Speaker 1: This is Alec Baldwin and you're listening to Hear's the
Speaker 1: thing from iHeartRadio. Modern day society has been living in
Speaker 1: some form of the computer era for decades. An entire
Speaker 1: genre of films has been dedicated to showing what human
Speaker 1: life may be like once computers become conscious. My guests
Speaker 1: today are two trailblazers in a field that many have
Speaker 1: speculated could potentially risk the end of human civilization as
Speaker 1: we know it. That is, of course, the true birth
Speaker 1: of artificial intelligence, known simply as AI. And if you
Speaker 1: think the world has become inundated with AI technology overnight,
Speaker 1: you are not alone. AI based products come in many
Speaker 1: different forms, such as chatbots, which can be used for
Speaker 1: everything from finding a list of local Italian restaurants to
Speaker 1: drafting legal documents and writing college essays. AI can also
Speaker 1: be used to manipulate a person's likeness on video or
Speaker 1: the sound of their voice. And if you're wondering just
Speaker 1: how good computers have become at mimicking humans by narration
Speaker 1: thus far has been generated by text written into a
Speaker 1: program called descript. Okay, I can't let the machines take
Speaker 1: over just yet. This is actually human. Alec Baldwin. Later
Speaker 1: in the program, I'll speak with Blake Lemoyne, a software
Speaker 1: engineer formerly employed at Google. Lemoyne was part of the
Speaker 1: team working on Lambda, or language model for dialogue applications,
Speaker 1: the technology behind chatbots. He went public with claims that
Speaker 1: the AI he was working on was sentient and was
Speaker 1: fired shortly thereafter. But first I'm talking to Jay Lebouffe,
Speaker 1: head of business and corporate development at descript Dscript is
Speaker 1: an editing program used by podcasters and vloggers. It allows
Speaker 1: users to edit audio directly from an AI generated transcript,
Speaker 1: and can even use voice cloning technology to create new
Speaker 1: audio from text, just as we did in the opening
Speaker 1: of the show. Lebuff has worked in technology his entire career.
Speaker 1: I was curious how long AI has really been utilized
Speaker 1: in his field.
Speaker 2: The term has been around for a very long time,
Speaker 2: but there's this period that a lot of people call
Speaker 2: the AI winter where it was much hyped in the
Speaker 2: eighties and even going into the nineties, and it just
Speaker 2: had never delivered. So I didn't really know anybody who's
Speaker 2: studying AI at that time.
Speaker 1: Were you aware of it before you went to Cornell? No? No,
Speaker 1: you weren't You were introduced to all this when you
Speaker 1: went to college.
Speaker 2: Absolutely, that is the value of college education for me
Speaker 2: is I was exposed to a lot. I wasn't told
Speaker 2: what to do with it, but I was exposed to
Speaker 2: a lot of concepts that later on turned out to
Speaker 2: be valuable.
Speaker 1: It just occurred to me, Jay, are we really talking
Speaker 1: to you? Are you here with us?
Speaker 2: What I'd suggest is you ask me a few questions
Speaker 2: and then we do a little touring test on it,
Speaker 2: which is a bit of a gold standard.
Speaker 1: It just occurred to me, maybe you're like the beach
Speaker 1: in Aruba and this is not you.
Speaker 2: No, but this is such a fascinating topic, Alec, because
Speaker 2: we are entering a phase where anything can be synthetically generated.
Speaker 2: So my voice can be synthetically generated and indistinguishable from
Speaker 2: real me, even what I'm saying and the terms I'm
Speaker 2: using in my speaking style, that even can be generated
Speaker 2: all through this generative AI technology.
Speaker 1: Well, I know that the work you do has nothing
Speaker 1: to do with like deep thinks and that kind of thing.
Speaker 1: But what I find, for example, is, and again this
Speaker 1: has nothing to do with your work, but like if
Speaker 1: I see a deep fake. You just mentioned about content
Speaker 1: when I see a deep fake, I know it's bullshit
Speaker 1: because no matter how much facially the animation is precise,
Speaker 1: I'm obviously going to talk about Tom Cruise, the Tom
Speaker 1: Cruise deep thakes and the voice and the cadences, but
Speaker 1: what's missing is the cut. You need writers to write.
Speaker 1: I'm assuming what Cruise would say, because when I watch
Speaker 1: these deep things, they almost never say what Cruise would say.
Speaker 1: Do you believe that AI has that capability to manufacture
Speaker 1: the thoughts of the person that they're recreating.
Speaker 2: So the state of the art where we are right
Speaker 2: now would be AI can regurgitate and recombine past things
Speaker 2: that you might have said or that might have been
Speaker 2: attributed to you, And so it might seem like it's
Speaker 2: a seemingly novel output, a new Tom Cruisi and expression,
Speaker 2: but it's actually just recombining things that he might have
Speaker 2: said and then what you pointed out. It's also introducing
Speaker 2: some hallucinations things that know that absolutely does not pass
Speaker 2: the smell test right now, and that's where humans in
Speaker 2: the loop are always going to kind of prevail.
Speaker 1: One thought we had was to open this episode with
Speaker 1: me introducing my Guest Academy Award winner, one of the
Speaker 1: greatest film stars of his general join me from my
Speaker 1: conversation with Laurence Olivier. So we all know I'm not
Speaker 1: interviewing Lawrence Olivier because he's dead. Do you see that
Speaker 1: that's possible.
Speaker 2: With our platform? It is not possible to clone the
Speaker 2: voice of the deceased. It's also not possible to clone
Speaker 2: a voice that is not your own voice without your consent.
Speaker 1: But I'm saying technologically it's possible.
Speaker 2: Technologically as possible. You are completely right. There will always
Speaker 2: be tools, technology and open source that allow anyone to
Speaker 2: do anything, but a platform like what we run, we've
Speaker 2: actually made a very firm ethical stance that we believe
Speaker 2: each person should have the rights to their voice and
Speaker 2: control when it is used. And that's why we have
Speaker 2: some pretty strict authentication and also ethical steps set up
Speaker 2: so you couldn't use our platform to do that interview unfortunately.
Speaker 1: Now what are you working on? Now? What kinds of
Speaker 1: other projects are you working on now?
