Cancer Straight Talk is a podcast for Memorial Sloan Kettering Cancer Center. We're host doctor Diane Reedy. Lagunis has intimate conversations with patients and experts about topics like dating and sex, exercise and diet, the power of gratitude, and more. I love being her guest back in April. Listen to Cancer Straight Talk. You'll learn so much. Hi everyone, I'm Kitty Kuric and this is next question. So I have to confess friends that if I sound weird, it's because I have a terrible cold. So I apologize in advance. Luckily, my plus one isn't sitting next to me catching my germs, but she is at a remote location. Where are you, Vivian.

Cancer Straight Talk is a podcast for Memorial Sloan Kettering Cancer Center. We're host doctor Diane Reedy. Lagunis has intimate conversations with patients and experts about topics like dating and sex, exercise and diet, the power of gratitude, and more. I love being her guest back in April. Listen to Cancer Straight Talk. You'll learn so much. Hi everyone, I'm Kitty Kuric and this is next question. So I have to confess friends that if I sound weird, it's because I have a terrible cold. So I apologize in advance. Luckily, my plus one isn't sitting next to me catching my germs, but she is at a remote location. Where are you, Vivian.

I'm in Bethesta, Maryland, in my home.

I'm in Bethesta, Maryland, in my home.

Oh nice, Well, Vivian Schiller, as you probably heard, is my plus one today. And Vivian, I thought i'd start by telling folks how we know each other? Do you want to start?

Oh nice, Well, Vivian Schiller, as you probably heard, is my plus one today. And Vivian, I thought i'd start by telling folks how we know each other? Do you want to start?

Well? Actually, I think we knew each other when you were at CNN, but you would not remember me. Well, you knew my husband?

Well? Actually, I think we knew each other when you were at CNN, but you would not remember me. Well, you knew my husband?

Yes? Was this in Atlanta?

Yes? Was this in Atlanta?

This is in Atlanta? You worked with my husband.

This is in Atlanta? You worked with my husband.

In the early days of CNN. Was that at Take two.

In the early days of CNN. Was that at Take two.

In the mid eighties, So I think that's how we initially met.

In the mid eighties, So I think that's how we initially met.

Vivian and I have cross paths at various times in our lives. I think Vivian is the only person I know who has worked at more news organizations than I have. Vivian give us The Rundown.

Vivian and I have cross paths at various times in our lives. I think Vivian is the only person I know who has worked at more news organizations than I have. Vivian give us The Rundown.

CNN, New York Times, NPR, NBC, The Guardian well as a board member, and also at Twitter doing a news role there, and at Discovery running a news documentary channel.

CNN, New York Times, NPR, NBC, The Guardian well as a board member, and also at Twitter doing a news role there, and at Discovery running a news documentary channel.

So basically, like me, Vivian cannot hold a job. So we are going to have a conversation today that actually was prompted by a conversation I heard at an Aspen Institute board meeting. Vivian and I are both very involved in the Aspen Institute. In fact, she's got a paying job there. Vivian, what exactly is your role at Aspen?

So basically, like me, Vivian cannot hold a job. So we are going to have a conversation today that actually was prompted by a conversation I heard at an Aspen Institute board meeting. Vivian and I are both very involved in the Aspen Institute. In fact, she's got a paying job there. Vivian, what exactly is your role at Aspen?

I run a program at the Aspens too, called Aspen Digital, and our focus is on all things technology and media and their impact on society, so exactly all the stuff we're talking about today.

I run a program at the Aspens too, called Aspen Digital, and our focus is on all things technology and media and their impact on society, so exactly all the stuff we're talking about today.

And Vivian and I got to know each other even better when we both served on the Aspen Institute Commission on Disinformation, which has a more formal title, which is go Ahead.

And Vivian and I got to know each other even better when we both served on the Aspen Institute Commission on Disinformation, which has a more formal title, which is go Ahead.

Vivian Commission on Information Disorder.

Vivian Commission on Information Disorder.

Thank you, Yes, And we got to know each other well during that time, but we've known each other for a long time, so we are excited to have this conversation for all of you. I learned a great deal and we have two incredible experts who are coming on to talk about not all what AI is, but obviously the promise and the perils of this new technology. And of course we're going to touch on Sam Altman's auster and then reinstatement by the board at Open AI, which is full of all sorts of intrigue. And this is hopefully a podcast that is AI for dummies, but I fear the only dummy in the conversation is yours truly. So without further ado, let's invite in our guests, Chris Wiggins and the Last Star. Welcome to the podcast. Thank you so much for being here. I should note that November thirtieth today is the one year anniversary of chat GPT, so we actually have a newspeg for this podcast. And before we dig in, I thought i'd ask you briefly about what you all do and why you're qualified to have this conversation Chris, Why don't we start with you?

Thank you, Yes, And we got to know each other well during that time, but we've known each other for a long time, so we are excited to have this conversation for all of you. I learned a great deal and we have two incredible experts who are coming on to talk about not all what AI is, but obviously the promise and the perils of this new technology. And of course we're going to touch on Sam Altman's auster and then reinstatement by the board at Open AI, which is full of all sorts of intrigue. And this is hopefully a podcast that is AI for dummies, but I fear the only dummy in the conversation is yours truly. So without further ado, let's invite in our guests, Chris Wiggins and the Last Star. Welcome to the podcast. Thank you so much for being here. I should note that November thirtieth today is the one year anniversary of chat GPT, so we actually have a newspeg for this podcast. And before we dig in, I thought i'd ask you briefly about what you all do and why you're qualified to have this conversation Chris, Why don't we start with you?

Sure?

Sure?

Fair question. So for the last twenty two years have been on the fact ficulty. At Columbia, I teach applied mathematics. My research is in machine learning, mostly apply to biology. For the last ten years, I've also been the Chief Data Scientist to The New York Times, which means I lead a team that develops and deployees machine learning.

Fair question. So for the last twenty two years have been on the fact ficulty. At Columbia, I teach applied mathematics. My research is in machine learning, mostly apply to biology. For the last ten years, I've also been the Chief Data Scientist to The New York Times, which means I lead a team that develops and deployees machine learning.

And I'm a lost star. Twenty odd years ago, I started my career as a computer science researcher, working on what we called artificial intuligence back then. Since then, I've spent my career building private and public organizations that focus on using tech as a way to advance justice and equity, and now lead probably one of the largest film propic organizations focused on funding AI that makes the world a better place.

And I'm a lost star. Twenty odd years ago, I started my career as a computer science researcher, working on what we called artificial intuligence back then. Since then, I've spent my career building private and public organizations that focus on using tech as a way to advance justice and equity, and now lead probably one of the largest film propic organizations focused on funding AI that makes the world a better place.

Well, I'm very excited to have you both, as well as my friend Vivian, who you both know well. And I thought i'd start with a very basic question, what is AI? Who wants to take a shot at that?

Well, I'm very excited to have you both, as well as my friend Vivian, who you both know well. And I thought i'd start with a very basic question, what is AI? Who wants to take a shot at that?

I could try a historical view. AI is one of my favorite drifting targets meeting It's a term that means different things to different people in different decades, in different communities. So when the term was coined in nineteen fifty five by John McCarthy, it was a proposal that the idea that any feature of intelligence can be, in principle, be so precisely described that a machine can be made to simulate it. So the conception of what artificial intelligence meant in nineteen fifty five is so different than what it's come to me now even in twenty twenty one, let alone a year ago today when chat TPT was launched, And now everybody when they think of AI, they're thinking of a chatbot, which is really chatbot is a small example of machine learning, which is a small example of artificial intelligence. So the term has come to mean different things in different times, which is why the term never feels like you're standing on solid ground when you're saying it, because different audiences can mean very different things. When you say those two letters.

