Speaker 1: Today on the Big Take. Concerns about AI are growing
Speaker 1: from whistleblowers and from industry leaders. I'm Craig Gordon in
Speaker 1: for West Kosova. Artificial intelligence or AI has been getting
Speaker 1: quite a bit of attention lately. Even as it promises
Speaker 1: to revolutionize the way we think and work, AI's positioned
Speaker 1: to bring headaches as well.
Speaker 2: Pictures online of a bombing at the Pentagon in the US,
Speaker 2: and it was AI generated, but obviously so many people
Speaker 2: very quickly panicked thinking that'd been a bombing that happened.
Speaker 3: AI has even infiltrated music. Now there's a new song
Speaker 3: I don't know if you've heard about by Drake in
Speaker 3: the Weekend that wasn't made by Drake or the Weekend
Speaker 3: was created by artificial intelligence.
Speaker 1: In March, thousands of tech leaders signed an open letter
Speaker 1: calling for a pause in AI development. Even more recently,
Speaker 1: three and fifty industry leaders signed a second open letter
Speaker 1: urgent caution. Their letter was just one sentence, mitigating the
Speaker 1: risk of extinction from AI should be a global priority
Speaker 1: alongside other societal scale risks such as pandemics and nuclear war.
Speaker 1: How realistic is this ominous warning? I spoke to Bloomberg
Speaker 1: AI reporters Dina Bass and Rachel Mattz to find out.
Speaker 1: I would love to hear from you, Dina, a little
Speaker 1: reality check. What is AI capable of doing right now?
Speaker 1: What is it not capable of doing? How can we
Speaker 1: put this technology to use for humankind on an individual basis.
Speaker 4: AI is not.
Speaker 5: New, and we've been talking about both promise and the
Speaker 5: challenges and concerns of lots of different types of AI
Speaker 5: for a number of years.
Speaker 4: What has happened in the last year that has.
Speaker 5: Started the current hype churn and excitement cycle that we
Speaker 5: have is something called generative AI.
Speaker 4: And the difference in terms of what we're talking about out.
Speaker 5: Here is that there are AI algorithms, AI models that
Speaker 5: basically suck up a lot of information. You know, pictures
Speaker 5: aren't text from across the Internet, from you know, Reddit
Speaker 5: and social media things like that, and what these models
Speaker 5: do is use all of that information to generate new
Speaker 5: content in some respect. And so when you start talking
Speaker 5: about a picture that purports to be an explosion near
Speaker 5: the Pentagon, that's new content that's created in some way
Speaker 5: by the.
Speaker 4: Artificial intelligence systems.
Speaker 5: What people are getting a little tripped up in, though,
Speaker 5: is you know people will use these sort of human
Speaker 5: like terms for what the AI is doing. They'll and
Speaker 5: thropple morphize things because it's the easiest way to understand it,
Speaker 5: and they'll ascribe kind of human like intelligence to these systems.
Speaker 5: They are not human, they are not thinking, they aren't
Speaker 5: producing art. What they're doing, in many cases is making predictions,
Speaker 5: making guesses, generating things that are sort of an imitation
Speaker 5: of the other things that they've seen. And so we
Speaker 5: need to be very careful to understand.
Speaker 4: It's definitely not human. It's not even human like.
Speaker 1: That said, as we have seen some examples, it can
Speaker 1: sort of mimic human speech, It appears to mimic sort
Speaker 1: of human thought. How can people sort of draw the distinction.
Speaker 1: How do we tell the difference between a human and
Speaker 1: an AI voice that sounds like a human.
Speaker 5: That's a real concern. I think people are grappling with
Speaker 5: what we're going to do there. At the beginning of
Speaker 5: the Senate hearing to talk about the artificial intelligence, Senator
Speaker 5: Blumenthal had chat GPT from open AI write his introductory
Speaker 5: speech and if he had an audio generation artificial intelligence
Speaker 5: algorithm actually speak it sounding like it was him.
Speaker 6: And how the lack of transparency can undermine public trust.
Speaker 6: This is not the future we want. If you were
Speaker 6: listening from home, you might have thought that voice was
Speaker 6: mine and the words from me, But in fact that
Speaker 6: voice was not mine, the words were not mine, and
Speaker 6: the audio was an AI voice cloning software trained on
Speaker 6: my floor speeches. The remarks were written by chat gpt.
