided that what they care about more
Speaker 3: than anything else is the existential risk to humanity. Let's
Speaker 3: just put that in context. There are billions of people
Speaker 3: on the planet today that could use what AI offers
Speaker 3: as a promise to fundamentally change what their lives look like.
Speaker 3: They're access to economic opportunity, to any number of other things.
Speaker 3: There are hundreds of thousands or millions of people who
Speaker 3: work in companies that could fundamentally change their relationship with customers.
Speaker 3: So what do we have. We have a small set
Speaker 3: of people with direct access to some of these tools,
Speaker 3: and we'll talk about how that came about shortly, who
Speaker 3: have come up with this common idea that the thing
Speaker 3: we should care about more than any else is that
Speaker 3: AI will kill us all. And in the meantime, we're
Speaker 3: living in a world where there are so many opportunities
Speaker 3: and challenges here and now that we should be spending
Speaker 3: our time thinking about.
Speaker 2: So there are the here and now, there is the
Speaker 2: long term future, and there's that whole middle ground that
Speaker 2: I think many of us haven't addressed. But before we
Speaker 2: come back to that, just to help our listeners a
Speaker 2: little bit, we hear the term AGI artificial general intelligence,
Speaker 2: not to be confused with generative artificial intelligence. Very confusing
Speaker 2: even for people that pay attention to these things. AGI
Speaker 2: is this sort of robot overlord saying we're talking about
Speaker 2: that may or may not ever happen. Is that right?
Speaker 1: Yeah? What is that? You guys help me out. I'm
Speaker 1: the dumb one here.
Speaker 4: I think one thing that's useful is to remember the
Speaker 4: two different g's in those two acronyms. The GAI is generitive,
Speaker 4: but AGI is general. So when people talk about AGI
Speaker 4: for general intelligence, part of which is exciting is the
Speaker 4: idea that in the last fifty years we've done species,
Speaker 4: not me personally, but the human being species have done
Speaker 4: a really good job building algorithms that are good for
Speaker 4: individual tasks. Like you can build an app that can
Speaker 4: take a picture and say does this have a dog
Speaker 4: face in it? Or a cat face in it? That's
Speaker 4: a specific use of statistical modeling, which is good for
Speaker 4: that specific use case. So the dream of AGI is
Speaker 4: that you can produce one algorithm, one machine, one model
Speaker 4: that's good not only for disinbiguating cat faces from dog faces,
Speaker 4: but also composing a sonnet or enjoying strawberries and cream,
Speaker 4: or whatever general problem you would like the machine to solve.
Speaker 4: So that's the g of agis general. It's very easy
Speaker 4: for us to make machine learning models that are good
Speaker 4: for one specific task. It's much harder to make a
Speaker 4: machine learning model that's general and is able to do
Speaker 4: anything that we consider an intelligent task.
Speaker 1: So where are we in terms of And I didn't
Speaker 1: really understand that explanation, Chris, can you try it again
Speaker 1: in a more like I'm not a Columbia student, or
Speaker 1: just pretend like I'm in sixth grade? Help me out, sure,
Speaker 1: help me out.
Speaker 4: So for many decades, we've been able to build specific
Speaker 4: machine learning models. So a specific algorithm that can tell
Speaker 4: the difference between a picture of a dog and a
Speaker 4: picture of a cat, say, that's a specific problem. And
Speaker 4: we've been very good for decades at building algorithms that
Speaker 4: can do very specific and focused tasks. One of the
Speaker 4: things that we've seen with chatbots that are trained on
Speaker 4: a wide variety of documents is that you can have
Speaker 4: a plausible conversation with a chatbot about a wide variety
Speaker 4: of topics. So if you've trained a chatbot only on
Speaker 4: chemistry textbooks, you will have a great conversation about chemistry
Speaker 4: and not about any other subject. But by training a
Speaker 4: chatbot on a wide variety of topics chemistry, philosophy, and
Speaker 4: all points in between, people are experiencing this shock that
Speaker 4: you can interact with an AI, meaning an algorithm that
Speaker 4: works not only solving a specific problem, solving a general problem,
Speaker 4: in this case, the general problem of having having an
Speaker 4: intelligent sounding conversation about a general breath of topics.
Speaker 1: After a quick break, we'll be back with my co
Speaker 1: pilot and plus one Vivian Shiller, talking to Chris Wiggins
Speaker 1: and velost Star. If you want to get smarter every
Speaker 1: morning with a breakdown of the news and fascinating takes
Speaker 1: on health and wellness and pop culture, sign up for
Speaker 1: our daily newsletter, Wake Up Call by going to Katiecuric
Speaker 1: dot com. We're back with Chris Wiggins and velost Star,
Speaker 1: along with my plus one Vivian Schiller. Have you guys
Speaker 1: used chat GPT or bard. Have you tried to have
Speaker 1: it write speeches for you or come up with any
Speaker 1: kind of documents. I'm sure you've tested it, Vivian, what
Speaker 1: has your experience been like.
Speaker 2: I've used chat gpt to develop an itinerary. I took
Speaker 2: it to Japan. I knew I needed I had some
Speaker 2: time between two places I needed to be, and I
Speaker 2: had certain things that I was interested in, certain things
Speaker 2: I was less interested in, didn't know how long it
Speaker 2: took to get paced the place, and actually chat Gipt
Speaker 2: gave me an amazing itinerary, so it was very useful.
Speaker 1: Travel agents probably don't like that, how about you, guys?
Speaker 3: Yeah, you know, I mean, I've used every LM out there,
Speaker 3: and so I'm like, I'm no longer, you know. I
Speaker 3: wish I could say it was at the emergent frontier
Speaker 3: of AI, but I'm no longer Now I have a
Speaker 3: different role. But I spent a lot of time with
Speaker 3: the smartest people that are working on this stuff, and
Speaker 3: I've used them all. I've used them to do really
Speaker 3: basic and pedantic things like oh, give me some talking points.
