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Speaker 2: Always read the leafless Oh hey, it's the tag that
Speaker 2: you wish you'd cut out of your shirt, Ali Ward,
Speaker 2: and for every one of us that has seen AI
Speaker 2: become more and more present in our lives and wondered,
Speaker 2: is anyone driving this bus? I have here for you
Speaker 2: a chat with an expert who tells us exactly who
Speaker 2: is driving the bus and where it could be headed.
Speaker 2: Is AI evil? Does AI even care about us? Is
Speaker 2: it going to kill us? Should we feel bad for it?
Speaker 2: Don't ask me, I'm not theologist. We're going to get
Speaker 2: to it now. This expert is a Senior Fellow in
Speaker 2: Trustworthy AI and an assistant professor at the School of
Speaker 2: Computer Science and Statistics at Trinity College in Dublin, Ireland.
Speaker 2: And they're cognitive scientists. They research ethics in artificial intelligence,
Speaker 2: and they've published papers with such titles as The Forgotten
Speaker 2: Margins of AI, Ethics, Toward Decolonizing Computational Sciences, the Unseen
Speaker 2: Blackfaces of AI algorithms, and the Values Encoded in Machine
Speaker 2: Learning research. So they're on it, and I got to
Speaker 2: sit down and visit and chat in person when I
Speaker 2: was in Ireland just last month. Also just a pleasing
Speaker 2: aesthetic side note. They were born in Ethiopia but lives
Speaker 2: in Ireland, and this expert has the most melodic cadence,
Speaker 2: just like pyork I was mesmerized. We're going to get
Speaker 2: to all that in a sec but first, if you
Speaker 2: ever need shorter, kid friendly episodes with no adult language,
Speaker 2: we have Smologies their episodes and their own feed wherever
Speaker 2: you get podcasts. Also linked in the show notes. That's Smologies.
Speaker 2: Also thank you to patrons for supporting ologies and sending
Speaker 2: in questions ahead of time. You can join for as
Speaker 2: little as a dollar a month at patreon dot com
Speaker 2: slash Ologies. Thanks also to everyone who's ever left a
Speaker 2: review for the show. They helped so much, and I
Speaker 2: read all of them weirdly, including this recent one by Remily,
Speaker 2: who wrote, this podcast will expand your horizons and help
Speaker 2: you indulge in your hyperfrixations, both known and yet to
Speaker 2: be discovered. Remily also says sorry has taken me over
Speaker 2: five years to finally write a review. Emily anytime, it's
Speaker 2: a good time. Thanks for that. Okay, let's get right
Speaker 2: into artificial intelligence ethicology. It's the ethics of machine cognition.
Speaker 2: Is a cognition, we'll talk about it. What does chat
Speaker 2: CHPT stand for? Why is Siri a lady? Can you
Speaker 2: ask a robot for a cheeseburger?
Speaker 4: Yet?
Speaker 2: What happens when you're rude to a chatbot? Also? How
Speaker 2: do artists prevent getting ripped off? How much energy does
Speaker 2: AI take up when we all lose our jobs? Booby traps,
Speaker 2: doorbell marks commonly used fallacies. How the creators of AI
Speaker 2: feel about AI? What is hype and what is horror?
Speaker 2: What are the benefits of AI? What happens if you
Speaker 2: assign a chat bot your homework? And whether or not
Speaker 2: AI is the root of all evil or a pocket
Speaker 2: pal with embodied Cognitive scientist professor, scholar and artificial intelligence ethicologist,
Speaker 2: doctor Ababa Burhane.
Speaker 4: Some questions that would take hours to one robin, and
Speaker 4: they expect you to answer everything like one minute, two
Speaker 4: minute marks.
Speaker 2: They're rushing it again, So no worries.
Speaker 4: I am above Abrahani, she.
Speaker 2: Her great and AI. You have been an expert in
Speaker 2: this field for a while, but I haven't known about
Speaker 2: AI for that long. How long have you been studying it?
Speaker 4: Technically speaking? I am a cognitive scientist, so I finished
Speaker 4: my PhD about three years ago in cognitive science. So
Speaker 4: halfway through my PhD, then around the end of my
Speaker 4: second year, I left the cognitive science department and joined
Speaker 4: a lab where people do a lot of tasting if
Speaker 4: I eating and tasting of you know, chatboats in various
Speaker 4: AI models.
Speaker 2: And what is exactly cognitive science.
Speaker 4: Cognitive science is very broad, so traditional cognitive science tends
Speaker 4: to be about you know, understanding cognition, understanding you know,
Speaker 4: human behavior, understanding human interaction, and so on. In cognitive
Speaker 4: science often is not taught at graduate level. It's either
Speaker 4: at a master's livery or a PhD level because cognitive
Speaker 4: science is really interdisciplinary.
Speaker 2: And doctor Brownie says that cognitive science is actually a
Speaker 2: mishmash of disciplines or what sometimes called the cognitive hexagon,
Speaker 2: besides representing philosophy, psychology, linguistics, neuroscience, artificial intelligence, and anthropology
Speaker 2: according to some institutions, and anthropology can also mean social sciences.
Speaker 2: Different institutions will phrase it their own way, but broadly speaking,
Speaker 2: cognitive science is a lot of stuff.
Speaker 4: So the idea is you will take you know, important
Speaker 4: or helpful aspects from these various disciplines, and cognitive science
Speaker 4: then allows you to synthesize, to combine these various theories
Speaker 4: even computational models to understand human cognition. That's the idea.
Speaker 4: So from philosophy, for example, you will learn how to
Speaker 4: question assumptions. You will go down into the various questions
Speaker 4: around what's cognition, what's intelligence? You know, what's human emotions.
Speaker 4: So philosophy really lends you the analytic tools. Same from neuroscience.
Speaker 4: The idea is you get to learn how the brain
Speaker 4: works and use it in a way to synthesize from
Speaker 4: all these different disciplines. So that's traditional cognitive science.
Speaker 2: You can call cognitive science cogsie if you want to,
Speaker 2: And she says that she's in a really niche specialty
Speaker 2: within COGSI, and it's called embodied cognitive science, which isn't
Speaker 2: just about understanding the human brain.
Speaker 4: Whereas embodied cognitive science is moving away from this idea
Speaker 4: of treating cognition in isolation. Your cognition doesn't end at
Speaker 4: your brain, and your sense of self doesn't end at
Speaker 4: the skin, but rather it's extended into the tools you use.
Speaker 4: Anything you do, you do it as an embodied self,
Speaker 4: as an embodied person. So your body, your social circle,
Speaker 4: your history, your culture, even your gender and sexuality, all
Speaker 4: these are important factors that play into you know, your understanding,
Speaker 4: your cognition, your intelligence, your emotion and so on.
Speaker 2: And when you're studying cognitive science, how do you not
Speaker 2: use your brain to think about your brain all the time?
Speaker 2: How do you get out of your brain you doing?
Speaker 4: Yeah, yeah, yeah, I mean you have to, you have
Speaker 4: to use your brain. So you're familiar, you know with
Speaker 4: the cards famous quote collegito or gossam, so I think
Speaker 4: therefore I am. The idea is you can't know who
Speaker 4: you are. You can know you are thinking big, you
Speaker 4: can know that you are someone. So that is like
Speaker 4: using your brain to understand your brain, so to speak. Whereas,
Speaker 4: again the emphasis with embodied cognitive science is that you know,
Speaker 4: all that is really very individualistic. Even to confirm our existence,
Speaker 4: it's through conversations with others. So that's why embodied cognitive
Speaker 4: science emphasizes others and communities and your body as really
Speaker 4: important factors in understanding cognition.
Speaker 2: So embodied cognitive science kind of goes downstairs a bit,
Speaker 2: and it considers how a person's body experiences the world
Speaker 2: and how the environment shapes thinking and perception. And cognitive
Speaker 2: scientists have this thought experiment that tickled me. And it
Speaker 2: envisions a little person in your brain interpreting inputs. But
Speaker 2: then who is in the brain inside that little person's brain?
Speaker 2: Like does it have its own brain inside the little
Speaker 2: person's It's like Russia nesting dolls, and there are just
Speaker 2: infinity little humans inside humans brains to interpret what the
Speaker 2: brain inside the brain is braining. And this is called
Speaker 2: endearingly the homunculous argument. And yes, embodied cognitive science is like,
Speaker 2: it's more than a tiny person in your skull. And
Speaker 2: how can we truly understand artificial intelligence if we don't
Speaker 2: first grasp intelligence intelligently. And when we're thinking about who
Speaker 2: we interface with with AI? Way back in the day,
Speaker 2: there used to be a search engine called ask Jeeves
Speaker 2: and it was like a butler who would find you
Speaker 2: the answers to things. And now we have Siri and Alexa.
Speaker 2: Why do you think with AI they've gendered and they've
Speaker 2: personified these voices that are actually a huge network of
Speaker 2: artificial intelligence? Is that to help our brains understand It's
Speaker 2: a little bit of like.
Speaker 4: A marketing strategy, but it's also a little bit of
Speaker 4: appealing to human nature. We tend to kind of gender
Speaker 4: and personify objects. So if you are interacting with chat
Speaker 4: ChiPT for example, we tend to just naturally, you know,
Speaker 4: treat it as another person, an as a being, an
Speaker 4: ather entity. On the one hand, as you said, these
Speaker 4: chat boats, you know, there is no intention, there is
Speaker 4: no understanding, there is no soul in these machines. There
Speaker 4: are just pure machines. But also the developers and vendors
Speaker 4: of these systems, they tend to market them as kind
Speaker 4: of personified entities because it's much more appealing to think
Speaker 4: that you are interacting withan asar synthy and think.
