Welcome to tech Stuff, a production from I Heart Radio. Hey there, and welcome to tech Stuff. I'm your host Jonathan Strickland. I'm an executive producer with I Heart Radio. And how the tech are Young. Well, since it's been in the news quite a bit so far this year, I thought today we would look into open ai, both the for profit company and it's parent not for profit organization. So, for those of y'all who have managed to dodge all the hubbub, open ai is the company behind chat gpt. That's the chat bot that's been making headlines for everything from offending the musician Nick Cave of Nick Cave and the Bad Seeds Fame, to worrying teachers that their students are just going to use a chat bot to cheat on assignments rather than actually bother to learn something. But what about the company that made this thing in the first place. Well, the history of open ai dates back to twenty when a bunch of very wealthy tech entrepreneurs got together and said, you know what, maybe we should create an organization that aims to make helpful artificial intelligence before someone opens Pandora's box and Unleasha's malevolent you know, or at least uncaring super intelligence upon us all or something to that effect. Essentially, the goal was to develop AI and AI applications in a way that would be beneficial to humanity and try to avoid all the scary sky net terminator kind of stuff. But to talk about this requires us to define some terms, like terms that you might think are obvious on the face of it, but I would argue are not. So. The big one here would be artificial intelligence. There are certain words and phrases out in the world that have lots of different meanings, and this can sometimes cause confusion and miscommunication. I would argue artificial intelligence is a real doozy among these. You hear about someone working in AI and you start immediately getting preconceived ideas of what that means, and you're probably wrong. Actually, now that we're just talking about even just the word intelligence has some ambiguity to it. So what do we mean when we say that something is intelligent. Well, let's take a look at what some dictionaries say. So Webster defines intelligence as the ability to learn or understand or to deal with new or trying situations, or the ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria such as tests. Thanks Webster Oxford defines it as the ability to learn, understand, and think in a logical way about things. The ability to do this, well, it's a little more succinct. But then if we really want to boil it down, the American Heritage Dictionary defines it as the ability to acquire, understand, and use knowledge. That's what intelligence is, according to those. Dr dah lyoel Lee, and I apologize Dr Lee for butchering your name is a professor of neuroscience and author of Birth of Intelligence, and he defines intelligence as the ability to solve complex problems or make decisions with outcomes that benefit the actor. Dr Lee also acknowledges that intelligence is actually pretty hard to define, and that there are many different definitions, which you know, we've just seen. Like even though all the definitions I mentioned have significant overlap between them and they all seem to be dancing around the same kind of concept, you might feel like none of them quite get it right. And that's where some of these challenges come from. Is that just defining intelligence before we even get to artificial intelligence is hard. All right, Well, let's let's say that intelligence generally is the ability to learn and to acquire knowledge and then to use that knowledge in new situations. Let's just use it by that and say that, you know, it's got an element of problem solving that goes with that, which I think is pretty much implied. So artificial intelligence, then will artificial suggests that it's something that's created by humans rather than found in nature. Oxford Languages defines artificial intelligence as the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision making, and translation between languages. So that's a fairly decent definition. Uh, But here's where we run into some more ambiguity. When we talk about artificial intelligence. We're not necessarily using the word intelligence to mean the exact same thing when we apply it to a human context. You know, a person working in artificial intelligence isn't necessarily trying to make a machine think or appear to think like a human does. In fact, they're probably not doing anything of the sort. They might be working on something that, when collected with the work of countless others, ends up contributing to that kind of machine but that's different, so AI involves a lot of different disciplines and technologies. Facial recognition is a type of AI. Speech recognition is a type of AI. Text to speech is related to artificial intelligence. Robotics share a lot of features with AI, although you could also have robots that are fully programmed to complete precise routines and and that cases they're just following a list of instructions and there's no decision making component right there, just literally following step one, step two, step three, step four, repeat. So those kinds of robots aren't really in the artificial intelligence realm, but there are other robots that are now frequently I find that the general public associates the concept of artificial intelligence with a machine that appears to have knowledge gathering and problems solving capabilities, usually paired with some method to put solutions into action, so often in the form of a robot or a computer system that's connected to stuff that can actually get crap done. I almost said the other phrase, but this is a family show, so they're thinking about what is often referred to as strong AI. These are machines that have a form of intelligence that is to all practical purposes. Indistinguishable from human intelligence. Now that's not to say that it's processing information the exact same way that we peep process information, but that the outcome is the same that at the end of the day, if the machine and the person were to come to the same conclusion, doesn't really matter what steps in the middle were taken. Now, if such a thing as possible, we're not there yet. We aren't at the point where we have this. But the work done in AI right now, which is really in the field of weak AI, that is, artificial intelligent solutions designed for specific purposes, is contributing toward the creation of strong AI. Now there's another phrase for strong AI that we need to talk about, which is artificial general intelligence or a g I. And I know there are a lot of initialisms, that's always the case when we talk about tech. But a g I general intelligence that kind of tells you, Okay, this is an AI that's meant to do lots of different stuff. Right, It's not designed to do a specific task and just get better and better and better at doing that task. It's meant to handle lots of different things, maybe any thing. And it's just like if you put a human and you have that human go into a situation they've never experienced before, how do they cope? Well, it's the goal is to create an artificial intelligence that would be able to handle new situations in a similar way to the way humans do. That's the artificial general intelligence. Again, no one has made one of these yet, but that would become open a eyes. Primary goal is to create an a g I, the first to create an a g I. Now, week AI does not mean that artificial intelligence is bad at its job or its inferior in some way. In fact, week AI might be much better at doing its specific task than humans are at completing that specific task. It's just that this is all the week AI can do. It can't do other things things it's operating under constraints. So as an example, let's just think of something that's really simple that you wouldn't even think of as being intelligent, like a basic calculator, not even a scientific calculator, a basic calculator like one that might be handed out by a bank, and you can enter a pretty tough mathematical problem into the calculator and it will provide a solution in a fraction of the time it would take your average human to do the same work, but that same human could do other stuff like maybe that human can play the guitar or juggle or paint or play a video game or any of an endless number of other tasks. But the calculator can't do that. It can just calculate. That's all it can do, and it can do it really well, but it's unable to extend this capability to anything beyond that purpose. Now, sometimes when we