Speaker 5: There's a lot of discussion about how to kind of
Speaker 5: watermark these things. It's potentially a little bit easier with
Speaker 5: images text it can be very difficult to tell.
Speaker 4: Open AI itsself released an.
Speaker 5: Algorithm that was intended to help people figure out if
Speaker 5: chat gpt had authored a piece of content, but it's
Speaker 5: not terribly accurate, and as a decently high false positive
Speaker 5: rate as well. But there is some work right now
Speaker 5: in technology to sort of help flag to people that
Speaker 5: something was generated by an artificial intelligence algorithm and is
Speaker 5: not human authored or human drawn.
Speaker 1: So, Rachel, I know a lot of these different technologies
Speaker 1: get lumped together, but maybe you could help us understand
Speaker 1: the difference between the sort of flavors of AI.
Speaker 7: I like to think of it as different kinds of architecture.
Speaker 7: I like it a lot because it's easy for us
Speaker 7: as people to understand architecture in general. Right, we have
Speaker 7: different types of buildings, they serve different purposes, They change
Speaker 7: over time, and things might fall out of fashion and
Speaker 7: then come back into fashion.
Speaker 1: So, Diana, what are some of the leading industries that
Speaker 1: are adapting this technology.
Speaker 4: The newest generative AI stuff.
Speaker 5: I think we're just seeing industries kind of climb on
Speaker 5: board with I mean, there's a little bit of a
Speaker 5: panic for every company to figure out what their AI
Speaker 5: strategy is and how to use it. But we're starting
Speaker 5: to see it, certainly in the graphic design space because
Speaker 5: some of the image generation stuff is a little bit
Speaker 5: older than the Chat GPT stuff. It preceded it by
Speaker 5: a few months, and so we're definitely seeing a lot
Speaker 5: of that in the kind of the graphic design and artwork.
Speaker 5: But we're seeing people use it for finance, for legal Obviously,
Speaker 5: there's been a lot of discussion about academic use cases.
Speaker 5: There's been a lot of focus on cheating, but there are,
Speaker 5: you know, non cheating applications that can help students learn
Speaker 5: or help them draft as long as they're clear with
Speaker 5: their teachers what they're doing. There really are I think
Speaker 5: a pretty wide array of use cases I hear, you know,
Speaker 5: lots of banking executives, lots of Wall Street executives tripping
Speaker 5: over each other to talk about who is going to
Speaker 5: have these smart generative BAI strategy. So it's pretty across
Speaker 5: the board in terms of people trying to say that
Speaker 5: they're adopting it. What will actually get used in practice?
Speaker 5: I think it's going to take a little while longer
Speaker 5: to know, and potentially even longer than that to have
Speaker 5: a sense of whether these replace workers, replace entry level
Speaker 5: jobs or the rosier scenario that Silicon Valley and Microsoft
Speaker 5: up here in the Seattle area like to talk about.
Speaker 5: The rosier scenario is that they make people more productive
Speaker 5: but don't put them out of work.
Speaker 1: What specifically do banks do with it? Can they use
Speaker 1: it to decide whether I should get my mortgage or
Speaker 1: what would be a good use for a bank.
Speaker 5: Banks have actually been using AI to decide whether you
Speaker 5: get your mortgage for a long time, and it's found
Speaker 5: to be problematic because it has racial and geographic bias
Speaker 5: implications when you do things like that. Also, banks produce
Speaker 5: a tremendous volume of written content, analysis notes to clients,
Speaker 5: research reports, things like that, But people are that trade
Speaker 5: are always looking for anything that can give them an
Speaker 5: advantage as well. So for several years, probably more than that,
Speaker 5: people been trying to figure out how they can use
Speaker 5: algorithms to give them a better you know, trade a
Speaker 5: little bit faster, get information a little bit faster. The
Speaker 5: mortgage and lending scenario there's a real problematic one.
Speaker 1: So we hear a lot about this technology known as
Speaker 1: generative AI, and that seems to be kind of the
Speaker 1: umbrella topic for a lot of the things that people
Speaker 1: talk about and write about in the media. Explain what
Speaker 1: that is and where is that leading us in the future.
Speaker 7: A lot of this is just still an experimentation phase,
Speaker 7: and I think what we'll probably see is these models
Speaker 7: used increasingly to train on very specific data sets. Like
Speaker 7: right now you have large language models such as that
Speaker 7: that underpins chat GPT. It's meant to be kind of
Speaker 7: a general purpose language model. You know, you can sort
Speaker 7: of ask it or you know, type to it any
Speaker 7: kind of question. It'll give you some kind of answer
Speaker 7: that may or may not be accurate. It's basically trying
Speaker 7: to give you what you want based on its training data.