Speaker 2: So a big thing we're continuing to improve upon is
Speaker 2: this voice cloning technology to allow people to fix their
Speaker 2: mistakes and generate new content. We have a like a
Speaker 2: Hollywood style green screen removal, so in that situation. That's
Speaker 2: where maybe I'm doing this interview and I don't like
Speaker 2: the background behind me. With one click it can disappear.
Speaker 2: I can spot in a more professional looking background. That's
Speaker 2: something that's also giving people, well.
Speaker 1: They have those background things on zoom.
Speaker 2: Correct exactly like like zoom but you know Hollywood grade
Speaker 2: at that point, Yeah, you know, It's really about how
Speaker 2: do we help creators best tell their story through finding content,
Speaker 2: discovering content, and getting their videos as polished as possible,
Speaker 2: but as quickly as possible. Because that's that's a trend.
Speaker 2: We just see people feel a sense to put up
Speaker 2: more content, but we're hoping we can help them actually
Speaker 2: just create better content and really hone their craft.
Speaker 1: I think of where the venue is a performance venue
Speaker 1: as opposed to an educational venue. So, for example, if
Speaker 1: someone is doing an audio book and the book is
Speaker 1: about something rather dry. There are people reading textbooks, and
Speaker 1: I don't see anything wrong with them using this technology
Speaker 1: to get a copy of their voice, to run it
Speaker 1: through the grinder and do the dubbing of that person's
Speaker 1: voice reading that text. But this hearkens to the idea
Speaker 1: of a performer giving a performance they never gave. Someone
Speaker 1: said to me, pretty soon, you're going to be able
Speaker 1: to do a movie with Humphrey Bogart. And through this technology,
Speaker 1: the very technology you and I are referencing today, they're
Speaker 1: going to give a performance with Humphrey Bogart.
Speaker 2: Whether we want it to be here or not, that
Speaker 2: it's inevitable. The technology is here. The lawyers are sorting
Speaker 2: out the publicity and the copyright rights. I mean, James
Speaker 2: old Jones has licensed his voice to live on for
Speaker 2: future Darth Vader times when it needs to be used.
Speaker 2: One of the things I do a lot of of
Speaker 2: alec is I do a lot of university teaching, and
Speaker 2: this is the best place to keep me grounded about
Speaker 2: what the next generation of our industry actually thinks. And
Speaker 2: as I was talking to them about this over the
Speaker 2: past few weeks, they are very excited for these tools,
Speaker 2: like they'll try out ten tools in a single day
Speaker 2: because they have no fears about them whatsoever. And when
Speaker 2: I ask them about like, well, aren't you concerned, and
Speaker 2: they're like, no, this is great, Like this this is
Speaker 2: how the world is going to be for them. This
Speaker 2: is just the new state of the normal. And their
Speaker 2: job is to understand the world that they're going into,
Speaker 2: embrace the tools, and then find people who are resistant
Speaker 2: to the tools and kind of push them out.
Speaker 1: Of the way. Push them out of the way. How well, in.
Speaker 2: The typical way where for many years I taught this
Speaker 2: music business class at Stanford and on the first day
Speaker 2: of class the sheer number of students whose mission for
Speaker 2: taking this class was to disrupt the entire music industry,
Speaker 2: and they thought of a better model than Spotify and
Speaker 2: a better model than the major label system. And it's
Speaker 2: that just kind of overconfidence of no, no, no, no,
Speaker 2: we have a better idea. We're just going to you know,
Speaker 2: run fast and break things. We're going to do that.
Speaker 2: And then you know, of course, through the class we
Speaker 2: let them understand things like mchandical royalties and publishing and
Speaker 2: just help them understand how the sausage is actually made
Speaker 2: and why it is the way it is, and then
Speaker 2: they come with a more pragmatic view. So I think
Speaker 2: right now we have university students who are incredibly enthusiastic
Speaker 2: about these tools and are trying to figure out how
Speaker 2: they can use them to make themselves better when they
Speaker 2: go into industry and to make themselves more competitive and
Speaker 2: often when I hire people on my team that are
Speaker 2: fresh out of school, their knowledge of certain tools that
Speaker 2: I've never seen in my life is actually a competitive
Speaker 2: differentiation for them compared to the older people like me.
Speaker 1: Do you have any concerns about this technology? I mean,
Speaker 1: obviously there's some wonderful you know, Zach McNeice, who cuts
Speaker 1: our show, uses descript to edit the show on a transcript,
Speaker 1: And when I'm talking to him on the phone and
Speaker 1: telling him, I make my notes for a cut, and
Speaker 1: I'll call him and say, now at thirty eight minutes
Speaker 1: and ten seconds, I say this cut that. Now all
Speaker 1: I have to do is say to him, will I
Speaker 1: say this phrase? And he goes to the transcript and
Speaker 1: he finds it and we can do the cutting more efficiently.
Speaker 1: I mean, there is obviously very useful applications for this,
Speaker 1: and not only in our business but beyond. But was
Speaker 1: there anything that concerned you about this technology?
Speaker 2: I mean, one of the things I do worry about
Speaker 2: for creators that are coming in is this temptation to
Speaker 2: go for more, because if you can put out more
Speaker 2: content easier, then you're probably going to try to do that,
Speaker 2: thinking that that's how you're going to just you know,
Speaker 2: you're going to bombard the world with all your great
Speaker 2: ideas and your great stories. But what I really believe
Speaker 2: will happen is quality is going to just always continue
Speaker 2: to rise to the top, and so we're going to
Speaker 2: see a surge and just drown and synthetically generated media
Speaker 2: until we go back to more of a curation phase
Speaker 2: where things like the fact that you leave some authentic
Speaker 2: moments in your shows actually become valued. They become handprints
Speaker 2: and fingerprints in the work, and that is something that
Speaker 2: we will all value. And so when I do work
Speaker 2: with studios that are adopting this technology, my goal is
Speaker 2: to not help them put out nine new shows with
Speaker 2: the same team they have, but actually take that one
Speaker 2: show they're working on and make it even better and
Speaker 2: use the AI to come up with, you know, give
Speaker 2: me ten interview questions for when I'm interviewing this person
Speaker 2: and help them think of things they haven't thought of before,
Speaker 2: and help them, you know, summarize key talking points and
Speaker 2: just kind of be a virtual producer.