Speaker 3: I no longer do that, having tried it a few
Speaker 3: times and realizing how bad it is. I spent a
Speaker 3: lot of time using these generative AI tools my nieces
Speaker 3: and nephews. I'm doing really fun things like saying, hey,
Speaker 3: let's come up with married a scene and let's see
Speaker 3: if we can get an EI to draw it for us,
Speaker 3: and then ask the question, hey, is this kind of
Speaker 3: what you pictured in your mind? Side? How do we
Speaker 3: make it better? And we actually iterate with genitive AI
Speaker 3: to create new artworks or even basic things sometimes like Hey,
Speaker 3: I want to tell you a bedtime story, what do
Speaker 3: you want it to be about? And then we work
Speaker 3: with an AI to kind of come up with a
Speaker 3: nice little Kate. Look, these are all really fun, But again,
Speaker 3: I want to make sure that we understand that we're
Speaker 3: kind of missing the point a little bit, right. These
Speaker 3: tools that have changed our lives, and Vivian said, have
Speaker 3: done so really in an amazing way, but not because
Speaker 3: the technology has already changed our lives, but because it's
Speaker 3: opened our eyes to what's possible when these tools get
Speaker 3: to be really amazing. We talk all the time about
Speaker 3: how these tools have hallucinations, right, the idea that you
Speaker 3: might ask it a question and it doesn't check whether
Speaker 3: the answer is real or not. It just kind of
Speaker 3: spews some language back at you and you say, okay,
Speaker 3: well that sounds reasonable and you move on. The tools
Speaker 3: that we have today aren't products yet. There's still kind
Speaker 3: of the very early days of what generative AI will
Speaker 3: look like. And my hope is when we start training
Speaker 3: these models on medical data that includes all of the
Speaker 3: kind of published medical literature, we'll get to a much
Speaker 3: better sense of what a generative AI can do to
Speaker 3: help the doctor diagnose it. But at the end of
Speaker 3: the day, I can't imagine a world in which we say,
Speaker 3: the genitor of AIS we have today are directly diagnosing
Speaker 3: a patient. The only thing they can do is help
Speaker 3: a doctor or a medical professional whose trained use it
Speaker 3: as an input into their process to figure out what's
Speaker 3: going on. And that's the moment that we're stuck in
Speaker 3: right now, because I know so many of us want
Speaker 3: to jump into a future where we say that AIS
Speaker 3: are going to do everything for us, but we're really
Speaker 3: in a moment where we're saying, the only way this
Speaker 3: works is that the AIS support human decision makers. They
Speaker 3: can use what the technology gives them, but their own experience,
Speaker 3: their lived wisdom. They're you know, working with patients for
Speaker 3: hour many years to actually make a decision.
Speaker 2: You know.
Speaker 1: I tried to get chat ept to write a poem
Speaker 1: for my husband's birthday and it was very, honestly not
Speaker 1: very good. I gave it information about my husband, but
Speaker 1: it was quite pedantic and not very clever. It was
Speaker 1: sort of honestly Hallmark CARDI quality. And I think it's
Speaker 1: because it didn't have the breadth of knowledge about him
Speaker 1: that I do so so it couldn't really compete with that,
Speaker 1: but it was fun to try it. And another example,
Speaker 1: when I interviewed Carl Rove at the Aspen Ideas Festival,
Speaker 1: I was trying to come up with a fun title
Speaker 1: for the conversation. I asked chat GPT and it came
Speaker 1: up with a great title, which was the Elephant in
Speaker 1: the Room because it was on the future of the
Speaker 1: Republican Party. And I was like, that is genius. So,
Speaker 1: you know, I think you're right what you were saying,
Speaker 1: Velss about it being helpful but not determinative. And one
Speaker 1: example is, you know, I'm very into cancer screening and
Speaker 1: some of the things that they're going to be able
Speaker 1: to do that is beyond the ability of a human
Speaker 1: to see things, is to take these massive data sets
Speaker 1: and look at scans and figure out actually predict if
Speaker 1: someone may or may not get breast cancer in the
Speaker 1: next five years. I mean, that really blows my mind.
Speaker 1: But that obviously has to be done in conjunction with
Speaker 1: an experienced medical professional. Right, So is that what you
Speaker 1: mean veloc by kind of being an aid?
Speaker 3: It is, And let me add a little bit of nuance, o, Katie.
Speaker 3: I mean, you've been such a courageous kind of leader
Speaker 3: on this topic. When we started looking at breast cancer
Speaker 3: in particular with AI through our lens as a civil
Speaker 3: society institution, we learned about this fundamental problem that's just
Speaker 3: going to kind of blow your hairback. We have all
Speaker 3: these algorithms today that have been trained to do exactly
Speaker 3: what you described to take a mammogram or a scan
Speaker 3: and say hey, can we do early prediction of cancer risk?
Speaker 3: But all of these tools we learned very quickly have
Speaker 3: been trained on global north populations. They've been trained on
Speaker 3: American data and European data, and so when an organization
Speaker 3: like Instituto Protea, which is a partner of ours, took
Speaker 3: these to Brazil and tried to use them on low
Speaker 3: cost machines that were already recent settings, they found the
Speaker 3: algorithms didn't work at all. So even in that aspirational
Speaker 3: moment that you've created this idea that we might have
Speaker 3: this massive break through, we come back to a very
Speaker 3: human kind of fundamental problem that until we train this
Speaker 3: data on ways that are representative about all of the
Speaker 3: people in the world, not just those who have privileged
Speaker 3: access to Western medicine, right, we're never going to realize
Speaker 3: the promise. You talked about.
Speaker 1: How biased is AI? How biased are these large language models,
Speaker 1: because I remember doing a documentary on our tech addiction,
Speaker 1: and this was just starting to be talked about, and
Speaker 1: I think this was like in twenty eighteen, Chris, do
Speaker 1: you see this as a major problem that it doesn't
Speaker 1: really represent people like so much in society?
Speaker 4: I mean, the problem is always how something is used
Speaker 4: or interpreted. I would say in the context of medical
Speaker 4: usage of AI, there's additional challenges around responsibility or attribution
Speaker 4: and decision making. So I think for all of these tools,
Speaker 4: they're going through this very inefficient part of our hype cycle.
Speaker 4: So in a hype cycle, there's a moment where you
Speaker 4: discover a technology and you have this moment of irrationally
Speaker 4: zuprints and you think it's going to be great, and
Speaker 4: then you have some trough of despair as you realize
Speaker 4: it's actually not that good about generating a poem about
Speaker 4: your husband in your case, And then we get to
Speaker 4: some efficient place where we all have an understanding of
Speaker 4: what these technologies can do and cannot do. So I
Speaker 4: do think we all need to limit our trust in
Speaker 4: all these technologies in in that'stry for technology in general.
Speaker 4: But I think Veloso is making a good, great point,
Speaker 4: which is form machine learning in general, which again is
Speaker 4: the strategy that actually works for artificial intelligence. Where you
Speaker 4: train an algorithm on lots of data, it is extremely biased,
Speaker 4: and this is that it's well suited to the data
Speaker 4: st you have, and there are many complex problems in
Speaker 4: the world where when you train it on one data set,
Speaker 4: it will not generalize to some other very different data set.
Speaker 4: And the different data set could be you've trained a
Speaker 4: language model in chemistry and then you try to test
Speaker 4: it on poetry, or it could be that you've trained
Speaker 4: it on genetic information from one demographic group and then
Speaker 4: you realize it says nothing about, say, predicting phenotype from
Speaker 4: genotype for a different demographic group. That is a real problem.
Speaker 4: It often undergoes the name of buis, but in the
Speaker 4: case of machine learning, it's built into the system. If
Speaker 4: you train it on one data set, you're going to
Speaker 4: have a bad time if you try to use it
Speaker 4: on a very different data set.