Speaker 7: But I think that what we're going to be starting
Speaker 7: to see more and more of is these large language
Speaker 7: models used in more like specific ways with more specific
Speaker 7: types of trains data, and then you might actually be
Speaker 7: able to get better answers, like maybe able to use
Speaker 7: it with certain subsets of medical data. Let's say, so
Speaker 7: you can use it to answer medical questions in a
Speaker 7: more accurate way than you could with just the basic
Speaker 7: model as it exists now.
Speaker 5: Yeah, and I feel like the other thing that would
Speaker 5: be an interesting shift about that as we stop competing
Speaker 5: on size, like you know, like right now everything is
Speaker 5: size matters. The bigger models do better if we are
Speaker 5: able to train on smaller, more specific to task models.
Speaker 5: They don't have to compete on size, and that can
Speaker 5: be useful in a few ways. Running these large language
Speaker 5: models is very expensive, both in a dollar sense and
Speaker 5: an environmental sense, and because the data sets are so large,
Speaker 5: they become almost impossible to check for harmful content for
Speaker 5: biased content. Smaller could be both cheaper to run and
Speaker 5: have greater quality control.
Speaker 1: Rachel Beck. In March, an open letter was signed by
Speaker 1: thousands of tech leaders regarding some of the apprehensions they
Speaker 1: have over AI technology. What are some of the specific
Speaker 1: concerns they laid out in that.
Speaker 7: Letter, the basic sort of overarching theme there was there
Speaker 7: should be a time period pause, like a six month
Speaker 7: pause on developing AI more powerful than GPT four I
Speaker 7: believe it was, which was the currently released state of
Speaker 7: the art model from open Ai. Right now, OpenAI is
Speaker 7: very much acknowledged as one of just a few leaders
Speaker 7: in the AI industry. I think what's actually really interesting
Speaker 7: about this, besides the fact that, as you pointed out,
Speaker 7: a lot of people who have been working in and
Speaker 7: around the AI industry for years signing onto this letter,
Speaker 7: there have already been lots in lots of people that
Speaker 7: have been shouting from the rooftops about existing very real
Speaker 7: current issues with the AI systems that are already in place,
Speaker 7: and that seemed to have gotten lost in the conversation there,
Speaker 7: And I felt like, to me that was kind of
Speaker 7: like a big kind of question mark.
Speaker 1: With all of this talk about a six month pause,
Speaker 1: is anyone actually hitting the brakes on their efforts to
Speaker 1: develop up the next version of this I.
Speaker 5: Saw something the other day that just said that open
Speaker 5: ai is not yet working on GPT five or something
Speaker 5: like that. But to Rachel's point, some of the motivation
Speaker 5: behind the letter was a little unclear. Some of the
Speaker 5: people signing it were potentially working on competing products.
Speaker 4: It's not really clear.
Speaker 5: Also, what does a six month pause do? Why six months?
Speaker 5: What do you do at the end of six months?
Speaker 5: And is that really the way to go? I think,
Speaker 5: you know, to Rachel's point, a lot of the AI
Speaker 5: ethics and responsible AI people who've been working in this
Speaker 5: field for five, six or longer years, and many of
Speaker 5: them are in the populations that are most affected by
Speaker 5: the current problems with AI.
Speaker 4: It's a number of women, a number of people of color.
Speaker 5: You know, many of them have been calling for slower
Speaker 5: approaches for a long time, asking companies developing these things
Speaker 5: to take the time to make sure that products are
Speaker 5: responsible it don't cause serious harm before they release them,
Speaker 5: and also calling for regulation of some sort by various governments.
Speaker 5: So there's been a call for a while to slow
Speaker 5: down and make sure that you're not going by the
Speaker 5: typical tech adage of move fast and break things.
Speaker 4: But the idea of.
Speaker 5: A specific six month pause, and then what and how
Speaker 5: would you even enforce that six month pause. I don't
Speaker 5: know if they just seized on that because it was
Speaker 5: attention getting or it was something you could just say, oh,
Speaker 5: this is a specific proposal, but it wasn't completely clear
Speaker 5: what that actually would look like in practice and.