Speaker 2: I always wonder if they made some of them female voices,
Speaker 2: because we're more accepting, we're less threatened by females. We're
Speaker 2: socialized to have a mommy figure come and help us
Speaker 2: with something. It's not as threatening that it'll turn on us.
Speaker 2: And also it's like, oh, Lydia, don't get it for you,
Speaker 2: you know.
Speaker 4: Yeah, yeah, I mean, we naturally tend to think women
Speaker 4: are much more like nurturing and they have the role
Speaker 4: of helping you. So it is kind of related to
Speaker 4: the social norms that really dictate society. So it's really
Speaker 4: also to some extent, leaning on that stereotype that if
Speaker 4: it's a woman, you know, it's approachable, it's there to
Speaker 4: help you.
Speaker 2: And yes, I am far from the first person to
Speaker 2: notice that digital assistance tend to be ladies. And some
Speaker 2: histrians and media scholars think that it starts early by
Speaker 2: hearing a female voice while you're cooking in the womb,
Speaker 2: and that's why people love a lady voice, or that
Speaker 2: the first telephone operator in the late eighteen hundreds happened
Speaker 2: to be a woman with a great voice and then
Speaker 2: it just stuck. But as these AI avatars start to
Speaker 2: have faces and personalities and pastimes and instagrams, why do
Speaker 2: we see mostly younger, hotter avatars. Well, there was a
Speaker 2: twenty twenty four article published by Reuter's Institute and it
Speaker 2: reached out to this multimedia professor April Newton, who noted
Speaker 2: that a gentle, well modulated woman's voice is usually the
Speaker 2: default for AI assistants and avatars because, quote, we order
Speaker 2: those devices to do things for us, and we are
Speaker 2: very comfortable ordering women to do stuff for us. And also,
Speaker 2: just a side note for me, did you know that
Speaker 2: the word robot it comes from a Slavic root for
Speaker 2: forced labor or slaves. Creepy. Historically humans have loved capable service,
Speaker 2: but not too capable or else you're just begging for
Speaker 2: an uprising. So the future, it's also the past. Am
Speaker 2: I rooting for the robots? Now? I don't know. When
Speaker 2: it comes to a sery or virtual assistance? How is
Speaker 2: that different than the AI that's been ramped up in
Speaker 2: the last couple of years. Is Siri and Alexa? Are
Speaker 2: those ais? Or are those just search engines? Is Google
Speaker 2: an AI? We call something's AI and then other things
Speaker 2: just computing? What's the difference?
Speaker 4: Yeah, so what's really difference between say traditional AI whizard
Speaker 4: it's you know, implemented as a chat boat or as
Speaker 4: a predictive system, you know, generative AI that has really
Speaker 4: exploded over the past two years, think of chat gipt,
Speaker 4: you know, Gemini in cloud and so on. The fundamental
Speaker 4: difference is that do I go into the technical details
Speaker 4: or remain clear of the.
Speaker 2: You can give us some technical details.
Speaker 4: I'll try to keep it light. There is no way
Speaker 4: of explaining the difference without getting into reinforcement learning. A
Speaker 4: typical classification system is an algorithm that you give it
Speaker 4: massive amounts of data. Think of like a face identification system,
Speaker 4: and these days data sets have to be really big.
Speaker 4: You might even have you know, trillions of tokens of
Speaker 4: images of faces, and you train your algorism like this
Speaker 4: is a face, this is a face, this is not
Speaker 4: a face, this is a face, this is not a face.
Speaker 4: Called machine learning. If you have succeeded in your training,
Speaker 4: then your AI should be able to tell you it's
Speaker 4: a face or it's not a face. So this is
Speaker 4: like a typical you know, classification system. What we consider
Speaker 4: AI is a very broad term that encompasses so many
Speaker 4: different subcategories.
Speaker 2: Just side out. How alone am I in not knowing
Speaker 2: that Apple Virtual assistance theory stands for something each interpretation
Speaker 2: and recognition interface. Did you know SIRI was an acronym?
Speaker 2: I didn't. Also, Apple is reportedly freaking out behind the
Speaker 2: scenes about Chat giput for the last three years being
Speaker 2: generative and having Chat bought capabilities and longer conversations and Siri.
Speaker 2: So apparently a lot of their efforts in self driving
Speaker 2: cars got shuttled over to their AI division, and a
Speaker 2: lot of people at Apple are like, I can't even
Speaker 2: speak publicly about this.
Speaker 4: The other big subcategory under the broad m brilav AA
Speaker 4: is NLP or natural language processing. So this is the
Speaker 4: area that deals with human language. So you have audio
Speaker 4: data that you feed into the AI system, and the
Speaker 4: idea there is that you know, you are building an
Speaker 4: AI system that even make predictions about human language, and
Speaker 4: ptools would learn, for example, predictive texts, so what the
Speaker 4: algorithm is doing is kind of predict what the next
Speaker 4: world is likely to be. So it's just predicting the
Speaker 4: nix stoken the next stoken. So that's kind of traditional
Speaker 4: classification or predictive systems.
Speaker 2: And that was machine learning or NLP, which is natural
Speaker 2: language processing, and those deal with visual data turned into
Speaker 2: tokens and they predict language like when your phone knows
Speaker 2: you better than you know yourself, and it's heartwarming and
Speaker 2: it's scary. It's like love now chat GPT, for example,
Speaker 2: I didn't know this, but the GPT stands for generative
Speaker 2: pre Trained Transformer, and a transformer is this type of
Speaker 2: deep learning system and it converts info into tokens and
Speaker 2: can handle more complex processing from language to vision to
Speaker 2: games and audio generation. So it's definitely a step up
Speaker 2: from just simple prediction.
Speaker 4: Whereas over the past year with generative AI, these are
Speaker 4: AI systems that do more than classification, more than prediction,
Speaker 4: more than aggregation, that are called generative A systems. They produce,
Speaker 4: you know, something new. So image generators, for example, you
Speaker 4: can put a TICS description as a prompt in the
Speaker 4: AI system will produce an image that resembles based on
Speaker 4: your description. The same with language systems, so chuch BT
Speaker 4: for example, you put in any prompt and it's able
Speaker 4: to produce new answers. So this is where this is
Speaker 4: what's new with generative systems.
Speaker 2: And the systems also need to learn which of the
Speaker 2: words in a language model are the important ones, which
Speaker 2: is part of the self attention mechanism, and then they
Speaker 2: generate based on statistics like what is the most probable
Speaker 2: way based on the data setts learned from from completing
Speaker 2: a certain sentence or prompt.
Speaker 4: So the training data really has a really significant impact
Speaker 4: in what outputs, whether it's image or text. What outputs
Speaker 4: these those can't generate.
Speaker 2: Let's get to the juicy parts. So with any tokens,
Speaker 2: would that data be in tokens? Like you were saying,
Speaker 2: facial recognition might have a trillion tokens? Does AI kind
Speaker 2: of scrub what we know of impressionist art and science
Speaker 2: fiction and anime does it scrub it and grab a
Speaker 2: bunch of tokens, so then it can have those as
Speaker 2: reference points.
Speaker 4: So the training data sets is one of the most
Speaker 4: contested issues in AI because data is constantly harvested. So
Speaker 4: like your search history feeds into some kind of AI
Speaker 4: one way or anazon I don't know if you are
Speaker 4: like me, if you signed up for some kind of
Speaker 4: bonus point, you go to a grocery shop and you
Speaker 4: tap that, so that is kind of like in the background.
Speaker 4: That's kind of conllicting your behavioral data.
Speaker 2: So that data may breeze through infrastructure like Google and
Speaker 2: then just be on its merry way to a third
Speaker 2: party aggregator or a broker or an AI company itself,
Speaker 2: and they're just yam yam yam gobble of the data.
Speaker 4: So this kind of practice puts a lot of data
Speaker 4: gathering for the purpose of training AI systems either illegal
Speaker 4: or borderline illegal. Yes, because you know, first of all,
Speaker 4: there is no consent. People are not even aware that
Speaker 4: you know their data is being used to train AI.
Speaker 2: So that's just number one on a general level. Let's
Speaker 2: get to art.
Speaker 4: But you will also have noticed over the past year
Speaker 4: or two the creative community, writers and artists are realizing
Speaker 4: that their work, their novels, their writing, their their arts
Speaker 4: are being used to train AI systems, again without any compensation,
Speaker 4: or with very little compensation after the fact if they
Speaker 4: go and contest you know, the use of their data.
Speaker 4: And because large companies that are kind of developing AI,
Speaker 4: you know, think of Google, Deepmined, Meta, open AI, anthropic,
Speaker 4: they are businesses. They operate under the business model. They
Speaker 4: are you know, commercial entities. Their objective is to maximize profits.
Speaker 4: My work is auditing, for example, large scale training data sets.
Speaker 4: People don't have access to what kind of data set
Speaker 4: this company is hold, So we don't have any mechanism
Speaker 4: to kind of take a look to scrutinize what's what
Speaker 4: you know, what's in the data is it, where does
Speaker 4: it come from? How large is it? These are the
Speaker 4: kind of questions that simply there is just no mechanism
Speaker 4: for tach corporations to be transparent, to open up, for
Speaker 4: us to have an objective understanding.