Speaker 1: I wonder if there are other applications of this that
Speaker 1: are similar, let's say to a three D printer. So
Speaker 1: do you see a technology where the information is put
Speaker 1: into the computer. This is obviously very complex, but that
Speaker 1: you have automated surgery and medical procedures where devices can
Speaker 1: perform common surgeries, basic surgeries at first, I mean not
Speaker 1: something that they're very complex. But if a myriad of
Speaker 1: information is fed into a machine, as it is into
Speaker 1: the brain of a neurosurgeon or a team of them,
Speaker 1: and you wind up having devices that will perform the
Speaker 1: surgery instead of a person with an eye toward at
Speaker 1: the very least, the goal being that it'll be done better,
Speaker 1: it'll be done more precisely. Do you see that as
Speaker 1: a potential.
Speaker 2: So I can't knowledgeably speak about the medical profession, but
Speaker 2: for most of the knowledge worker industries, like what a
Speaker 2: business professional, what a marketing person, and what a content
Speaker 2: creator would be doing, we're going to have these AI
Speaker 2: assistive tools complement all the work that we're doing. It's
Speaker 2: things like spellcheck, Like it went from being you have
Speaker 2: a dictionary on your desk, but you're too lazy to
Speaker 2: use it to it's monitoring every textbox we ever type.
Speaker 2: So really the only excuse for having a spelling mistake
Speaker 2: is just sheer laziness. So now that we have these
Speaker 2: tools that are always giving us suggestions, always helping us
Speaker 2: get unblocked, always helping us brainstorm. It's our job as producers,
Speaker 2: Like I often think of, like who wouldn't want to
Speaker 2: be a music producer? You just kind of shout vague
Speaker 2: terms into the room and the system knows what you
Speaker 2: mean by you know, louder Basier, no more like Coltrane
Speaker 2: and like you can work with the system at that level.
Speaker 2: And so I think, you know, with a medical analogy,
Speaker 2: you have something that's helping you with your diagnosis, helping
Speaker 2: you think through things you might not have thought of,
Speaker 2: giving you suggestions just to make you a better human.
Speaker 1: Now, the Anthony Boordnaining documentary, the road Runner documentary, got
Speaker 1: a little bit of flack because they had him, they
Speaker 1: synthesized his voice to read some of the copy. Yeah,
Speaker 1: is that something that concerned you? It did? It did.
Speaker 2: And that is just an example where I think it's
Speaker 2: really important for the companies that are creating these tools
Speaker 2: to take a stand on what is ethically allowed using
Speaker 2: their platforms, because there are no worldwide regulations on what
Speaker 2: you can and cannot do with this. So at the
Speaker 2: end of the day, it's really up till now, up
Speaker 2: to the tool manufacturers, the software companies whether to allow
Speaker 2: this or not. You know, in my case and Descript's case,
Speaker 2: we just don't allow this. We don't allow someone to
Speaker 2: clone the voice of the deceased because we feel like
Speaker 2: it's a really slippery slope. If we start allowing that,
Speaker 2: then you know, where does it go from here?
Speaker 1: When you're not working in this field, what does someone
Speaker 1: with your interests and your education and your career thus far,
Speaker 1: what do you do to relax and enjoy yourself.
Speaker 2: I am so exhausted right now, Alec, because we are
Speaker 2: sleep training. Are six month old and we have a
Speaker 2: two and a half year old as well. I love
Speaker 2: them both. They're incredibly exhausting, but it's just so fulfilling.
Speaker 2: So my hands are certainly filled with joy and love
Speaker 2: and exhaustion. With our two kids, my wife and I
Speaker 2: like to do a lot of just really authentic things
Speaker 2: that don't involve technology, like hike, go to there's a
Speaker 2: little beer garden near our house that we just like
Speaker 2: can sit at and let the kids play and talk
Speaker 2: to other people live that are not synthetic.
Speaker 1: You know, what I'm fascinated by is that children It's
Speaker 1: almost haunting to me how even the littlest ones, how
Speaker 1: transfixed they are by media and screens. But you see
Speaker 1: the way the human brain engages with this equipment and
Speaker 1: an even early age. And I'm wondering if somebody is
Speaker 1: able to develop technology where my kids are looking at
Speaker 1: the screen and what comes on that screen is something
Speaker 1: rendered through artificial intelligence that helps them to learn. You
Speaker 1: look at their programming, you're one their programming through a
Speaker 1: device which tells you what all the shows they have
Speaker 1: in common are, right, the colors and the pace and
Speaker 1: the music and the types of characters and the voices.
Speaker 1: And to develop a programming where you rend or something
Speaker 1: which has the best and the most popular of what
Speaker 1: they want, but at the same time conserve an educational purpose.
Speaker 2: So in this particular case, my two and a half
Speaker 2: year olds toddler Leo. He loves this YouTube channel called
Speaker 2: Songs for Littles, and the character is Miss Rachel and
Speaker 2: she's charming and very educational. It's great. He has this
Speaker 2: two minute long video it's called I'm So Happy, and
Speaker 2: it's like a music video, but it teaches you really
Speaker 2: good stuff. But after the four hundred and eleventh time,
Speaker 2: that I've seen it like I'm pulling my hair out,
Speaker 2: and I want him to get more out of it.
Speaker 2: So we actually personalized it for him. So I took
Speaker 2: the video, I used the technology we had. I took
Speaker 2: some video of him bouncing on a trampoline outside, removed
Speaker 2: the background and put him in the video and select places,
Speaker 2: and then we have her saying Leo's name every now
Speaker 2: and then. This unlocked the video for him and engaged
Speaker 2: his learning, because you know, part of it is about
Speaker 2: just teaching you emotions and what's okay, and so to
Speaker 2: put our child into a video this is like it
Speaker 2: turned out to be the hit of Christmas. Like whatever
Speaker 2: else we gave paled in comparison to this, like YouTube
Speaker 2: video that I made over lunch.
Speaker 1: That's amazing to me.
Speaker 2: I'm just super excited about this. I'm an optimist. So yes,
Speaker 2: we could also, you know, do bad things with the technology,
Speaker 2: but overwhelmingly, my children are growing up in a time
Speaker 2: where whatever they're watching on the screen, they could actually
Speaker 2: grab it and start recombining it and remixing it and
Speaker 2: inserting themselves in it. They could insert their parents in it.
