Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Heydarren, Welcome
Speaker 1: to tech Stuff. I'm your host, Jonathan Strickland. I'm an
Speaker 1: executive producer with iHeartRadio and how the tech are you? So?
Speaker 1: Last week I said I would do an episode about
Speaker 1: doctor Jeffrey Hinton, the so called godfather of AI. My
Speaker 1: dog is very interested in this, as he winds in
Speaker 1: the background, and as I published this. We're into the
Speaker 1: fifth month of twenty twenty three, and I still feel
Speaker 1: pretty good about calling this the Year of AI. While
Speaker 1: artificial intelligence has obviously been a discipline for decades, with
Speaker 1: lots of impressive displays and exhibitions and developments throughout the years,
Speaker 1: the buzz around and attention to aifields has really hit
Speaker 1: a high point this year, largely driven by stuff like
Speaker 1: large language models LMS, as well as the chatbots built
Speaker 1: on top of them that seem to be pretty knowledgeable,
Speaker 1: almost human in their capabilities. Plus you throw in some
Speaker 1: image and video and audio capabilities that allow us to
Speaker 1: use a machine to create all sorts of stuff, and
Speaker 1: you got yourself something that the average person can at
Speaker 1: least recognize as AI. A lot of AI applications historically
Speaker 1: have been so far behind the scenes that you might
Speaker 1: not even recognize it as artificial intelligence, or you might
Speaker 1: not think of it in that context. But now we're
Speaker 1: getting to a point where there's at least the appearance
Speaker 1: of machines behaving similarly to people within certain contexts, and
Speaker 1: it becomes way easier for the average person to say, Wow,
Speaker 1: hang on, what's going on now. I say that because,
Speaker 1: as I have pointed out in this show so many
Speaker 1: many times, there are lots of different aspects of artificial intelligence,
Speaker 1: some of which have been around for many years, and
Speaker 1: some of them have even been causing problems for many years.
Speaker 1: See also facial recognition technology and the fact that bias
Speaker 1: and systems can lead to really terrible consequences in the
Speaker 1: real world. And today I wanted to talk about how
Speaker 1: some of the folks in the AI field are voice
Speaker 1: and concerns that they have around AI and AI's evolution
Speaker 1: and also its deployment and how it could be a
Speaker 1: destructive tool in the future. Now, if you've been listening
Speaker 1: to me for a while, you know I try to
Speaker 1: take a very thoughtful approach to this. I think it's
Speaker 1: important to understand the capabilities of AI, and it's also
Speaker 1: important to understand the potential misuses of AI, or at
Speaker 1: least the unintended consequences of using AI. But I also
Speaker 1: want to try to avoid fud that is, fear, uncertainty
Speaker 1: and doubt that can air on the side of being
Speaker 1: an alarmist. So I think that we should be concerned,
Speaker 1: But so far I haven't been ready to push the
Speaker 1: panic button just yet for AI. But maybe that's about
Speaker 1: to change, because while I've been trying to wrap my
Speaker 1: brain around this, a person like doctor Jeffrey Hinton has
Speaker 1: come forward with his own concerns about AI. And if
Speaker 1: doctor Hinton is concerned, I should probably listen. And that's
Speaker 1: because doctor Hinton has been at the cutting edge of
Speaker 1: AI development for years for decades, particularly in fields like
Speaker 1: artificial neural networks and deep neural networks. In particular, he
Speaker 1: recently resigned from his position at Google, where he had
Speaker 1: been working in AI research, at age seventy five. He's
Speaker 1: certainly at a point in his life where retirement would
Speaker 1: seem pretty natural. You would just think that he would
Speaker 1: come to the conclusion of, yes, it's time for me
Speaker 1: to rest. But his decision was made at least in
Speaker 1: part so that he could speak out about AI and
Speaker 1: the dangers he considers to be important without considering how
Speaker 1: it would impact Google. And that's from doctor Hinton himself.
Speaker 1: He posted that on Twitter, where he said he was
Speaker 1: doing this without considering how it would impact on Google.
Speaker 1: He was addressing a New York Times article that implied
Speaker 1: he had left Google so that he could criticize Google
Speaker 1: in particular. He was quick to say that he felt
Speaker 1: Google had been pretty responsible in its pursuit of AI,
Speaker 1: at least arguably until relatively recently. So let's learn a
Speaker 1: bit about doctor Hinton and his background, the work that
Speaker 1: he pursued, and what his concerns around AI actually cover,
Speaker 1: and maybe along the way we'll figure out some questions
Speaker 1: that we need to answer at some point, implications that
Speaker 1: need to be considered, and perhaps choices we absolutely should
Speaker 1: not make if we want to create helpful AI that
Speaker 1: provides a net benefit rather than something that you know,
Speaker 1: creates the terminator or how or whatever. And I am
Speaker 1: being a bit flippant, but there are reasons we should
Speaker 1: have some concerns, even if they don't involve single minded
Speaker 1: cyborg soldiers. Jeffrey Hinton was born in nineteen forty seven
Speaker 1: in London, England. He attended the University of Edinburgh and
Speaker 1: graduated with a degree in psychology in nineteen sixty nine,
Speaker 1: which is an interesting starting point for someone who had
Speaker 1: become deeply involved in computer science, and the background in
Speaker 1: psychology is probably an important component for someone who would
Speaker 1: contribute to the advancement of artificial intelligence in general and
Speaker 1: neural networks in particular. In nineteen seventy eight, he earned
Speaker 1: a PhD in artificial intelligence at the University of Sussex.