Speaker 2: So big companies are like, no, you can't see it.
Speaker 2: But auditors like doctor Bhaney and colleagues can observe open
Speaker 2: source publicly available similar data sets as proxies or substitutes
Speaker 2: to kind of figure out what might be going on
Speaker 2: organizationally behind the locked Willi Wanka factory gates, the other
Speaker 2: big tech companies.
Speaker 4: So we do get an idea, but through proxy data sets.
Speaker 4: So to answer your question, I mean, if you are
Speaker 4: an artist, a writer, if you are produced novels and
Speaker 4: so on, it's very very likely that your work is
Speaker 4: being used to train AI. But there's very little legal
Speaker 4: mechanism to actually have a clear idea.
Speaker 2: Is it like they're stealing a bunch of ingredients and
Speaker 2: then making something but they're like, you can't see our recipe,
Speaker 2: and you're like, you stole the ingredients and they're like, yeah,
Speaker 2: their resume, we made something with it. That is that
Speaker 2: sort of great.
Speaker 4: Yeah, So the fact that they are protected by proprietary
Speaker 4: rights as a commercial entity means that there is virtually
Speaker 4: no mechanism to, you know, to force these companies to
Speaker 4: open up their data sets. Of course, you can encourage them,
Speaker 4: you can kind of you know, appeal to their good
Speaker 4: sites and so on.
Speaker 2: But.
Speaker 4: Yeah, there is no there is no legal mechanism. There
Speaker 4: is no law or regulation that says you have to
Speaker 4: open access or you have to you know, share your.
Speaker 2: Data set, and why not you ask?
Speaker 4: Of course, if that is to happen, then you know,
Speaker 4: all these AI companies would go out of business. As
Speaker 4: it is, a lot of them are under a lot
Speaker 4: of lawsuits. Oh yeah, you know, from from Meta to OPENAEI.
Speaker 4: Openae itself is under a lot of lawsuits, including from
Speaker 4: the New York Times. So the minute they open up
Speaker 4: their data, it becomes clear that a lot of people's suspicions,
Speaker 4: especially you know, the creative and artists communities. The minute
Speaker 4: the data sets are open, why they are going to court?
Speaker 2: Is there any recourse that artists writers have like, is
Speaker 2: there anything they can do other than trying to open
Speaker 2: a really big expensive lawsuit.
Speaker 4: Yeah, so writers in creatives are organizing for class action suits.
Speaker 4: I know there are a bunch of class action lawsuits.
Speaker 4: Was in the US and in the UK.
Speaker 2: Now last August there was this landmark case and it
Speaker 2: decided in favor of artists. It found that generative AI
Speaker 2: systems like mid Journey and Devian Arts, dream Up and
Speaker 2: Stability AI's Stable Diffusion, that those were violating copyright law
Speaker 2: by using data sets A billions of artistic examples scraped
Speaker 2: from the web, and getty Images sued Stable Diffusion for
Speaker 2: copyright infringements, and the evidence was like almost funny if
Speaker 2: it were such a bummer. But apparently the Generative AI
Speaker 2: so relied on getty images that it started adding a
Speaker 2: blurry gray box to some of its AI output, which
Speaker 2: was learned from the iconic getty images watermarks. Embarrassing now.
Speaker 2: In March of this year, a judge ordered that the
Speaker 2: lawsuit between the New York Times and open ai can proceed,
Speaker 2: despite open ai begging it not to, and the newspaper
Speaker 2: New York Times, along with a few other journalism outlets,
Speaker 2: alleges that open ai scraped a lot of their work
Speaker 2: to train chat GPT. So there are lawsuits, but there
Speaker 2: are also just huge glaring holes.
Speaker 4: But also in the UK, for example, they are considering
Speaker 4: a regulation that leaves massive loophole for intellectual copyright issues
Speaker 4: that leaves artists and writers with absolutely no protection at all.
Speaker 4: So people are really organizing on the ground and they
Speaker 4: have massive amounts of petitions signed and so on. But
Speaker 4: also there are technical, i don't want to say solutions,
Speaker 4: technical kind of remedies. So you can use data poisoning tools.
Speaker 4: For example, you have oh yeah, so you have like
Speaker 4: booby trap like one of them is called to night shed.
Speaker 4: So for images, for example, you would insert various adversarial
Speaker 4: attacks that we make the data unusable for machines because
Speaker 4: it's maybe a tiny pixel that's been altered so that's
Speaker 4: not visible to the human eye, but it kind of
Speaker 4: misses with the automatage system or how machines kind of
Speaker 4: use this data as it. So there are various tools
Speaker 4: like that.
Speaker 2: Another type of AI booby trap is called a tarpit,
Speaker 2: and it sends AI crawlers in this infinite loop where
Speaker 2: they just get stuck and they can't escape. And I
Speaker 2: just love to think of like the AI system on
Speaker 2: the toilet, just scrolling, just not being able to exit
Speaker 2: and get back to work.
Speaker 4: Even if you find tachn care solutions, now, the big
Speaker 4: companies are likely to come back with another solution that
Speaker 4: makes your own solution defunct. So yeah, you really have
Speaker 4: to be adapting with that constantinly, so, I think a
Speaker 4: vibrent solution has to come from the regulation from the
Speaker 4: legal space.
Speaker 2: And how do you feel about is that?
Speaker 4: Sam s Oh my gosh, thank you.
Speaker 2: I almost said Sam Edelman, which is a shoemaker, how
Speaker 2: do you feel about kind of some recent changes, Like
Speaker 2: last May. I believe he went before Congress in the
Speaker 2: US saying like, hey, we better watch out, and now
Speaker 2: I think he was like at the inauguration. So we
Speaker 2: chatted about this in twenty twenty three's Neurotechnology episode, because
Speaker 2: just a few months prior, in May twenty twenty three,
Speaker 2: Sam Altman was in the news a lot as a
Speaker 2: cautionary voice, and as we said in that episode, if
Speaker 2: you're wondering why this was a big deal. Sam Altman
Speaker 2: is the head of open Ai, which invented chat GPT,
Speaker 2: and in spring of twenty twenty three, he spoke at
Speaker 2: the Senate Judiciary Committee Subcommittee on Privacy, Technology and the Law.
Speaker 2: Hearing called Oversight of AI Rules or Artificial Intelligence. He
Speaker 2: also signed a statement about trying to mitigate the risk
Speaker 2: of extinction, and he told the committee that quote, AI
Speaker 2: could cause significant harm to the world.
Speaker 5: My worst fears are that we cause significant we the field,
Speaker 5: the technology, the industry caused significant harm to the world.
Speaker 5: I think that could happen in a lot of different ways.
Speaker 5: I think if this technology goes wrong, it can go
Speaker 5: quite wrong, and we want to be vocal about that.
Speaker 5: We want to work with the government to prevent that
Speaker 5: from happening.
Speaker 2: And ultimately Altman urged the committee to help establish a
Speaker 2: new framework for this new technology. And though in twenty
Speaker 2: sixteen Altman declared that Donald Trump was terrible, he recently
Speaker 2: backpedaled on that and Altman said that he's changed his
Speaker 2: mind and donated one million dollars to Trump's reelection campaign
Speaker 2: in twenty twenty four. So Altman's thoughts on AI regulations
Speaker 2: likely have pivoted in the last few years since that hearing.
Speaker 2: It doesn't seem like regulations are gonna happen very fast.
Speaker 4: Yeah, So this idea of oh, we gotta watch out
Speaker 4: because our AIS might become sentient and might be out
Speaker 4: of control, might cause existential risk and lead to human extinction.
Speaker 4: So unfortunately, this is a very popular and commonly dissimilated
Speaker 4: worry emerging out of AI, but there is very little
Speaker 4: scientific evidence for it. People have done a Toto analysis
Speaker 4: how such a possibility is just zero percent likely there,
Speaker 4: it's just.
Speaker 2: Really yeah, but human beings can be so terrible and
Speaker 2: they're learning from us.
Speaker 4: Yeah, so there is no intention there is no wish
Speaker 4: or there is no desire to act on something, to
Speaker 4: do something. But at the end of the day, it
Speaker 4: really is a massive, complex algorithm. Of course, and that
Speaker 4: is to some extent, I'm predictable, But that doesn't mean
Speaker 4: you know AI systems as they develop further, you know,
Speaker 4: all of a sudden develop intentionality or wishes or interests
Speaker 4: or needs. I mean, you and I are you know,
Speaker 4: as human beings, we do something and we get satisfaction
Speaker 4: out of it. I have a motivation for doing the
Speaker 4: research I do, and if that doesn't happen, I feel disappointed.
Speaker 4: I can feel sad. I also feel accountable when I
Speaker 4: put out, you know, a research paper. I know if
Speaker 4: there are errors in it, I know I'm the one
Speaker 4: responsible for it. So there is none of that. So
Speaker 4: when an AI system gives you an output, it's not
Speaker 4: because it might be worried about if it's an incorrect answer,
Speaker 4: or it's because it wants to please you. It's just
Speaker 4: a chatbot that is designed to provide, you know, give
Speaker 4: answers again based on prompt So this idea of a
Speaker 4: systems causing existential risk leans on this huge leap of
Speaker 4: faith that requires you to believe that there is intention,
Speaker 4: there is emotion, there is motivation, all these human characters,
Speaker 4: all these things that makes us human, but it's just
Speaker 4: not there. It can't emerge out of nowhere. That's part
Speaker 4: of what makes us different and unique as individuals, as
Speaker 4: biological organisms. These are things that are hardware on us.