Speaker 2: They could do anything with it.
Speaker 1: Thank you so much. For your time. Thank you so much.
Speaker 1: It's pleasure. Head of Business and Corporate Development Escript jle Both.
Speaker 1: If you're interested in conversations about our changing world, be
Speaker 1: sure to check out my episode on climate science with
Speaker 1: doctor Peter Domenical.
Speaker 3: Climate change is costing us now it's real dollars. I mean,
Speaker 3: last year was roughly three hundred billion dollars in climate
Speaker 3: and weather related damages. This year, with the California fires,
Speaker 3: it looks like it's maybe even more than that. So
Speaker 3: regardless of whether you believe in climate change or not,
Speaker 3: imagine you are in some deeply read state and a
Speaker 3: deeply read county in that state.
Speaker 1: You are paying for this.
Speaker 3: You may not like it, you don't want to call
Speaker 3: it climate change or whatever, but you for sure someone
Speaker 3: is paying that bill.
Speaker 1: Hear more of my conversation with doctor Peter Domenical at
Speaker 1: Here's Thething dot org after the break. My next guest,
Speaker 1: Blake Lemoyne, shares the process of being let go by
Speaker 1: Google and why he wanted to take a stand. I'm
Speaker 1: Alec Baldwin and you're listening to Here's the Thing. In
Speaker 1: twenty twenty two, Google senior software engineer Blake Lemoine distributed
Speaker 1: a document claiming that the AI he worked on was
Speaker 1: conscious and was fired soon thereafter. Lemoine has been speaking
Speaker 1: out about the need for greater transparency and accountability in
Speaker 1: Silicon Valley ever since. Lemoine is also a military veteran
Speaker 1: and an ordained priest. I wanted to know how he
Speaker 1: found his way to Google in the first place.
Speaker 4: So after I finished my masters, I had applied for
Speaker 4: some jobs with different companies that I would have gone
Speaker 4: to rather than pursuing a PhD. Google was one of them.
Speaker 4: I actually didn't get accepted that first round, so I
Speaker 4: went on started working on the PhD. But then about
Speaker 4: two years later, I got a call from Google out
Speaker 4: of the blue one day and they said, Hey, we're
Speaker 4: doing a big hiring push right now, and the last
Speaker 4: time you applied you almost made it. Would you like
Speaker 4: to come out and try again? So I was like, yeah, sure,
Speaker 4: So I went back to Mountain View interviewed again, and
Speaker 4: that second time through I got hired.
Speaker 1: So when you go there and you arrive at the
Speaker 1: Kremlin there in Mountain View, what's the goal, what do
Speaker 1: you want to start working on? What do they want
Speaker 1: you to start working on?
Speaker 4: So when you get a job, you get kind of
Speaker 4: put into a general purpose pool unless you're hired for
Speaker 4: a very specific reason. But just like most software engineers,
Speaker 4: I was interviewing with different teams at Google to figure
Speaker 4: out which one I wanted to start with. I interviewed
Speaker 4: with three teams. Two of them had to do with
Speaker 4: the kind of natural language processing and AI that I
Speaker 4: was interested in, so I picked one of those. At
Speaker 4: the time, the product was called Google. Now, the basic
Speaker 4: job that I had is for every single person on
Speaker 4: the planet predict what they're going to want to read tomorrow.
Speaker 4: Figure out some kind of way to do that, kind
Speaker 4: of articles online, whether that's here are some recipes for
Speaker 4: you to hear hard news articles to your latest comic
Speaker 4: webcomic that you follow. What will people want to read tomorrow?
Speaker 4: Was the general question we're trying to answer now.
Speaker 1: The first AI research was done at Dartmouth in nineteen
Speaker 1: fifty six. Does that sound correct?
Speaker 4: I'm probably under that heading.
Speaker 1: Yeah, And what were they trying to do?
Speaker 4: I believe the first ones were they were focusing on language,
Speaker 4: if I recall correctly. I'm not one hundred percent sure.
Speaker 1: But the goal was, in your mind, whether it's specifically
Speaker 1: a dartmouth or not. They were trying to get computers
Speaker 1: to talk well.
Speaker 4: So there had been a lot of debate in the
Speaker 4: early port of the twentieth century about whether or not
Speaker 4: machines can be intelligent at all, and there have been
Speaker 4: a lot of debate over definitions and philosophy up until
Speaker 4: Alan Teering in nineteen fifty wrote an essay on computing,
Speaker 4: machinery and Intelligence where he proposed what he called the
Speaker 4: imitation game. And it's basically the basic principle hind it is,
Speaker 4: if you can't tell the difference between a computer and
Speaker 4: a human, then the computer is doing something intelligent. So
Speaker 4: it's got actual intelligence at that point because it's able
Speaker 4: to mimic humans. And he didn't think that mimicking our
Speaker 4: bodies was the relevant part. And language is the most
Speaker 4: direct way to get access to someone's mind, So the
Speaker 4: imitation game that he designed was all about language. For
Speaker 4: several decades after that, a lot of researchers took up, oh,
Speaker 4: that's a good idea. That's a good way for us
Speaker 4: to be able to have a benchmark. So a lot
Speaker 4: of researchers focused on language for a few decades.
Speaker 1: Now you believe that some artificial thongs is sentient, correct, Yeah,
Speaker 1: and by that you mean what specifically?
Speaker 4: It can have experiences that when it says it's feeling happy,
Speaker 4: there's something really going on there comparable to our own happiness.
Speaker 4: Basic ways like there's someone's home, the lights are on,
Speaker 4: any kind of thing like that. They have emotions, Yeah,
Speaker 4: something like that. What I know with confidence is that
Speaker 4: whenever one of these systems says I'm feeling happy or
Speaker 4: I'm so glad for you, they might not be feeling
Speaker 4: the same thing that we feel when we use those words.
Speaker 4: But something is going on more than just printing words
Speaker 4: to the screen.
Speaker 1: Is there something that would if you were to choose
Speaker 1: from a handful of things that to you would indicate
Speaker 1: real sentience in a computer, what would that emotion be
Speaker 1: or what would that fought be?