Speaker 1: What it would do when we come back. Worries about
Speaker 1: the direction of AI have been around as long as
Speaker 1: the technology has. We'll look at what the concerns are
Speaker 1: and who has been raising them. I wanted to go
Speaker 1: a little bit deeper on this idea. One of these whistleblowers,
Speaker 1: if we can call them that, Timny Gibrew from Google,
Speaker 1: was probably one of the most prominent early critics of
Speaker 1: this for some of the reasons that you cited related
Speaker 1: to the questions of whether women and people of color
Speaker 1: would be treated fairly or treated responsibly by some of
Speaker 1: these technologies. Dina, maybe you could tell us bit about
Speaker 1: her story and what her concerns were.
Speaker 5: So she was one of the pioneers of sort of
Speaker 5: looking at the harms of various AI systems.
Speaker 4: She co authored with.
Speaker 5: Joy Bloom Winnie a landmark paper in twenty eighteen that
Speaker 5: showed that a lot of the most popular facial recognition
Speaker 5: products were just performing spectacularly badly when they were looking
Speaker 5: at images of people of color in general, but particularly women,
Speaker 5: and choose at Microsoft. At that point, she moves to
Speaker 5: Google and along with Margaret Mitchell, they co found an
Speaker 5: ethical AI group at Google, and they start trying to
Speaker 5: make Google's AI scientists pay more attention to some of
Speaker 5: the problems in the algorithms that they were working on.
Speaker 5: This ultimately ends up in her dismissal from Google. Several
Speaker 5: months later, Margaret Mitchell is also dismissed from Google, basically
Speaker 5: decapitating this ethical AI group. One of the things that
Speaker 5: this sort of raises is some of these, you know,
Speaker 5: people that are now speaking up about concerns in the
Speaker 5: AI work, including Jeff Hinton, who's one of the you know,
Speaker 5: the pioneers of the current generation of AI that we
Speaker 5: have are Google people, and they're you know, waiting essentially
Speaker 5: to twenty twenty three to speak up about this and
Speaker 5: did not, in any way that I know of, really
Speaker 5: back or stick up for or anything else. The people
Speaker 5: that were at Google several years ago that were very
Speaker 5: much whistle lowers and the truest sense of the word
Speaker 5: that they were fired for airing their concern.
Speaker 1: And so what is Google's response to all this?
Speaker 5: So there is you know, a dispute about what happened
Speaker 5: between Google and doctor Timmy Debrew. Google said at the
Speaker 5: time that they accepted her resignation. Doctor Gabrew maintains that
Speaker 5: she offered no such resignation. With regard to doctor Mitchell,
Speaker 5: Google says that they fired her.
Speaker 1: Is there anything that we could point to in the
Speaker 1: technology that exists now where these concerns have been addressed?
Speaker 5: Both Google and Microsoft have significant groups of people that
Speaker 5: work on responsible AI. All of the new things that
Speaker 5: Microsoft has put out they have taken and pains to
Speaker 5: tell us have gone through their responsible AI reviews and
Speaker 5: continue to as people test the products and they get
Speaker 5: more feedback about what is and isn't working. Microsoft continually
Speaker 5: every time they announce a new AI product, will tell
Speaker 5: the press and the public, here are the ways in
Speaker 5: which we know it does not work. They're trying very
Speaker 5: hard to make it clear that they know that there
Speaker 5: are limitations and that they are working on many of them.
Speaker 5: Open ai does the same thing. I mean, when OpenAI
Speaker 5: released Dolli, its image generation tool, they did some work
Speaker 5: to make sure that the images that are generated by
Speaker 5: Dolli are you know, as they sort of explained it,
Speaker 5: more representative of the world. So when you ask DOLLI
Speaker 5: to generate a picture of doctors. They went in and
Speaker 5: made sure manually that the doctors were both men and
Speaker 5: women of different races. And so there are things that
Speaker 5: companies are trying to do to address these issues. I
Speaker 5: think there's a continued push and pull between the company's
Speaker 5: own ethical AI people and people externally. The are looking
Speaker 5: at their work about whether what's enough, whether it's enough,
Speaker 5: whether things are moving too quickly to possibly ensure safety.
Speaker 1: Just a quick quote from the letter that kind of
Speaker 1: sums up to me the kind of the heart of it.
Speaker 1: It says quote recent months have seen AI labs locked
Speaker 1: in an out of control race to develop and deploy
Speaker 1: ever more powerful digital minds that no one, not even
Speaker 1: their creators can understand, predict or reliably control.