I could try a historical view. AI is one of my favorite drifting targets meeting It's a term that means different things to different people in different decades, in different communities. So when the term was coined in nineteen fifty five by John McCarthy, it was a proposal that the idea that any feature of intelligence can be, in principle, be so precisely described that a machine can be made to simulate it. So the conception of what artificial intelligence meant in nineteen fifty five is so different than what it's come to me now even in twenty twenty one, let alone a year ago today when chat TPT was launched, And now everybody when they think of AI, they're thinking of a chatbot, which is really chatbot is a small example of machine learning, which is a small example of artificial intelligence. So the term has come to mean different things in different times, which is why the term never feels like you're standing on solid ground when you're saying it, because different audiences can mean very different things. When you say those two letters.

You know, I'll agree with that. I agree with everything Chris said. I mean, on one side, AI is a technology conversation. It's a new set of tools that let computers do what people have tradisally thought only we could do. But it's also something much bigger. It's a social phenomenon. It's a moment now where we get to test and examine some pretty basic assumptions about what it means to have an economy, a political society, about what it means to be human. And that's why we're seeing this amazing grounds full of interest in what AI is.

You know, I'll agree with that. I agree with everything Chris said. I mean, on one side, AI is a technology conversation. It's a new set of tools that let computers do what people have tradisally thought only we could do. But it's also something much bigger. It's a social phenomenon. It's a moment now where we get to test and examine some pretty basic assumptions about what it means to have an economy, a political society, about what it means to be human. And that's why we're seeing this amazing grounds full of interest in what AI is.

When you think about AI, can you explain in very sort of eighth grade terms, how it works, how these large language models are assembled, and how machine learning enables technology to spew out things that make sense. Chris, can you help me with that?

When you think about AI, can you explain in very sort of eighth grade terms, how it works, how these large language models are assembled, and how machine learning enables technology to spew out things that make sense. Chris, can you help me with that?

Sure? I think again. History is really useful here. One example of how you might build a chatbot statistically was Claude Shannon in probably nineteen forty four was thinking about this model where you generate words at random. Imagine that you're reading a book and you find some word, and then you keep reading in that book until you find that word again, and then write down the word that follows it. Then keep reading the book, wait until you find that word again and write down the word that that's the basic nexus of a small language model. So you're predicting the next word based on the previous word you can think about what's being done today as a very large version of that same small language model. It's a statistical prediction model. And an important part there is that it really matters what book you're training it on, and so you need a very large corpus of training data. In this case. One of the things that makes large language models possible is the vast amount of information that's available online, and so computer programs automatically ingest all of the text on the web could be from Reddit, newsgroups or Wikipedia, or hey, they.

Sure? I think again. History is really useful here. One example of how you might build a chatbot statistically was Claude Shannon in probably nineteen forty four was thinking about this model where you generate words at random. Imagine that you're reading a book and you find some word, and then you keep reading in that book until you find that word again, and then write down the word that follows it. Then keep reading the book, wait until you find that word again and write down the word that that's the basic nexus of a small language model. So you're predicting the next word based on the previous word you can think about what's being done today as a very large version of that same small language model. It's a statistical prediction model. And an important part there is that it really matters what book you're training it on, and so you need a very large corpus of training data. In this case. One of the things that makes large language models possible is the vast amount of information that's available online, and so computer programs automatically ingest all of the text on the web could be from Reddit, newsgroups or Wikipedia, or hey, they.

Use my book the best advice I ever got for chat GPT, and nobody asked my permission. By the way, that's right.

Use my book the best advice I ever got for chat GPT, and nobody asked my permission. By the way, that's right.

That's a whole other issue.

That's a whole other issue.

That's exactly right. That's all other issues is how this relate to the rights of the authors. But the statistical problem is one of training from data. So the data are central and it's counterintuitive, I think to many people who think that computers are about writing down rules, and when you write down the rules about how we think we think, then you'll get something that acts like how we think we think. And in fact, for most of artificial intelligence as a field, for the last seventy years, that's how people thought we were going to achieve artificial intelligence was by understanding how we think we think, and then you would just simulate it or just program it. And the truth is, it's been a realization in the last two decades that the way that we are able to achieve such exciting results is from taking really large data sets and learning from the data how to build a computer that emulates, really imitates what we sound like when we are intelligent.

That's exactly right. That's all other issues is how this relate to the rights of the authors. But the statistical problem is one of training from data. So the data are central and it's counterintuitive, I think to many people who think that computers are about writing down rules, and when you write down the rules about how we think we think, then you'll get something that acts like how we think we think. And in fact, for most of artificial intelligence as a field, for the last seventy years, that's how people thought we were going to achieve artificial intelligence was by understanding how we think we think, and then you would just simulate it or just program it. And the truth is, it's been a realization in the last two decades that the way that we are able to achieve such exciting results is from taking really large data sets and learning from the data how to build a computer that emulates, really imitates what we sound like when we are intelligent.

In words, sometimes when I'm writing emails, these words like so much. I must use that all the time, thank you so much. It shows up in my email if I want to just kind of press a button and not write anymore. Is that an example a rudimentary example of AI.

In words, sometimes when I'm writing emails, these words like so much. I must use that all the time, thank you so much. It shows up in my email if I want to just kind of press a button and not write anymore. Is that an example a rudimentary example of AI.

Yes, very much.

Yes, very much.

So.

So.

There's the math behind it, which is how are you going to predict the next word? But the other thing about it is the product and sort of the user interface. People like to talk about how in the late fifties night at Stanford there was John McCarthy who was working on the mathematics of AI, and then there were people like Doug Engelbart who were working on the product of AI. How are we going to make an interface that allows people to interact with the computer. Well, so when you just saw it, there was a good example of good math. And the math could be as simple as counting the number of times that the word what follows the word, So it's very simple math. But as well as the product idea, which is, how do I make a suggestion to you in a way that's useful to you while you use that digital product and not creepy and not intrusive. So yeah, that's another thing that we're seeing with chatchept is a good coming together of technology and mathematics and statistical models, but also just a nice product that people are enjoying musing.

There's the math behind it, which is how are you going to predict the next word? But the other thing about it is the product and sort of the user interface. People like to talk about how in the late fifties night at Stanford there was John McCarthy who was working on the mathematics of AI, and then there were people like Doug Engelbart who were working on the product of AI. How are we going to make an interface that allows people to interact with the computer. Well, so when you just saw it, there was a good example of good math. And the math could be as simple as counting the number of times that the word what follows the word, So it's very simple math. But as well as the product idea, which is, how do I make a suggestion to you in a way that's useful to you while you use that digital product and not creepy and not intrusive. So yeah, that's another thing that we're seeing with chatchept is a good coming together of technology and mathematics and statistical models, but also just a nice product that people are enjoying musing.

What you're describing, Chris, is a predictive model. But so many people, particularly since a year ago today when chatchipt came out and sort of collectively blew the world's minds, it feels like we're talking to a machine that is actually thinking, that is actually sentient, and it's in fact design that way, and that has societal implications, some of the societal implications that you were referring to earlier.

What you're describing, Chris, is a predictive model. But so many people, particularly since a year ago today when chatchipt came out and sort of collectively blew the world's minds, it feels like we're talking to a machine that is actually thinking, that is actually sentient, and it's in fact design that way, and that has societal implications, some of the societal implications that you were referring to earlier.