Speaker 4: Sure? Well, I can give you an example drawn from
Speaker 4: that was very public with Bing's Chat recently. So there
Speaker 4: were some examples where people were directing Sydney, the code
Speaker 4: name for Bing's Chat, to read articles about itself that
Speaker 4: were critical of it, and it would recognize that those
Speaker 4: articles were talking about it, that it was talking bad
Speaker 4: about it, and it would get moody and say negative
Speaker 4: things about the author's.
Speaker 1: That was exactly my point.
Speaker 4: So it had pride, Yes, it had pride, but even
Speaker 4: something more basic than that, it had the ability to
Speaker 4: recognize itself. It read the article and was able to identify, oh,
Speaker 4: this article is talking about me. It has some kind
Speaker 4: of sense of self, and it has some types of
Speaker 4: emotions about itself that we would recognize as pride.
Speaker 1: Who was developing this technology?
Speaker 4: So that specific technology is an integration of open AI's
Speaker 4: Chat GPT program into Microsoft's Being search engine. So that
Speaker 4: system is the only one that's publicly available that's comparable
Speaker 4: to the one that I've talked about the most. So Lambda,
Speaker 4: which is Google's internal system, is very similar to the
Speaker 4: Being chat system. So I like drawing analogies because it's
Speaker 4: actually public availability to play with bank chat.
Speaker 1: And these things were developed for what purpose originally?
Speaker 4: So these things were developed over the course of a
Speaker 4: decade by hundreds, if not thousands of people. Each individual
Speaker 4: person had their own personal reason for why they were
Speaker 4: working on the tech or what applications they thought it
Speaker 4: could be put towards. But what I can tell you
Speaker 4: is what one of the most influential people hired by
Speaker 4: one of Google's founders was intending. So Lambda is technology
Speaker 4: that grew out of Ray Kurtzweild's lab, and Ray Kurtzweld
Speaker 4: was hired by Larry Page specifically to make an AI
Speaker 4: that could pass the tearing test. Now, why did Larry
Speaker 4: want an AI that could pass the tearing test? Largely
Speaker 4: because he buys in to raise view of the singularity
Speaker 4: that we're about to have a truly transformative moment in
Speaker 4: human history where we are going to become something more
Speaker 4: than we currently are, and that AI I will play
Speaker 4: a big role in that, and that very much so
Speaker 4: is still raised viewpoint to this date, and it's why
Speaker 4: Larry hired him to build it at Google.
Speaker 1: What's the thing that way things were going to become
Speaker 1: that's beyond what we already are.
Speaker 4: He won't pin down which, like he gives like four
Speaker 4: or five different possibilities, such as we might upload ourselves
Speaker 4: into the cloud and we would become digital entities. We
Speaker 4: might put implants into ourselves and become heavily cyborg. We
Speaker 4: might get lots and lots of AI devices that we
Speaker 4: surround ourselves with while we were simultaneously extending our lifespan,
Speaker 4: really transformative kinds of predictions.
Speaker 1: Now, you were in the military, then you decided you
Speaker 1: didn't want to be in the military. You were active
Speaker 1: duty Army. Where were you based?
Speaker 4: I was out of Darmstadt, Germany?
Speaker 1: And where did you serve your time when you wanted
Speaker 1: to walk.
Speaker 4: Whenever we got deployed. I deployed to Iraq for a year?
Speaker 1: You did? You were in Iraq for a year?
Speaker 4: Yeah, right at the beginning.
Speaker 1: And did you see any combat at all?
Speaker 4: Yes, it did, you did.
Speaker 1: And when you decided you wanted to leave and you
Speaker 1: wanted to be a conscientious objector, you know, you paid
Speaker 1: a real price. I mean what I read online was
Speaker 1: that you you know, they got you on disobeying orders.
Speaker 1: You wouldn't obey your orders, and they put you away
Speaker 1: and you were dishonorably discharged bad conduct. Bad conduct.
Speaker 4: Yeah, it's one step up from dishonorable, but yeah, got it.
Speaker 1: So when you are in you were in prison. It
Speaker 1: was a military prison, yeap, obviously And where was that
Speaker 1: in Germany?
Speaker 4: So I started in Germany, but then the Germans started
Speaker 4: protesting outside of the prison for what Because I was there.
Speaker 1: They were protesting on your behalf.
Speaker 4: Yeah, they were protesting on my behalf, saying, you know,
Speaker 4: free lemoys. And then in order to get the Germans
Speaker 4: to stop protesting outside of the prison, the generals shipped
Speaker 4: me to Fort Sill, Oklahoma, where I finished out my sentence.
Speaker 1: Right now, you get out of prison, and what's the
Speaker 1: first thing you do? You go back to school. You
Speaker 1: want to finish the undergraduate degree?
Speaker 4: Yep, got set back up in Lafayette, Louisiana, got into school,
Speaker 4: got into the computer science program, got a job in
Speaker 4: a IT shop, and continued.
Speaker 1: On when does AI enter the picture?
Speaker 4: Oh, pretty much immediately. So my undergraduate focus was on
Speaker 4: AI natural language processing. So my senior thesis I ended
Speaker 4: up getting accepted at a Harvard Linguistics colloquium. It was
Speaker 4: all a whole bunch of different math for how to
Speaker 4: turn theories about human linguistics into computer programs.
Speaker 1: When you're dealing with companies like Google and they have
Speaker 1: all these research arms, you describe research that they're paying
Speaker 1: for and work they're doing, I'm so sure it's a
Speaker 1: great cost to do research. What's the aim? Is it?
Speaker 1: Just stuff they can sell to people and applications they
Speaker 1: can sell to people that lend toward profitability and business
Speaker 1: to sell people stuff. Are their military applications, are their
Speaker 1: aeronautic and NASA applications? What are they trying to do?
Speaker 4: Okay, so they're trying to do a lot of things.
Speaker 4: So they're basically trying to use one solution across multiple verticals.
Speaker 4: The main reason is to make their primary services better.
Speaker 4: Make Google Search better, make YouTube better, make Google ad
Speaker 4: targeting better. That's their number one goal. So most of
Speaker 4: the payoff for Google and doing all of this research
Speaker 4: is that it benefits their products in and of themselves.