Speaker 4: These are also things that makes us human.
Speaker 2: This is why I fret.
Speaker 4: Still, of course, we have to worry about powerful people
Speaker 4: using AI to do terrible things. And what worries me
Speaker 4: is over the past year and especially since the rise
Speaker 4: of Trump and since the Trump administration came to power,
Speaker 4: you have a lot of large corporations really abandoning their
Speaker 4: voluntary place to protect basic fundamental rights. So Meta has worked,
Speaker 4: for example, walked back their commitment for DEI, their commitment
Speaker 4: to fact check and monitor their social media platforms.
Speaker 2: Against hate speech two as well, which is.
Speaker 4: What exactly what really is worrying also now is you
Speaker 4: have superpowers and powerful governments like the US government, the
Speaker 4: UK government, even the European Union itself using AI, moving
Speaker 4: into AI for surveillance for military purposes, for warfares, and
Speaker 4: a lot of AI companies starting from you know, Open Ai, Meta, Amazon, Google,
Speaker 4: they had voluntary principles not to use AI for military purposes,
Speaker 4: but over the past year all of them have abandoned that.
Speaker 4: So even here across in the EU you have a
Speaker 4: French AI company called Mistral announcing that they are open
Speaker 4: to working with European governments to provide military AI. So
Speaker 4: of course we have to worry about governments using AI
Speaker 4: under the guise of national security, which really means you know,
Speaker 4: monitoring and surveillance and squishing dissent and really this is
Speaker 4: against you know, fundamental rights for freedom of expression, freedom
Speaker 4: of movement, and so on. So we have to worry
Speaker 4: about AI. But AI in the hands of powerful governments
Speaker 4: and people in position of power rather than the AI itself,
Speaker 4: because the AI can't do anything by itself.
Speaker 2: Is it sort of like the guns don't kill people,
Speaker 2: people kill people kind of a situation, Yeah, exactly. How
Speaker 2: is that working out for us in the US? Though? Well?
Speaker 2: Death by firearm is the leading cause of mortality for
Speaker 2: teens and children in the US, according to the Pew
Speaker 2: Research Center, and over half of our nearly fifty thousand
Speaker 2: gun deaths a year in the US are suicides. That's
Speaker 2: not going well, and that's because the NRA slogan guns
Speaker 2: don't kill people, people do is what is known as
Speaker 2: bumper sticker logic or a misdirection, also called a false
Speaker 2: dichotomy or plainly speaking, a fallacy according to philosophers. So
Speaker 2: giving AI a sense of technological neutrality is a bit misguided.
Speaker 2: The regulations being walked back is terrifying, especially trying to
Speaker 2: put trust in a government to stop things when a
Speaker 2: lot of our people in power, like don't know how
Speaker 2: to use their own printers, you know. So some of
Speaker 2: the questions, and like the congressional hearings are like, how
Speaker 2: does this even work to track my movement?
Speaker 3: Does Google through this phone know that I have moved
Speaker 3: here and moved over to the left.
Speaker 2: Which is terrifying. So maybe they don't have morals and
Speaker 2: guilts and things like that and ambitions. But I was
Speaker 2: looking at some research showing that AI is being trained
Speaker 2: to become more and more sexist, more and more xenophobic,
Speaker 2: more and more racist us more and more hate speech.
Speaker 2: And is it learning from the worst of humanity? Is
Speaker 2: it amplifying it? Is that just exposing how much hate
Speaker 2: is in the world.
Speaker 4: Yeah, so let's maybe walk back twenty years because that's
Speaker 4: when you know, real progress in AI started to emerge.
Speaker 4: I mean, we've had a lot of the core principles
Speaker 4: for AI, you know, since in the nineteen fifty sixty, seventies, eighties,
Speaker 4: Like some of the foundational papers about reinforcement learning deep
Speaker 4: learning were written in the nineteen eighties. So jeff Hinton's
Speaker 4: famous paper on I think what was convulsional learning was written,
Speaker 4: you know, late nineteen eighties.
Speaker 2: We don't have to go too deep into it, but
Speaker 2: I do want to tell you that Jeffrey Hinton is
Speaker 2: apparently considered the godfather of AI and a leading figure
Speaker 2: in the deep learning world, and in twenty twenty four
Speaker 2: he won the Nobel Prize for his work. He's also
Speaker 2: worked for Google Brain, and then he quit Google because
Speaker 2: he wanted to quote freely speak about the risks of AI.
Speaker 2: Quit Google so he could talk about it now. In
Speaker 2: twenty twenty three, during a CBS Saturday Morning news segment,
Speaker 2: he warned about deliberate misuse by malicious actors, unemployment, and
Speaker 2: existential risk involving AI. He is very much in favor
Speaker 2: of research on the risks of what could become a
Speaker 2: monster that he helped create. He's like, you know, we
Speaker 2: need some safety guidelines and regulations, buddies, and that it's
Speaker 2: not really happening, but yes, he is among you who
Speaker 2: over the last many decades drove these innovations.
Speaker 4: But what really made the AI revolution possible is the
Speaker 4: world Wide Web. With the emergence of the world Wide Web,
Speaker 4: became possible to kind of script to kind of gather
Speaker 4: harvest massive amounts of data from the world Wide Web,
Speaker 4: you know, through chat forums or domains like Wikipedia. They
Speaker 4: are really a core element of training AI, at least
Speaker 4: for text data. So that means that a lot of
Speaker 4: our training material for AI comes from the world Wide Web,
Speaker 4: whether it's our digital traces, whether you know, it's the
Speaker 4: pictures we put on social media, pictures of your kids,
Speaker 4: your dogs, yourselves and so on, or the kind of infrastructure.
Speaker 4: Digital infrastructure like Google is everywhere has dominated whether you
Speaker 4: want to email or you know, prepare a presentation or
Speaker 4: write a document. Google has provided the infrastructure that means
Speaker 4: they have the infrastructure to constantly harvest training data. This
Speaker 4: means that a lot of the data that we are
Speaker 4: using for training reflects, you know, humanities beauty, but also our.
Speaker 6: In the ugliness of humanity, and just last week tech
Speaker 6: report released by Google admitted that its Gemini two point
Speaker 6: five flash model is more likely than its predecessor model
Speaker 6: two point zero to generate results outside of its safety guidelines,
Speaker 6: and images are even worse than text at that and
Speaker 6: I mentioned this in a twenty twenty three episode we
Speaker 6: did with doctor Nita Farhani about neurotechnology.
Speaker 2: But around June teenth of that year, I saw this
Speaker 2: viral tweet about chatgept not acknowledging that the Texas and
Speaker 2: Oklahoma border the Panhandle was in fact influenced by Texas
Speaker 2: desiring to stay a slave state, which is fact that
Speaker 2: chat GPT would not acknowledge. So doctor Barhani notes that
Speaker 2: when an AI is built on racist, sexist, xenophobic, et
Speaker 2: cetera data set, the results, like history itself, are not
Speaker 2: kind to minoritized identities.
Speaker 4: She says, it reflects, you know, societal norms, its reflections,
Speaker 4: you know, historical injustices and so on, unless you really
Speaker 4: delve into the data set and ensure that you do
Speaker 4: a thorough job of cleaning the data set. We've audited
Speaker 4: numerous data sets, and you find content that shouldn't be there.
Speaker 4: You find, you know, images of genocide, images of you know,
Speaker 4: child rip. One of the early data sets we audited
Speaker 4: back in twenty nineteen was a data set called eighty
Speaker 4: million Tiny Images. It was held by Mighty, and we
Speaker 4: found several thousands of images, really problematic images, Images of
Speaker 4: black people labeled with the N word, images of women
Speaker 4: labeled with the B word, the C word, and words
Speaker 4: I can't really say on air.
Speaker 2: So while the upside of AI is detecting cancer from
Speaker 2: scans earlier or predicting tornado patterns, there's also so much
Speaker 2: concern now. Doctor Martin Luther King Junior observed and proclaimed
Speaker 2: that the arc of the moral universe is long, but
Speaker 2: it bends toward justice. I think we might consider that
Speaker 2: the arc of the Internet is short, and it bends
Speaker 2: towards smart and hate.
Speaker 4: So you can't assume any datail you collect from the
Speaker 4: web is really horrible. And in one of the reason audits,
Speaker 4: actually we found an overwhelming amount of women concepts really
Speaker 4: represented by images that come from the pornographic space. So
Speaker 4: massive amounts of the web is also really you know,
Speaker 4: pornographic and really you know, problematic content, So you have
Speaker 4: to do a lot of filtering. So as a result,
Speaker 4: this is why you know DEI initiatives. This is why
Speaker 4: obligations to audit your data a set to ensure that
Speaker 4: you know toxic contents have been removed and so on.
Speaker 4: This is why it's so critical.
Speaker 2: So an AI is only as ethical as its data sets.
Speaker 2: And the Internet is a weird dark place where people
Speaker 2: say things they would never say in person, So the
Speaker 2: data sets are feeding that.