Speaker 2: Let me follow up with that to both the Loss
Speaker 2: and Chris, which is how much of that has to
Speaker 2: do with the people who are selecting the data sets,
Speaker 2: who are creating the technology, who are deploying technology, most
Speaker 2: of whom are in Silicon Valley. Are they maybe in
Speaker 2: a few centralized companies. How much of that is an issue?
Speaker 2: And how do we get out of that jam?
Speaker 3: Yeah, Vivian, I'm going to take that. I'm going to
Speaker 3: go one step bigger. I'm going to give you an
Speaker 3: example for it. We talk a lot about you've probably
Speaker 3: heard about hiring algorithms, about how companies are using AI
Speaker 3: to screen resumes about who they want to hire. And
Speaker 3: there's a story that's been well told there about the
Speaker 3: fact that these algorithms are often biased, they often pick
Speaker 3: men over women particular types of technical competency. That's one story,
Speaker 3: and we get it. But there's a bigger story here
Speaker 3: that we have a really hard time engaging with those algori.
Speaker 3: We're trained on twenty years of data about how human
Speaker 3: recruiters picked candidates, and yet we never talk about the
Speaker 3: fact that for twenty years we've lived in a world
Speaker 3: where our own recruiters are showing these biases day in
Speaker 3: and day out. The question we should be asking is
Speaker 3: not why is the algorithm biased? It's why is a
Speaker 3: society have we been so okay for twenty years with
Speaker 3: this set of outcomes, and now that we have a
Speaker 3: tool that shows us just how bias we've been, we're
Speaker 3: not having a public conversation about restructuring our entire hiring
Speaker 3: mechanism across the private sect. This is just one analog
Speaker 3: of a lot of things like this that I think
Speaker 3: are emerging across the board, where AI, because of the
Speaker 3: bias in the algorithm, is putting a spotlight on the
Speaker 3: bias in our human behavior. We should be using AI
Speaker 3: as an investigative tool, as a magnifying glass that lets
Speaker 3: us look at all kinds of decisions and say, how
Speaker 3: do we build a more just and equitable society. Let's
Speaker 3: have a conversation with a bias in AI. We absolutely should,
Speaker 3: and the answer to that, we kind of know what
Speaker 3: the answer is, right. More representative data, presentive talent that
Speaker 3: designs these algorithms, making sure there's public compute that allows
Speaker 3: these people to develop products. But let's take the bigger
Speaker 3: picture here. This is what we're going into over the
Speaker 3: next twenty years is a world in which these tools
Speaker 3: demonstrate to us why we're okay with the society we've built,
Speaker 3: and let us question if we actually want to make
Speaker 3: some fundamental changes in them.
Speaker 1: But maybe AI can be an instrument for change for losso.
Speaker 1: I mean, you know that is such a massive undertaking
Speaker 1: to uproot bias in society. I mean, it's so baked in,
Speaker 1: so maybe this is one entry way to address it.
Speaker 3: Absolutely, It's one of the things I'm most optimistic about
Speaker 3: right is when we look at things like we're going
Speaker 3: to have a conversation I'm sure here about some of
Speaker 3: the recent developments in the AI world, one of which
Speaker 3: has just been the continued silencing of women underrepresented characters
Speaker 3: in building these tools. I'm deeply optimistic about the fact
Speaker 3: that we could invest in creating a new capacity to
Speaker 3: build AI that's really representative a lot of those problems
Speaker 3: would one we signed a spotlight on them, and two
Speaker 3: we'd very quickly move to fix them.
Speaker 1: Well, when we come back, we're going to talk about
Speaker 1: how do you regulate artificial intelligence, What in the world
Speaker 1: is going on with Sam Altman and open AI, and
Speaker 1: how quickly is this technology going to evolve. That's right
Speaker 1: after this, I want to tell you all about the
Speaker 1: Cancer Straight Talk podcast for Memorial Sloan Cattering Cancer Center
Speaker 1: with MSK oncologist doctor Diane Reedy Lagunis. I was a
Speaker 1: guest and we had a totally candid conversation about my
Speaker 1: family's experiences with cancer, including my husband's illness, my own
Speaker 1: treatment for breast cancer, and of course that time I
Speaker 1: got a colonoscopy. On TV, Cancer straight Talk features life
Speaker 1: affirming conversations with experts and patients alike about topics affecting
Speaker 1: everyone touched by cancer. If that includes you, I hope
Speaker 1: you'll listen into my episode and every episode of Cancer
Speaker 1: Straight Talk. We're back with Chris Wiggins and Velos star
Speaker 1: along with my plus one Vivian Shiller. Chris is the
Speaker 1: Chief Data Science of The New York Times, Associate Professor
Speaker 1: of Applied Mathematics and Systems Biology at Columbia, and he
Speaker 1: wrote the book How Data Happened, A History from the
Speaker 1: Age of Reason to the Age of Algorithms, which frankly
Speaker 1: I read in two days, just kidding. Chris the Loss
Speaker 1: is President and trustee of the Patrick J. McGovern Foundation,
Speaker 1: which focuses on AI and data solutions. And my plus
Speaker 1: one today is my good friend Vivian Schiller, who has
Speaker 1: worked in many media organizations and has really dug into
Speaker 1: AI and technology, media and society. So you gave me
Speaker 1: the perfect segue the loss in our last conversation before
Speaker 1: the break, and that was what is happening right now
Speaker 1: in various technology companies. So, Chris and velos, who wants
Speaker 1: to kind of explain this Sam Altman drama which is
Speaker 1: being watched with baited breath by everyone in technology and
Speaker 1: I think in media right now. Chris, you want to
Speaker 1: give it a shot, I.
Speaker 4: Can try, with the warning that you know, we're all
Speaker 4: outside the company and so all of it is speculative.
Speaker 4: You know, there's a set of about four people who
Speaker 4: really know what happened. There are the people who were
Speaker 4: on the board that were voting to oust the CEO,
Speaker 4: and so there's a very small number of people who
Speaker 4: really were in the room when it happened and can
Speaker 4: tell us.
Speaker 1: Having said that, Chris, though there's been some pretty strong
Speaker 1: reporting on it that I've read, and let me try
Speaker 1: to set it up if I could. So, Sam Altman,
Speaker 1: this young genius head of open Ai, who I think
Speaker 1: is very well liked by the press, considered obviously a
Speaker 1: real leader in the field, was the CEO of open Ai.
Speaker 1: Two members of the board who were very concerned that
Speaker 1: the business model was superseding the ethical considerations of AI.
Speaker 1: Is my understanding. Okay, Vivian, you look like you want
Speaker 1: to add something, Is that right?