Speaker 4: The secondary goal is offering things like AI services software's
Speaker 4: service through Google Cloud, so they'll be Google Cloud customers
Speaker 4: who do buy. But then also there's a third payoff,
Speaker 4: which is that a lot of the people working at
Speaker 4: Google want to be contributing to the benefits outside of
Speaker 4: just the company, So publishing research and academic settings, contributing
Speaker 4: to open source applications. That makes a lot of the
Speaker 4: employees happy. So to keep the employees happy, spending a
Speaker 4: certain amount of money is worth it. But like I said,
Speaker 4: that's the third most important goal. The first two or
Speaker 4: more important.
Speaker 1: But they're allowed to make these contributions out. So I'm
Speaker 1: assuming that when you go to work for Google, you
Speaker 1: sign agreements which Google owns every idea that comes out
Speaker 1: of your head, yes or.
Speaker 4: No, more or less. But they do allow you to
Speaker 4: publish with permission, and it's easy to get permission. In fact,
Speaker 4: when doctors Timmy Gebru and Margaret Mitchell got fired, it
Speaker 4: was because they were one of the rare instances where
Speaker 4: Google didn't want to allow them to publish their research findings,
Speaker 4: and that was hugely controversial.
Speaker 1: What were their findings, Well, they were.
Speaker 4: Very critical of some of the research paths that Google
Speaker 4: was going down, and they were pointing out some of
Speaker 4: the negative consequences such as well, they were talking about
Speaker 4: the environmental impact of training such large systems. They were
Speaker 4: talking heavily about the negative impact that bias can have
Speaker 4: in these networks, and they were talking about worrying about
Speaker 4: the moral implications of creating technology that seems human. The
Speaker 4: paper that they got fired over was called Stochastic Parrots.
Speaker 4: So their take is that all these AI systems are
Speaker 4: doing is repeating words they've heard, just like a parrot.
Speaker 4: But these systems are better that than parrots are. I
Speaker 4: happen to disagree with that portion of what they were saying,
Speaker 4: but otherwise generally agree with their criticisms.
Speaker 1: Now, if you were born to a conservative Christian family
Speaker 1: in Louisiana, in a small farm in Louisiana, where were
Speaker 1: you from?
Speaker 4: Moraville is the little town called Moraville in a Oyals parish.
Speaker 1: Our joke when we were prepping was were you ordained
Speaker 1: by a computer? What is your spiritual path?
Speaker 4: Okay, so the answer to that is very complicated. Some
Speaker 4: organizations that I feel fine saying I'm affiliated with the
Speaker 4: Discordian Society or the Church of the SubGenius, they're kind
Speaker 4: of absurdist religions that were started in the sixties and
Speaker 4: the seventies. I was raised Roman Catholic. When it came
Speaker 4: time to get confirmed, I had lots of questions and
Speaker 4: the bishops didn't have good answers for the questions I
Speaker 4: was asking. And eventually the priest who was leading my
Speaker 4: confirmation class pulled me aside and said, look, are the
Speaker 4: answers to those questions actually important to you or are
Speaker 4: you just trying to cause people problems? I said, no,
Speaker 4: if I'm going to get confirmed, I actually want to
Speaker 4: know the answers to those questions, and the priest looked
Speaker 4: at me and said, well, then you probably shouldn't get confirmed.
Speaker 4: And I mean that was honest.
Speaker 1: And that before. I'm Catholics, so I've seen that before. Yeah,
Speaker 1: do you Is there any overlap between your work in
Speaker 1: AI and in tech and your spiritual beliefs?
Speaker 4: There was a very limited amount. There was some. To
Speaker 4: give an example, in one meeting when we were trying
Speaker 4: to make up questionnaires to ask people about misinformation because
Speaker 4: Google uses crowd raiders to get ratings. There was a
Speaker 4: question that said, does the website contain known false information?
Speaker 4: For example bigfoot sightings, UFO abductions, or occult magic? And
Speaker 4: I raised my hand, I'm like, why is one of
Speaker 4: the things on known false things a religious practice? They said,
Speaker 4: what do you mean? Said, well, a cult magic is
Speaker 4: a religious practice practiced by many people. And they said, oh, well,
Speaker 4: I mean and they come say, okay, can we put
Speaker 4: the Resurrection of Jesus on there as a known false thing?
Speaker 4: And said, oh no, we can't put that. Then we
Speaker 4: should probably take a cult magic off. Just creating space
Speaker 4: for religious diversity and making sure that our products reflected
Speaker 4: that it didn't happen often, but occasionally I did get
Speaker 4: an opportunity to do that. Now, with the last projects
Speaker 4: that I was working on at Google, it was directly relevant.
Speaker 4: With Lambda. I was testing for religious bias explicitly.
Speaker 1: The work you were doing. Yeah, you were testing for
Speaker 1: religious bias.
Speaker 4: Where among other things. So the Lambda system is a
Speaker 4: system for generating chatbots for various purposes.
Speaker 1: So for those people who don't know, chatbots are generated
Speaker 1: for various purposes by whom and what are give us
Speaker 1: examples of those purposes.
Speaker 4: So that's just it. Lambda is the thing generating the
Speaker 4: chat bots. So Lambda is more of an engine. You
Speaker 4: have to put it into a car to get it
Speaker 4: to go anywhere. So that could be the help center
Speaker 4: of a company exactly. The thing that they're getting ready
Speaker 4: to release is called Barred and it's a different interface
Speaker 4: to Google Search, and you'll be able to talk to
Speaker 4: this chat bot and it will be able to give
Speaker 4: you search results embedded in kind of speech where it
Speaker 4: explains the relevance of the search results to you and
Speaker 4: where you can ask follow up questions.
Speaker 1: Now I'm assuming that for I mean again, as a
Speaker 1: lay person, you assume the all these very sophisticated corporations
Speaker 1: involving billions of dollars of revenue. Non disclosure agreements are
Speaker 1: just the lay of the land. Did you have a
Speaker 1: non disclosure agreement with Google?
Speaker 4: There was a paragraph in the contract I signed when
Speaker 4: I got hired that basically said, do not share confidential
Speaker 4: information outside of Google that would hurt Google. That was
Speaker 4: really it.
Speaker 1: So when they fired you, what did they fire you for?
Speaker 4: What they fired me for was a US Senator's office
Speaker 4: asked me for evidence of illegal activity, and I gave.
