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

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