encounter a really good week AI, we can fool ourselves into thinking that the AI is doing something really magical, or that it's matching our own capabilities to think. It can actually be pretty easy to fall into this trap. A sufficiently sophisticated chatbot might fool listen to thinking that the machine we're chatting with is actually thinking itself. But it's not, at least not in the same way that people do. Now, why did I go through all of that trouble to define all these things? Well? The founding principle of open AI is to create artificial general intelligence and AI applications and technologies through a responsible, thoughtful approach, and that implies that there's an irresponsible way to do this, and that following such an irresponsible way could lead to disaster. And that's where we get to our science fiction stories, and that certainly tracks. You know, I'm not here to tell you that that's an unreasonable fear. That fear is totally reasonable. In fact, we've been seeing how weak AI can and does cause problems, or maybe how I should say, are our reliance upon week AI can cause problems. The AI on its own may not be able to cause a problem by itself, but because we rely on it, then we go and we create these problems. So let's go with facial recognition for this one. It has been shown time and again that many of the facial recognition technologies that are actively deployed in the world today have bias built into them. They are fairly reliable at identifying people within certain populations, like white people primarily, but then with people of color, these systems aren't nearly as accurate. So what happens is that these facial recognition systems can generate false positives more frequently for say, black people. And because we have law enforcement agencies that are making active use of facial recognition technologies when looking for suspects, this means that police can and do end up harassing innocent people, all based off of this misidentification. So imagine one day you're just going about your business and then suddenly law enforcement swoops in and arrests you for a crime not only you didn't commit, but you also have no knowledge of this crime. And it's all because a machine somewhere said this is the person you want. Now, imagine how your life would be affected. What if it happened while you were at work or at school. How do you think the people around you would react when police come in and arrest you. How many of those people would treat you differently even after hearing that the whole thing was just a mistake. What kind of stress would that put on you and the people in your life? Now? The reason I'm really nailing this home is because this stuff is happening right. This problem is a real problem. This is not a theoretical it's not a hypothetical. Real people have had their lives up ended because police have relied upon faulty facial recognition technology and saying, oops, it was our mistake doesn't fix your life when it's been turned upside down. Or as Matthew Grissinger of the Institute for Safe medication practices has put it quote. The tendency to favor or give greater credence to information supplied by technology e g. And a d C display, and to ignore a manual source of information that provides contradictory information e g. Handwritten entry on the computer generated m a R, even if it is correct, illustrates the phenomenon of automation bias. Automation complacency is a closely linked, overlapping concept that refers to the monitoring of technology with less frequency or vigilance because of a lower suspicion of error and a stronger belief in its accuracy end quote. So in other words, we have a tendency to trust the output of machines, and that trust is not always warranted. This can get us into trouble. We can trust that the machines know what they're doing and that the way they process information is reliable and even infallible, and by acting upon that we can create terrible consequences. Mr griss Singer's context was within the field of medication prescriptions, which, obviously, if you were to rely solely upon automated output and that automated output was wrong, could result in terrible consequences. But I'm sure you can imagine countless other scenarios in which an over reliance on technology could lead to disaster. We'll talk about another one when we come back from this quick break. We're back, and before the break, I was talking about how we have a tendency to put too much trust inte knowlogy in general and AI in particular, and how this can come back to haunt us. So an example that leaps to my mind is autonomous cars. And I'm going to be the first to admit I jumped on the autonomous car bandwagon without applying nearly enough critical thinking. I was really considering just the surface level of what it would mean to have autonomous cars. So here's how my flawed logic went. This is why I was so like Gung Ho on autonomous cars several years ago now and have subsequently changed my my thinking. So the way I originally thought was computer processors are wicked fast, right like a CPU in your computer can complete calculations so quickly, millions of them every second, billions in fact, depending upon the the sophistication of the of the operations. And then you have parallel processing, right like if you have a multi core processor could have lots of functions all being performed simultaneously by this processor. Then, on top of that, you could have sensors on your car that cover three sixty degrees of view around the vehicle, so you would be able to have the system pay attention in every single direction simultaneously, whereas a human driver can only pay attention within their field of view and then with the help of some mirrors, get a little extra you know, awareness around them. You could have mechanical systems that could react immediately upon receiving a command from the processors with no delay, so you don't have that delay of action between when you sense something happening and when you are able to act on that. So, surely such a system with incredible processing power, with three sixty degrees of awareness, with this immediate ability to react, would be able to engage in defensive driving faster, more effectively, and safer than in a human ever could. Clearly, machines are superior. We should all be in autonomous cars. This is where I ran into the problem of overreliance on technology. Sure, in isolated cases, everything I was thinking might be at least partly true, but when you take it together and you start to apply it in the field in a vehicle, things are far more complicated than I ever gave it credit for. And as we have seen with advanced driver assist features, if we rely too much on this technology, it can and does lead to tragedy. So we've seen this play out where people have depended too heavily upon this tech and have paid for it with their lives. So we know that this is more complex than what I initially thought of back in my naive days of being so, you know, flag bearing for the whole autonomous car our movement, and I still believe in autonomous cars and how they could contribute to greater safety, but I also recognize that it's a far more complex problem than what I originally imagined. All right, so we have thoroughly defined the problem at this point. Right. Artificial intelligence has the potential to help us do amazing things, but only if we develop and deploy it properly. Otherwise it could exacerbate existing problems or even create all new problems. So there's a need to be thoughtful about design and application and deployment and distribution. So who decided to codify this philosophy of being careful about AI and create an organization dedicated to doing that. Well, the two people who are frequently cited as the co founders for open ai are Elon Musk and Sam Altman, though I would hate and add there were many other people who are really co founders as well, but these are the two that, you know, everyone says, these are the guys who started talking and kind of generated the initial idea that became open AI. So let's start with Musk. So years before he decided to drop billions of dollars in an effort to troll the Internet whenever he wanted to, Mr Musk was something of an AI doomsayer. You know, he was warning that artificial intelligence could potentially pose an existential threat to humans. Kind of this idea of we create a human level or even superhuman level strong AI, and then it turns on us and wipes us out. And certainly bad AI can be a huge issue. We just talked about how even weak AI can be a really big