Speaker 1: He transitioned into being an AI researcher, but it was
Speaker 1: kind of a tough go in the UK. There just
Speaker 1: really wasn't that much support and funding for AI research
Speaker 1: over in the UK, so it was hard for him
Speaker 1: to make much progress. So he decided to immigrate to
Speaker 1: the United States, where he first worked as a researcher
Speaker 1: at the University of California in San Diego, and then
Speaker 1: he moved on to Carnegie Mellon University and he worked
Speaker 1: at Carnegie Mellon as a professor from nineteen eighty two
Speaker 1: to nineteen eighty seven, but by the late eighties he
Speaker 1: made a decision to relocate to Canada and you might say, well,
Speaker 1: why would you go to Canada when you were already
Speaker 1: working in AI in the United States. I mean, the
Speaker 1: US has spent billions of dollars in research and development
Speaker 1: in the technology field. Well, his primary reason was because
Speaker 1: the main source for research funding in AI at the
Speaker 1: time came from the Department of Defense, and doctor Hinton
Speaker 1: wasn't comfortable with the idea of working on machine intelligence
Speaker 1: that was through military backing, because the presumption is whatever
Speaker 1: our work you create is ultimately going to be put
Speaker 1: to use by the Department of Defense, and it's reasonable
Speaker 1: to assume that at least some of those uses could
Speaker 1: be weaponized, and Hinton didn't want to contribute to work
Speaker 1: that could later be used to harm or kill others,
Speaker 1: so he would rather sidestep that and get funding from
Speaker 1: other sources. So he settled in Toronto, Canada. He took
Speaker 1: on more academic roles. He continued to be professor. He
Speaker 1: also continued to work in the field of AI research,
Speaker 1: specifically in artificial neural networks and deep learning approaches, and
Speaker 1: we will talk more about those in just a little bit.
Speaker 1: In twenty twelve, he co founded a company with two
Speaker 1: of his students after publishing a paper on deep learning.
Speaker 1: So his paper got the attention of some really smart
Speaker 1: people around the world, and before Hinton knew it, he
Speaker 1: was being courted by some really big companies, companies that
Speaker 1: had super deep pockets and wanted to hire him and
Speaker 1: a couple of his students on to work in the
Speaker 1: field of AI research. So he got one offer from
Speaker 1: the Chinese company Baidu that would have had him and
Speaker 1: his two students work for the company for a few
Speaker 1: years in return for twelve million smackaroo's worth of compensation.
Speaker 1: That's a healthy salary. But Hinton also had other folks
Speaker 1: who were potentially interested in his work, and he also
Speaker 1: figured it would be far more lucrative if he created
Speaker 1: a company with his students, if they made a company
Speaker 1: together that could then be acquired, so instead of getting
Speaker 1: hired as individuals, they would have a company that would
Speaker 1: have to be purchased. And so that's when he and
Speaker 1: these two students incorporated into DNN Research. The DNN stands
Speaker 1: for deep Neural Networks. Hinton then took this brand new
Speaker 1: company which really just had three employees, including himself, and
Speaker 1: had no products and no services, no business plan, nothing
Speaker 1: other than the fact that it was incorporated, and then
Speaker 1: he put it up for auction. The actual auction took
Speaker 1: place in Lake Tahoe during a conference on AI and
Speaker 1: machine learning, and by Do participated, but so did Microsoft, Google,
Speaker 1: and an AI research company called DeepMind, which a couple
Speaker 1: of years later would become a Google subsidiary of its own. Now,
Speaker 1: DeepMind was the first company to bow out of the auction.
Speaker 1: It just did not have the resources of these three
Speaker 1: giant tech companies. Microsoft then followed. Actually, Microsoft kind of
Speaker 1: bounced in and out of the auction a couple of
Speaker 1: times before finally throwing in the towel, and the bidding
Speaker 1: war came down to Google versus by Do, and it
Speaker 1: just kept going and going and going. Once the price
Speaker 1: hit an astounding forty four million dollars. And keep in
Speaker 1: mind DNN Research had only been around for a very
Speaker 1: short while and had no products or services to its name,
Speaker 1: doctor Hinton called the auction closed and the company went
Speaker 1: to its new owner, that of Google. Reportedly, the people
Speaker 1: at Google were actually surprised that he stopped the auction
Speaker 1: at that point, because they figured that he was leaving
Speaker 1: millions of dollars on the table that the bidding war
Speaker 1: would have continued between Google and by Doo, and he
Speaker 1: could have gotten more for it, but doctor Hinton was
Speaker 1: more concerned with working for Google rather than for by
Speaker 1: Do and felt that forty four million dollars was more
Speaker 1: than enough. So that's a pretty you know, mature approach
Speaker 1: as opposed to let's take every cent we can grab.