Speaker 4: But as we are seeing now a lot of these
Speaker 4: companies are abandoning their plages and we're really walking backwards.
Speaker 4: But for any given AI system, whether it's a predictive
Speaker 4: system or classification or generating, you can't assume that deeply
Speaker 4: heled societal injustices and norms will be reflected in how
Speaker 4: that AI performs in the kind of output the AI
Speaker 4: gives you. So that's the default. So we have to
Speaker 4: work backwards to ensure we're removing those biases.
Speaker 2: Let's say that some of the comments online, some of
Speaker 2: the head online is AI generated comments, which I sometimes
Speaker 2: I'll look at now X and I'll say, who are
Speaker 2: all these people?
Speaker 4: Like?
Speaker 2: Why are comments getting meaner and meaner? With Facebook, with
Speaker 2: a lack of fact checking, more and more sort of
Speaker 2: hateful speech. Does that mean that the next tokens and
Speaker 2: data sets pick up on that and say, oh, this
Speaker 2: is how people think. And then the next one. So
Speaker 2: does it get amplified like mercury toxicity and like a
Speaker 2: tuna fish.
Speaker 4: That's one way I'm putting you. Yeah, okay, yes, yes,
Speaker 4: you are encoding those biases and you are exaggerating them.
Speaker 2: Yeah.
Speaker 4: The technical drawback is that, so we train given AI
Speaker 4: for a next world prediction, for example, it's based on
Speaker 4: you know, these massive amounts of data that kind of
Speaker 4: tells you how people taket how people use language for
Speaker 4: English for example, how people you know, construct a core
Speaker 4: and sentence. That data, that training data comes from actual
Speaker 4: people activities, people interactions. That is your baseline, so to speak,
Speaker 4: in when you are modeling how you know language operates.
Speaker 4: But now, as you said, as the world wide wave
Speaker 4: is feel more and more with you know, synthetic text
Speaker 4: or synthas data that comes from generative AI system itself,
Speaker 4: then your AI system has no frame of reference. It
Speaker 4: tends to forget, so the quality of the output starts
Speaker 4: to deterior it.
Speaker 2: That's so scary.
Speaker 4: So this is called model collapse.
Speaker 2: Okay, does this keep you aver night?
Speaker 4: Does that.
Speaker 2: I mean, I know that it's like, don't be afraid,
Speaker 2: don't be afraid, but it's also like, this is very
Speaker 2: new territory for humanity, right, Yeah.
Speaker 4: But at the end of the day, I mean, people
Speaker 4: should be in control. If an AI system starts producing
Speaker 4: outputs that is rubbish, that is irrelevant. I don't think
Speaker 4: it should scare us. It should make people like, Okay,
Speaker 4: that's not helpful to me anymore, so I'm not gonna
Speaker 4: use it.
Speaker 2: Maybe the more it unravels and crashes out, the less
Speaker 2: people will rely on it. But of course that hinges
Speaker 2: on being able to tell that it's spitting nonsense at you.
Speaker 2: And in this day and age, the world is so
Speaker 2: profoundly absurd that truly anything is believable. And doctor Burhannig
Speaker 2: says that public education is key and just getting the
Speaker 2: word out that a lot of what we think about
Speaker 2: AIS capabilities are just big corporations humping out hype and
Speaker 2: pr But the auditors on the inside, like her in
Speaker 2: her lab, know that boy, Harry hot damn, it is
Speaker 2: a bunch of horse packy flim flam, and not to
Speaker 2: believe the hype.
Speaker 4: The actual performance is nowhere near what the developers claim,
Speaker 4: So these are the facts that we really have to communicate.
Speaker 4: A lot of the AI systems that we are interacting
Speaker 4: with are actually subpar in terms of performance, in terms
Speaker 4: of what they are supposed to do, in terms of
Speaker 4: what people expect them to do. Because these big corporations
Speaker 4: have really mastered public communication in PR, A lot of
Speaker 4: like the failures or the drawbacks of AI systems are
Speaker 4: new to people when you actually communicate it. But this
Speaker 4: should really be like common knowledge, and if people want
Speaker 4: to use AI, they should know both the strengths and
Speaker 4: what they can do with it, but also where the
Speaker 4: limitations are or what it can't do for them.
Speaker 2: Does abstention work? Does not going on matter and giving
Speaker 2: them more fadder? Does not using chat GBT? Does any
Speaker 2: sort of like boycott work?
Speaker 4: Yes? And no. On the one hand, so a lot
Speaker 4: of these AI systems have really cleverly been integrated into
Speaker 4: the social infrastructure.
Speaker 2: Yeah.
Speaker 4: So for example, I'm not on Facebook. I haven't been
Speaker 4: on Facebook for over ten years, but the apartment complex
Speaker 4: I live in can only be communicated via Facebook groups.
Speaker 4: I still refuse to create a Facebook account, But situations
Speaker 4: like this really gives you very little option to abstain
Speaker 4: to not use these platforms, and you can't avoid Google,
Speaker 4: for example, Google Search and like you know, gimming and
Speaker 4: like Google Docs. It makes it really difficult if you
Speaker 4: want to apply for a job. Almost all companies now
Speaker 4: use some kind of AI to filter your CV before
Speaker 4: it reaches human So in some senses you don't even
Speaker 4: have the option to opt out. If you are, you know,
Speaker 4: someone looking for a job, you can't say, oh, I
Speaker 4: don't want you to use an AI system to sift
Speaker 4: through my CV.
Speaker 2: It's just like it's gonna happen.
Speaker 4: Yeah, it's gonna happen.
Speaker 2: Doctor Burhanne says that it's pretty unavoidable. And I have
Speaker 2: asked tech lawyers and even they don't read Apple's terms
Speaker 2: and conditions. They're like, I just checked the box.
Speaker 4: So instead of using WhatsApp, which is owned by Meta,
Speaker 4: which you know really gathers all your texts, all your information,
Speaker 4: we can't move to other messaging apps like Signal. So
Speaker 4: Signal has you know, an to end encryption. There is
Speaker 4: no backdoor. Nobody can access it, not even governments. This
Speaker 4: is one of the things. Meridith Witiker, the CEO of Signal,
Speaker 4: has been really strong and standing up to large governments
Speaker 4: is that nobody should have a back end access that
Speaker 4: gives them the opportunity to gather data.
Speaker 2: And yes, Signal is run by a nonprofit foundation, a
Speaker 2: Signal Foundation dot org and Meredith Whittaker is Signal's president
Speaker 2: and she had worked at Google for ten years and
Speaker 2: she was raising concerns about their AI and she was
Speaker 2: also a core organizer of the twenty eighteen Google walkout
Speaker 2: in protest of sexual harassment there and pay inequities. She
Speaker 2: also advises government agencies on AI safety and privacy laws.
Speaker 2: So signal, good, yay signal And many recently laid off
Speaker 2: government staff that I know of will only communicate for
Speaker 2: you a signal, which is kind of telling in terms
Speaker 2: of their own safety concerns. But yes, you signal, so
Speaker 2: we can.
Speaker 4: Do some things. We can use less and less of
Speaker 4: these large corporations infrastructure, and we can use you know,
Speaker 4: more open source tools. But also sometimes you know, just
Speaker 4: out of your control. But every little helps in every awareness,
Speaker 4: you know, it kind of culminates and it will eventually
Speaker 4: lead to, you know, this massive switch.
Speaker 2: I hope at least that's encouraging. I hope. Yeah, And
Speaker 2: can I ask you some questions from listeners. Is that okay,
Speaker 2: okay for sure? But before we do, let's give away
Speaker 2: some money. And this week doctor Behani selected the cause
Speaker 2: the Municipality of Gaza and un r WA, which directly
Speaker 2: supports Palestine refugees and displaced families in Gaza. They say
Speaker 2: every donation, no matter the amount, helps them reach families
Speaker 2: with life saving food, assistance, shelter, healthcare and more. And
Speaker 2: for more info you can see donate dot un r
Speaker 2: WA dot org, which is linked in the show notes.
Speaker 2: And for more on the ongoing humanitarian crisis in Gaza,
Speaker 2: please see our Genocideology episode with global expert in Crimes
Speaker 2: of a Trosty doctor Dirk Moses, which we will also
Speaker 2: link in the show notes. So a quick break. Now, okay,
Speaker 2: we are back. Let's run through some questions from your
Speaker 2: real squishy brains made of human beings out there. There's
Speaker 2: some great ones job replacement Carlyn de Azevo, Alia Meyer's
Speaker 2: Red Tongue, Jennifer Grogan, Ian, Jenna Congen, Rosa, Rebecca Rome,
Speaker 2: Other Maya, Sam Nelson, Howard Newness. All these people wanted
Speaker 2: to know, in Ian's words, will all jobs be obsolete soon?
Speaker 2: Do the people working on AI give any thought to
Speaker 2: compensating people for the lost income? Jenna Congden said, when
Speaker 2: will AI get so good that human writers are basically
Speaker 2: crowded out of a job? This goes for visual art
Speaker 2: as well. In a capitalist economy, when you got a
Speaker 2: hustle to make money as it is, what is going
Speaker 2: to happen job wise? Do you think or do AI
Speaker 2: experts such as yourself.
Speaker 4: So some of the worry about your displacement is is
Speaker 4: genuine and grounded on, you know, real worry. You hear
Speaker 4: even the so called godfathers saying things like you shouldn't
Speaker 4: bozer learning code, or like the job of software engineering,
Speaker 4: for example, will become obsolete and so on.