VELAs know, you're pointing out the critical kind of missing element in what Chris described as what AI is today. At the end of the day, every version of AI that we have today. It's a mathematical model that predicts what happens next based on what's happened before. It doesn't reason, it doesn't think, it doesn't have agency, it doesn't have preferences, all of the things that people now try to scare us with that. I imagine we'll talk a little bit about that crazy term AGI. Today's AI is none of that. I often like to say, do you all remember that movie Honey, I Stroked the Kids?

VELAs know, you're pointing out the critical kind of missing element in what Chris described as what AI is today. At the end of the day, every version of AI that we have today. It's a mathematical model that predicts what happens next based on what's happened before. It doesn't reason, it doesn't think, it doesn't have agency, it doesn't have preferences, all of the things that people now try to scare us with that. I imagine we'll talk a little bit about that crazy term AGI. Today's AI is none of that. I often like to say, do you all remember that movie Honey, I Stroked the Kids?

Yeah, Rick Moranez, Yeah.

Yeah, Rick Moranez, Yeah.

Great movie. Right, Today's AI. All it is is this, take everything that's ever been written, put it in a giant library, beam a rate gun at that library, and enough power to power a small city for months and months, and say, how do we compress that entire library down? And give us one little map? And all the math does is it says this. If before, when people said a word, they often followed it with another word, that's all we're going to do right now. Now, what that does. It's an amazing magic trick. It's a great illusion. It makes you think you're talking to somebody who wants to talk back to you. But at the end of the day, all the machine is doing is predicting what the next word and the answer should be. This is so critical because it reframes how we engage with these tools, and that's really all they are. They're just tools. They're not, you know, all knowing entities. They're not partners, they're not conversational and sparring buddies. They're just tools that help us maybe be better.

Great movie. Right, Today's AI. All it is is this, take everything that's ever been written, put it in a giant library, beam a rate gun at that library, and enough power to power a small city for months and months, and say, how do we compress that entire library down? And give us one little map? And all the math does is it says this. If before, when people said a word, they often followed it with another word, that's all we're going to do right now. Now, what that does. It's an amazing magic trick. It's a great illusion. It makes you think you're talking to somebody who wants to talk back to you. But at the end of the day, all the machine is doing is predicting what the next word and the answer should be. This is so critical because it reframes how we engage with these tools, and that's really all they are. They're just tools. They're not, you know, all knowing entities. They're not partners, they're not conversational and sparring buddies. They're just tools that help us maybe be better.

Let me ask you this because I thought this was interesting. Bill Gates recently noted that AI as it exists today is quote still pretty dumb. Chris. Do you agree with that?

Let me ask you this because I thought this was interesting. Bill Gates recently noted that AI as it exists today is quote still pretty dumb. Chris. Do you agree with that?

Yeah?

Yeah?

Absolutely so. I think what VELAs is saying is apt, which is language, and the ability to produce language is a great imitation of what thinking. And in fact, I use the word imitation because in Hellan Turing's original nineteen fifty paper on can MA Machines think he basically set out this problem. Imagine a computer that could imitate what it's like to talk to somebody. That's sort of an operaginalization of what it means to think. But there's still many things a I can't do. As often described, planning is difficult, Compositional thinking is difficult, Working with multiple modes at once is difficult. Meaning like words and images together. So I think you're right that it's uncanny. Right, we're in the uncanny valley of conversations right now with chatbots. But it's very difficult for people not to impose this belief that it is intelligent or thoughtful. And the truth is people have been having that experience for as long as they've been building chatbots. Even in the nineteen sixties, people were building chatbots based on simple rules, and users using that chatbot had the same experience of feeling like even though they knew it was just a very simple computer program, there was the emotional resonance was one as though you were talking to an intelligent agent.

Absolutely so. I think what VELAs is saying is apt, which is language, and the ability to produce language is a great imitation of what thinking. And in fact, I use the word imitation because in Hellan Turing's original nineteen fifty paper on can MA Machines think he basically set out this problem. Imagine a computer that could imitate what it's like to talk to somebody. That's sort of an operaginalization of what it means to think. But there's still many things a I can't do. As often described, planning is difficult, Compositional thinking is difficult, Working with multiple modes at once is difficult. Meaning like words and images together. So I think you're right that it's uncanny. Right, we're in the uncanny valley of conversations right now with chatbots. But it's very difficult for people not to impose this belief that it is intelligent or thoughtful. And the truth is people have been having that experience for as long as they've been building chatbots. Even in the nineteen sixties, people were building chatbots based on simple rules, and users using that chatbot had the same experience of feeling like even though they knew it was just a very simple computer program, there was the emotional resonance was one as though you were talking to an intelligent agent.

Hear this word sentient a lot, which of course is capable of sensing or feeling conscious of, or responsive to the sensations of seeing, hearing, feeling, tasting, or smelling sentient beings, which is really what it means to be a human. Does AI have the capacity to be sentient?

Hear this word sentient a lot, which of course is capable of sensing or feeling conscious of, or responsive to the sensations of seeing, hearing, feeling, tasting, or smelling sentient beings, which is really what it means to be a human. Does AI have the capacity to be sentient?

I think what we've shown is that it does a great imitation of it. But I think it's important for us all to remember that it is, as Vela said, just math right. It is a mathematical model that spits out words and it's optimized for generating words that sound like what a human being would say and given the same prompt. But we should remember that it is a machine and it's executing a mathematical act that we trained it on.

I think what we've shown is that it does a great imitation of it. But I think it's important for us all to remember that it is, as Vela said, just math right. It is a mathematical model that spits out words and it's optimized for generating words that sound like what a human being would say and given the same prompt. But we should remember that it is a machine and it's executing a mathematical act that we trained it on.

But yet there's a lot of experts out there and researchers and some pretty serious people who are trying to warn us that these machines may become sentient. Now, is that just a matter of seeing too many sci fi movies or is that something that is possible obviously not today, but on the horizon.

But yet there's a lot of experts out there and researchers and some pretty serious people who are trying to warn us that these machines may become sentient. Now, is that just a matter of seeing too many sci fi movies or is that something that is possible obviously not today, but on the horizon.

I think there's a couple of reasons why people are warning us of that possibility. I mean, again, part of it is wordplay, and I think that's what Alan during was getting at in nineteen fifty when he said, can machines think? Is an ill posed problem, so let's try to operationalize it. But I think the warnings are often distractions from real problems that automated inequality and other downsides of using algorithms today are causing. In the here and now. It's sometimes very difficult to think about our existing challenges in sociotechnical systems, and somehow more pleasant to think about this terminator doomsday future which is not cured yet. I think also there's.

I think there's a couple of reasons why people are warning us of that possibility. I mean, again, part of it is wordplay, and I think that's what Alan during was getting at in nineteen fifty when he said, can machines think? Is an ill posed problem, so let's try to operationalize it. But I think the warnings are often distractions from real problems that automated inequality and other downsides of using algorithms today are causing. In the here and now. It's sometimes very difficult to think about our existing challenges in sociotechnical systems, and somehow more pleasant to think about this terminator doomsday future which is not cured yet. I think also there's.

A yet yet that's scary, that's right.

A yet yet that's scary, that's right.

I think there's also a concern that people are putting forward this idea of a doomsday because there's only a small number of companies at present which are able to afford amassing lots of data and producing really good products. And often these companies are saying, we are the ones who can tell you how to regulate this. So there's concern that some of the doomsday scenario might be coupled to re trey capture or getting ahead of potential regulation both domestically and internationally.