problem. Now, I don't think we're close to having a human, let alone superhuman intelligence determined to wipe out humanity emerge, but you know, you can definitely have bad AI contribute to human suffering. See also Tesla, one of Mr Musk's companies. One might even argue that Elon Musk knows the danger that artificial intelligence poses to humanity because one of his companies is leading the charge in that field in the form of Tesla autopilot and full self driving modes. Now again, you could say that I'm being unkind, because we do need to remember that Tesla, despite the languages it uses for marketing purposes, does alert drivers that they are not supposed to take their hands off the wheel or stop paying attention to the road, and that at least in all the accounts I have read about terrible accidents involving Tesla vehicles that were in driver assists mode, it sounds like the driver wasn't following those directions. So you could argue that, you know, the driver ultimately is at fault because they're failing to adhere to the instructions that Tesla gives. The flip side of that is that Tesla markets these features as if they are more than you know, sophisticated driver assist features. The other co founder of open Ai that's frequently mentioned is Sam Altman, the current CEO of open Ai. Sam Altman was previously president of y Combinator. He became president of y Combinator in fourteen, which was the year before he co founded open Ai with Elon Musk. And you might say, well, what is why Combinator. It's a startup accelerator, which doesn't really mean anything either, right, Well, that's a company that helps people who have startup business ideas get the support they need in order to launch their idea and make it a reality. So that can include stuff like mentoring the startup leaders so that they can build a good business model and create the right corporate structure that they're going to need in order to do business, all the way up to prepping them and connecting them with people that they can pitch their idea to in order to get investment into their startup. So one of the big valuable services that companies like y Combinator provide is access to the investor community that you might not otherwise be able to get to without that kind of support. Now, Altman would continue to serve as y Combinator president until twenty nineteen. At that point he stepped down from that position to focus on open Ai. H Elon Musk would sit on the board of directors for open Ai until ten. We'll talk about that in just a bit. Now. I mentioned that there were also other co founders, So in addition to these two entrepreneurs, early founders in the open Ai initiative included Greg Brockman, who's still there. I believe he's a former chief technology officer of Stripe, the payment processing company. The PayPal co founder Peter Thiel was also one of the early investors in open Ai. LinkedIn co founder read Garrett Hoffman, another one one of Altman's y Combinator colleagues. Jessica Livingston was another, and there were a few more. Now collectively, the founders and partners all pledged one billion dollars to fund open ai, which again was meant to be a nonprofit organization dedicated to developing productive, friendly AI and not the scary pew pew lasers kind of AI. But then there's also the open part of open ai. So during the brainstorming that would lead to the founding of this organization, the co founders talked about how big tech companies typically do all their AI development behind closed doors with no transparency, and that their version of AI was meant to benefit the parent company, not humanity as a whole. The open Ai organization is going to take a different approach. The idea was to share the benefits of AI research with the world and do that as much as possible on an effort to evolve AI in a way that helps but doesn't harm. Researchers would be encouraged to publish their work in various formats as frequently as they could, and any patents that open ai would secure would similarly be shared with the world. The message appeared to be the goal is more important than the organization, that friendly AI is the chief important goal here, and that open ai only exists to see that become reality, and that open ai was really kind of more of a a shepherd of pushing AI into this direction rather than brazenly forging a path into the wilderness, although that's not how things would turn out now. Early on, the organization grew mostly through connections in the AI research community, with Luminaries and x birds joining the organization, but the organization itself kind of lacked a real sense of leadership or direction. There was this noble goal, right, Everyone knew that they were trying to make reliable, safe, friendly, beneficial AI, but how there wasn't really any plan for how to get to where they wanted to be. Google researcher Dariomday visited open ai in mid and he came away thinking that no one at the organization really had any idea of what they were doing. Despite that, or maybe because of it, i'm a Day would join the organization a couple of months later and became head of research there. Now. One of the first things to emerge from open ai was in ten like it was founded in late and in twenty sixteen they were already producing some interesting stuff. And the first up was a testing environment that the organization called Jim Jim as a gymnasium, not as in Jimmy Jim Jim Jim Hawkins. So what was being tested, well, they were testing learning agents. This brings us to a discipline that's within artificial intelligence. It's called machine learning, and basically machine learning is what it says on the TIN. It's finding ways to make machines learn so that they discover how to do certain tasks and how to improve at doing them over time. And there is no single way that this is done. It's not like there's one and only one way for machine learning to happen. There are actually lots of different models. For example, there's the generative adversarial model of machine learning. Basically, this is a model that involves having two machines set against each other. One machine is set up to try and accomplish a specific task. This is the generative part, and the other machine is set up to foil that task is the adversarial part. So, for example, maybe you're training the generative model to create a digital painting mimicking the style of famous impressionists, and the adversarial system's job is to figure out which images that are fed to it are real impressionist paintings from history and which ones were generated by the computer system. And you run these trials over and over, with each system getting better over time. The generative one gets better at making Impressionist style paintings and the adversarial one gets better at finding little hints that indicate this was not an actual painting but was computer generated. The open ai jem specializes in learning agents that rely on reinforcement learning, and when you break it down, it sounds a lot like your typical kind of school work. That is, when the learning agent performs well, it is rewarded when it performs poorly, it is punished. So it's kind of like getting your test paper back and finding out you aced the exam, or if things didn't go well that you totally whiffed it and you'll be going to summer school to make up for that. Also in open Ai introduced a platform humbly called Universe. This platform helps track progress and train learning agents to problem solve, starting with the most serious of all problems, finding the fun in Atari video games. I'm talking about classic Autari video games like Pitfall, which, let's be honest, awesome game. You don't have to find the fund there, it's right there. But let's say et the Extraterrestrial or their version of pac Man. Yeah, you have to really find the fun in those. And I'm being a little facetious here, but Universe really does train learning agents by having them learn how to play video games. They started with the Tari games and then they began to build from there, and Universe trains these agents to play the games, and the ideas that by learning how to play games, as the agents encounter new games, they can apply the previous learnings from the experiences of playing everything before to the new game. Just like we humans, will try and apply our knowledge and experience with certain tasks. When we face a totally new situation. You come into something you've never done before, and you might think, well, when I do this other thing, I do it this way, So let me try that here first. Maybe that skill