Speaker 2: Uh? Well, all they have said publicly, and I think
Speaker 2: I've seen a lot of that there has been some
Speaker 2: fantastic reporting, is that sam Aldman was not communicating in
Speaker 2: a way that made the boy I forget the wordy exactly,
Speaker 2: but communicating to the board in a way that made
Speaker 2: them feel comfortable. They didn't specifically say they were worried
Speaker 2: the AI was getting out ahead of his skis. I
Speaker 2: think there's one other interesting twist in all of this,
Speaker 2: which is not to get too technical, but the structure
Speaker 2: of open AI is fascinating.
Speaker 1: Well, it's really important, I think to mention that.
Speaker 2: Yeah, it's a not for profit organization of five oh
Speaker 2: one C three, which, as someone that has been part
Speaker 2: of and led five O one C three, has very
Speaker 2: specific governance. They have a governance to a mission, a
Speaker 2: stated mission that is part of how the organization is
Speaker 2: set up.
Speaker 1: Let me just say interject that their work should benefit
Speaker 1: quote unquote humanity as a whole exactly.
Speaker 2: So a not for profit organization is not there to
Speaker 2: return shareheld value. It is there for the greater good.
Speaker 2: In this case, the exact words that you just quoted,
Speaker 2: that not for profit owned, among other things, this for
Speaker 2: profit entity that was set up because the resources that
Speaker 2: are needed in order to continue to evolve open AI
Speaker 2: requires tremendous billions of dollars of resources. So they've set
Speaker 2: this up and that entity was able to then bring
Speaker 2: in a lot of outside money, billions of dollars to
Speaker 2: continue to evolve and see the developments that we've seen
Speaker 2: come out of Open Eye AI since then, Chat, GPT
Speaker 2: and many many, many other tools. That's not that unusual
Speaker 2: a set up. There are other organizations that are set
Speaker 2: up like that and worked just fine. But in this
Speaker 2: case there was really a lack of alignment, and that
Speaker 2: not for profit organization management that I think it was
Speaker 2: a four person board decided they were either not in
Speaker 2: a loop or not comfortable with where the for profit
Speaker 2: entity was, and so they apparently without any consultation with anyone, fired.
Speaker 1: The booted him.
Speaker 2: They booted him, and they didn't really understand what well anyway,
Speaker 2: they clearly didn't foresee what the rebound would be.
Speaker 1: Then there's a huge uprising among the employees. I think
Speaker 1: eighty percent said they were going to quit if he
Speaker 1: was gone, and they were going to follow him to Microsoft,
Speaker 1: and then suddenly he's back in business at open AI.
Speaker 1: Can you guys help us make sense of it? Chris,
Speaker 1: do you want to start.
Speaker 4: Yeah again with the warning that a lot of this
Speaker 4: is speculation because only you know the four members who
Speaker 4: voted to out him, and the six board members total
Speaker 4: really know what happened in the room where it happens.
Speaker 4: But the popular understanding right now is that it was
Speaker 4: a concern over movie too fast versus having safeguards. But
Speaker 4: it may come out with future reporting that it was
Speaker 4: about product moves, or about the decision to open up
Speaker 4: so much of the access to the technology that they
Speaker 4: had to slow down new signups. I mean, I've seen
Speaker 4: many people speculate on what the causes were. Also the
Speaker 4: possibility that some sort of particularly quantum leap in the
Speaker 4: technology caused the board to have anxiety, but at this
Speaker 4: point we don't know. I think future reporting, good investigative,
Speaker 4: shoe works, shoe leather work right is needed right now
Speaker 4: to figure out what actually went.
Speaker 1: Down the loss. I know that Chris just mentioned sort
Speaker 1: of a new technology, and I've been reading about this
Speaker 1: project q asterisk. I don't even know how you say it, Vivian,
Speaker 1: how do you say that?
Speaker 4: Q star?
Speaker 1: Q star has been described as a major breakthrough in
Speaker 1: the company's pursuit of artificial general intelligence. So can you
Speaker 1: help me understand veloss, what the hell that means and
Speaker 1: what that technology was? Do you know?
Speaker 3: Sure? Super happy too. I've read, I mean, all of
Speaker 3: the public reporting and some of the peoper is behind it.
Speaker 3: But can I give you my spicy take first before
Speaker 3: I tell you about q start.
Speaker 1: Oh we love spicy takes. Here a question the.
Speaker 3: Two line here, like this is the telenovella of twenty
Speaker 3: twenty three. None of this matters, right, but we love
Speaker 3: our tabloid headlines that we have spent so much time
Speaker 3: I got to say, hundreds of millions of hours of
Speaker 3: human time thinking about Sam Altman and open Ai. Let's
Speaker 3: put this in context, and it's so important that we
Speaker 3: get this right. Open Ai is a company that was
Speaker 3: based on a public paper that taught you how to
Speaker 3: do LLLMS large language models, these like these chat GPT
Speaker 3: type things. Right. They raised billions of dollars, which they
Speaker 3: spent pretty much exclusively on what we call compute right
Speaker 3: access to a bunch of computers, and they built the
Speaker 3: first product that people could see. Nothing revolutionary happened at
Speaker 3: open ai except for the fact that they took this
Speaker 3: incredible paper that was done by some amazing scientists and
Speaker 3: then just threw money at the problem. And once they did,
Speaker 3: what did everybody else do? While then Microsoft threw a
Speaker 3: lot of money at the problem at Facebook threw a
Speaker 3: lot of money at it, Google threw a lot of
Speaker 3: money at it, and they all came up with technologies
Speaker 3: that are pretty similar, some are slightly better than ours. Okay,
Speaker 3: it's important for us to say this because we spend
Speaker 3: a lot of time daifying open ai as if it's
Speaker 3: the most amazing thing that's ever happened. And it turns
Speaker 3: out that when you have a pretty complex problem and
Speaker 3: a pretty complex way to solve it, and you spend
Speaker 3: a couple of billion dollars, you can come out with
Speaker 3: an answer pretty easily. Okay, I say all that to
Speaker 3: you and excuse the mini rant, because now we have
Speaker 3: a real question in front of us, right, why is it, Katie,
Speaker 3: that we're okay with the world in which a technology
Speaker 3: that could change every human life on the planet is
Speaker 3: held by seven companies that have these kinds of like
Speaker 3: human personal dramas that drive what will happen with them.
Speaker 1: And that's all about regulation. But philosoph before you do that,
Speaker 1: you can to start what is QStar before we talk
Speaker 1: about regulation, because I've read about it and I'm it's
Speaker 1: sort of shrouded in mystery and interest and.