I think there's also a concern that people are putting forward this idea of a doomsday because there's only a small number of companies at present which are able to afford amassing lots of data and producing really good products. And often these companies are saying, we are the ones who can tell you how to regulate this. So there's concern that some of the doomsday scenario might be coupled to re trey capture or getting ahead of potential regulation both domestically and internationally.

Let's zoom out a little bit. I agree with what Chris said, but maybe and you spoke to experts and serious people who are trying to scare us all, and I just want to put us in context. There are, you know, eight billion people on the planet. There are maybe a few hundred thousand that really understand how AI works. And there's maybe on the order of a few thousand people who have decided that what they care about more than anything else is the existential risk to humanity. Let's just put that in context. There are billions of people on the planet today that could use what AI offers as a promise to fundamentally change what their lives look like. They're access to economic opportunity, to any number of other things. There are hundreds of thousands or millions of people who work in companies that could fundamentally change their relationship with customers. So what do we have. We have a small set of people with direct access to some of these tools, and we'll talk about how that came about shortly, who have come up with this common idea that the thing we should care about more than any else is that AI will kill us all. And in the meantime, we're living in a world where there are so many opportunities and challenges here and now that we should be spending our time thinking about.

Let's zoom out a little bit. I agree with what Chris said, but maybe and you spoke to experts and serious people who are trying to scare us all, and I just want to put us in context. There are, you know, eight billion people on the planet. There are maybe a few hundred thousand that really understand how AI works. And there's maybe on the order of a few thousand people who have decided that what they care about more than anything else is the existential risk to humanity. Let's just put that in context. There are billions of people on the planet today that could use what AI offers as a promise to fundamentally change what their lives look like. They're access to economic opportunity, to any number of other things. There are hundreds of thousands or millions of people who work in companies that could fundamentally change their relationship with customers. So what do we have. We have a small set of people with direct access to some of these tools, and we'll talk about how that came about shortly, who have come up with this common idea that the thing we should care about more than any else is that AI will kill us all. And in the meantime, we're living in a world where there are so many opportunities and challenges here and now that we should be spending our time thinking about.

So there are the here and now, there is the long term future, and there's that whole middle ground that I think many of us haven't addressed. But before we come back to that, just to help our listeners a little bit, we hear the term AGI artificial general intelligence, not to be confused with generative artificial intelligence. Very confusing even for people that pay attention to these things. AGI is this sort of robot overlord saying we're talking about that may or may not ever happen. Is that right?

So there are the here and now, there is the long term future, and there's that whole middle ground that I think many of us haven't addressed. But before we come back to that, just to help our listeners a little bit, we hear the term AGI artificial general intelligence, not to be confused with generative artificial intelligence. Very confusing even for people that pay attention to these things. AGI is this sort of robot overlord saying we're talking about that may or may not ever happen. Is that right?

Yeah? What is that? You guys help me out. I'm the dumb one here.

Yeah? What is that? You guys help me out. I'm the dumb one here.

I think one thing that's useful is to remember the two different g's in those two acronyms. The GAI is generitive, but AGI is general. So when people talk about AGI for general intelligence, part of which is exciting is the idea that in the last fifty years we've done species, not me personally, but the human being species have done a really good job building algorithms that are good for individual tasks. Like you can build an app that can take a picture and say does this have a dog face in it? Or a cat face in it? That's a specific use of statistical modeling, which is good for that specific use case. So the dream of AGI is that you can produce one algorithm, one machine, one model that's good not only for disinbiguating cat faces from dog faces, but also composing a sonnet or enjoying strawberries and cream, or whatever general problem you would like the machine to solve. So that's the g of agis general. It's very easy for us to make machine learning models that are good for one specific task. It's much harder to make a machine learning model that's general and is able to do anything that we consider an intelligent task.

I think one thing that's useful is to remember the two different g's in those two acronyms. The GAI is generitive, but AGI is general. So when people talk about AGI for general intelligence, part of which is exciting is the idea that in the last fifty years we've done species, not me personally, but the human being species have done a really good job building algorithms that are good for individual tasks. Like you can build an app that can take a picture and say does this have a dog face in it? Or a cat face in it? That's a specific use of statistical modeling, which is good for that specific use case. So the dream of AGI is that you can produce one algorithm, one machine, one model that's good not only for disinbiguating cat faces from dog faces, but also composing a sonnet or enjoying strawberries and cream, or whatever general problem you would like the machine to solve. So that's the g of agis general. It's very easy for us to make machine learning models that are good for one specific task. It's much harder to make a machine learning model that's general and is able to do anything that we consider an intelligent task.

So where are we in terms of And I didn't really understand that explanation, Chris, can you try it again in a more like I'm not a Columbia student, or just pretend like I'm in sixth grade? Help me out, sure, help me out.

So where are we in terms of And I didn't really understand that explanation, Chris, can you try it again in a more like I'm not a Columbia student, or just pretend like I'm in sixth grade? Help me out, sure, help me out.

So for many decades, we've been able to build specific machine learning models. So a specific algorithm that can tell the difference between a picture of a dog and a picture of a cat, say, that's a specific problem. And we've been very good for decades at building algorithms that can do very specific and focused tasks. One of the things that we've seen with chatbots that are trained on a wide variety of documents is that you can have a plausible conversation with a chatbot about a wide variety of topics. So if you've trained a chatbot only on chemistry textbooks, you will have a great conversation about chemistry and not about any other subject. But by training a chatbot on a wide variety of topics chemistry, philosophy, and all points in between, people are experiencing this shock that you can interact with an AI, meaning an algorithm that works not only solving a specific problem, solving a general problem, in this case, the general problem of having having an intelligent sounding conversation about a general breath of topics.

So for many decades, we've been able to build specific machine learning models. So a specific algorithm that can tell the difference between a picture of a dog and a picture of a cat, say, that's a specific problem. And we've been very good for decades at building algorithms that can do very specific and focused tasks. One of the things that we've seen with chatbots that are trained on a wide variety of documents is that you can have a plausible conversation with a chatbot about a wide variety of topics. So if you've trained a chatbot only on chemistry textbooks, you will have a great conversation about chemistry and not about any other subject. But by training a chatbot on a wide variety of topics chemistry, philosophy, and all points in between, people are experiencing this shock that you can interact with an AI, meaning an algorithm that works not only solving a specific problem, solving a general problem, in this case, the general problem of having having an intelligent sounding conversation about a general breath of topics.

After a quick break, we'll be back with my co pilot and plus one Vivian Shiller, talking to Chris Wiggins and velost Star. If you want to get smarter every morning with a breakdown of the news and fascinating takes on health and wellness and pop culture, sign up for our daily newsletter, Wake Up Call by going to Katiecuric dot com. We're back with Chris Wiggins and velost Star, along with my plus one Vivian Schiller. Have you guys used chat GPT or bard. Have you tried to have it write speeches for you or come up with any kind of documents. I'm sure you've tested it, Vivian, what has your experience been like.

After a quick break, we'll be back with my co pilot and plus one Vivian Shiller, talking to Chris Wiggins and velost Star. If you want to get smarter every morning with a breakdown of the news and fascinating takes on health and wellness and pop culture, sign up for our daily newsletter, Wake Up Call by going to Katiecuric dot com. We're back with Chris Wiggins and velost Star, along with my plus one Vivian Schiller. Have you guys used chat GPT or bard. Have you tried to have it write speeches for you or come up with any kind of documents. I'm sure you've tested it, Vivian, what has your experience been like.