Speaker 7: I don't one hundred percent buy that the fact that
Speaker 7: they use the word mind in there is to me
Speaker 7: it's self a little bit suspicious. I think that it's
Speaker 7: extremely out of date at this point to act as
Speaker 7: though the current AI systems are black boxes that we
Speaker 7: can't understand and we can't interrogate. We very much can,
Speaker 7: And I think that anybody who says otherwise is either
Speaker 7: hopelessly naive or as kidding themselves. People who are making
Speaker 7: these systems understand that there may be aspects of them
Speaker 7: that they don't quite get, but in general I think
Speaker 7: they very much do understand them. Whether they're putting in
Speaker 7: proper safeguards before releasing them is a whole other thing.
Speaker 7: But we're nowhere near a point of like complete not understanding,
Speaker 7: or AI at a point where it's like out of control.
Speaker 7: This very much boils down to applications of mas Tina.
Speaker 1: Let's talk about the recent congressional testimony of open Ai
Speaker 1: CEO Sam Altman. What prompted him to come before Congress
Speaker 1: at all and share a little bit about the concerns
Speaker 1: that he raised with a rather unusual call that the
Speaker 1: Congress should actually regulate his own company, not something to
Speaker 1: hear every day in the halls of Congress.
Speaker 4: I think he was summoned. I think that was what
Speaker 4: brought him. I want to argue a little bit with
Speaker 4: the notion.
Speaker 5: I know that Congress doesn't frequently have captains of industry
Speaker 5: come in and say regulate me, but in the AI
Speaker 5: space that's actually been going on for a few years.
Speaker 5: Open Ai, Microsoft IBM, who were also on that panel,
Speaker 5: and even Amazon somewhat surprisingly, have been asking for regularly
Speaker 5: on parts or all of the AI field for several
Speaker 5: years now. Now we can discuss why they want that,
Speaker 5: to what extent they really want to be regulated, but
Speaker 5: the fact is they have been asking for it, and
Speaker 5: some of it is because some of the larger companies
Speaker 5: really want some guidelines and a level playing field so
Speaker 5: that they know what they can do and they can't do.
Speaker 4: And other companies.
Speaker 5: If Microsoft is going to be in their minds a
Speaker 5: good corporate citizen and not do certain things that they
Speaker 5: aren't undermined by other companies that are willing to do that.
Speaker 5: And in fact, the US is behind here. Europe has
Speaker 5: still not passed anything. It's wending its way through the
Speaker 5: European Parliament right now, but Europe's been working on an
Speaker 5: AI law for a couple of years now, and everybody
Speaker 5: has a kind of different version of it. But what
Speaker 5: Sam Altman was talking about is that Opening Eye feels
Speaker 5: that there needs to be a separate US agency, that
Speaker 5: the current agencies in government are not fit for this,
Speaker 5: and that there should be through that agency or otherwise
Speaker 5: some sort of licensing for these kinds of algorithmstra.
Speaker 2: Here's your shot, Thank you, Senator. Number one, I would
Speaker 2: form a new agency that licenses any effort above a
Speaker 2: certain scale of capabilities and can take that license away
Speaker 2: and ensure compliance with safety standards. Number two, I would
Speaker 2: create a set of safety standards focused on what you
Speaker 2: said in your third hypothesis as the dangerous capability evaluations.
Speaker 2: And then third, I would require independent audits, so not
Speaker 2: just from the company or the agency, but experts who
Speaker 2: can say the model is or isn't in compliance with
Speaker 2: these state and safety thresholds and these percentages of performance
Speaker 2: on question.
Speaker 6: X or Y.
Speaker 4: Would you be quantified if we promulgated those rules to
Speaker 4: administer those rules.
Speaker 5: I love my.
Speaker 1: Current job, Rachel. First of all, is any of this realistic? Like,
Speaker 1: how much of this can truly be regulated? The agency
Speaker 1: doesn't exist, Congress moves very slowly, so what is even
Speaker 1: realistic about regulation? And then even if the US was
Speaker 1: able to come up with a regulatory scheme, are there
Speaker 1: fears of any unintended consequences?
Speaker 7: I guess there's a few different ways we can look
Speaker 7: at it. One is there's some existing rules, laws, different
Speaker 7: agencies that could probably take on a lot of different aspects.