Speaker 2: So whether you're a software developer or a writer or
Speaker 2: an artist, Doctor Behannie says.
Speaker 4: I don't think AI will fully automate AI will fully
Speaker 4: replace human task force, because at the end of the day,
Speaker 4: what even the most advanced AI systems do is really
Speaker 4: kind of aggregate information and kind of outputs a very
Speaker 4: mediocre whether it's image or text.
Speaker 2: Some of them are so good, though, some of the
Speaker 2: artists so good, and you're like, like.
Speaker 4: The arts, Yeah, it's not just the pure the row outputs.
Speaker 4: People have tweaked probably like a thousand times. People have
Speaker 4: twik dates, people have spent hours perfecting the right prompt
Speaker 4: and so on. So there is always people in the loop.
Speaker 4: There is always whether it's data preparing, you know, data
Speaker 4: and notation, data curation, to building the AI system itself,
Speaker 4: to them kind of ensuring the output is something appealing.
Speaker 4: You really you need people through and through.
Speaker 2: So for me, as a former newspaper journalist and I
Speaker 2: was also a newspaper illustrator, I I'm not as optimistic.
Speaker 2: So so many writers are copywriters who are making content
Speaker 2: and articles for websites to raise their profile. And now
Speaker 2: I'm hearing from those people that articles are just written
Speaker 2: by AI and they are full of shit. And just
Speaker 2: doing this acide is making me depressed and my chest hurts.
Speaker 2: But doctor Burhani is an expert, so I'm gonna try
Speaker 2: to find some bright spots. And before she had mentioned
Speaker 2: that lawsuit with open AI and the New York Times,
Speaker 2: and I was looking for it and I found a
Speaker 2: recent art this was published literally yesterday, which had the
Speaker 2: headline AI is getting more powerful, but its hallucinations are
Speaker 2: getting worse. A new wave of reasoning systems from companies
Speaker 2: like open AI is producing incorrect information more often even
Speaker 2: the companies don't know why that's the headline. And this
Speaker 2: New York Times article explained that AI systems do not
Speaker 2: and cannot decide what is true and what is false,
Speaker 2: and sometimes they just make stuff up, a phenomenon that
Speaker 2: some AI researchers call hallucinations. And in one test, the
Speaker 2: article says, hallucination rates of newer AI systems were as
Speaker 2: high as seventy nine percent. And I also want to
Speaker 2: note that my spellcheck tried to get me to change
Speaker 2: that it's in the headline to one with an apostrophe,
Speaker 2: which is incorrect. So computers, what's going on? But yes,
Speaker 2: doctor Bahani says that a lot of journalism has been
Speaker 2: replaced by AI, even though we all know that the
Speaker 2: generative system is unreliable.
Speaker 4: Loosen it. A lot of the time. It gives you
Speaker 4: information that sounds coherent, that seems fuctual, but it's just
Speaker 4: absolutely made up. It just there is. It even sometimes
Speaker 4: gives you citations and so on of things that don't exist.
Speaker 4: So we always need people to babysit AI, so to speak.
Speaker 4: So a writer might be, you know, your hours might
Speaker 4: be reduced and you might be getting paid less, and
Speaker 4: your company might be bringing AI to kind of do
Speaker 4: the bulk of the job. But still you can't put
Speaker 4: out the raw outputs because most of the time it's
Speaker 4: not even you know, legible. So the role of writers
Speaker 4: and artists and journals and so on becomes more of
Speaker 4: kind of a babysitter for AI, verifying the information that's
Speaker 4: been put out, kind of ensuring it makes sense and
Speaker 4: so on.
Speaker 2: Right, Kenny, the babysitter is dead to.
Speaker 4: Some extent, The answer is yes, and no. Humans will
Speaker 4: always remain at the heart of AI. The minut human
Speaker 4: involvement seizes the minute AI stops operating, because AI is
Speaker 4: human thru and through. As I said, you know, from
Speaker 4: the data as gathered from humans, and so much work
Speaker 4: goes into data preparation, data annotation, cleaning up the data,
Speaker 4: detoxifying the data, and unfortunately a lot of these tasks
Speaker 4: allocated to you know, the developing world. So you have
Speaker 4: a lot of data workers in Kenya and Nigeria and Ethiopia,
Speaker 4: in India for example, that really do the dirty work
Speaker 4: of AI. There are even a bunch of stories where
Speaker 4: you have Amazon checkout for example AI checkout or self checkout,
Speaker 4: where Amazon was introducing this AI where you can just
Speaker 4: collect groceries and your items and just walk out, and
Speaker 4: the AI is supposed to kind of identify what you
Speaker 4: have picked up and charge you from your credit cards
Speaker 4: for whatever you have used. But then it turns out
Speaker 4: that it was actually data workers in India that's where
Speaker 4: scanning every item you are picking up.
Speaker 2: So oh man, what word?
Speaker 4: Yeah? Yeah, and I mean MacDonald also recently partnered with
Speaker 4: IBM or one of those companies to have like an
Speaker 4: AI drive through where AI systems take order and they
Speaker 4: have to close it within the next few weeks because
Speaker 4: people were getting orders of like, you know, bacon on
Speaker 4: top of ice cream and things.
Speaker 2: Like that, you added bacon to my ice cream? I
Speaker 2: don't want I bake it. I raised the national minimum
Speaker 2: wage for the first time since two thousand and nine
Speaker 2: when you can just spend billions of dollars tweaking unpaid
Speaker 2: machines like welcome to the future. Maybe so, I.
Speaker 4: Guess the point I'm trying to make is like you
Speaker 4: always need humans for AI to to function and operate
Speaker 4: as it's supposed to, because at the end of the
Speaker 4: stem these days, these are like really mare machines that
Speaker 4: don't have you know, intense understanding, motivation and so on,
Speaker 4: Like we human star So.
Speaker 2: Maybe our jobs will look different, but there will be jobs. Yes,
Speaker 2: I know a lot of people myself included, wanted to
Speaker 2: know the environmental impact Lily a bunch of folks and
Speaker 2: first time question askers elean Or Bundy and Meghan m
Speaker 2: And we also did a recent episode of Earth Day
Speaker 2: with this climate activist and humanitarian rightsler Adam Met, who
Speaker 2: said that AI could be solving some environmental concerns, which
Speaker 2: is optimistic. But what does an AI experts take on that?
Speaker 2: Megan Walker asked environmentally, how bad is AI when compared
Speaker 2: to the current computing we do? Yeah, what's going on?
Speaker 4: Yeah? Yeah yeah?
Speaker 2: How much energy does it use?
Speaker 3: Yeah?
Speaker 4: So again, like we have very little information about training data.
Speaker 4: The kind of energy consumption used by AI systems is
Speaker 4: very OPQ. There is very little transparency.
Speaker 2: Okay, but what is the damage generally?
Speaker 4: So, generals of AI really conceives massive lout of power
Speaker 4: compared to traditional AI. For example, if you are using
Speaker 4: Google to put a prompt say you know, how many
Speaker 4: glasses of water should I drink per day, and if
Speaker 4: you do the exact same prompt and you ask the
Speaker 4: generative systems such as chat ChiPT, people estimate you use
Speaker 4: about ten times more energy to process that query into
Speaker 4: generated answers.
Speaker 2: I wanted to go straight to the source, so I
Speaker 2: used Google AI and chatchipt for the first time, asking
Speaker 2: them both how much energy does chatchipt use as opposed
Speaker 2: to Google now. Google AI said in what I hope
Speaker 2: is a snotty tone that chat gpt consumes significantly more
Speaker 2: energy per query, five to ten times more electricity than
Speaker 2: a standard Google search. Now. It cited a twenty twenty
Speaker 2: four Goldman SAX report titled AI has poised to drive
Speaker 2: one hundred and sixty percent increase in data center power demand.
Speaker 2: Then I asked chat schiapt and it said that its
Speaker 2: version four can use up to thirty times more energy
Speaker 2: than a basic Google search, and it also noted, I
Speaker 2: like to think defensively that Google has had decades to
Speaker 2: optimize for a lower footprint now. Doctor Behanne says that
Speaker 2: the energy consumption of generative AI systems has become indeed
Speaker 2: a big issue, and that in countries like Ireland, the
Speaker 2: data centers are power hogs.
Speaker 4: The computer resources required to running AI systems equals or
Speaker 4: is more than the total amount of energy that's required
Speaker 4: to run Irish households. But in places like takes Us
Speaker 4: sometimes that energy consumption is taken away from households to
Speaker 4: run data centers. Wow, so it kind of results in
Speaker 4: reduced energy for households. And this is before we even
Speaker 4: get into the massive tons of water that you need
Speaker 4: to cool down data centers.
Speaker 2: Oh yeah, didn't even think about that.
Speaker 4: Yeah, and the water also has to be pure because
Speaker 4: you can't use say, you know, ocean water or seawater
Speaker 4: because of the sea salt that might damage the servers
Speaker 4: and so on. So again there is competition. It tends
Speaker 4: to be when you use water that is used for households.
Speaker 4: As a result, you know, people tend to pay for
Speaker 4: the consequences of that. So yeah, water consumption is another
Speaker 4: massive area as well.