I've used chat gpt to develop an itinerary. I took it to Japan. I knew I needed I had some time between two places I needed to be, and I had certain things that I was interested in, certain things I was less interested in, didn't know how long it took to get paced the place, and actually chat Gipt gave me an amazing itinerary, so it was very useful.

I've used chat gpt to develop an itinerary. I took it to Japan. I knew I needed I had some time between two places I needed to be, and I had certain things that I was interested in, certain things I was less interested in, didn't know how long it took to get paced the place, and actually chat Gipt gave me an amazing itinerary, so it was very useful.

Travel agents probably don't like that, how about you, guys?

Travel agents probably don't like that, how about you, guys?

Yeah, you know, I mean, I've used every LM out there, and so I'm like, I'm no longer, you know. I wish I could say it was at the emergent frontier of AI, but I'm no longer Now I have a different role. But I spent a lot of time with the smartest people that are working on this stuff, and I've used them all. I've used them to do really basic and pedantic things like oh, give me some talking points. I no longer do that, having tried it a few times and realizing how bad it is. I spent a lot of time using these generative AI tools my nieces and nephews. I'm doing really fun things like saying, hey, let's come up with married a scene and let's see if we can get an EI to draw it for us, and then ask the question, hey, is this kind of what you pictured in your mind? Side? How do we make it better? And we actually iterate with genitive AI to create new artworks or even basic things sometimes like Hey, I want to tell you a bedtime story, what do you want it to be about? And then we work with an AI to kind of come up with a nice little Kate. Look, these are all really fun, But again, I want to make sure that we understand that we're kind of missing the point a little bit, right. These tools that have changed our lives, and Vivian said, have done so really in an amazing way, but not because the technology has already changed our lives, but because it's opened our eyes to what's possible when these tools get to be really amazing. We talk all the time about how these tools have hallucinations, right, the idea that you might ask it a question and it doesn't check whether the answer is real or not. It just kind of spews some language back at you and you say, okay, well that sounds reasonable and you move on. The tools that we have today aren't products yet. There's still kind of the very early days of what generative AI will look like. And my hope is when we start training these models on medical data that includes all of the kind of published medical literature, we'll get to a much better sense of what a generative AI can do to help the doctor diagnose it. But at the end of the day, I can't imagine a world in which we say, the genitor of AIS we have today are directly diagnosing a patient. The only thing they can do is help a doctor or a medical professional whose trained use it as an input into their process to figure out what's going on. And that's the moment that we're stuck in right now, because I know so many of us want to jump into a future where we say that AIS are going to do everything for us, but we're really in a moment where we're saying, the only way this works is that the AIS support human decision makers. They can use what the technology gives them, but their own experience, their lived wisdom. They're you know, working with patients for hour many years to actually make a decision.

Yeah, you know, I mean, I've used every LM out there, and so I'm like, I'm no longer, you know. I wish I could say it was at the emergent frontier of AI, but I'm no longer Now I have a different role. But I spent a lot of time with the smartest people that are working on this stuff, and I've used them all. I've used them to do really basic and pedantic things like oh, give me some talking points. I no longer do that, having tried it a few times and realizing how bad it is. I spent a lot of time using these generative AI tools my nieces and nephews. I'm doing really fun things like saying, hey, let's come up with married a scene and let's see if we can get an EI to draw it for us, and then ask the question, hey, is this kind of what you pictured in your mind? Side? How do we make it better? And we actually iterate with genitive AI to create new artworks or even basic things sometimes like Hey, I want to tell you a bedtime story, what do you want it to be about? And then we work with an AI to kind of come up with a nice little Kate. Look, these are all really fun, But again, I want to make sure that we understand that we're kind of missing the point a little bit, right. These tools that have changed our lives, and Vivian said, have done so really in an amazing way, but not because the technology has already changed our lives, but because it's opened our eyes to what's possible when these tools get to be really amazing. We talk all the time about how these tools have hallucinations, right, the idea that you might ask it a question and it doesn't check whether the answer is real or not. It just kind of spews some language back at you and you say, okay, well that sounds reasonable and you move on. The tools that we have today aren't products yet. There's still kind of the very early days of what generative AI will look like. And my hope is when we start training these models on medical data that includes all of the kind of published medical literature, we'll get to a much better sense of what a generative AI can do to help the doctor diagnose it. But at the end of the day, I can't imagine a world in which we say, the genitor of AIS we have today are directly diagnosing a patient. The only thing they can do is help a doctor or a medical professional whose trained use it as an input into their process to figure out what's going on. And that's the moment that we're stuck in right now, because I know so many of us want to jump into a future where we say that AIS are going to do everything for us, but we're really in a moment where we're saying, the only way this works is that the AIS support human decision makers. They can use what the technology gives them, but their own experience, their lived wisdom. They're you know, working with patients for hour many years to actually make a decision.

You know.

You know.

I tried to get chat ept to write a poem for my husband's birthday and it was very, honestly not very good. I gave it information about my husband, but it was quite pedantic and not very clever. It was sort of honestly Hallmark CARDI quality. And I think it's because it didn't have the breadth of knowledge about him that I do so so it couldn't really compete with that, but it was fun to try it. And another example, when I interviewed Carl Rove at the Aspen Ideas Festival, I was trying to come up with a fun title for the conversation. I asked chat GPT and it came up with a great title, which was the Elephant in the Room because it was on the future of the Republican Party. And I was like, that is genius. So, you know, I think you're right what you were saying, Velss about it being helpful but not determinative. And one example is, you know, I'm very into cancer screening and some of the things that they're going to be able to do that is beyond the ability of a human to see things, is to take these massive data sets and look at scans and figure out actually predict if someone may or may not get breast cancer in the next five years. I mean, that really blows my mind. But that obviously has to be done in conjunction with an experienced medical professional. Right, So is that what you mean veloc by kind of being an aid?

I tried to get chat ept to write a poem for my husband's birthday and it was very, honestly not very good. I gave it information about my husband, but it was quite pedantic and not very clever. It was sort of honestly Hallmark CARDI quality. And I think it's because it didn't have the breadth of knowledge about him that I do so so it couldn't really compete with that, but it was fun to try it. And another example, when I interviewed Carl Rove at the Aspen Ideas Festival, I was trying to come up with a fun title for the conversation. I asked chat GPT and it came up with a great title, which was the Elephant in the Room because it was on the future of the Republican Party. And I was like, that is genius. So, you know, I think you're right what you were saying, Velss about it being helpful but not determinative. And one example is, you know, I'm very into cancer screening and some of the things that they're going to be able to do that is beyond the ability of a human to see things, is to take these massive data sets and look at scans and figure out actually predict if someone may or may not get breast cancer in the next five years. I mean, that really blows my mind. But that obviously has to be done in conjunction with an experienced medical professional. Right, So is that what you mean veloc by kind of being an aid?

It is, And let me add a little bit of nuance, o, Katie. I mean, you've been such a courageous kind of leader on this topic. When we started looking at breast cancer in particular with AI through our lens as a civil society institution, we learned about this fundamental problem that's just going to kind of blow your hairback. We have all these algorithms today that have been trained to do exactly what you described to take a mammogram or a scan and say hey, can we do early prediction of cancer risk? But all of these tools we learned very quickly have been trained on global north populations. They've been trained on American data and European data, and so when an organization like Instituto Protea, which is a partner of ours, took these to Brazil and tried to use them on low cost machines that were already recent settings, they found the algorithms didn't work at all. So even in that aspirational moment that you've created this idea that we might have this massive break through, we come back to a very human kind of fundamental problem that until we train this data on ways that are representative about all of the people in the world, not just those who have privileged access to Western medicine, right, we're never going to realize the promise. You talked about.