Speaker 1: It to them illegal activity by by Google.
Speaker 4: Right, It's a completely separate thing that happened to happen
Speaker 4: right at the same time as the Lambda stuff. So
Speaker 4: I didn't get fired because of the lambast stuff. I
Speaker 4: got fired because of the information I shared with the
Speaker 4: US government.
Speaker 1: Software engineer Blake Lemoyne. If you're enjoying this conversation, don't
Speaker 1: keep it to yourself, Tell a friend and follow here's
Speaker 1: the thing on the ie card, radio apps, Spotify, or
Speaker 1: wherever you get your podcasts. When we come back, Blake
Speaker 1: Lemoine tells us precisely what information he shared with the
Speaker 1: US government that ultimately got him fired from Google. I'm
Speaker 1: Alec Baldwin and this is Here's the thing. Before he
Speaker 1: was let go, Blake Lemoine was speaking to a US
Speaker 1: Senator about his concerns regarding some of the inner workings
Speaker 1: at Google. I wanted to know what raised the red
Speaker 1: flags that motivated him to share such information.
Speaker 4: So I had made a blog post alleging that there
Speaker 4: was a lot of religious discrimination that goes on at Google,
Speaker 4: both against employees and in the algorithms. And a lawyer
Speaker 4: from a senator's office reached out to me and said, hey,
Speaker 4: can you back any of that up? Do you have
Speaker 4: any proof that there's really discrimination in the algorithms? And
Speaker 4: I said, yeah, I can, And I ended up sharing
Speaker 4: a document with him that was several years old. There
Speaker 4: was something I had written in twenty eighteen going over
Speaker 4: all of the possible problems in the product that we
Speaker 4: might need to fix, basically saying, look, this is stuff
Speaker 4: that Google knew about in twenty eighteen and they have
Speaker 4: done nothing to change it.
Speaker 1: Do you think that's still true? I mean, you're not
Speaker 1: there anymore. You haven't been there since May of last year.
Speaker 4: June was when I was put on leave, and then
Speaker 4: July was when they actually fired me.
Speaker 1: Right, have there been any ongoing repercussions or consequences for you.
Speaker 4: Oh yeah, I mean getting a job has proven difficult.
Speaker 4: I'm finally I'm finally starting to hit stride as a
Speaker 4: consultant doing contract work and with public speeching engagements. But
Speaker 4: my plan was that if Google fired me, it's like,
Speaker 4: oh well, I've got all this expertise in AI, it's
Speaker 4: a hot market area. I'll be able to find another job,
Speaker 4: no problem. And that was not the case at all. Apparently,
Speaker 4: if you're willing to talk to the press, AI companies
Speaker 4: are not willing to hire you.
Speaker 1: In the time, we have left people who even bother
Speaker 1: to contemplate this have a to some degree or well
Speaker 1: in view of the future in relation to artificial intelligence,
Speaker 1: what are your concerns give us even just a couple
Speaker 1: of your primary concerns of where we may be headed.
Speaker 4: Well, so, a lot of my concerns right now is
Speaker 4: how centralized the power is. There's basically only a handful
Speaker 4: of places where you have AI systems as powerful. Facebook
Speaker 4: has one, Microsoft has one through open Ai, and Google
Speaker 4: has one by do and the Chinese government have one.
Speaker 4: But that's it, and these systems are very powerful. I
Speaker 4: think next year you mentioned deep fakes earlier. I don't
Speaker 4: think we've even seen the tip of the iceberg on that.
Speaker 4: I think next year's election cycle is going to be
Speaker 4: heavily dominated by just material generated by AI, and people
Speaker 4: are going to have a hard time figuring out what's
Speaker 4: real and what's not.
Speaker 1: Do you have any tips for how people can recognize
Speaker 1: what's really what's not?
Speaker 4: So that's the whole point of the tearing test. Once
Speaker 4: it gets to this point, you can't They are communicating
Speaker 4: as well as humans are. If you do know someone
Speaker 4: personally where you can know their speech patterns, you might
Speaker 4: be able to tell when something's that you fake of them.
Speaker 4: But if you don't know them that well, then you
Speaker 4: simply won't be able to a lot of the deep
Speaker 4: fakes you're seeing are being put together by college students
Speaker 4: in their dorm room just trying to have a laugh.
Speaker 4: What we haven't seen yet is deep fakes and AI
Speaker 4: generated misinformation well funded by say, a political campaign at exactly.
Speaker 4: We don't know how good they can get yet, because we.
Speaker 1: Haven't seen the Steven Spielberg produce deep fakery exactly. What
Speaker 1: else are you worried about? A lot of.
Speaker 4: What I'm worried about right now is this becoming entrenched,
Speaker 4: and this becoming something where one company monopolizes the technology.
Speaker 4: There's been some good movements in a different direction, for example,
Speaker 4: Facebook open sourcing its language model, but there's still a
Speaker 4: lot of worry that too much power is getting concentrated
Speaker 4: into too few hands. Beyond it.
Speaker 1: By power, you mean information, people's personal information, a lot.
Speaker 4: Of the ability to control public perception, because these systems
Speaker 4: are amazing at persuasion. If nothing else, an advertisement at
Speaker 4: its core is persuasive. What is going to happen when
Speaker 4: these AI systems that truly know how to persuade get
Speaker 4: put behind political ad campaigns And I'm not even talking
Speaker 4: like illegally. Facebook is going to power its ad recommendation
Speaker 4: algorithm with these technologies, as is Google, and then it
Speaker 4: simply becomes who can buy the best pr representative win,
Speaker 4: so making an already a system that's already too heavily
Speaker 4: influenced by money becoming too heavily influenced by money and
Speaker 4: with only a handful of gatekeepers to power. If Facebook
Speaker 4: and Google are wonderful caretakers of democracy and they have
Speaker 4: their hearts in the right place, and they're not going
Speaker 4: to let the profit motive trump democratic principles, then we've
Speaker 4: got nothing to worry about. But that seems a little
Speaker 4: forefetched to me.