translates to this new situation, and maybe it works, maybe it doesn't, but either way, that informs you and then you can start branching out from there to learn how to master this new task. That's the idea with Universe. Jim and Universe both gave a glimpse at the big plans open Ai had in store. But there was a looming problem on the horizon. And it wasn't a levolent Ai that was hell bent on destroying humanity. It was a far more mundane threat. Open Ai was in danger of running out of money. I'll explain more, but before I run out of money, let's take a quick break. We're back, okay, So we're up to and leaders in open Ai realized that they were facing their own existential crisis in the form of funding. So in order to remain relevant and competitive in the fast paced world of AI development, and in order to achieve the goal of creating an a g I before anyone else. The company was going to have to spend enormous amounts of money on computer systems and other assets like training, databases or else it was going to get left behind. It just wasn't possible to do this while also being a strictly not for profit company, so the leaders started to think about how they might address this. Meanwhile, in Elon Musk stepped down from the board of directors. Now officially, the reason given was that Musk wanted to avoid a potential conflict of interest because Tesla was pursuing its own AI research and Tesla was bound to compete for the same talent pool that Opened a I wanted to tap into, so in order to avoid a conflict of interest, he resigned from the board of directors. However, Musk also subsequently tweeted out that he felt open ai was falling short, mostly on the open part, and that he had disagreements regarding the direction of the organization's efforts. It was also in when open ai released its charter, the company charter, which started to hint at upcoming changes. The charter read, in part quote, we anticipate needing to marshal substant ential resources to fulfill our mission, but will always diligently act to minimize conflicts of interest among our employees and stakeholders that could compromise broad benefit end quote. It was like the leaders were starting to couch things in an effort to explain what was going to be coming up next. So the following year, twenty nineteen, saw open Ai create a new for profit company as a subsidiary. So the parent company, Open Eye Eye, Incorporated, remains a not for profit organization, but open Ai l P is a for profit company. Open Ai published a blog post that tried to explain this decision, saying, quote, we want to increase our ability to raise capital while still serving our mission, and no pre existing legal structure we know of strikes the right balance. Our solution is to create open Ai LP as a hybrid of a for profit and nonprofit, which we are calling a capped profit company end quote. So the idea here is that an investor can pour money into open Ai LP and can potentially earn up to one hundred times that investment as the company releases and generates revenue from products. But that's the limit. Once an investor hits one hundred times their investment. That's they're done. You ain't getting a hunter and one times return on your investment, bucko. So all the additional money over that one hundred times return would go toward nonprofit work. But um, that's that's a lot, right. One hundred times return on investment is huge, to the point where some people say, like, when would you ever hit that? I mean, Google, I think is somewhere in the realm of twenty times return on investment if you got in early on. So um, it's hard to imagine a hundred time return. So some people say, well, this is just language to make it seem like they're still dedicated to this nonprofit but aren't. Really, that's one of the criticisms I've I've read. Now, just imagine that you know that initial investment into open ai was a billion dollars, so presumably you'd have to see more than a hundred billion dollars in profit, uh in order to return that to investors before they were all paid out, and then the rest could go toward nonprofit That's just that initial investment, because believe me, open ai has received subsequent funding. In fact, in twenty nineteen, Microsoft board an additional billion dollars into the company, although only half of that was cash, so it was only like five million. The other five million was in like cloud computing credit, so that open ai could make use of Microsoft's Azure platform without having to pay for it because they had five hundred million dollars in credit. Yalza. And of course we've heard recently that Microsoft is considering a ten billion all our investment into open ai, and there ain't a yells a big enough to express how princely that sum is. In twenty nineteen, open Ai did something strange, at least strange if you remember that open is part of the company's name. The PR Department released information that open ai had been sitting on a language model named Generative pre Trained Transformer TO or GPT two that developed this and not talked about it, and now they were finally talking about it, and that this language model was capable of generating text in response to props, including stuff like it could create fake news articles or alternative takes on classic literature. Further, open ai said that it was actually too dangerous to release the code because people might then use the code to create misinformation or worse, which seemed to fly in the face of open Aiyes, purpose that the company had fostered a published, often and transparently culture, and that was keeping certain projects secret, and when finally talking about them, denying access to the research that seemed counter to the founding principles of open ai. The folks in open ai had sort of shifted their perspective a little bit. In their eyes, some secrecy and restrictions were needed to ensure safety and security, as well as to maintain a competitive advantage over others in the field of AI research. Open ai would eventually release GPT two in several stages before the full code finally came out in November twenty nineteen. Critics accused open ai of relying on publicity stunts to hype up what their research and work had created, and thus pumping unrealistic expectations into the investor market, like, in other words, by saying, oh, this is really dangerous, I don't know if I can let you have this. It got people really excited about it, and so investors were willing to pour more money into open Ai. That's what the critics were saying, that you're just doing this to get people worked up into a frenzy and that the staged release process for GPT two was open AIS way to capitalize on all this height gradually so as not to just deflate expectations by releasing it and then everyone say, oh, that's it. Later, in a paper released in early open AI revealed another secret that the company was essentially using the more power approach of trying to achieve artificial general intelligence or a g I. So a quick word on what they were doing. This was called foresight, by the way, So broadly speaking, there are two big schools of thought on how the world will see a true a g I emerge. That is, an artificial intelligence that can perform very much like a human intelligence, you know, perhaps not in the same way, but again achieving the same outcomes. So one way, the one school of thought is that we already have all the off that we need in all the AI research that has been done over the years. We have all the pieces, They're all there. We just need to amp it up by providing more computational resources behind it and larger training sets. So everything's good to go. We just got to provide the power to push it into the realm of a g I. Now, the other school of thought is that we're still missing something or maybe several some things, and that until we figure those out and we incorporate them into our AI strategy, we just are not going to see an a G I. It won't matter how much power you put behind it. We're still missing elements that will actually allow us to hit a GI status. Now open ai subscribes to the more power philosophy generally speaking, and the research paper kind of explained us. And again this was something that open ai was holding in secret. They even compelled employees to stay quiet about the work. And what was essentially going on was that open ai researchers were taking AI work that was developed in other research labs and companies. These were tools that other competitors were offering, and so they essentially got hold of these tools, and then they jacked up the power