Speaker 2: And do you think that more companies will look toward
Speaker 2: some sort of nuclear power for their supercomputers or is
Speaker 2: that still too highly regulated?
Speaker 4: I think companies like Google are actually talking about using
Speaker 4: nuclear power, but yes, that option is being considered.
Speaker 2: Yeah, how about healthcare? Several people Benjamin Burnish weared analyst
Speaker 2: to young Nikki g asked, how can AI be ethically
Speaker 2: apply to healthcare like data analytics, treatment options, medical imaging interpretations.
Speaker 2: Second opinions, Is there some hope there for it?
Speaker 4: Yeah, I think there is some hope. There is some
Speaker 4: hope for sure. I think there is some hope in
Speaker 4: you know, numerous domains for AI to be useful. Okay, However,
Speaker 4: that just remains a theory. It's it's possible in theory.
Speaker 4: But the problem there are a bunch of problems. One
Speaker 4: of them is that generative systems are fundamentally unreliable. So,
Speaker 4: for example, there is a new audit that came out
Speaker 4: I think towards the end of January where they looked
Speaker 4: at this new AI tool where the system kind of
Speaker 4: records your conversations between say a healthcare provider and a patient,
Speaker 4: and it summarizes the conversation, and it kind of it's
Speaker 4: supposed to reduce a lot of the work for nurses
Speaker 4: and so on. And what they found was that in
Speaker 4: some cases, eight out of the teen summaries where hallucinations.
Speaker 2: No.
Speaker 4: So generative systems tend to be unreliable. And the other
Speaker 4: thing is because a lot of these tools that are
Speaker 4: supposed to be used in health care tend to be
Speaker 4: built by businesses with the objective of maximizing profits. They
Speaker 4: tend to have a different kind of objective than say,
Speaker 4: you know, what's good for the patient. So another famous
Speaker 4: case is United healths that is in court at the
Speaker 4: moment where they were using a suit of about fifty
Speaker 4: algorithms to look at mental health services, and what they
Speaker 4: found was that they were using you know, costs as
Speaker 4: a proxy rather than the need of the patient as
Speaker 4: a proxy, and they were cutting a lot of services,
Speaker 4: a lot of like, you know, therapy services and meditations
Speaker 4: and other necessary services. Again because they are you know,
Speaker 4: they're looking at the wrong motivation, their own proxy. They're
Speaker 4: looking to save the company money rather than to ground
Speaker 4: what they do in the needs of the patient. So
Speaker 4: if we correct, for example, hallucinations and biases in AI systems,
Speaker 4: and if we kind of it's impossible to strip down
Speaker 4: all you know, capitalist motivations. But if you know, capitalist
Speaker 4: motivations come second to you know, the needs of the patient,
Speaker 4: then it's possible to kind of develop a systems in
Speaker 4: various areas of fALS care that prioritize patients that prioritize
Speaker 4: people as opposed to just you know, inserting technology for
Speaker 4: the sake of having technology, and also for using technology
Speaker 4: to maximize profit, as opposed to ensuring patient safety.
Speaker 2: Which is once again good luck. I mean, our healthcare
Speaker 2: is above and beyond frustrating. But a lot of people
Speaker 2: wanted to know. Sam Wise, Emily heard, Amalia Magda Casawka
Speaker 2: wanted to know. Kier Henrickson asked for some question, asker,
Speaker 2: how do we feel about AI and chatbots and using
Speaker 2: them in high schools schoolwork? Same ways AI used in
Speaker 2: schools thoughts is there a way to flag it? Or
Speaker 2: are we doing education and injustice?
Speaker 1: Yeah?
Speaker 4: So, on the one hand, I know some people that
Speaker 4: find using AI chatboats really helpful. You give it a prompt,
Speaker 4: it gives you just a bunch of answers. Of course,
Speaker 4: these are people that know how to craft the perfect prompts,
Speaker 4: that know where AI can be useful and where it
Speaker 4: might fail you. So with all that in mind, it
Speaker 4: can't be useful, but you need to be an expert.
Speaker 4: Having said that, for young kids, studies are starting to
Speaker 4: emerge that for example, they did a control study I
Speaker 4: think we is over three thousand students where some of
Speaker 4: the students were given chatbots to help them with I
Speaker 4: think it's maths problems. The others weren't, and they did
Speaker 4: a taste. So what they found was that the kids
Speaker 4: that had chat boats did better than the kids that
Speaker 4: didn't have. Then they performed another taste a few weeks later,
Speaker 4: and they found that the kids that used chat boats
Speaker 4: performed way worse than the kids that didn't have. So
Speaker 4: people are realizing that these systems inhibit learning. Of course,
Speaker 4: you know, education is not just information dissemination, the teacher
Speaker 4: going into class and just like taining the students facts,
Speaker 4: but rather it's an interaction. It's a two way street,
Speaker 4: both for the student and the teacher, developing the skills,
Speaker 4: especially critical skills to analyze and to decipher fact from fiction.
Speaker 4: You know, pormon for misinformation and so on. And when
Speaker 4: you use AI chatbots without knowing their limitations, you tend
Speaker 4: to kind of trust the output, you tend to treat
Speaker 4: it as facts. But also it inhibits your learning. It
Speaker 4: inhibits your critical skills. And if you don't have the
Speaker 4: knowledge to begin wish to verify the answer, you have
Speaker 4: no way of knowing what you have, what you are
Speaker 4: getting is correct or incorrect. So in the long term,
Speaker 4: studies are coming out to show that they might seem
Speaker 4: helpful in the immediate term, but in the long term,
Speaker 4: these chatbots might be inhibiting the learning process.
Speaker 2: Last listener question and I know my husband has this
Speaker 2: question too. DV and C share rample and Chelsea and
Speaker 2: her doctor Charlie want to know. Chelsea asked, why does
Speaker 2: AI do better when you threaten it? Is that ethical?
Speaker 2: Because it doesn't feel like a good president to set
Speaker 2: in any part of life. VNC asked, is it weird
Speaker 2: that I feel the need to say please and thank you?
Speaker 2: We're talking to chatbots. Will the AI overlords be nicer
Speaker 2: to me when they take over? I assume all of
Speaker 2: my conversations will be logged for eternity and Jarett, my
Speaker 2: husband also doesn't use chat ChiPT very much, but when
Speaker 2: it came out, he was trying to teach it to
Speaker 2: be civil and I was like, boy, I don't think
Speaker 2: that's gonna work.
Speaker 4: Yeah.
Speaker 2: Do manners matter?
Speaker 7: Yeah?
Speaker 4: Yeah, yeah yeah. So for models like chat chipet, they
Speaker 4: have what they call a knowledge cut off dates, so
Speaker 4: the training data, the you know, your interaction won't really
Speaker 4: input into into the learning system of the model. It's
Speaker 4: the training data set for I think Chad Chipet that
Speaker 4: ends I think around twenty twenty one or twenty twenty two.
Speaker 4: So Jadechipt, for example, can't give you a cohorrent answer
Speaker 4: for any event that has happened recently.
Speaker 2: So different models are using data from different timelines. They
Speaker 2: have to collect it and clean it and process it first,
Speaker 2: so it's not as real time as I thought it was,
Speaker 2: or some people might expect.
Speaker 4: For I for kind of when you speak to it,
Speaker 4: was it aggressively cratly?
Speaker 2: Yeah, threateningly? Why does it do better? Threaten it?
Speaker 4: So it's the first time I'm hearing this, so I
Speaker 4: should I should try it out. I should check it
Speaker 4: out and see if that also happens to me. But yeah,
Speaker 4: it's it's the first time I'm hearing it.
Speaker 2: Okay, let's hit the books for this. Specifically a twenty
Speaker 2: twenty four study about how to interact with large language models,
Speaker 2: and it's titled should We Respect lms Across Lingual Study
Speaker 2: on the Influence of prompt Politeness on LM performance. So
Speaker 2: this abstract explains that they did testing in English, Japanese,
Speaker 2: and Chinese language models and that as the politeness level descends,
Speaker 2: the answers generated get shorter. However, on the far side
Speaker 2: of the rude scale, impolite prompts often results in poor performance,
Speaker 2: but overly polite language does not guarantee better outcomes, and
Speaker 2: the best politeness level is different according to the language.
Speaker 2: And they say that this suggests that lllms not only
Speaker 2: reflect human being behavior, but are also influenced by language,
Speaker 2: particularly in different cultural contexts. So what about the future.
Speaker 2: The researchers say that it is conjectured that GBT four,
Speaker 2: being a superior model, might prioritize the task itself and
Speaker 2: effectively control its tendency to quote argue at a low
Speaker 2: politeness level, so as it matures, it just won't engage.
Speaker 2: It's like this AI new generation has been the therapy
Speaker 2: if it were a person, which it's not.
Speaker 4: And as to the AI overlords, again, I mean, it's
Speaker 4: just models. It's just you know, data sets in algorithms,
Speaker 4: and you know connection of networks. Okay, there is no
Speaker 4: kind of all knowing godlike or seeing AI. But of
Speaker 4: course you know, the people that are running AI companies
Speaker 4: come close to that because they have access to the data,
Speaker 4: because they have access to the algorithms, so you might
Speaker 4: worry about those people. You know, using your data has
Speaker 4: like almost invisible but very nuanced downstream impact. So in
Speaker 4: the US, for example, authorities as you are forcing companies
Speaker 4: like Meta to give up data so that authorities are
Speaker 4: hunting down women that had abortions. For example, in areas
Speaker 4: where abortion is prohibited. Law enforcement is working with Amazon
Speaker 4: for example, for was it Amazon bail ring bail ring?