Speaker 3: Again, right, and one of the things I really appreciate
Speaker 3: about Chris and neither of us really want to be
Speaker 3: a pundit. Right, We've both been experts in this field
Speaker 3: for a long time. What I can glean from what's
Speaker 3: been publicly reported and from some of the sources I've
Speaker 3: talked to, is that it's a shift from focusing on
Speaker 3: language as a predictive model to being able to focus
Speaker 3: on things like math problems as a reasoning model. So
Speaker 3: instead of saying, hey, I've got a sentence you know
Speaker 3: twinkle twinkle, Well, we know the next words are probably
Speaker 3: little star, it's instead a way to say, well, what
Speaker 3: is two plus two? And you might say probabilistically, because
Speaker 3: I've looked at everything humans have ever written, Well, when
Speaker 3: you say two plus two, it usually followed my equals four.
Speaker 3: But if somebody along the way, in some book had
Speaker 3: written two plus two equals five, then one in ten
Speaker 3: million times the large language model might say, oh, two
Speaker 3: plus two equals five. We're trying to fix that, And
Speaker 3: so q star says, can we actually reason if we
Speaker 3: have two of one thing and two of another what
Speaker 3: happens when you put them together. This is a big breakthrough.
Speaker 3: It is something that gets us closer to what Chris
Speaker 3: described as AGI right, that idea that you've got one
Speaker 3: model that can talk about language and I can do
Speaker 3: a little bit of math. We don't have any sense yet.
Speaker 3: There hasn't been public reporting yet of just how good
Speaker 3: of a breakthrough this is. But again, you take seven
Speaker 3: or eight hundred really smart people, you give them a
Speaker 3: lot of compute, and you say, hey, go figure some
Speaker 3: stuff out, and this is what the next breakthrough looks like.
Speaker 3: I don't think it's the thing that's going to lead
Speaker 3: to terminator style robots and helicopters that are out there
Speaker 3: trying to kill us all. That's all I'm saying.
Speaker 1: That's good to know. I appreciate that. Well, I think
Speaker 1: you raised the big question, and that obviously is regulation
Speaker 1: something that Vivian and I dealt with a lot when
Speaker 1: we were on this asping commission.
Speaker 2: They ask in Commission on Information Disorder.
Speaker 1: Thank you very much, Vivian, where it's very, very difficult
Speaker 1: to regulate these things. And maybe I see veloss your scowling,
Speaker 1: and so you think that's not an accurate statement. I'm
Speaker 1: good at reading facial expressions, Belosto.
Speaker 3: Let's start with the question, though, like, why are we
Speaker 3: so focused on regulating? Right? What does it mean to regulate?
Speaker 3: It means figure out all the ways it can harm
Speaker 3: us and limit them from doing so, let me ask
Speaker 3: you a different question, like, look, I grew up in
Speaker 3: rural Illinois as like a very proud American, but my
Speaker 3: parents or not well off. For me, the biggest thing
Speaker 3: in the world was being able to access a library.
Speaker 3: And I'll tell you why this matters, right, I'd go
Speaker 3: to a library that was paid for by a pedance
Speaker 3: of tax dollars, that took books and knowledge and all
Speaker 3: of these public assets and made them available to me
Speaker 3: as a curious YOUMKID. Today, if we're sitting here talking
Speaker 3: about AI, you and I are fixed in a conversation
Speaker 3: that says AI is owned by private companies. We don't
Speaker 3: know how private companies make decisions. Well, our tool is
Speaker 3: to regulate them. What if we ask a different question,
Speaker 3: why are governments investing in building public purpose AI that's
Speaker 3: done transparently, that's actually said. This is like a library,
Speaker 3: it's a part of public infrastructure. And when we make
Speaker 3: decisions about how AI will be used, that's a public
Speaker 3: and democratic conversation, not for a board of four people.
Speaker 3: We've seen what happens when you let them make decisions
Speaker 3: about AI companies.
Speaker 1: Well, by Chris, why isn't government getting more involved?
Speaker 4: Well, there's a couple of reasons. I mean. One is
Speaker 4: at the scale of the US federal government, which I
Speaker 4: think is what you mean by government. The response by
Speaker 4: the US federal government is often reactive and sectoral. So
Speaker 4: what I mean by that is reactive, meaning that often
Speaker 4: the US federal government doesn't move in a large scale
Speaker 4: until something clearly bad has happened, and something that's so
Speaker 4: bad that everyone accepts that it was bad, and thereafter
Speaker 4: the US federal government will make a new agency to
Speaker 4: govern a particular sector. So bisectoral, what I mean is
Speaker 4: we have a sector of the law and in a
Speaker 4: branch of the US government around say finance or transportation
Speaker 4: or other sectors of our lives, rather than having a
Speaker 4: branch of government that works on technologies read large. A
Speaker 4: counter example to what I just said is FTC. So
Speaker 4: Federal Trade Commission works on antitrust, but under the current
Speaker 4: leadership of FTC they have sort of reasserted that part
Speaker 4: of the purpose of FDC is to think about consumer
Speaker 4: protection as well, so there's an option there for FTC
Speaker 4: to be responsive. That said, there are other ways that
Speaker 4: the US government operates other than laws, for example, executive orders,
Speaker 4: which can be an opportunity for the President to say
Speaker 4: this is really important, and I'm demanding that other people
Speaker 4: who are in the White House respond or commission reports
Speaker 4: on something, and by the spending power of the US government.
Speaker 4: So when the US government says we will no longer
Speaker 4: give money to any company that doesn't meet this bar
Speaker 4: in terms of safety or transparency or other things that
Speaker 4: we may want from technologies in general, that has a
Speaker 4: huge market effect because without passing any laws, the White
Speaker 4: House in this case can actually drive companies to behave
Speaker 4: differently for market reasons.
Speaker 3: Here's the thing, right again. I know it's a big statement,
Speaker 3: and we're kind of nipping around the edges and we're
Speaker 3: kind of saying about what can government do today? But
Speaker 3: I'm going to ask the question again, why are we
Speaker 3: so okay with the fact that we've just given up
Speaker 3: as a public citizen rey to say that we could
Speaker 3: actually own and build these tools. There's three things government
Speaker 3: could do that I don't think risk touchedock. The first
Speaker 3: is that could invest in public compute resources to make
Speaker 3: supercomputing available to lots of communities and groups that are
Speaker 3: working on AI. I worked with an amazing group called
Speaker 3: Indigity Genius. It's a number of indigenous AI scientists. We're
Speaker 3: building tools for people to use on reservations that use
Speaker 3: AI for their public purpose. There's not compute resources between
Speaker 3: Boise and Chicago that they can get access to. Right
Speaker 3: we should be spending money on this. There's a bill
Speaker 3: in Congress right now. The second is data representation, mandating
Speaker 3: that these companies actually include public data sets that are
Speaker 3: truly represented with guidelines. This could happen through regulation, it
Speaker 3: could happen through policy, it could happen through an EO.
Speaker 3: And the last is talent. Why are we so confident
Speaker 3: that the only way that you can make a career
Speaker 3: in AI to go get a degree and then go
Speaker 3: work for one of these companies making whatever six figures.