Speaker 1: Also in twenty twelve, he won an award when he
Speaker 1: co invented a deep learning model called alex Net, named
Speaker 1: after the other co creator, his student, Alex Krzewski. This
Speaker 1: was bigger than just a two person operation. By the way,
Speaker 1: it's not like Alex Krzewski and doctor Hinton were the
Speaker 1: only two to work on it, but they were the
Speaker 1: leads on this project and it was named after Alex. Alex,
Speaker 1: by the way, was also one of the two students
Speaker 1: who was part of DNN research, the other being a
Speaker 1: student named Iliya Sutzkever. And my apologies for the butchering
Speaker 1: of pronunciation, but the learning model Alex Natt focused quite literally,
Speaker 1: i guess you could say, on image recognition and participated
Speaker 1: in a competition in which the model proved to have
Speaker 1: an eighty five percent accuracy rate. And while it's a
Speaker 1: trivial thing for a human to look at a photo
Speaker 1: and say something like that's a bunny rabbit. It's not
Speaker 1: so trivial to create a way for computers to be
Speaker 1: able to do the same thing. So this eighty five
Speaker 1: percent accuracy rate was like that was like a stake
Speaker 1: in the ground saying we have made a massive leap
Speaker 1: ahead with machine learning and artificial intelligence. It was one
Speaker 1: of the reasons why DNA research was so highly sought after,
Speaker 1: and alex Nett wasn't just an impressive approach toward machine learning.
Speaker 1: It really got enough buzz that money began to pour
Speaker 1: into deep learning projects everywhere, not just with doctor Hinton
Speaker 1: and his students, Like we literally started to see more
Speaker 1: development in the discipline as a whole because this was
Speaker 1: such an impressive display. From twenty twelve until just this year,
Speaker 1: doctor Hinton worked in AI research and deep learning, in
Speaker 1: particular over at Google. One of his two students, Ilijas Skiver,
Speaker 1: actually would leave Google to join a little AI nonprofit
Speaker 1: called open Ai. I mean, originally it was a nonprofit,
Speaker 1: and technically the nonprofit part of open ai is still
Speaker 1: a parent organization, but really the for profit arm of
Speaker 1: open AI is in the news way more frequently these days.
Speaker 1: In twenty eighteen, doctor Hinton was a co recipient of
Speaker 1: the award. This is a prestigious honor for those in
Speaker 1: the computing field. Some people even refer to it as
Speaker 1: being the equivalent to a Nobel Prize. And now he's
Speaker 1: stepping forward with concerns relating to the work he dedicated
Speaker 1: his life too. Now we're going to take a quick break.
Speaker 1: When we come back, we're going to talk about deep
Speaker 1: neural networks and what they do within the realm of
Speaker 1: machine learning. But first these messages. Okay, we're back. Let's
Speaker 1: talk about doctor Hinton's work and deep neural networks. Now,
Speaker 1: as you might imagine, this subject gets really complicated. It's
Speaker 1: really nuanced, it's technical, and as I'm sure you have
Speaker 1: no need to imagine, my understanding of deep neural networks
Speaker 1: is pretty limited. I mean, you could call it surface
Speaker 1: level and I wouldn't be able to disagree. So we're
Speaker 1: going to paint this topic in broad strokes. And I'm
Speaker 1: doing this not to dumb it down, but rather to
Speaker 1: do my best to kind of get across the general
Speaker 1: way it works without making too many egregious errors. Along
Speaker 1: the way. So first up, the goal of a deep
Speaker 1: neural network is to provide a learning mechanism that mimics
Speaker 1: the human brain, but using a computer rather than a
Speaker 1: human brain. So, for the purposes of an overly simple
Speaker 1: thought experiment, imagine you've got a black box. It's an
Speaker 1: opaque black box. Now, one side of the box allows
Speaker 1: you to put something in, and the other side of
Speaker 1: the box allows stuff to come out. And let's say
Speaker 1: that you are putting in one thing and it transforms
Speaker 1: in some way inside the box and comes out as
Speaker 1: something else. That's the general thought here, because I'm feeling
Speaker 1: a little peckish and a little puckish. Let's say that
Speaker 1: you decide to put in the inputs as the ingredients
Speaker 1: you would need for a pizza, and you're shoving that
Speaker 1: into the box. So we're talking stuff like pizza dough
Speaker 1: and some sauce and some cheese and any toppings you like,
Speaker 1: and you shove that into the input in the box,
Speaker 1: and then the output shoots out a cowl zone. Well shucks,
Speaker 1: you think, unless you're Ben Wyatt from Parks and rec
Speaker 1: in which case you celebrate because you think col zones
Speaker 1: are superior to pizza in every way. But assuming you're
Speaker 1: not Ben Wyatt, you say, that's not what I wanted.
Speaker 1: I wanted to get a pizza, not a calzone. So
Speaker 1: you have to open up the box, you have to
Speaker 1: adjust some stuff inside it, you have to close it
Speaker 1: all up and try it again, and you keep doing
Speaker 1: this over and over until you get a pizza, a
Speaker 1: properly cooked and prepared pizza. This is sort of similar
Speaker 1: to how computer scientists perform supervised learning with artificial neural networks,
Speaker 1: because that box represents what we call hidden layers. They
Speaker 1: could be lots of hidden layers, and these are layers
Speaker 1: of computer nodes that serve as artificial neurons, and pathways
Speaker 1: form between these different nodes as they process information. So
Speaker 1: when you put input into the system, that input goes
Speaker 1: to a node and it begins to sort the data
Speaker 1: based on some criteria that the system has been trained on.
Speaker 1: Whatever the purpose of the system actually is. It's kind
Speaker 1: of like, you know, let's say it's for recognizing bunnies,
Speaker 1: since we use that example earlier. So you feed it
Speaker 1: a whole bunch of images, and the node takes the
Speaker 1: data and passes the data to another node. A layer
Speaker 1: down and it does it. It chooses the node based
Speaker 1: on some transformational function at that artificial neuron, right, so
Speaker 1: you can think data comes in, the neuron, performs a
Speaker 1: transformational function on this data. Based on that result, it
Speaker 1: goes to one node or another, and then the process
Speaker 1: repeat and it does this again and again until it
Speaker 1: comes out the output side. Where like in our example,
Speaker 1: we figure out whether the machine is able to recognize
Speaker 1: if a picture has a bunny in it or not.