of the tools by training them on larger data sets and providing more compute computational power to see if, oh, maybe what we already have is the way there and we just gotta give it the extra oomph to get it to in open ai announced the next generation of its Generative pre Trained Transformer. This would be GPT three and that it would make available in Application Programming Interface or a p I, which would be the company's first commercial product, so customers developers in this case could get access to the GPT three language model through this ap I and then integrate that with their app. So if it was an app would help you do things like I don't know book meetings, then the language model would be part of what would power this app. The following year, we got open aies tool that would generate digital images, which is doll E. That's d A L L E kind of a combination of Wally the Pixar character and Salvador Dolly, the absurdist artist with the incredible mustache. So you would feed Dolly a text prompt and it would try to create images based on that prompt. Sometimes it was delightful and sometimes it was disturbing. Sometimes it was a combination. But it was really impressive that it was able to do this at all, and similar to that of other generative image AI services like mid Journey, which would actually debut a year later in two and open Ai updated Dolly and released Dolly two. In the new version of Dolly is able to combine find more concepts together to create images and also to imitate specific styles. So you know, if you wanted a style that imitated a photograph from the nineteen twenties, it would try to create that that effect, or if you were to say, like a painting from the Cubist movement, that it would try and and accomplish that. In late two, open Ai introduced chat GPT, a chat bought built on top of the GPT three point five language model. That's the one that stirred up conversations around transparency, trusting AI output, and worrying about students cheating off an AI s AST. Now we've already touched on this in this episode about you know, a lot of the concern here, and I think a great deal of it rises not from Chat GPTs incredible abilities, which are genuinely impressive, but rather our human tendency to trust automated output implicitly when a fact it's sometimes wrong. In fact, as many reports have said, sometimes Chat GPT gets things very very wrong, but it presents it in a way that appears to be authoritative and trustworthy. So if we do trust the output of such a system and then we act on that output, where we're falling far short of that AI that's supposed to be beneficial to humanity, right. Open ai was built around that, So this seems again to be a contradiction to open a eyes goal that if it has a chat bot that occasionally produces incorrect information and then people act on it, wouldn't you argue that this AI could be potentially harmful to humanity not beneficial. Now you could say that it's the people who are relying too heavily on chat GPT that are the problem, and that's not really open aiyes fault. They can't control how people use their tools. That, just like the test law owners, people are not properly making use of the technology with enough awareness of that technology's limitations. But others might argue that open ai hasn't exactly made people aware of the limitations at all, at least not in a way that's equal to the hype that surrounds their various products. That open Ai is benefiting from this excitement around the undeniably impressive achievements, but that the company is failing to live up to this commitment to creating beneficial AI because they're not being good stewards of this tool and the outcome of people using it. And it is a very complicated problem, and AI isn't likely to solve this one right away. Open ai is currently developing GPT four, so that's the next generation of the language model it's been developing all these years. CEO Sam Altman has already said that people are likely going to be disappointed by GPT for not the cause the model won't be impressive. I have no doubt it will be, but because people have already built up in their minds a bar that GPT four simply will not be able to reach. And while that is a fair observation, I can't help but think that open ai is at least partly responsible for encouraging the fervor that led to this impossibly high bar. I don't think people said it all on their own. I think open aiyes own approach has kind of encouraged this sort of reaction. I mean, there's already this tendency for us to hype stuff when we just get a hint of what is possible and we start to extrapolate from that. That's true all the time. You can see it over and over and over again in lots of different technologies throughout the years. But at the same time, I feel open ai takes a kind of almost coy approach, and that helps encourage this behavior rather than discourage it. The company is openly doing the goal of building the first a g I, though as we've seen, it's not doing so in quite as transparent away as the organization first set out to follow. But if you're pursuing that goal, it means you've got like really big ambitions, and that again, I think helps to fuel the hype cycle. Now, I guess I can conclude this episode by just reflecting on the fact that open ai is a company that Elon Musk has criticized for failing to be transparent. That's something, y'all. Now. I don't wish to disparage the people who work for open ai or even the goal of the organization itself. I think it's a worthy goal. I think there are a lot of people who truly believe in that goal who are working for open Ai. I think the leadership believes in the goal and that that's what they're pursuing. It's just the realities of trying to achieve that in a world where you need to make money in order to fuel that pursuit creates complications, and there are no perfect solutions unless you just happen to have, you know, a a bottomless pit of a benefactor who can just pour money into the organization and allow it to pursue these these developments without having to worry about the commercial aspect of it. Unless you have that, then you have to deal with these real world complications. And just like the autonomous cars that you know, on the surface should be able to maneuver without any driver in the driver's seat and do so perfectly safely, we learned that once you put it into the real world, there are so many other variables and complications at play. It's never as simple as you first thought. So I know I've dogged on open Ai a lot. There are a lot of really great critical articles about the company. But I do believe in the work they're doing. I just the way they go about it has some elements to it that I find troubling. But it's not like I can suggest a better approach. I just think that it's important for us to pay attention and to criticize when necessary, and to ask questions and to hold the organization accountable because it has claimed to be this organization founded with a pursuit of developing beneficial AI and doing so in an open, transparent way. And if it fails to do that, I think we have to call them on it, because otherwise what we get may not be that beneficial AI we've been hoping for. All Right, that's it for this episode. Hope you enjoyed it, and if you have suggestions for topics I should cover in future episodes of tech Stuff, please reach out to me. You can download the i heart Radio app for free and navigate over to tech Stuff. Just put tech stuff in the search field. That will bring you over to our little page on that app, and you will find a microphone icon on the tech stuff page. If you click on that, you can leave a voice message up to thirty seconds in length let me know what you would like to hear, or if you prefer, you can head on over to Elon Musk's Twitter and you can send me a Twitter message. The handle for the show is tech Stuff H s W and I'll talk to you again really soon, y. Tech Stuff is an I Heart Radio production. 