Speaker 7: Of regulating AI. As of now, there's this idea of
Speaker 7: creating a new agency. I think it sort of depends
Speaker 7: on who you talk to whether or not they think
Speaker 7: that's necessary. Some people would say that could make things
Speaker 7: even harder and even worse, because it's like, oh, a
Speaker 7: new agency, like you know, we already have quite a
Speaker 7: number of those in the US, and that could possibly
Speaker 7: bog things down even more. That's a little tricky to
Speaker 7: start a whole new thing. I mean, I think what's
Speaker 7: important to keep in mind is that up to now,
Speaker 7: there hasn't been any like AI specific legislation in the US.
Speaker 7: As as Dina pointed out, there's some like application specific stuff,
Speaker 7: but it's more at the local level. But other than that,
Speaker 7: there isn't much so far in the US, and it's
Speaker 7: going to be interesting to see, especially because we have
Speaker 7: a few like AI related court cases right now sort
Speaker 7: of winding their way through various courts. Over the next
Speaker 7: few years, I wouldn't be surprised if things change quite
Speaker 7: a bit as far as having some kind of either
Speaker 7: federal legislation or more rules at the state level.
Speaker 1: I think it's also fair to ask the level of
Speaker 1: sincerity of these calls. Sam Altman's own company released an
Speaker 1: iPhone and Android app about three days after his testimony
Speaker 1: to make chat GPT available to all of us on
Speaker 1: all of our phones. But at the same time he's
Speaker 1: saying we need to be careful how we use this,
Speaker 1: he's sort of making it even more available. Rachel, How
Speaker 1: since here is the industry when it says please regulate.
Speaker 7: Us, I mean, I think part of it is probably
Speaker 7: a actual desire for regulation to know what the rules are,
Speaker 7: what can you do, what you can you not do?
Speaker 7: And some of it I think is definitely aimed at
Speaker 7: keeping control for the parties that are already in control.
Speaker 7: You know, companies like open ai and Google and Microsoft,
Speaker 7: they like their positions in the industry and I can't
Speaker 7: imagine that they want to fall back. So having legislation
Speaker 7: could be helpful to them in certain ways. And I
Speaker 7: feel like keeping some control might be some of it.
Speaker 5: For all these companies that want a licensing scheme, they
Speaker 5: don't have to wait for Congress to act to come
Speaker 5: up with some sort of mechanisms for outside auditing that
Speaker 5: ensures people that they are closed source algorithms are fair
Speaker 5: and safe.
Speaker 1: When we return, what do Rachel and Dina see coming
Speaker 1: down the line when it comes to regulating artificial intelligence.
Speaker 1: As all of our listeners think about these topics, and
Speaker 1: I think they are on a lot of our minds.
Speaker 1: What are you watching for in the next weeks and
Speaker 1: months as this story continues to unfold, whether it's on
Speaker 1: the regulatory scheme, the new technologies that might come out,
Speaker 1: the new uses we might learn about. Dina, why don't
Speaker 1: you go first tell us what you're watching for right now?
Speaker 5: My sense is that the first thing we're going to
Speaker 5: see from a regulatory standpoint is this European law. One
Speaker 5: of the things that Sam Outman suggested is that if
Speaker 5: we do something in the US, it shouldn't impact every
Speaker 5: single kind of AI. In the same way he wanted
Speaker 5: some carbouts for open source so as not to stifle innovation,
Speaker 5: he wanted some carbouts for startups.
Speaker 4: Things like that. The European proposal that's being.
Speaker 5: Looked at has several different tiers of the type of
Speaker 5: algorithm and the sort of related level of scrutiny that
Speaker 5: it gets. For algorithms that do things that the European
Speaker 5: Union considers completely unacceptable, those would just flat out the
Speaker 5: outlawed in the block and so that would definitely be new,
Speaker 5: and then below the absolutely not. There's three other tiers
Speaker 5: that have different levels of scrutiny. I'm interested to see
Speaker 5: if that passes and in what form and what action
Speaker 5: the US companies take as a result, because US companies
Speaker 5: can't just continue to do something here that they cannot
Speaker 5: do in Europe. This will completely impact the way that
Speaker 5: they do business around artificial intelligence.
Speaker 1: Rachel, what are you watching for?