Speaker 3: What if we built a public service core of computer
Speaker 3: scientists and data scientists and we're seeing the start of
Speaker 3: that under the Biden Prris administration, to actually say, let's
Speaker 3: go work in government and let's work in communities to
Speaker 3: build AI products. These are three things we could do
Speaker 3: that actually have nothing to do with limiting the safety
Speaker 3: of EI tools. That's important, but that can't be the
Speaker 3: only conversation. And it feels like it is right now.
Speaker 1: And Vivian, don't you think it's weird that this is
Speaker 1: all handled by the FTC? I mean, why isn't there
Speaker 1: cabinet level position kind of overseeing technology. It seems to
Speaker 1: me it's such a huge issue that, you know, new
Speaker 1: departments have been established historically, the Department of the Interior,
Speaker 1: you know, HHS. I don't even know when they were established,
Speaker 1: but it seems to me it's time to establish a
Speaker 1: new cabinet level position and a whole infrastructure that can
Speaker 1: help manage these issues. Right.
Speaker 2: Yeah, Well, Biden's executive order doesn't quite go that far,
Speaker 2: but it's starting to walk towards those space sort of.
Speaker 2: Among the many things that the Executive Order says is
Speaker 2: deep coordination among various parts of government, more AI expertise
Speaker 2: in all of these federal offices. I mean, that's part
Speaker 2: of the problem. You don't have people that understand the technology,
Speaker 2: it's going to be hard to make do any kind
Speaker 2: of regulation. I think they're also limited by what can
Speaker 2: be done without the ascent of Congress, since Congress doesn't
Speaker 2: seem to be assenting to just about anything right now.
Speaker 4: There's good and bad this idea of focusing new creation
Speaker 4: and of branches of government on AI. I like the
Speaker 4: idea that government is taking consumer protection seriously. Like that
Speaker 4: sounds good, but a loss there is realizing the ways
Speaker 4: in which AI is just another technology. So we already
Speaker 4: have a Presidential Office of Science and Technology Policy. We
Speaker 4: already have funding agencies. I'll show my biases as an academic,
Speaker 4: but we have the National Science Foundation. It's been writing
Speaker 4: checks since the mid fifties. So there are already ways
Speaker 4: for the US government to spur innovation. So I like
Speaker 4: the idea of US recognizing that AI is having a
Speaker 4: big impact. Again, that's partly about technology, but also part
Speaker 4: of the power of markets in our own norms. But
Speaker 4: I also don't want to make AI so exceptional that
Speaker 4: we don't profit from the lessons learned for dealing with
Speaker 4: technologies in general. We've regulated and made safe and made
Speaker 4: productive so many forms of technology through both positive and
Speaker 4: negative regulation. So I feel like there's lessons to be
Speaker 4: learned there that we might lose out at if we
Speaker 4: somehow think of AI as being magic and not just
Speaker 4: another form of technology.
Speaker 2: HETI I have a quick follow up, which is the
Speaker 2: issue with speed. These tech companies are moving really fast,
Speaker 2: and government, often for very good reasons, move slowly. Governments,
Speaker 2: I should say, because you've also got actions coming out
Speaker 2: of the European Union, in the UK, other parts of
Speaker 2: the inter governmental organizations like the United Nations, which I
Speaker 2: know you're part of that group that's working on this philosophy,
Speaker 2: I mean, can they possibly keep up, let alone get
Speaker 2: out ahead of this.
Speaker 3: Yeah, you know, I think you're asking exactly the right question.
Speaker 3: I'm going to disagree with Chris just in a matter
Speaker 3: of degree, which is the sense that AI is exceptional
Speaker 3: only in exactly what you refer to, Vivian is the
Speaker 3: speed of transformation that's creating in our society, and so yes,
Speaker 3: there's a lot to be learned by how we've dealt
Speaker 3: with this in the past. But we don't have one
Speaker 3: hundred years between the introduction of the Cotton gen and
Speaker 3: the creation of the National Labor Relations Board, right, we
Speaker 3: don't have that much time now. Look, I think the
Speaker 3: question is what are we reacting to and why are
Speaker 3: we spending so much more time reacting to tech companies?
Speaker 3: Where is there public leadership that says, what's the vision
Speaker 3: for what AI should be in human society and how
Speaker 3: do we create policy that gets us there. The mandates
Speaker 3: of the Secretary General of the UN, Antonio bu Terras
Speaker 3: has given us on this high level advisory board to
Speaker 3: which I've been avoided is to move beyond just thinking
Speaker 3: about the risks of what happens when AI is deployed
Speaker 3: by private companies and say, what does it actually look
Speaker 3: like to build a governance mechanism they use this AI
Speaker 3: to create a better future. That's not how our particular
Speaker 3: government system is set up at the moment. I think
Speaker 3: the Biden Harris Executive Order, which we refer to a
Speaker 3: couple of times on here, was actually a really meaningful
Speaker 3: attempt to take a lot of this language and push
Speaker 3: it into one hundred page document. It's a start, but
Speaker 3: we need a new public conversation. This isn't something to
Speaker 3: say government should go figure this out. I think we
Speaker 3: need to actually have a public American conversation about what
Speaker 3: a future driven by AI looks like, and we need
Speaker 3: to figure out where to start that.
Speaker 1: I'd love to follow up by asking how quickly is
Speaker 1: it moving? I mean, how different will the world look, say,
Speaker 1: in one to five years, Chris, I mean, what are
Speaker 1: you seeing in terms of how quickly this technology is evolving?
Speaker 1: What's going to look different in a few years.
Speaker 4: I often like to talk about norms, laws, markets, and architecture,
Speaker 4: which is this idea from the legal scholar Larry Lesseik
Speaker 4: about the forces acting on us can be clustered into
Speaker 4: those four groups and they all have their own time scales.
Speaker 4: So architecture in this case includes technology which moves. It
Speaker 4: feels like it moves really faster, and there might be
Speaker 4: some sort of paradigm shift where we're confronted with the
Speaker 4: new technology. Markets react very quickly. For example, we create
Speaker 4: new job titles like prompt engineer and start paying people
Speaker 4: to do that, and we start writing books about how
Speaker 4: to use lllms, and then our norms adapt much more
Speaker 4: slowly as we get to normative statements like should I
Speaker 4: use in court a bunch of citations that were generated
Speaker 4: by llms? Is it okay to write the eulogy for
Speaker 4: my friend using chatgypt? Those are normative things that we
Speaker 4: all have to react to. And then laws, and.
Speaker 1: I'll answer to that question, no, it's not. Go ahead.
Speaker 4: So our norms constantly evolve, and then the laws, as
Speaker 4: you pointed out, are generally much slower. Timescale for laws
Speaker 4: is much longer than timescale for those. So you know,
Speaker 4: chat GPT was a great product innovation. GPT three had
Speaker 4: been around for like a year or two before that.