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Welcome to tech Stuff, a production from I Heart Radio. Hey there, and welcome to tech Stuff. I'm your host Jonathan Strickland. I'm an executive producer with I Heart Radio. And how the tech are Young. Well, since it's been in the news quite a bit so far this year, I thought today we would look into open ai, both the for profit company and it's parent not for profit organization. So, for those of y'all who have managed to dodge all the hubbub, open ai is the company behind chat gpt. That's the chat bot that's been making headlines for everything from offending the musician Nick Cave of Nick Cave and the Bad Seeds Fame, to worrying teachers that their students are just going to use a chat bot to cheat on assignments rather than actually bother to learn something. But what about the company that made this thing in the first place. Well, the history of open ai dates back to twenty when a bunch of very wealthy tech entrepreneurs got together and said, you know what, maybe we should create an organization that aims to make helpful artificial intelligence before someone opens Pandora's box and Unleasha's malevolent you know, or at least uncaring super intelligence upon us all or something to that effect. Essentially, the goal was to develop AI and AI applications in a way that would be beneficial to humanity and try to avoid all the scary sky net terminator kind of stuff. But to talk about this requires us to define some terms, like terms that you might think are obvious on the face of it, but I would argue are not. So. The big one here would be artificial intelligence. There are certain words and phrases out in the world that have lots of different meanings, and this can sometimes cause confusion and miscommunication. I would argue artificial intelligence is a real doozy among these. You hear about someone working in AI and you start immediately getting preconceived ideas of what that means, and you're probably wrong. Actually, now that we're just talking about even just the word intelligence has some ambiguity to it. So what do we mean when we say that something is intelligent. Well, let's take a look at what some dictionaries say. So Webster defines intelligence as the ability to learn or understand or to deal with new or trying situations, or the ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria such as tests. Thanks Webster Oxford defines it as the ability to learn, understand, and think in a logical way about things. The ability to do this, well, it's a little more succinct. But then if we really want to boil it down, the American Heritage Dictionary defines it as the ability to acquire, understand, and use knowledge. That's what intelligence is, according to those. Dr dah lyoel Lee, and I apologize Dr Lee for butchering your name is a professor of neuroscience and author of Birth of Intelligence, and he defines intelligence as the ability to solve complex problems or make decisions with outcomes that benefit the actor. Dr Lee also acknowledges that intelligence is actually pretty hard to define, and that there are many different definitions, which you know, we've just seen. Like even though all the definitions I mentioned have significant overlap between them and they all seem to be dancing around the same kind of concept, you might feel like none of them quite get it right. And that's where some of these challenges come from. Is that just defining intelligence before we even get to artificial intelligence is hard. All right, Well, let's let's say that intelligence generally is the ability to learn and to acquire knowledge and then to use that knowledge in new situations. Let's just use it by that and say that, you know, it's got an element of problem solving that goes with that, which I think is pretty much implied. So artificial intelligence, then will artificial suggests that it's something that's created by humans rather than found in nature. Oxford Languages defines artificial intelligence as the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision making, and translation between languages. So that's a fairly decent definition. Uh, But here's where we run into some more ambiguity. When we talk about artificial intelligence. We're not necessarily using the word intelligence to mean the exact same thing when we apply it to a human context. You know, a person working in artificial intelligence isn't necessarily trying to make a machine think or appear to think like a human does. In fact, they're probably not doing anything of the sort. They might be working on something that, when collected with the work of countless others, ends up contributing to that kind of machine but that's different, so AI involves a lot of different disciplines and technologies. Facial recognition is a type of AI. Speech recognition is a type of AI. Text to speech is related to artificial intelligence. Robotics share a lot of features with AI, although you could also have robots that are fully programmed to complete precise routines and and that cases they're just following a list of instructions and there's no decision making component right there, just literally following step one, step two, step three, step four, repeat. So those kinds of robots aren't really in the artificial intelligence realm, but there are other robots that are now frequently I find that the general public associates the concept of artificial intelligence with a machine that appears to have knowledge gathering and problems solving capabilities, usually paired with some method to put solutions into action, so often in the form of a robot or a computer system that's connected to stuff that can actually get crap done. I almost said the other phrase, but this is a family show, so they're thinking about what is often referred to as strong AI. These are machines that have a form of intelligence that is to all practical purposes. Indistinguishable from human intelligence. Now that's not to say that it's processing information the exact same way that we peep process information, but that the outcome is the same that at the end of the day, if the machine and the person were to come to the same conclusion, doesn't really matter what steps in the middle were taken. Now, if such a thing as possible, we're not there yet. We aren't at the point where we have this. But the work done in AI right now, which is really in the field of weak AI, that is, artificial intelligent solutions designed for specific purposes, is contributing toward the creation of strong AI. Now there's another phrase for strong AI that we need to talk about, which is artificial general intelligence or a g I. And I know there are a lot of initialisms, that's always the case when we talk about tech. But a g I general intelligence that kind of tells you, Okay, this is an AI that's meant to do lots of different stuff. Right, It's not designed to do a specific task and just get better and better and better at doing that task. It's meant to handle lots of different things, maybe any thing. And it's just like if you put a human and you have that human go into a situation they've never experienced before, how do they cope? Well, it's the goal is to create an artificial intelligence that would be able to handle new situations in a similar way to the way humans do. That's the artificial general intelligence. Again, no one has made one of these yet, but that would become open a eyes. Primary goal is to create an a g I, the first to create an a g I. Now, week AI does not mean that artificial intelligence is bad at its job or its inferior in some way. In fact, week AI might be much better at doing its specific task than humans are at completing that specific task. It's just that this is all the week AI can do. It can't do other things things it's operating under constraints. So as an example, let's just think of something that's really simple that you wouldn't even think of as being intelligent, like a basic calculator, not even a scientific calculator, a basic calculator like one that might be handed out by a bank, and you can enter a pretty tough mathematical problem into the calculator and it will provide a solution in a fraction of the time it would take your average human to do the same work, but that same human could do other stuff like maybe that human can play the guitar or juggle or paint or play a video game or any of an endless number of other tasks. But the calculator can't do that. It can just calculate. That's all it can do, and it can do it really well, but it's unable to extend this capability to anything beyond that purpose. Now, sometimes when we encounter a really good week AI, we can fool ourselves into thinking that the AI is doing something really magical, or that it's matching our own capabilities to think. It can actually be pretty easy to fall into this trap. A sufficiently sophisticated chatbot might fool listen to thinking that the machine we're chatting with is actually thinking itself. But it's not, at least not in the same way that people do. Now, why did I go through all of that trouble to define all these things? Well? The founding principle of open AI is to create artificial