Speaker 7: I mean, I think we're gonna see just more and
Speaker 7: more experiments and people actually using these systems like chat,
Speaker 7: GPT in practice, and as we've seen with some of
Speaker 7: the examples, like the stuff's getting better and better, it's
Speaker 7: getting harder and harder to determine what is a genuine
Speaker 7: human made article from what is created using AI. So
Speaker 7: I think we are going to see more sort of
Speaker 7: disinfo misinfo stuff related to these systems going forward. But
Speaker 7: I'm also optimistic that the detection is going to get
Speaker 7: better and we may be seeing companies increasingly using various
Speaker 7: watermarking technologies to stamp things. Essentially, there are ways to
Speaker 7: stamp both text and images so that they can be
Speaker 7: detected and be seen as created with the help of AI,
Speaker 7: and then I think we'll probably see more applications of
Speaker 7: things like generative video. That's something that's really in its
Speaker 7: infancy right now and has been getting better rapidly, so
Speaker 7: it's going to be really interesting to keep an eye
Speaker 7: on that. There's all kinds of interesting things that are
Speaker 7: happening in the open source community right now now, such
Speaker 7: as the idea of automating these systems, so that, like
Speaker 7: AUTOGBT is one thing that people are paying attention a
Speaker 7: lot too lately, So sort of setting an end goal
Speaker 7: for a large language model and having it sort of
Speaker 7: go off and create additional tasks toward that goal to
Speaker 7: eventually reach the goal, maybe connecting it to other services.
Speaker 4: So, yeah, there's a lot.
Speaker 7: Of interesting stuff there, some potentially scary stuff, but also
Speaker 7: some potentially really cool stuff.
Speaker 1: I'll throw it out there to either of you. The
Speaker 1: Supreme Court did recently avoid ruling on section two thirty,
Speaker 1: the law that protects, you know, tech companies being liable
Speaker 1: for every single thing that's on their platforms. Does that
Speaker 1: tell us that at least the highest court of the
Speaker 1: land doesn't really want to wade into being the big
Speaker 1: referee on tech issues and or how does that ruling
Speaker 1: or non ruling actually affect the conversation around AI.
Speaker 4: So two things.
Speaker 5: One, we're still not sure that section two thirty would
Speaker 5: or should apply to AI. That came up a bit
Speaker 5: in the recent Senate hearing. I know Altman said he
Speaker 5: didn't think two thirty was the right way to regulate AI.
Speaker 5: But you know, interestingly enough that day that really came out.
Speaker 5: Rachel and I were actually discussing the AI implications of
Speaker 5: the other ruling that came out that morning, which also
Speaker 5: has some AI implications, maybe even clearer ones, which was
Speaker 5: a case where a photographer had sued the Andy Warhol estate.
Speaker 5: Andy Warhol had taken a photo that this photographer had
Speaker 5: taken of prints and turned it into one of his
Speaker 5: you know, usual pieces of art silkscreen, et cetera, and
Speaker 5: the photographer sued over it. And the question was was
Speaker 5: the Warhol work transformative enough of the photographer's original work
Speaker 5: that accounted as a new work. And the Supreme Court
Speaker 5: ruled in favor of the photographer's claim. And that has
Speaker 5: implications for AI because there are a number of lawsuits
Speaker 5: right now from artists and computer programmers whose works either
Speaker 5: their software code or their works of art, their photography
Speaker 5: has been used in the training data of some of
Speaker 5: these generative AI systems that we're discussing, and in some
Speaker 5: cases the artist or the computer programmer claims that not
Speaker 5: only was their work used in the training data, but
Speaker 5: the out put, the thing that the AI algorithm generated
Speaker 5: looked suspiciously like their original product, and so there are
Speaker 5: suits over whether that's allowed now. And the companies that
Speaker 5: create these AI models are basically making a fair use
Speaker 5: argument that they are allowed to use these things in
Speaker 5: the training data, that they're transforming it into something else
Speaker 5: that it doesn't resemble the original work. So you know,
Speaker 5: Rachel and I were actually spent a lot more of
Speaker 5: that day discussing that Supreme Court cases implications for AI models,
Speaker 5: rather than the section two thirty one.
Speaker 1: Well, I want to say thank you to our two
Speaker 1: guests today, Dina bas who covers Microsoft and AI for
Speaker 1: Bloomberg News, and of course Rachel Metz, who covers AI
Speaker 1: for US as well.
Speaker 4: Thank you so much for joining me, Thank you for
Speaker 4: having us, thank you thanks for.
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Speaker 1: great weekend.