Speaker 1: So you feed millions, tens of millions, hundreds of millions
Speaker 1: of pictures to this system to train it. When you
Speaker 1: start off, you might be doing this with a bunch
Speaker 1: of images that you've already determined whether or not there
Speaker 1: are bunnies in them. So you've got to control amount
Speaker 1: of data that you're feeding just for the purposes of
Speaker 1: training your system. And at the end, after it's sorted
Speaker 1: through those images, you evaluate the system to see how
Speaker 1: well it did in figuring out whether an image had
Speaker 1: a bunny in it or not. Maybe in some of
Speaker 1: the pictures it misses a bunny. Maybe in some pictures
Speaker 1: it thinks there's a bunny there and there's not. And
Speaker 1: then you might go in and start to adjust the
Speaker 1: weights on those artificial neurons. This is the thing that
Speaker 1: creates that transformational function. You might tweak those transformational functions
Speaker 1: a little bit. You might start closest to the output
Speaker 1: and work your way back. That's called back propagating. And
Speaker 1: what you're trying to do is adjust all these settings
Speaker 1: so that it is more accurate the next time you
Speaker 1: feed all the images through, and you do it again,
Speaker 1: and you might do this dozens or hundreds of times
Speaker 1: in an effort to really refine your model so that
Speaker 1: it gets better and better at identifying the pictures that
Speaker 1: have bunnies in them. And then ideally you get the
Speaker 1: system to a point where you could just feed it
Speaker 1: raw data, like you haven't even looked at these images.
Speaker 1: You're just dumping millions of images in and you're letting
Speaker 1: it sort it through. And because it has reached the
Speaker 1: level of accuracy that it's at, because you've trained it
Speaker 1: for so long, you don't even have to worry so
Speaker 1: much about whether or not it caught all the images
Speaker 1: or if it misidentified some there's probably going to be
Speaker 1: some error in there, but if your accuracy level is
Speaker 1: high enough, then it's possibly good enough for whatever purpose
Speaker 1: you've built it for. And yeah, the more data you
Speaker 1: use to train your machine learning model in general, the
Speaker 1: better it will perform, because it'll start to eliminate things
Speaker 1: like outliers. And while image recognition is just one of
Speaker 1: the more famous uses for deep neural networks in machine learning,
Speaker 1: it is clearly not the only one. The one we've
Speaker 1: been hearing about a lot lately involves large language models
Speaker 1: or llms, like I mentioned at the top of the show.
Speaker 1: So imagine feeding millions or even billions of documents to
Speaker 1: a neural network that's trained to recognize patterns in language.
Speaker 1: So you're feeding all sorts of stuff to this model,
Speaker 1: and as you do, the system quote unquote learns how
Speaker 1: words follow each other, like which words are likely to
Speaker 1: follow other words. You probably wouldn't go so far as
Speaker 1: to say the system under stands a language like English,
Speaker 1: but it does have an incredibly sophisticated statistical model that
Speaker 1: breaks down how likely one word is to follow another.
Speaker 1: So you can think of it a little bit like
Speaker 1: a word association game. You've probably played something like this
Speaker 1: at some point or another. Someone gives you a word
Speaker 1: and you're supposed to say the first word that comes
Speaker 1: to your mind. So if I were to say the
Speaker 1: word nuclear, you might think power or bomb or radiation.
Speaker 1: You probably wouldn't think penguin or Chesterfield sofa just not
Speaker 1: likely to pop up statistically, it's unlikely. Well, you can
Speaker 1: kind of think of the large language model as being
Speaker 1: an enormous version of that. So as these large language
Speaker 1: models process increasing amounts of information, and as the neural
Speaker 1: network experiences refinement over countless learning runs, you end up
Speaker 1: with a system that is capable of doing some pretty
Speaker 1: extraordinary things, at least on the surface life. It can
Speaker 1: pull information together to answer questions about practically any topic. Unfortunately,
Speaker 1: it can also invent answers by following a statistical probability
Speaker 1: when it doesn't actually contain the answers to the question
Speaker 1: you asked. This means you can end up with an
Speaker 1: answer that isn't accurate at all, but it follows a
Speaker 1: statistical model where each word is from a probability standpoint,
Speaker 1: the perfect word to go in that point in a sentence,
Speaker 1: which is a weird thing to think about, right, Like,
Speaker 1: the answer you get isn't right, but each word is,
Speaker 1: statistically speaking, the best one to put in that place
Speaker 1: in lieu of any actual information. Now here's another thing
Speaker 1: to consider. These tools can do stuff like build code.