general intelligence and AI applications and technologies through a responsible, thoughtful approach, and that implies that there's an irresponsible way to do this, and that following such an irresponsible way could lead to disaster. And that's where we get to our science fiction stories, and that certainly tracks. You know, I'm not here to tell you that that's an unreasonable fear. That fear is totally reasonable. In fact, we've been seeing how weak AI can and does cause problems, or maybe how I should say, are our reliance upon week AI can cause problems. The AI on its own may not be able to cause a problem by itself, but because we rely on it, then we go and we create these problems. So let's go with facial recognition for this one. It has been shown time and again that many of the facial recognition technologies that are actively deployed in the world today have bias built into them. They are fairly reliable at identifying people within certain populations, like white people primarily, but then with people of color, these systems aren't nearly as accurate. So what happens is that these facial recognition systems can generate false positives more frequently for say, black people. And because we have law enforcement agencies that are making active use of facial recognition technologies when looking for suspects, this means that police can and do end up harassing innocent people, all based off of this misidentification. So imagine one day you're just going about your business and then suddenly law enforcement swoops in and arrests you for a crime not only you didn't commit, but you also have no knowledge of this crime. And it's all because a machine somewhere said this is the person you want. Now, imagine how your life would be affected. What if it happened while you were at work or at school. How do you think the people around you would react when police come in and arrest you. How many of those people would treat you differently even after hearing that the whole thing was just a mistake. What kind of stress would that put on you and the people in your life? Now? The reason I'm really nailing this home is because this stuff is happening right. This problem is a real problem. This is not a theoretical it's not a hypothetical. Real people have had their lives up ended because police have relied upon faulty facial recognition technology and saying, oops, it was our mistake doesn't fix your life when it's been turned upside down. Or as Matthew Grissinger of the Institute for Safe medication practices has put it quote. The tendency to favor or give greater credence to information supplied by technology e g. And a d C display, and to ignore a manual source of information that provides contradictory information e g. Handwritten entry on the computer generated m a R, even if it is correct, illustrates the phenomenon of automation bias. Automation complacency is a closely linked, overlapping concept that refers to the monitoring of technology with less frequency or vigilance because of a lower suspicion of error and a stronger belief in its accuracy end quote. So in other words, we have a tendency to trust the output of machines, and that trust is not always warranted. This can get us into trouble. We can trust that the machines know what they're doing and that the way they process information is reliable and even infallible, and by acting upon that we can create terrible consequences. Mr griss Singer's context was within the field of medication prescriptions, which, obviously, if you were to rely solely upon automated output and that automated output was wrong, could result in terrible consequences. But I'm sure you can imagine countless other scenarios in which an over reliance on technology could lead to disaster. We'll talk about another one when we come back from this quick break. We're back, and before the break, I was talking about how we have a tendency to put too much trust inte knowlogy in general and AI in particular, and how this can come back to haunt us. So an example that leaps to my mind is autonomous cars. And I'm going to be the first to admit I jumped on the autonomous car bandwagon without applying nearly enough critical thinking. I was really considering just the surface level of what it would mean to have autonomous cars. So here's how my flawed logic went. This is why I was so like Gung Ho on autonomous cars several years ago now and have subsequently changed my my thinking. So the way I originally thought was computer processors are wicked fast, right like a CPU in your computer can complete calculations so quickly, millions of them every second, billions in fact, depending upon the the sophistication of the of the operations. And then you have parallel processing, right like if you have a multi core processor could have lots of functions all being performed simultaneously by this processor. Then, on top of that, you could have sensors on your car that cover three sixty degrees of view around the vehicle, so you would be able to have the system pay attention in every single direction simultaneously, whereas a human driver can only pay attention within their field of view and then with the help of some mirrors, get a little extra you know, awareness around them. You could have mechanical systems that could react immediately upon receiving a command from the processors with no delay, so you don't have that delay of action between when you sense something happening and when you are able to act on that. So, surely such a system with incredible processing power, with three sixty degrees of awareness, with this immediate ability to react, would be able to engage in defensive driving faster, more effectively, and safer than in a human ever could. Clearly, machines are superior. We should all be in autonomous cars. This is where I ran into the problem of overreliance on technology. Sure, in isolated cases, everything I was thinking might be at least partly true, but when you take it together and you start to apply it in the field in a vehicle, things are far more complicated than I ever gave it credit for. And as we have seen with advanced driver assist features, if we rely too much on this technology, it can and does lead to tragedy. So we've seen this play out where people have depended too heavily upon this tech and have paid for it with their lives. So we know that this is more complex than what I initially thought of back in my naive days of being so, you know, flag bearing for the whole autonomous car our movement, and I still believe in autonomous cars and how they could contribute to greater safety, but I also recognize that it's a far more complex problem than what I originally imagined. All right, so we have thoroughly defined the problem at this point. Right. Artificial intelligence has the potential to help us do amazing things, but only if we develop and deploy it properly. Otherwise it could exacerbate existing problems or even create all new problems. So there's a need to be thoughtful about design and application and deployment and distribution. So who decided to codify this philosophy of being careful about AI and create an organization dedicated to doing that. Well, the two people who are frequently cited as the co founders for open ai are Elon Musk and Sam Altman, though I would hate and add there were many other people who are really co founders as well, but these are the two that, you know, everyone says, these are the guys who started talking and kind of generated the initial idea that became open AI. So let's start with Musk. So years before he decided to drop billions of dollars in an effort to troll the Internet whenever he wanted to, Mr Musk was something of an AI doomsayer. You know, he was warning that artificial intelligence could potentially pose an existential threat to humans. Kind of this idea of we create a human level or even superhuman level strong AI, and then it turns on us and wipes us out. And certainly bad AI can be a huge issue. We just talked about how even weak AI can be a really big problem. Now, I don't think we're close to having a human, let alone superhuman intelligence determined to wipe out humanity emerge, but you know, you can definitely have bad AI contribute to human suffering. See also Tesla, one of Mr Musk's companies. One might even argue that Elon Musk knows the danger that artificial intelligence poses to humanity because one of his companies is leading the charge in that field in the form of Tesla autopilot and full self driving modes. Now again, you could say that I'm being unkind, because we do need to