Speaker 4: I looked at my notes and saw that I was
Speaker 4: teaching GPT three to my class in the spring of
Speaker 4: twenty twenty two. And you know, GPT two was around
Speaker 4: before that, and chatbots, as I said, were around since
Speaker 4: the sixties. So many of these things are not new,
Speaker 4: But what changes quickly is our norms and also the
Speaker 4: way these things become products. So Opdai has done a
Speaker 4: great job of making GPT four the basis for other
Speaker 4: plugins and for API access, and other companies have been
Speaker 4: built sort of on top of that technology.
Speaker 1: What does that need help translate? What do you mean?
Speaker 4: Sorry? Yeah, sorry, I was a nerdy tangent there. So
Speaker 4: katis are application programming interfaces. It basically means I'm going
Speaker 4: to allow one program to interact with another program, and
Speaker 4: those two programs could be run by totally different companies.
Speaker 4: So I could have one company make a computer talk
Speaker 4: to a different companies computer, and all sorts of creativity
Speaker 4: is unlocked.
Speaker 2: There.
Speaker 1: Can you give me an example.
Speaker 4: Vivian had an itinerary, Now hook it up to Expedia
Speaker 4: or Kayak or some other company that will buy the
Speaker 4: ticket for you. So you had GPT right us at
Speaker 4: poem hook it up to a company that will already
Speaker 4: print it for you and mainly your card.
Speaker 1: Yeah, exactly, got it.
Speaker 4: So all of those interfaces are such an onlock to
Speaker 4: different people's creativity, and the people again could be you know,
Speaker 4: artists or students or other companies. So that's the thing
Speaker 4: that's easy to move quickly is you know, let's say
Speaker 4: we were all stuck with GPT three from spring of
Speaker 4: twenty twenty two. Now that we've had this normative change
Speaker 4: that everybody has had their eyes open to, their creativity,
Speaker 4: open to the market, open to which means a bunch
Speaker 4: of capital flowing to this new opportunity. There's so much
Speaker 4: room for things to change real fast. Not because the
Speaker 4: tech is advancing so fast and scientists are so smart,
Speaker 4: whether or not they are. It's because all of our
Speaker 4: norms and our markets are changing so fast. There's very
Speaker 4: little viscosity to stop us from coming up with new
Speaker 4: ways of doing things now that we have accepted, for example,
Speaker 4: moderately hallucinatory and somewhat truthful generative technologies.
Speaker 3: Let me give you two facts that take what Chris
Speaker 3: said with them in stark relief. He talked about GPT three,
Speaker 3: a lot a model that came out in twenty twenty
Speaker 3: out of twenty twenty two. Rather, it took about eighteen
Speaker 3: months to train that model. I'll spare you what that means,
Speaker 3: but it took about eighteen months of people working with computers.
Speaker 3: The newest supercomputer from Nvideo can now train a GPT
Speaker 3: three equivalent in four minutes. We have increased the amount
Speaker 3: of compute capacity exists on the planet by fifty five
Speaker 3: million times in the last ten years. The pace of
Speaker 3: change is so incredible here, and when Chris talks about
Speaker 3: the human components of that, the pieces of connecting and creativity,
Speaker 3: we also have to acknowledge that even just what's possible
Speaker 3: is changing almost by the day or by the month.
Speaker 3: Q Star that we talked about wouldn't even have been
Speaker 3: conceivable two years ago. So who knows what two years
Speaker 3: from now will look like. And that's my one last
Speaker 3: thought on regulation is we are so we're working so
Speaker 3: hard regulating what AI looked like two years ago. Maybe
Speaker 3: in the most frontier places, the most brilliant congress people
Speaker 3: are saying, what does AI look like today? We have
Speaker 3: no idea how to build a policy that regulates what
Speaker 3: AI will look like in five years, So we take
Speaker 3: control of building AI ourselves.
Speaker 1: In fact, I wanted to ask you both. Jeffrey Hinton,
Speaker 1: known as the godfather of AI, spent decades advancing AI,
Speaker 1: but we're recently cautioned about the potential existential dangers that
Speaker 1: could pose. I feel like you all have kind of
Speaker 1: diminished the bad stuff that goes with AI, and I'm
Speaker 1: curious if you can give us some sense of how
Speaker 1: it could be misused or abused in the wrong hands.
Speaker 4: To be clear, there's lots of bad stuff, it's just
Speaker 4: not that particular bad stuff. So there's bad stuff happening
Speaker 4: right now all the time. And so Jeff Hinton and
Speaker 4: others have portrayed a possible bad thing in the future
Speaker 4: that has some unknowable but I think very small probability.
Speaker 4: So there are other existential risks right now that don't
Speaker 4: evolve anything involving AI. Let's worry about those. But also
Speaker 4: when we're talking about AI, there's all sorts of bad
Speaker 4: things happening right now with AI. You know, if you automate,
Speaker 4: as philosophic saying earlier sexism or it doesn't make it
Speaker 4: less sexist. Right for you to have a biased algorithm
Speaker 4: and then you automate it so it can be you know,
Speaker 4: sexist or show biases at high efficiency at scale, that
Speaker 4: doesn't make it any less biased, right, It's still bad.
Speaker 4: So I wouldn't say that we're, at least on my point,
Speaker 4: trying to minimize the bad stuff. It's just it's not
Speaker 4: Jeff Hint, it's bad stuff that I'm more concerned about.
Speaker 3: Let me tell you, when I spend my time on
Speaker 3: I spend my time on making sure that communities around
Speaker 3: the world are just totally left out of the air revolution.
Speaker 3: I'd spend my time thinking about making sure that AI
Speaker 3: decisions that affect people's lives have the contours of human
Speaker 3: ethics around them. I spend my time making sure that
Speaker 3: the people who are building these tools are representative of
Speaker 3: all of us. I spend my time making sure that
Speaker 3: AI is not being used to run autonomous weapons and
Speaker 3: run warfare. These are things that we can all spend
Speaker 3: our time on to make sure that AI doesn't actually
Speaker 3: make the world worse and maybe makes the world better.
Speaker 3: I don't have time to be thinking about what happens
Speaker 3: in twenty five years when one man's conception of a
Speaker 3: risk comes true. There's a lot of risks that actually
Speaker 3: affect our daily lives today that we should be spending
Speaker 3: our time making better.
Speaker 2: One of the areas that we're very, very focused on
Speaker 2: at the Aspen Institute is the intersection of artificial intelligence,
Speaker 2: the upcoming twenty twenty four elections, and societal trust. And
Speaker 2: it's a big area of concern. We've seen you even
Speaker 2: just from recent elections outside the United States, recently in
Speaker 2: Argentina and in Netherlands, Slovakia and Poland. We've seen how
Speaker 2: some of the parties, the candidates, the campaigns are using AI,
Speaker 2: and there is some significant concern about our twenty twenty
Speaker 2: four elections and the ways that AI might impact what
Speaker 2: population thinks, how they vote, where they show up. Can
Speaker 2: you just share with us, both of you a little
Speaker 2: bit about what you're seeing there and what you think
Speaker 2: we should be most worried about here?