Speaker 1: This code isn't always reliable, it's not always right, but
Speaker 1: sometimes it is. So maybe you use a tool like
Speaker 1: GPT to look over the code that was made by
Speaker 1: a group of engineers, and you do it to search
Speaker 1: for errors, like you're using this to look for mistakes
Speaker 1: that were made in the code. Or maybe you use
Speaker 1: it to see if there's a way to make the
Speaker 1: code that was written more elegant or efficient. Maybe you
Speaker 1: figure you've reached a point where you don't even need
Speaker 1: human engineers because the AI agent performs at a standard
Speaker 1: that's high enough to replace them. Maybe you think it's
Speaker 1: even better than what human engineers can do, and that
Speaker 1: it's far faster, and that you can therefore develop and
Speaker 1: deployee software at a pace that you couldn't before. So
Speaker 1: the IT industry is in a particularly delicate place as
Speaker 1: companies begin to explore how AI could augment or potentially
Speaker 1: replace people. I go back to what IBM's CEO recently said.
Speaker 1: He said that for nearly eight thousand job offerings that
Speaker 1: the company has now put on a hiring freeze, he
Speaker 1: might never hire a human to take one of those jobs. Instead,
Speaker 1: he might rely on automation and AI to cover that job.
Speaker 1: So it's not quite the same thing as firing someone
Speaker 1: and then replacing him with a robot, but it is
Speaker 1: given a robot a job instead of a human being. Okay,
Speaker 1: let's switch gears. Let's talk about AI in the arts,
Speaker 1: because that's also a really relevant conversation right now. So
Speaker 1: last year we already started to see debates about the
Speaker 1: validity of AI generated images. Should an AI generated image
Speaker 1: be considered art? We saw people submit AI generated paintings
Speaker 1: into competitions, some of which ended up receiving awards, and
Speaker 1: then we're subsequently either stripped of those awards or you know,
Speaker 1: people got in trouble for using AI even when they
Speaker 1: were you know, admitting to it in an effort to say, hey,
Speaker 1: we're trying to start a conversation about AI and its
Speaker 1: role in arts. So is art actually art if the
Speaker 1: image is a product of a complex series of decisions
Speaker 1: that aren't driven by imagination or creativity, but rather some
Speaker 1: really weird statistical model that's so complicated that no one
Speaker 1: really understands it. Or is it just a meaningless image?
Speaker 1: You know, maybe it's an image that mimics specific artists,
Speaker 1: but in itself it's nothing more than just a picture.
Speaker 1: I mean, you know, drawing a perfect circle freehand with
Speaker 1: no tools is really really hard for a human to do,
Speaker 1: but it's a piece of cake for a computer. So
Speaker 1: should we be astounded by a computer's ability to generate
Speaker 1: a perfect circle? What about a computer's ability to mimic
Speaker 1: the style of say, you know, Picasso or Dali. Beyond
Speaker 1: visual arts, there are examples like writing and music. There's
Speaker 1: the case of the song Hard on My Sleeve that
Speaker 1: features the deep fake voices of Drake and the Weekend,
Speaker 1: so it sounds like Drake in the Weekend the song.
Speaker 1: But these are just computer generated voices. So what happens
Speaker 1: when people can create new songs that feature an imitation
Speaker 1: of an established artist's voice or style. You could have
Speaker 1: fun finding out what it would sound like if the
Speaker 1: Beatles wrote a song in the style of the Ramones.
Speaker 1: But this kind of distraction can become really harmful to
Speaker 1: actual human artists. Honestly, what this illustrates is a need
Speaker 1: to create more comprehensive right to publicity and right to
Speaker 1: personality laws to protect people from being imitated without their consent.
Speaker 1: Going a bit further, recently, Spotify had to purge a
Speaker 1: whole bunch of songs from its streaming service because AI
Speaker 1: was gaming the system. So there's this company called Boomy,
Speaker 1: and Boomy lets you create a song based on a prompt,
Speaker 1: kind of similar to how chat GPT will create a
Speaker 1: text response to a prompt you type in a little
Speaker 1: text field. So you could type something up like country
Speaker 1: song in the style of Hank Williams with vocals like
Speaker 1: Billie Eilish about going home after being away for many years,
Speaker 1: and then Boomy would take this prompt and generate a
Speaker 1: musical track for you, and then Boomy would actually release
Speaker 1: that track on streaming services like Spotify. Now that's already
Speaker 1: a bit sus because if you're using styles and voices
Speaker 1: that actually originate with other people without their involvement or consent,
Speaker 1: there's a problem with that. Even if there's not an
Speaker 1: obvious law that you're violating, it's still an ethical issue.
Speaker 1: But don't worry. It gets worse because someone maybe it
Speaker 1: was Boomy, maybe it was one of Boomy's customers, I
Speaker 1: don't know, but someone was trying to boost streams to
Speaker 1: these AI generated songs because Boomy's business model was you
Speaker 1: can create a song. You can use our AI to
Speaker 1: create a song based on your prompts, and then we'll
Speaker 1: post the song to streaming platforms and then we share
Speaker 1: the royalties that are generated from the AI song. So
Speaker 1: if your AI song is really popular, then you get
Speaker 1: a payout, but Boomy takes a cut, So some money
Speaker 1: goes to the user who's prompts served as the starting
Speaker 1: point for that song, and the rest goes to Boomy. Well,
Speaker 1: streaming royalties really don't amount to very much, so if
Speaker 1: you want to start generating royalties, you need to get
Speaker 1: like a crazy number of listens to a particular song.