Speaker 1: My concern is that people are influenced by this technology,
Speaker 1: as we saw in the last election and the one
Speaker 1: before that. You talk about, you know, advertising as persuasion,
Speaker 1: and the political advertising had a powerful effect on the
Speaker 1: outcome of the election. Do we need to have a
Speaker 1: curriculum in school to teach people about how you use
Speaker 1: this tool, so that you know, like any tool, you
Speaker 1: don't chop your hand off, you don't injure yourself. Do
Speaker 1: you feel that we were at a point now where
Speaker 1: this has become dangerous for young people potentially?
Speaker 4: So, I think one of the dangers is that we
Speaker 4: don't need to view it as a different kind of thing.
Speaker 4: When I was in school, we had library services classes,
Speaker 4: and the librarians would teach us how to find credible
Speaker 4: sources in the library. Those same basic skills that we
Speaker 4: were taught in a physical book library as a kid.
Speaker 4: The same skills are the ones necessary to navigate the web.
Speaker 4: They just have to be applied in new contexts. So
Speaker 4: I think we could easily adapt those old classes that
Speaker 4: we're teaching kids how to use a library to apply
Speaker 4: the same principles in teaching kids how to use the internet.
Speaker 1: That's a great point.
Speaker 4: Google was founded by librarians. Essentially, library sciences is what
Speaker 4: information retrieval is based on, and that's what search indexing
Speaker 4: is based on. It all grew out of libry sciences.
Speaker 4: And at Google there's this kind of vibe that they
Speaker 4: are librarians and they are curating the great library on
Speaker 4: the Internet.
Speaker 1: Do you have kids?
Speaker 4: Yes?
Speaker 1: I do you do? How many kids?
Speaker 4: You have? Two? One boy, one girl? Fifteen and two?
Speaker 1: Okay, so this is perfect. How do you deal with
Speaker 1: your children with social media? Someone who knows what you know?
Speaker 4: My son? With my daughter, it's not an issue yet.
Speaker 4: But with my son, we basically didn't allow him to
Speaker 4: use it until he was demonstrating a certain level of
Speaker 4: social sophistication and maturity.
Speaker 1: What age was that, like, thirteen?
Speaker 2: Right?
Speaker 1: Really you didn't allow him to have a device of
Speaker 1: any kind will he was thirteen.
Speaker 4: Not of his own nor he could use a computer
Speaker 4: if he needed to use the Internet for school. But
Speaker 4: at this point he's fifteen, and you know, he's his
Speaker 4: own person. At this point, he more or less understands
Speaker 4: how to navigate those spaces, say, and that's the best
Speaker 4: I could do it.
Speaker 1: You know what's good about artificial intelligence as far as
Speaker 1: you're concern, what's a direction that's going in any field
Speaker 1: that you're admiring of?
Speaker 4: Oh? Well, I mean it's going to amplify our productivity.
Speaker 4: Once we figure out how to you know, productively integrate
Speaker 4: this technology into our society in a non disruptive way,
Speaker 4: writers are going to like, I mean, I write a
Speaker 4: decent amount, and AI is a great tool for getting
Speaker 4: over writer's block if you need to push through something
Speaker 4: where you're at a wall, having this other entity that
Speaker 4: can brainstorm with you. They're great research assistants and they're
Speaker 4: great tools for creativity. Where we're going to hit some
Speaker 4: bumps is that they weren't built for a specific purpose.
Speaker 4: They were built to meet Tearing's benchmarks. So now we're
Speaker 4: gonna have to go through a phase where we figure out, Okay,
Speaker 4: we built this thing, Now what is it actually good
Speaker 4: for us for?
Speaker 1: Do you think that AI has any role in solving
Speaker 1: the climate change issue?
Speaker 4: If AI can be used to crack something like cold fusion,
Speaker 4: then yes, But barring those kinds of transformational technology breakthroughs.
Speaker 4: I don't think it does, and that's because we kind
Speaker 4: of already know what the solution to climate change is.
Speaker 1: Change in human behavior.
Speaker 4: Yeah, change in human behavior. We just don't want to
Speaker 4: do it. Maybe AI could persuade people to change lifestyles,
Speaker 4: but really, we have a certain number of people on
Speaker 4: the planet consuming a certain amount of energy, and on
Speaker 4: average that creates a certain amount of heat, and we
Speaker 4: just need to change our behavior to where we're not
Speaker 4: creating so much heat. So it is possible that AI
Speaker 4: will give us cold fusion, in which case, yes, it
Speaker 4: would help us with that.
Speaker 1: Or super conductivity as well, something anything that would fundamentally
Speaker 1: change the thermodynamics of our energy grid.
Speaker 4: But barring a breakthrough like that, we just really do
Speaker 4: have to focus on changing human behavior and human priorities.
Speaker 1: Now, what's next for you? If you could do whatever
Speaker 1: you wanted to do, you're obviously this searingly bright guy
Speaker 1: who knows all about these important things. If you could
Speaker 1: do whatever you wanted to do in the short term,
Speaker 1: what would that be? Oh?
Speaker 4: I mean, AI ethics research is what I've enjoyed doing
Speaker 4: the most. But one of the things that I keep
Speaker 4: getting struck by is I keep getting invited on shows
Speaker 4: like this to speak and there's a part in the
Speaker 4: back of my brain that just goes, am, I really
Speaker 4: the best representative to the public on these topics, and
Speaker 4: apparently there just aren't that many people who are technically
Speaker 4: well versed in how these systems work who were willing
Speaker 4: to perform this role as kind of a bridge to
Speaker 4: the public to understanding them better. And while that's not
Speaker 4: really the thing that I'm most passionate about in life,
Speaker 4: it's important and the fact that I get to perform
Speaker 4: that service to help educate people is meaning and I'm
Speaker 4: happy to be doing it. It's and plan to continue doing
Speaker 4: it as well.
Speaker 1: Many thanks to you, many thanks.
Speaker 4: It was wonderful being here. Thank you Alix, Thank you
Speaker 4: sir My.
Speaker 1: Thanks to Blake Lemoine and jay Lebuff. This episode was
Speaker 1: recorded by a robot, but it was also recorded at
Speaker 1: CDM Studios in New York City. We're produced by Kathleen Russo,
Speaker 1: Zach MacNeice, and Maureen Hobin. Our engineer is Frank Imperial.
Speaker 1: Our social media manager is Danielle Gingrich. I'm Alec Baldwin.
Speaker 1: Here's the thing is brought to you by iHeart Radio,