It is, And let me add a little bit of nuance, o, Katie. I mean, you've been such a courageous kind of leader on this topic. When we started looking at breast cancer in particular with AI through our lens as a civil society institution, we learned about this fundamental problem that's just going to kind of blow your hairback. We have all these algorithms today that have been trained to do exactly what you described to take a mammogram or a scan and say hey, can we do early prediction of cancer risk? But all of these tools we learned very quickly have been trained on global north populations. They've been trained on American data and European data, and so when an organization like Instituto Protea, which is a partner of ours, took these to Brazil and tried to use them on low cost machines that were already recent settings, they found the algorithms didn't work at all. So even in that aspirational moment that you've created this idea that we might have this massive break through, we come back to a very human kind of fundamental problem that until we train this data on ways that are representative about all of the people in the world, not just those who have privileged access to Western medicine, right, we're never going to realize the promise. You talked about.

How biased is AI? How biased are these large language models, because I remember doing a documentary on our tech addiction, and this was just starting to be talked about, and I think this was like in twenty eighteen, Chris, do you see this as a major problem that it doesn't really represent people like so much in society?

How biased is AI? How biased are these large language models, because I remember doing a documentary on our tech addiction, and this was just starting to be talked about, and I think this was like in twenty eighteen, Chris, do you see this as a major problem that it doesn't really represent people like so much in society?

I mean, the problem is always how something is used or interpreted. I would say in the context of medical usage of AI, there's additional challenges around responsibility or attribution and decision making. So I think for all of these tools, they're going through this very inefficient part of our hype cycle. So in a hype cycle, there's a moment where you discover a technology and you have this moment of irrationally zuprints and you think it's going to be great, and then you have some trough of despair as you realize it's actually not that good about generating a poem about your husband in your case, And then we get to some efficient place where we all have an understanding of what these technologies can do and cannot do. So I do think we all need to limit our trust in all these technologies in in that'stry for technology in general. But I think Veloso is making a good, great point, which is form machine learning in general, which again is the strategy that actually works for artificial intelligence. Where you train an algorithm on lots of data, it is extremely biased, and this is that it's well suited to the data st you have, and there are many complex problems in the world where when you train it on one data set, it will not generalize to some other very different data set. And the different data set could be you've trained a language model in chemistry and then you try to test it on poetry, or it could be that you've trained it on genetic information from one demographic group and then you realize it says nothing about, say, predicting phenotype from genotype for a different demographic group. That is a real problem. It often undergoes the name of buis, but in the case of machine learning, it's built into the system. If you train it on one data set, you're going to have a bad time if you try to use it on a very different data set.

I mean, the problem is always how something is used or interpreted. I would say in the context of medical usage of AI, there's additional challenges around responsibility or attribution and decision making. So I think for all of these tools, they're going through this very inefficient part of our hype cycle. So in a hype cycle, there's a moment where you discover a technology and you have this moment of irrationally zuprints and you think it's going to be great, and then you have some trough of despair as you realize it's actually not that good about generating a poem about your husband in your case, And then we get to some efficient place where we all have an understanding of what these technologies can do and cannot do. So I do think we all need to limit our trust in all these technologies in in that'stry for technology in general. But I think Veloso is making a good, great point, which is form machine learning in general, which again is the strategy that actually works for artificial intelligence. Where you train an algorithm on lots of data, it is extremely biased, and this is that it's well suited to the data st you have, and there are many complex problems in the world where when you train it on one data set, it will not generalize to some other very different data set. And the different data set could be you've trained a language model in chemistry and then you try to test it on poetry, or it could be that you've trained it on genetic information from one demographic group and then you realize it says nothing about, say, predicting phenotype from genotype for a different demographic group. That is a real problem. It often undergoes the name of buis, but in the case of machine learning, it's built into the system. If you train it on one data set, you're going to have a bad time if you try to use it on a very different data set.

Let me follow up with that to both the Loss and Chris, which is how much of that has to do with the people who are selecting the data sets, who are creating the technology, who are deploying technology, most of whom are in Silicon Valley. Are they maybe in a few centralized companies. How much of that is an issue? And how do we get out of that jam?

Let me follow up with that to both the Loss and Chris, which is how much of that has to do with the people who are selecting the data sets, who are creating the technology, who are deploying technology, most of whom are in Silicon Valley. Are they maybe in a few centralized companies. How much of that is an issue? And how do we get out of that jam?

Yeah, Vivian, I'm going to take that. I'm going to go one step bigger. I'm going to give you an example for it. We talk a lot about you've probably heard about hiring algorithms, about how companies are using AI to screen resumes about who they want to hire. And there's a story that's been well told there about the fact that these algorithms are often biased, they often pick men over women particular types of technical competency. That's one story, and we get it. But there's a bigger story here that we have a really hard time engaging with those algori. We're trained on twenty years of data about how human recruiters picked candidates, and yet we never talk about the fact that for twenty years we've lived in a world where our own recruiters are showing these biases day in and day out. The question we should be asking is not why is the algorithm biased? It's why is a society have we been so okay for twenty years with this set of outcomes, and now that we have a tool that shows us just how bias we've been, we're not having a public conversation about restructuring our entire hiring mechanism across the private sect. This is just one analog of a lot of things like this that I think are emerging across the board, where AI, because of the bias in the algorithm, is putting a spotlight on the bias in our human behavior. We should be using AI as an investigative tool, as a magnifying glass that lets us look at all kinds of decisions and say, how do we build a more just and equitable society. Let's have a conversation with a bias in AI. We absolutely should, and the answer to that, we kind of know what the answer is, right. More representative data, presentive talent that designs these algorithms, making sure there's public compute that allows these people to develop products. But let's take the bigger picture here. This is what we're going into over the next twenty years is a world in which these tools demonstrate to us why we're okay with the society we've built, and let us question if we actually want to make some fundamental changes in them.

Yeah, Vivian, I'm going to take that. I'm going to go one step bigger. I'm going to give you an example for it. We talk a lot about you've probably heard about hiring algorithms, about how companies are using AI to screen resumes about who they want to hire. And there's a story that's been well told there about the fact that these algorithms are often biased, they often pick men over women particular types of technical competency. That's one story, and we get it. But there's a bigger story here that we have a really hard time engaging with those algori. We're trained on twenty years of data about how human recruiters picked candidates, and yet we never talk about the fact that for twenty years we've lived in a world where our own recruiters are showing these biases day in and day out. The question we should be asking is not why is the algorithm biased? It's why is a society have we been so okay for twenty years with this set of outcomes, and now that we have a tool that shows us just how bias we've been, we're not having a public conversation about restructuring our entire hiring mechanism across the private sect. This is just one analog of a lot of things like this that I think are emerging across the board, where AI, because of the bias in the algorithm, is putting a spotlight on the bias in our human behavior. We should be using AI as an investigative tool, as a magnifying glass that lets us look at all kinds of decisions and say, how do we build a more just and equitable society. Let's have a conversation with a bias in AI. We absolutely should, and the answer to that, we kind of know what the answer is, right. More representative data, presentive talent that designs these algorithms, making sure there's public compute that allows these people to develop products. But let's take the bigger picture here. This is what we're going into over the next twenty years is a world in which these tools demonstrate to us why we're okay with the society we've built, and let us question if we actually want to make some fundamental changes in them.

But maybe AI can be an instrument for change for losso. I mean, you know that is such a massive undertaking to uproot bias in society. I mean, it's so baked in, so maybe this is one entry way to address it.