Speaker 1: So what better way to get a revenue bump than
Speaker 1: to create a bot that artificially hits replay on a
Speaker 1: track to get that number up into the stratosphere. And
Speaker 1: if you think about it, it's a case of robots
Speaker 1: making the music and robots listening to it. How insane
Speaker 1: is that? Now? Artificially running up those numbers hurts everyone
Speaker 1: in the long run, even if the streaming platforms didn't
Speaker 1: pick up on it, and don't worry, they did. Well,
Speaker 1: if you did this long enough the industry would have
Speaker 1: to revisit how royalties are paid out. The whole business
Speaker 1: would change, and ultimately that could hurt legitimate artists in
Speaker 1: the process, you know, artists who aren't relying on bots
Speaker 1: to artificially drive up the popularity of their music. There
Speaker 1: are a lot of negative consequences to that kind of scheme.
Speaker 1: But the platforms have already begun to remove those types
Speaker 1: of tracks in response to suspicious playback numbers. Like there's
Speaker 1: nothing inherently wrong with creating a track like that, at
Speaker 1: least not on a legal standpoint, but you know, illegally
Speaker 1: boosting the numbers, that is an issue. Okay, I've got
Speaker 1: more to say about this, but we're going to take
Speaker 1: another quick break and then we'll get back to doctor
Speaker 1: Hinton's specific concerns with AI. Okay, so I mentioned AI
Speaker 1: in the creative fields of things like, you know, the
Speaker 1: visual arts and music. We've also got the current situation
Speaker 1: here in the United States as I record this, it's
Speaker 1: in May of twenty twenty three, I think I said
Speaker 1: that at the top of the show, and the Writer's
Speaker 1: Guild of America or WGA, is on strike. So this
Speaker 1: union represents TV and film writers, and as they're on strike,
Speaker 1: they cannot do any work in those fields. They can't
Speaker 1: take any meetings, they can't discuss projects, nothing. One of
Speaker 1: their many concerns, it's not the only one, but it
Speaker 1: is one of them, is the role of AI in
Speaker 1: the writing process in Hollywood. So the fear is that
Speaker 1: studios will start to turn to AI in order to
Speaker 1: do stuff like generate script ideas or maybe even a
Speaker 1: first pass at a full story treatment, and then they
Speaker 1: would turn to human writers to polish that idea up
Speaker 1: into something that's con deceivably watchable. But see if you're
Speaker 1: hired to do that, If you're hired to come in
Speaker 1: and do a rewrite or a punch up on a script,
Speaker 1: you make less than you would if you were writing
Speaker 1: a new script from page one. So, in other words, studios,
Speaker 1: the fear is that studios will lean on AI to
Speaker 1: avoid having to pay people to come up with great ideas.
Speaker 1: They'll just use the AI to create ideas, and then
Speaker 1: the humans have to turn these AI generated ideas into
Speaker 1: something that's theoretically going to be a hit, and those
Speaker 1: ideas aren't always going to be great. Now. To be fair,
Speaker 1: the stuff humans make is not a always great see
Speaker 1: also pretty much anything coming out of asylum. That studio
Speaker 1: seems to be run by committee, specifically for the purposes
Speaker 1: of creating trash. But it's it's a real concern, right,
Speaker 1: the worry that, oh, you're going to undercut writers, You're
Speaker 1: going to make it even harder to make a living
Speaker 1: to be a professional writer in Hollywood, because you're going
Speaker 1: to take out one of the more lucrative parts of
Speaker 1: the job by shifting that over to AI, and then
Speaker 1: everyone ends up making even less money while cost of
Speaker 1: living continues to go up, and the studio ends up
Speaker 1: doing it all in the justification of cutting costs and
Speaker 1: increasing profits. It's those kinds of concerns that partly led
Speaker 1: to doctor Hinton to resign from Google. But again that's
Speaker 1: just part of it. There is this concern about how
Speaker 1: AI once was intended to be a thing to augment
Speaker 1: a person's capabilities in their job, but there's this legit
Speaker 1: fear that it could be more of a replacement than
Speaker 1: an augmentation. But there's more to it. So imagine a
Speaker 1: scenario in which an AI agent is not only able
Speaker 1: to design code to build a program, imagine that it's
Speaker 1: also able to execute that code. So it's not just
Speaker 1: creating software, it's able to run that software. Now, imagine
Speaker 1: an AI agent that creates code intended to improve the
Speaker 1: AI itself. Now, this is one of those concepts that's
Speaker 1: really popular in a lot of science fiction, and it
Speaker 1: also shows up in variations of the Singularity. I mentioned
Speaker 1: the singularity recently in an episode, but I didn't define
Speaker 1: it in that episode. So let me do that right now,
Speaker 1: because honestly, you don't hear the terms as frequently as
Speaker 1: you did like a decade ago. But the idea of
Speaker 1: the Singularity goes something like this, We eventually will reach
Speaker 1: a point in technological development where there is a tipping point,
Speaker 1: and after that tipping point, things will be evolving and
Speaker 1: advancing so quickly that it becomes impossible to define the
Speaker 1: present at any given moment, because from one minute to
Speaker 1: the next, so much is advancing and changing that there's
Speaker 1: no like the President is just change. That's it. It's
Speaker 1: an era of incredibly, unimaginably rapid and constant evolution, and
Speaker 1: it will encompass not just our technologies, but potentially even ourselves.