Speaker 3: If anything, Yeah, a look at a majority of the
Speaker 3: world's population is going to the polls next year. You
Speaker 3: just have putted out. What I'm concerned about is not
Speaker 3: how AI will go and change the elections. It's how
Speaker 3: bad actors are going to use AI to do what
Speaker 3: they've already done to perforate trust in our society, but
Speaker 3: do it even more effectively. I'm worried about things like
Speaker 3: somebody deciding to send out three hundred and fifty million
Speaker 3: individualized emails to manipulate the way people are going to vote.
Speaker 3: And here's the worst part. They don't have to include
Speaker 3: any misinformation or lies at all, because what they can
Speaker 3: do is look at real factual information, only give you
Speaker 3: a version of that story that affects your demographic as
Speaker 3: they understand you that analyzes your behaviors and tries to
Speaker 3: get you vote a certain way. We don't even have
Speaker 3: rules in place on what to do if somebody comes
Speaker 3: to you with something where not a single fact is incorrect,
Speaker 3: but is architected to manipulate you in some way. This
Speaker 3: is where we should be spending our time thinking about
Speaker 3: policy and regulation.
Speaker 1: Chris, any thoughts from you.
Speaker 4: It's been so far, just had to summarize it. Yeah,
Speaker 4: that's a real concern, and some of it is about
Speaker 4: AI as we understand it this year, but some of
Speaker 4: it is about the fact that our marketplace of ideas
Speaker 4: has become completely algorithmically empowered by a few private companies,
Speaker 4: and so all of our conceptions about how, you know,
Speaker 4: having lots of people have a free exchange of ideas,
Speaker 4: you know, so we're predicated on a very different sort
Speaker 4: of game theory of the way people are trading ideas.
Speaker 4: In addition to the fact that the digital assets are
Speaker 4: so easily manipulated that there's room for creating things that
Speaker 4: are that look trustworthy.
Speaker 1: Like Nancy Pelosi intoxicated.
Speaker 4: That's a good example, or.
Speaker 1: Tom Hanks talking and saying something any about a dentist
Speaker 1: or something some dental service, or.
Speaker 4: Simply taking video game footage from a video game and
Speaker 4: representing it as being from a war zone, which also
Speaker 4: has happened time and time again in different military conflicts
Speaker 4: and continues to happen. So it doesn't even have to
Speaker 4: be deep fixed, right, It can be absolutely cheap fix that,
Speaker 4: you know, accelerated by an information platform which is used
Speaker 4: to optimize engagement. Now I'm going to go down a
Speaker 4: slightly nerdy rant. I can tell anyways it's bad. So
Speaker 4: there's a lot of concern there, and there's a very
Speaker 4: difficult time for academic researchers to investigate it because the
Speaker 4: digital commons is now owned by a few private companies
Speaker 4: who are not particularly motivated to share information in a
Speaker 4: research friendly way. So it's difficult for us to do
Speaker 4: anything that even looks like experiments, which is the way
Speaker 4: science has been done for the last century, to do
Speaker 4: randomized control trials around different treatments. There's sort of no
Speaker 4: framework for doing that technologically nor ethically. The people who
Speaker 4: are most concerned about it are not particularly technologically able
Speaker 4: to get hold of lots of data and do statistical
Speaker 4: analyzes of them, so it's a concern. I mean, it's
Speaker 4: a concern politically, it's a concern for researchers who want
Speaker 4: to understand it. I'm concerned.
Speaker 2: I'll add one other thing, just as an addendum to this.
Speaker 2: There's much we can't accomplish between now and the elections
Speaker 2: next year. But one thing we can do, and this
Speaker 2: is work that we're taking on a little promotion for
Speaker 2: our for the Aspen Institute here is bringing groups together
Speaker 2: who are not talking to each other. We did a
Speaker 2: deep We spoke to a lot of experts, including both
Speaker 2: of the experts here. One of them said something that
Speaker 2: hit us, which is that election officials don't understand what
Speaker 2: is the potential of what AI can do to cause confusion,
Speaker 2: and the AI companies don't understand how democracy works. So
Speaker 2: we can bring these groups together to cross educate, cross trained,
Speaker 2: to understand each other's risks. That's at least something.
Speaker 1: And Chris, one of our recommendations from our commission, on
Speaker 1: which Vivian was a part and I was a co chair,
Speaker 1: was to open the doors for scientists and researchers to
Speaker 1: actually study these tech companies. But clearly, Vivian, that hasn't happened,
Speaker 1: has it?
Speaker 2: Well? We may need a whole other podcast for that,
Speaker 2: given the political pressures that are happening on those that
Speaker 2: are looking into miss and disinformation and the chilling effect
Speaker 2: that that has, but it's it's troubling.
Speaker 1: Well. On that note, Happy holidays everybody. Chris and the
Speaker 1: Loss and Vivian, thank you all so much for this conversation.
Speaker 1: I hope it's helpful to people who are trying to
Speaker 1: wrap their arms around this new technology and the ramifications
Speaker 1: it is going to have on all of us. To
Speaker 1: all three of you, thank you so much.
Speaker 4: Thanks for having us, Thank you, Thanks everybody.
Speaker 1: Vivian, you've become such an expert in this area. Did
Speaker 1: you hear anything new or interesting or are you as
Speaker 1: troubled as ever?
Speaker 2: That's a good question. It's not that I heard anything new,
Speaker 2: because I spend a lot of time on this space.
Speaker 2: But what to me was so revealing about this conversation
Speaker 2: is not sort of all the things that we're worried
Speaker 2: about are the robot overlords taking over or the deep
Speaker 2: fake that's going to, you know, make everybody in the
Speaker 2: world believe it. It's the second and third order effects
Speaker 2: and the fact that so much control over these incredibly
Speaker 2: powerful technologies are in the hands of just a few people.
Speaker 2: I think they both made those points very very strongly,
Speaker 2: and I think it's it's really hopeful focus on sort
Speaker 2: of the things that really matter. We get very distracted
Speaker 2: by shiny objects and maybe not focusing on the fundamentals.
Speaker 1: Like the telenovella story of Sam Altman, where we need
Speaker 1: to really focus on the long term implications of all
Speaker 1: of this. Well, I think they're both really nice, really smart.
Speaker 1: Thank you for introducing me to them, Vivian, and thank
Speaker 1: you for being part of the podcast.
Speaker 2: Well, thank you for letting me share Dinny's with you. Katie.
Speaker 2: It's an incredibly humbling honor, so thank you so much.
Speaker 1: Thanks for listening. Everyone. If you have a question for me,
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