But maybe AI can be an instrument for change for losso. I mean, you know that is such a massive undertaking to uproot bias in society. I mean, it's so baked in, so maybe this is one entry way to address it.

Absolutely, It's one of the things I'm most optimistic about right is when we look at things like we're going to have a conversation I'm sure here about some of the recent developments in the AI world, one of which has just been the continued silencing of women underrepresented characters in building these tools. I'm deeply optimistic about the fact that we could invest in creating a new capacity to build AI that's really representative a lot of those problems would one we signed a spotlight on them, and two we'd very quickly move to fix them.

Absolutely, It's one of the things I'm most optimistic about right is when we look at things like we're going to have a conversation I'm sure here about some of the recent developments in the AI world, one of which has just been the continued silencing of women underrepresented characters in building these tools. I'm deeply optimistic about the fact that we could invest in creating a new capacity to build AI that's really representative a lot of those problems would one we signed a spotlight on them, and two we'd very quickly move to fix them.

Well, when we come back, we're going to talk about how do you regulate artificial intelligence, What in the world is going on with Sam Altman and open AI, and how quickly is this technology going to evolve. That's right after this, I want to tell you all about the Cancer Straight Talk podcast for Memorial Sloan Cattering Cancer Center with MSK oncologist doctor Diane Reedy Lagunis. I was a guest and we had a totally candid conversation about my family's experiences with cancer, including my husband's illness, my own treatment for breast cancer, and of course that time I got a colonoscopy. On TV, Cancer straight Talk features life affirming conversations with experts and patients alike about topics affecting everyone touched by cancer. If that includes you, I hope you'll listen into my episode and every episode of Cancer Straight Talk. We're back with Chris Wiggins and Velos star along with my plus one Vivian Shiller. Chris is the Chief Data Science of The New York Times, Associate Professor of Applied Mathematics and Systems Biology at Columbia, and he wrote the book How Data Happened, A History from the Age of Reason to the Age of Algorithms, which frankly I read in two days, just kidding. Chris the Loss is President and trustee of the Patrick J. McGovern Foundation, which focuses on AI and data solutions. And my plus one today is my good friend Vivian Schiller, who has worked in many media organizations and has really dug into AI and technology, media and society. So you gave me the perfect segue the loss in our last conversation before the break, and that was what is happening right now in various technology companies. So, Chris and velos, who wants to kind of explain this Sam Altman drama which is being watched with baited breath by everyone in technology and I think in media right now. Chris, you want to give it a shot, I.

Well, when we come back, we're going to talk about how do you regulate artificial intelligence, What in the world is going on with Sam Altman and open AI, and how quickly is this technology going to evolve. That's right after this, I want to tell you all about the Cancer Straight Talk podcast for Memorial Sloan Cattering Cancer Center with MSK oncologist doctor Diane Reedy Lagunis. I was a guest and we had a totally candid conversation about my family's experiences with cancer, including my husband's illness, my own treatment for breast cancer, and of course that time I got a colonoscopy. On TV, Cancer straight Talk features life affirming conversations with experts and patients alike about topics affecting everyone touched by cancer. If that includes you, I hope you'll listen into my episode and every episode of Cancer Straight Talk. We're back with Chris Wiggins and Velos star along with my plus one Vivian Shiller. Chris is the Chief Data Science of The New York Times, Associate Professor of Applied Mathematics and Systems Biology at Columbia, and he wrote the book How Data Happened, A History from the Age of Reason to the Age of Algorithms, which frankly I read in two days, just kidding. Chris the Loss is President and trustee of the Patrick J. McGovern Foundation, which focuses on AI and data solutions. And my plus one today is my good friend Vivian Schiller, who has worked in many media organizations and has really dug into AI and technology, media and society. So you gave me the perfect segue the loss in our last conversation before the break, and that was what is happening right now in various technology companies. So, Chris and velos, who wants to kind of explain this Sam Altman drama which is being watched with baited breath by everyone in technology and I think in media right now. Chris, you want to give it a shot, I.

Can try, with the warning that you know, we're all outside the company and so all of it is speculative. You know, there's a set of about four people who really know what happened. There are the people who were on the board that were voting to oust the CEO, and so there's a very small number of people who really were in the room when it happened and can tell us.

Can try, with the warning that you know, we're all outside the company and so all of it is speculative. You know, there's a set of about four people who really know what happened. There are the people who were on the board that were voting to oust the CEO, and so there's a very small number of people who really were in the room when it happened and can tell us.

Having said that, Chris, though there's been some pretty strong reporting on it that I've read, and let me try to set it up if I could. So, Sam Altman, this young genius head of open Ai, who I think is very well liked by the press, considered obviously a real leader in the field, was the CEO of open Ai. Two members of the board who were very concerned that the business model was superseding the ethical considerations of AI. Is my understanding. Okay, Vivian, you look like you want to add something, Is that right?

Having said that, Chris, though there's been some pretty strong reporting on it that I've read, and let me try to set it up if I could. So, Sam Altman, this young genius head of open Ai, who I think is very well liked by the press, considered obviously a real leader in the field, was the CEO of open Ai. Two members of the board who were very concerned that the business model was superseding the ethical considerations of AI. Is my understanding. Okay, Vivian, you look like you want to add something, Is that right?

Uh? Well, all they have said publicly, and I think I've seen a lot of that there has been some fantastic reporting, is that sam Aldman was not communicating in a way that made the boy I forget the wordy exactly, but communicating to the board in a way that made them feel comfortable. They didn't specifically say they were worried the AI was getting out ahead of his skis. I think there's one other interesting twist in all of this, which is not to get too technical, but the structure of open AI is fascinating.

Uh? Well, all they have said publicly, and I think I've seen a lot of that there has been some fantastic reporting, is that sam Aldman was not communicating in a way that made the boy I forget the wordy exactly, but communicating to the board in a way that made them feel comfortable. They didn't specifically say they were worried the AI was getting out ahead of his skis. I think there's one other interesting twist in all of this, which is not to get too technical, but the structure of open AI is fascinating.

Well, it's really important, I think to mention that.

Well, it's really important, I think to mention that.

Yeah, it's a not for profit organization of five oh one C three, which, as someone that has been part of and led five O one C three, has very specific governance. They have a governance to a mission, a stated mission that is part of how the organization is set up.

Yeah, it's a not for profit organization of five oh one C three, which, as someone that has been part of and led five O one C three, has very specific governance. They have a governance to a mission, a stated mission that is part of how the organization is set up.

Let me just say interject that their work should benefit quote unquote humanity as a whole exactly.

Let me just say interject that their work should benefit quote unquote humanity as a whole exactly.

So a not for profit organization is not there to return shareheld value. It is there for the greater good. In this case, the exact words that you just quoted, that not for profit owned, among other things, this for profit entity that was set up because the resources that are needed in order to continue to evolve open AI requires tremendous billions of dollars of resources. So they've set this up and that entity was able to then bring in a lot of outside money, billions of dollars to continue to evolve and see the developments that we've seen come out of Open Eye AI since then, Chat, GPT and many many, many other tools. That's not that unusual a set up. There are other organizations that are set up like that and worked just fine. But in this case there was really a lack of alignment, and that not for profit organization management that I think it was a four person board decided they were either not in a loop or not comfortable with where the for profit entity was, and so they apparently without any consultation with anyone, fired.