Speaker 1: So some versions of the Singularity incorporate an idea where
Speaker 1: humans integrate technology into themselves and augment their abilities, like
Speaker 1: boosting their intelligence or giving them incredible skills, kind of
Speaker 1: like the matrix idea of I know, kung fu, right,
Speaker 1: like that sort of thing. Some versions of the Singularity
Speaker 1: instead say, nah, humans just kind of get rid of
Speaker 1: our fleshy, mortal bodies and we become digital beings. We
Speaker 1: find a way to transport our consciousness into the digital realm,
Speaker 1: and we become one with machines and potentially one with
Speaker 1: each other. It gets really speculative fictiony when you start
Speaker 1: talking about the Singularity, and I am not convinced that
Speaker 1: that kind of thing is ever going to happen, But
Speaker 1: I do see the potential danger of having machines design
Speaker 1: their own code and then be able to execute that code.
Speaker 1: That could include things like malware that is able to
Speaker 1: bypass antivirus detection because it's built on a new model
Speaker 1: and it's not based off some previous version of malware
Speaker 1: that could potentially be detected. That's a real possibility. It's
Speaker 1: something to really be concerned about. We've also seen already
Speaker 1: with AI hallucinations, how these systems can present misinformation as
Speaker 1: if it's the real deal, and with such unintended consequences
Speaker 1: in a pretty innocent application of AI, you are left
Speaker 1: to wonder what kind of problems would occur with coding? Right,
Speaker 1: we've already seen a problem that can occur just with
Speaker 1: simple text based interactions. What kind of problems could occur
Speaker 1: if we start to depend upon A to build code? Now,
Speaker 1: I think in a lot of cases we would just
Speaker 1: end up with bad code. Like we'd have stuff that works,
Speaker 1: but we'd also have stuff where, for some reason or another,
Speaker 1: the AI introduced code that doesn't do anything, or it
Speaker 1: causes the whole thing to crash, and so we just
Speaker 1: end up with software that doesn't really work in those cases,
Speaker 1: But there's enough doubt there to make us pause, Like
Speaker 1: maybe the code would work, but maybe it would do
Speaker 1: something malicious or ultimately harmful. I'm assuming that's how doctor
Speaker 1: Hinton feels based on his statements post resignation. I don't
Speaker 1: want to put words into his mouth, but this is
Speaker 1: kind of the what I'm inferring based upon what he said.
Speaker 1: Hinton is worried that deep neural networks and similar machine
Speaker 1: learning techniques could be put to use in harmful, aggressive ways.
Speaker 1: I mean this dates back to his decision to try
Speaker 1: and avoid taking funding from the Department of Defense. Right,
Speaker 1: he's worried about the stuff like AI controlled machines that
Speaker 1: could be used in warfare, and we've already seen some
Speaker 1: elements of that with things like drones. So it's a
Speaker 1: reasonable fear to have, and a lot of experts in
Speaker 1: AI have struggled with this and have you know, campaigned
Speaker 1: to have kind of bands put on for AI controlled
Speaker 1: weaponry and warfare materials. And there's a real fear that
Speaker 1: in some countries, the push to create such tools will
Speaker 1: be very hard to avoid, that there won't be these
Speaker 1: checks in place or people concerned. It will be more
Speaker 1: of a just an overall drive to develop those kinds
Speaker 1: of tools and to thus dominate by having those tools
Speaker 1: in your arsenal. That can lead to a situation where
Speaker 1: everybody else rushes to weaponize AI because they're worried that
Speaker 1: everyone else is already doing it and that they're going
Speaker 1: to get left behind and thus be in a vulnerable position.
Speaker 1: So it becomes kind of a self fulfilling prophecy. And
Speaker 1: in that case, it's the AI experts that we have
Speaker 1: to rely on to push back against that Trendeople who
Speaker 1: are actually building the systems. We have to hope that
Speaker 1: they will do so in a way that won't perpetuate harm.
Speaker 1: But that's a big hope to place on that particular
Speaker 1: group of people. Now, not everyone is as worried about AI,
Speaker 1: at least not in the short term. Stanford researchers recently
Speaker 1: published a paper titled are emergent abilities of large language
Speaker 1: models a mirage? So, an emergent ability refers to a
Speaker 1: system developing some sort of skill or function for which
Speaker 1: it was not formally trained or programmed to do so.
Speaker 1: For example, let's say you train a large language model
Speaker 1: to answer questions that are posed in English, but then
Speaker 1: you find out that it's also able to translate responses
Speaker 1: into Spanish perfectly, even though you didn't design the system
Speaker 1: to quote unquote understand Spanish. The researchers at Stanford concluded
Speaker 1: that these apparent emergent abilities are in fact mirages. They
Speaker 1: are not real, They are illusions. So the researchers are
Speaker 1: saying that companies like Google and open ai might look
Speaker 1: at the results of a model and then use a
Speaker 1: metric that suggests the ability that was displayed was an
Speaker 1: emergent one. But if they had chosen a different metric,
Speaker 1: if they had looked at the output from a different
Speaker 1: point of view. In other words, the illusion of emergent
Speaker 1: behavior would fade. So, in other words, it just depends
Speaker 1: on how you look at it, whether it looks like, oh,
Speaker 1: this system is doing something it wasn't designed to do,
Speaker 1: or oh, by looking at it this way, we see
Speaker 1: it's performing exactly as it was designed to do. So
Speaker 1: we're getting real obi wan kenobi here with a certain
Speaker 1: point of view. Stuff, all right, But how worried should
Speaker 1: we be about AI? Sadly, I think the answer to
Speaker 1: that is really complicated. I wish I could just give
Speaker 1: you a definitive answer from terrified to mift, But I
Speaker 1: think it largely depends upon who you are and what
Speaker 1: you do for a living, and how much you depend
Speaker 1: upon automated technology. Honestly, I think, for example, that folks
Speaker 1: who write code have a legitimate reason to be concerned,
Speaker 1: not because I think AI is going to do their
Speaker 1: job better, but rather because I have very little faith
Speaker 1: in software company executives to avoid the temptation to push
Speaker 1: their chips in on a big, long shot bet on AI. So,
Speaker 1: in other words, I worry that business leaders are going
Speaker 1: to make some poor decisions in an effort to cut
Speaker 1: costs and maximize efficiency, and then get rid of human
Speaker 1: engineers and rely on AI to build code, and then
Speaker 1: we're going to end up with a really rough period
Speaker 1: of subpar software. Now, in the long run, we might
Speaker 1: either see companies that previously had discarded their human programmers
Speaker 1: return to them and say, gosh, it turns out, yeah,
Speaker 1: we need you because what the stuff that AI is
Speaker 1: making is not consistent or good quality. But then again,
Speaker 1: we might see AI generated code improved to a point
Speaker 1: where you know, it is superior to what humans were making.