Speaker 2: Oh right, this is that camera enabled an Amazon owned
Speaker 2: ring door bell.
Speaker 4: So law enforcement like ice uses that kind of data
Speaker 4: from those two to do something to even deport people.
Speaker 4: So what we should worry about is not really AI overlords,
Speaker 4: but this company is working with powerful entities to really
Speaker 4: kind of identify people that might be in trouble with
Speaker 4: the law or that might be doing you know that
Speaker 4: that Violet the know because that data gives them access,
Speaker 4: gives them the knowledge about the whereabouts in the interactions
Speaker 4: and the activities of people.
Speaker 2: Yeah. And previously, according to a twenty twenty Newsweek article
Speaker 2: titled police are monitoring Black Lives Matter protests with ring
Speaker 2: doorbell data and drones, activists say it's reported that Amazon
Speaker 2: Ring has video sharing partnerships with more than thirteen hundred
Speaker 2: law enforcement agencies across the US. However, in January twenty
Speaker 2: twenty four, Ring said that it would stop letting police
Speaker 2: departments request and receive user's footage on its app. Now,
Speaker 2: on the flip side, some Ring doorbell owners are posting
Speaker 2: on the Ring Neighbors app when ice rates are going
Speaker 2: down locally and they're alerting their community. Now, Ring of
Speaker 2: course notes that those are user generated posts and has
Speaker 2: nothing to do with them. Whether or not they'll censor
Speaker 2: those user generated posts is like anyone's guess, Hey, let's
Speaker 2: take a welcome departure from reality for sex? Shall we
Speaker 2: do any movies get it right?
Speaker 7: Like?
Speaker 2: Does it make get it right?
Speaker 4: Any?
Speaker 2: Does AI? That old Spielberg movie? Does anyone actually get
Speaker 2: it AI right? Or does that drive you absolutely insane
Speaker 2: to watch TV?
Speaker 4: So I love science fiction actually, like The Matrix is
Speaker 4: one of my favorite movies.
Speaker 2: I knew it, I knew it.
Speaker 4: It's a good one. But also that's like, that's nowhere
Speaker 4: you have to treat science fiction as science fiction for
Speaker 4: the sake of like it's really some really good science
Speaker 4: fiction really brings you into a world that you couldn't
Speaker 4: even envision. So I love that element about science fiction.
Speaker 4: But a lot of these like Robot Uprising, Terminator like
Speaker 4: movies are really just for entertainment. There is nothing that
Speaker 4: can be extrapolated and said, oh, this could happen to
Speaker 4: really AI. But you have kind of very nuanced sci
Speaker 4: fi movies. That's nail it. So you have Continuum. It's
Speaker 4: not a movie, it's a series. It wasn't on Netflix
Speaker 4: a wild but a.
Speaker 2: Few years side for what for what you're going to do?
Speaker 4: So what happens here is that you know, as AI
Speaker 4: companies become powerful, they take over government and they become
Speaker 4: you know, the bodies that really govern society. So that
Speaker 4: kind of sci fi is very close to reality than
Speaker 4: you know, Terminator like movies. Yeah.
Speaker 2: How about Black Mirror?
Speaker 4: Oh black Mirror.
Speaker 2: Is so good?
Speaker 4: I mean Black Mirror. There are some things that are like, eh, Now,
Speaker 4: when Black Mirror came out, it was just like wow,
Speaker 4: this could happen. And now I was like, oh that
Speaker 4: has happened, or it's like oh yeah this is you know,
Speaker 4: this is what's happening? Is this in that government?
Speaker 2: So yeah, wow, yep. And the last two questions I
Speaker 2: always ask her is worst and best. I guess your
Speaker 2: your most loathed thing about AI and your favorite thing
Speaker 2: about it. I guess we've talked a lot about cautionary
Speaker 2: but like in terms of what you do or in
Speaker 2: terms of your job, worst and best thing.
Speaker 4: Yeah, so the worst thing is really just the hype.
Speaker 4: As a researcher, I have my own research agen, but
Speaker 4: the hype is so destructive. You see something that's not
Speaker 4: true being disseminated, going viral, and you know, as an expert,
Speaker 4: it's really traveling, so you have to stop what you
Speaker 4: are doing and do some work to kind of correct
Speaker 4: it or at least that tempt. So yeah, a lot
Speaker 4: of the hype really is what really gets on minor
Speaker 4: and it becomes also a problem in terms of getting
Speaker 4: my own work done. But what excites me about AI
Speaker 4: is I'm still extremely optimistic about AI, But unfortunately a
Speaker 4: lot of the AI I get excited about is not
Speaker 4: something that results in you know, massive profits. So you know,
Speaker 4: using AI for disaster mapping, using AI for soil heads monitoring,
Speaker 4: and so on, these are things that really excite me,
Speaker 4: but there is no monetary value in developing AI for
Speaker 4: these system So these are the things that really get
Speaker 4: me excited, that really make me feel like, Wow, this
Speaker 4: is powerful tool that we can use to actually do
Speaker 4: some good in the world.
Speaker 1: Yeah.
Speaker 2: We could make sure that everyone is fed and has
Speaker 2: healthcare and that resources are allocated in a way that's fair.
Speaker 2: And we just don't because of money.
Speaker 4: Yeah, because it doesn't make you money.
Speaker 2: Yeah, which is I think once again, money is the
Speaker 2: root of allie.
Speaker 4: Yes, yeah, yeah, yeah.
Speaker 2: Thank you so much for doing this. This has been
Speaker 2: so illuminating and it's great to talk to someone who
Speaker 2: knows their shit about it.
Speaker 4: Thank you so much for having me. I really enjoyed
Speaker 4: our conversations.
Speaker 2: So ask real people, real not smart and important questions,
Speaker 2: because how else are we supposed to learn anything. So
Speaker 2: thank you so much to Trinity College's doctor Rohney for
Speaker 2: sitting down with me and making the trip to Ireland
Speaker 2: so eventful. I loved this talk and you can find
Speaker 2: links to her and her work in the show notes,
Speaker 2: as well as to the Cause of the week. We
Speaker 2: are at ologies on Meta owned Instagram and on Blue Sky,
Speaker 2: and I'm giving my data as ali Ward with just
Speaker 2: one l on both, and our website has links to
Speaker 2: all the studies we talked about and that link is
Speaker 2: in the show notes. If you're looking to become a patron,
Speaker 2: you can go to patreon dot com slash Ologies and
Speaker 2: you can join up there. If you need shorter, kid
Speaker 2: friendly versions of Ologies episodes, we have them for free
Speaker 2: in their own feed. Just look for Smologies that's also
Speaker 2: linked to the show notes. Please spread the word on that,
Speaker 2: and we have Ologies merch at ologiesmerch dot com. Thank
Speaker 2: you to Aaron Talbert for adminting the Ologies podcast Facebook group.
Speaker 2: Aveline Mallick does our professional human made transcripts. Kelly R.
Speaker 2: Dwyerd is the website. Noel Dilworth is our Flesh and
Speaker 2: Blood scheduling producer, Human Organism. Susan Hale managing directs the
Speaker 2: whole show. A live editor Jake Chafe helps put it together,
Speaker 2: and the Connective Tissue lead editor is Mercedes Maitland of
Speaker 2: Maitland Audio and Nick Thorbern made the theme music using
Speaker 2: his brain and ears and fingers. And if you stick
Speaker 2: around to the end of the show, I tell you
Speaker 2: a secret, and this week it's two. One is that
Speaker 2: I think I'm going to be shooting something next week
Speaker 2: and I want to tell patrons about it. For a
Speaker 2: but also i'll do some posting on social media if
Speaker 2: and when it happens, I'm really excited. I don't mean
Speaker 2: to be secretive, but just send good vibes. Next week
Speaker 2: i'll tell you as a secret effort happens. And the
Speaker 2: other secret is, before I went to Ireland, I got
Speaker 2: a couple of those film cameras disposed because it's like, ooh,
Speaker 2: what is this a film in this? And I took
Speaker 2: all the pictures and I haven't gotten them developed yet,
Speaker 2: and I kind of feel like the longer you wait
Speaker 2: to get them developed, the more you'll like them. And
Speaker 2: so I don't know what the appropriate amount of time
Speaker 2: is to forget about this disposable camera and then get
Speaker 2: it developed. If it should be like a couple more months,
Speaker 2: or if I should get it developed in a year. And
Speaker 2: so now I just have this disposable camera in my
Speaker 2: backpack and I don't know how long I should I
Speaker 2: also don't know where to get it developed, if I'm
Speaker 2: being honest. But anyway, if anyone has thoughts about that,
Speaker 2: feel free to advise me. That is a very analog
Speaker 2: update here for me, all right, fucking please do not
Speaker 2: use chat GPT to write papers, or illustrate anything important.
Speaker 2: Hire an illustrator if you can. Illustrators, writers, artists, musicians,
Speaker 2: please let them live. They are alive, okay, be good.
Speaker 7: For by pacodermatology, homology or doo zoology, lithology, technology, meteorology, pertology, anthology, seriology, elinology.
Speaker 2: We marveled at our own magnificence as we gave birth
Speaker 2: to a r