So a not for profit organization is not there to return shareheld value. It is there for the greater good. In this case, the exact words that you just quoted, that not for profit owned, among other things, this for profit entity that was set up because the resources that are needed in order to continue to evolve open AI requires tremendous billions of dollars of resources. So they've set this up and that entity was able to then bring in a lot of outside money, billions of dollars to continue to evolve and see the developments that we've seen come out of Open Eye AI since then, Chat, GPT and many many, many other tools. That's not that unusual a set up. There are other organizations that are set up like that and worked just fine. But in this case there was really a lack of alignment, and that not for profit organization management that I think it was a four person board decided they were either not in a loop or not comfortable with where the for profit entity was, and so they apparently without any consultation with anyone, fired.

The booted him.

The booted him.

They booted him, and they didn't really understand what well anyway, they clearly didn't foresee what the rebound would be.

They booted him, and they didn't really understand what well anyway, they clearly didn't foresee what the rebound would be.

Then there's a huge uprising among the employees. I think eighty percent said they were going to quit if he was gone, and they were going to follow him to Microsoft, and then suddenly he's back in business at open AI. Can you guys help us make sense of it? Chris, do you want to start.

Then there's a huge uprising among the employees. I think eighty percent said they were going to quit if he was gone, and they were going to follow him to Microsoft, and then suddenly he's back in business at open AI. Can you guys help us make sense of it? Chris, do you want to start.

Yeah again with the warning that a lot of this is speculation because only you know the four members who voted to out him, and the six board members total really know what happened in the room where it happens. But the popular understanding right now is that it was a concern over movie too fast versus having safeguards. But it may come out with future reporting that it was about product moves, or about the decision to open up so much of the access to the technology that they had to slow down new signups. I mean, I've seen many people speculate on what the causes were. Also the possibility that some sort of particularly quantum leap in the technology caused the board to have anxiety, but at this point we don't know. I think future reporting, good investigative, shoe works, shoe leather work right is needed right now to figure out what actually went.

Yeah again with the warning that a lot of this is speculation because only you know the four members who voted to out him, and the six board members total really know what happened in the room where it happens. But the popular understanding right now is that it was a concern over movie too fast versus having safeguards. But it may come out with future reporting that it was about product moves, or about the decision to open up so much of the access to the technology that they had to slow down new signups. I mean, I've seen many people speculate on what the causes were. Also the possibility that some sort of particularly quantum leap in the technology caused the board to have anxiety, but at this point we don't know. I think future reporting, good investigative, shoe works, shoe leather work right is needed right now to figure out what actually went.

Down the loss. I know that Chris just mentioned sort of a new technology, and I've been reading about this project q asterisk. I don't even know how you say it, Vivian, how do you say that?

Down the loss. I know that Chris just mentioned sort of a new technology, and I've been reading about this project q asterisk. I don't even know how you say it, Vivian, how do you say that?

Q star?

Q star?

Q star has been described as a major breakthrough in the company's pursuit of artificial general intelligence. So can you help me understand veloss, what the hell that means and what that technology was? Do you know?

Q star has been described as a major breakthrough in the company's pursuit of artificial general intelligence. So can you help me understand veloss, what the hell that means and what that technology was? Do you know?

Sure? Super happy too. I've read, I mean, all of the public reporting and some of the peoper is behind it. But can I give you my spicy take first before I tell you about q start.

Sure? Super happy too. I've read, I mean, all of the public reporting and some of the peoper is behind it. But can I give you my spicy take first before I tell you about q start.

Oh we love spicy takes. Here a question the.

Oh we love spicy takes. Here a question the.

Two line here, like this is the telenovella of twenty twenty three. None of this matters, right, but we love our tabloid headlines that we have spent so much time I got to say, hundreds of millions of hours of human time thinking about Sam Altman and open Ai. Let's put this in context, and it's so important that we get this right. Open Ai is a company that was based on a public paper that taught you how to do LLLMS large language models, these like these chat GPT type things. Right. They raised billions of dollars, which they spent pretty much exclusively on what we call compute right access to a bunch of computers, and they built the first product that people could see. Nothing revolutionary happened at open ai except for the fact that they took this incredible paper that was done by some amazing scientists and then just threw money at the problem. And once they did, what did everybody else do? While then Microsoft threw a lot of money at the problem at Facebook threw a lot of money at it, Google threw a lot of money at it, and they all came up with technologies that are pretty similar, some are slightly better than ours. Okay, it's important for us to say this because we spend a lot of time daifying open ai as if it's the most amazing thing that's ever happened. And it turns out that when you have a pretty complex problem and a pretty complex way to solve it, and you spend a couple of billion dollars, you can come out with an answer pretty easily. Okay, I say all that to you and excuse the mini rant, because now we have a real question in front of us, right, why is it, Katie, that we're okay with the world in which a technology that could change every human life on the planet is held by seven companies that have these kinds of like human personal dramas that drive what will happen with them.

Two line here, like this is the telenovella of twenty twenty three. None of this matters, right, but we love our tabloid headlines that we have spent so much time I got to say, hundreds of millions of hours of human time thinking about Sam Altman and open Ai. Let's put this in context, and it's so important that we get this right. Open Ai is a company that was based on a public paper that taught you how to do LLLMS large language models, these like these chat GPT type things. Right. They raised billions of dollars, which they spent pretty much exclusively on what we call compute right access to a bunch of computers, and they built the first product that people could see. Nothing revolutionary happened at open ai except for the fact that they took this incredible paper that was done by some amazing scientists and then just threw money at the problem. And once they did, what did everybody else do? While then Microsoft threw a lot of money at the problem at Facebook threw a lot of money at it, Google threw a lot of money at it, and they all came up with technologies that are pretty similar, some are slightly better than ours. Okay, it's important for us to say this because we spend a lot of time daifying open ai as if it's the most amazing thing that's ever happened. And it turns out that when you have a pretty complex problem and a pretty complex way to solve it, and you spend a couple of billion dollars, you can come out with an answer pretty easily. Okay, I say all that to you and excuse the mini rant, because now we have a real question in front of us, right, why is it, Katie, that we're okay with the world in which a technology that could change every human life on the planet is held by seven companies that have these kinds of like human personal dramas that drive what will happen with them.

And that's all about regulation. But philosoph before you do that, you can to start what is QStar before we talk about regulation, because I've read about it and I'm it's sort of shrouded in mystery and interest and.

And that's all about regulation. But philosoph before you do that, you can to start what is QStar before we talk about regulation, because I've read about it and I'm it's sort of shrouded in mystery and interest and.

Again, right, and one of the things I really appreciate about Chris and neither of us really want to be a pundit. Right, We've both been experts in this field for a long time. What I can glean from what's been publicly reported and from some of the sources I've talked to, is that it's a shift from focusing on language as a predictive model to being able to focus on things like math problems as a reasoning model. So instead of saying, hey, I've got a sentence you know twinkle twinkle, Well, we know the next words are probably little star, it's instead a way to say, well, what is two plus two? And you might say probabilistically, because I've looked at everything humans have ever written, Well, when you say two plus two, it usually followed my equals four. But if somebody along the way, in some book had written two plus two equals five, then one in ten million times the large language model might say, oh, two plus two equals five. We're trying to fix that, And so q star says, can we actually reason if we have two of one thing and two of another what happens when you put them together. This is a big breakthrough. It is something that gets us closer to what Chris described as AGI right, that idea that you've got one model that can talk about language and I can do a little bit of math. We don't have any sense yet. There hasn't been public reporting yet of just how good of a breakthrough this is. But again, you take seven or eight hundred really smart people, you give them a lot of compute, and you say, hey, go figure some stuff out, and this is what the next breakthrough looks like. I don't think it's the thing that's going to lead to terminator style robots and helicopters that are out there trying to kill us all. That's all I'm saying.