Speaker 1: It's really impossible to say right now, we can't say
Speaker 1: for sure which direction it's going to go in, and
Speaker 1: it may even be more messy than that. Right It
Speaker 1: might be in some cases the code generated by AI
Speaker 1: is superior and in other cases it's inferior. It may
Speaker 1: not be, you know, an industry wide thing we can
Speaker 1: have a firm statement about, and maybe I should put
Speaker 1: more faith in companies. I just know I've seen a
Speaker 1: lot of decisions and a lot of different organizations over
Speaker 1: the years that have proven to be really short sighted
Speaker 1: strategies and ultimately harmful all in the name of returning
Speaker 1: shareholder value. So I guess you could call me a cynic,
Speaker 1: but I just feel like I've seen it a lot,
Speaker 1: so I would not be surprised, and in fact, that
Speaker 1: that IBM CEO statement of potentially filling around seveny eight
Speaker 1: hundred jobs total, I think is the real estimate with
Speaker 1: AI instead of with humans. That kind of speaks to
Speaker 1: my worries. I do not think that we are on
Speaker 1: the precipice of AI spiraling out of control and becoming
Speaker 1: this malevolent superhuman intelligence that's going to ultimately decide to
Speaker 1: get rid of all of us. However, that's just my opinion,
Speaker 1: and Goodness knows, I don't have the experience or the
Speaker 1: expertise of someone like doctor Henton, so I'm taking this
Speaker 1: from what you could argue is a largely uninformed opinion.
Speaker 1: I think that's a fair assessment. I do think AI
Speaker 1: is posing real problems right here and now, and that
Speaker 1: we have to consider those problems and we need to
Speaker 1: address them, either in how we are developing and deploying AI,
Speaker 1: or how we create legislation to protect humans who otherwise
Speaker 1: might see their livelihood threatened. I do think we need
Speaker 1: to revisit right to personality and right to publicity style
Speaker 1: laws to make sure that the laws incorporate things like
Speaker 1: deep fake video and audio and in the form you know.
Speaker 1: Right now, we have protections in place if someone uses
Speaker 1: your likeness without your permission. It's very specific rules about that.
Speaker 1: It's not like if your image pops up, you know,
Speaker 1: because someone took a photo and you happen to be
Speaker 1: in the background. It's not like that's a case that
Speaker 1: you're going to have a really strong, you know, legal
Speaker 1: backing on if you want to protest the use of it.
Speaker 1: But let's say you're a celebrity and someone runs your
Speaker 1: image next to a product that you did not agree
Speaker 1: to endorse. There are protections for that, but those protections
Speaker 1: are largely for just likenesses like your image. It doesn't
Speaker 1: necessarily cover things like the sound of your voice or
Speaker 1: the style of music you create. The laws need to
Speaker 1: be rewritten or tweaked in order to cover those cases, because,
Speaker 1: I mean, it's a new world where that sort of
Speaker 1: thing is possible. So right now, there's not like there's
Speaker 1: no recourse for someone who hears a song that sounds
Speaker 1: like they sang it, but they didn't there's nothing really
Speaker 1: they can do, and you have to be careful with
Speaker 1: how you word such laws because there are things like
Speaker 1: you know, parody being protected by fair use. So if
Speaker 1: you wanted to create a parody of a song and
Speaker 1: you hire someone who sounds kind of like the musical
Speaker 1: artist you're parodying, that shouldn't necessarily be illegal. But if
Speaker 1: you're trying to pass it off as if it were
Speaker 1: the artists themselves, that's a different story. So yeah, it's complicated,
Speaker 1: it's messy. It's complicated not just on the tech side,
Speaker 1: but on the legislative side, the cultural side, military as well.
Speaker 1: I do think that doctor Hinton has some legitimate concerns.
Speaker 1: I think some of them, at least I hope anyway,
Speaker 1: are a little premature. I hope it's the things he's
Speaker 1: worried about are far enough out into the future that
Speaker 1: we can actually steps to prevent bad outcomes and negative consequences.
Speaker 1: We'll never prevent all of them, because some of them
Speaker 1: will be completely unintended, but I would like to see
Speaker 1: them minimized at the very least in the meantime. I'm
Speaker 1: not going to panic about AI, but I'm giving it
Speaker 1: a lot of side eye so it knows it needs
Speaker 1: to stay in line. That's it. I hope you are
Speaker 1: all well, and I'll talk to you again really soon.
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