remember that Tesla, despite the languages it uses for marketing purposes, does alert drivers that they are not supposed to take their hands off the wheel or stop paying attention to the road, and that at least in all the accounts I have read about terrible accidents involving Tesla vehicles that were in driver assists mode, it sounds like the driver wasn't following those directions. So you could argue that, you know, the driver ultimately is at fault because they're failing to adhere to the instructions that Tesla gives. The flip side of that is that Tesla markets these features as if they are more than you know, sophisticated driver assist features. The other co founder of open Ai that's frequently mentioned is Sam Altman, the current CEO of open Ai. Sam Altman was previously president of y Combinator. He became president of y Combinator in fourteen, which was the year before he co founded open Ai with Elon Musk. And you might say, well, what is why Combinator. It's a startup accelerator, which doesn't really mean anything either, right, Well, that's a company that helps people who have startup business ideas get the support they need in order to launch their idea and make it a reality. So that can include stuff like mentoring the startup leaders so that they can build a good business model and create the right corporate structure that they're going to need in order to do business, all the way up to prepping them and connecting them with people that they can pitch their idea to in order to get investment into their startup. So one of the big valuable services that companies like y Combinator provide is access to the investor community that you might not otherwise be able to get to without that kind of support. Now, Altman would continue to serve as y Combinator president until twenty nineteen. At that point he stepped down from that position to focus on open Ai. H Elon Musk would sit on the board of directors for open Ai until ten. We'll talk about that in just a bit. Now. I mentioned that there were also other co founders, So in addition to these two entrepreneurs, early founders in the open Ai initiative included Greg Brockman, who's still there. I believe he's a former chief technology officer of Stripe, the payment processing company. The PayPal co founder Peter Thiel was also one of the early investors in open Ai. LinkedIn co founder read Garrett Hoffman, another one one of Altman's y Combinator colleagues. Jessica Livingston was another, and there were a few more. Now collectively, the founders and partners all pledged one billion dollars to fund open ai, which again was meant to be a nonprofit organization dedicated to developing productive, friendly AI and not the scary pew pew lasers kind of AI. But then there's also the open part of open ai. So during the brainstorming that would lead to the founding of this organization, the co founders talked about how big tech companies typically do all their AI development behind closed doors with no transparency, and that their version of AI was meant to benefit the parent company, not humanity as a whole. The open Ai organization is going to take a different approach. The idea was to share the benefits of AI research with the world and do that as much as possible on an effort to evolve AI in a way that helps but doesn't harm. Researchers would be encouraged to publish their work in various formats as frequently as they could, and any patents that open ai would secure would similarly be shared with the world. The message appeared to be the goal is more important than the organization, that friendly AI is the chief important goal here, and that open ai only exists to see that become reality, and that open ai was really kind of more of a a shepherd of pushing AI into this direction rather than brazenly forging a path into the wilderness, although that's not how things would turn out now. Early on, the organization grew mostly through connections in the AI research community, with Luminaries and x birds joining the organization, but the organization itself kind of lacked a real sense of leadership or direction. There was this noble goal, right, Everyone knew that they were trying to make reliable, safe, friendly, beneficial AI, but how there wasn't really any plan for how to get to where they wanted to be. Google researcher Dariomday visited open ai in mid and he came away thinking that no one at the organization really had any idea of what they were doing. Despite that, or maybe because of it, i'm a Day would join the organization a couple of months later and became head of research there. Now. One of the first things to emerge from open ai was in ten like it was founded in late and in twenty sixteen they were already producing some interesting stuff. And the first up was a testing environment that the organization called Jim Jim as a gymnasium, not as in Jimmy Jim Jim Jim Hawkins. So what was being tested, well, they were testing learning agents. This brings us to a discipline that's within artificial intelligence. It's called machine learning, and basically machine learning is what it says on the TIN. It's finding ways to make machines learn so that they discover how to do certain tasks and how to improve at doing them over time. And there is no single way that this is done. It's not like there's one and only one way for machine learning to happen. There are actually lots of different models. For example, there's the generative adversarial model of machine learning. Basically, this is a model that involves having two machines set against each other. One machine is set up to try and accomplish a specific task. This is the generative part, and the other machine is set up to foil that task is the adversarial part. So, for example, maybe you're training the generative model to create a digital painting mimicking the style of famous impressionists, and the adversarial system's job is to figure out which images that are fed to it are real impressionist paintings from history and which ones were generated by the computer system. And you run these trials over and over, with each system getting better over time. The generative one gets better at making Impressionist style paintings and the adversarial one gets better at finding little hints that indicate this was not an actual painting but was computer generated. The open ai jem specializes in learning agents that rely on reinforcement learning, and when you break it down, it sounds a lot like your typical kind of school work. That is, when the learning agent performs well, it is rewarded when it performs poorly, it is punished. So it's kind of like getting your test paper back and finding out you aced the exam, or if things didn't go well that you totally whiffed it and you'll be going to summer school to make up for that. Also in open Ai introduced a platform humbly called Universe. This platform helps track progress and train learning agents to problem solve, starting with the most serious of all problems, finding the fun in Atari video games. I'm talking about classic Autari video games like Pitfall, which, let's be honest, awesome game. You don't have to find the fund there, it's right there. But let's say et the Extraterrestrial or their version of pac Man. Yeah, you have to really find the fun in those. And I'm being a little facetious here, but Universe really does train learning agents by having them learn how to play video games. They started with the Tari games and then they began to build from there, and Universe trains these agents to play the games, and the ideas that by learning how to play games, as the agents encounter new games, they can apply the previous learnings from the experiences of playing everything before to the new game. Just like we humans, will try and apply our knowledge and experience with certain tasks. When we face a totally new situation. You come into something you've never done before, and you might think, well, when I do this other thing, I do it this way, So let me try that here first. Maybe that skill translates to this new situation, and maybe it works, maybe it doesn't, but either way, that informs you and then you can start branching out from there to learn how to master this new task. That's the idea with Universe. Jim and Universe both gave a glimpse at the big plans open Ai had in store. But there was a looming problem on the horizon. And it wasn't a levolent Ai that was hell bent on destroying humanity. It was a far more mundane threat. Open Ai was in danger of running out of money. I'll explain more, but before I run out of money, let's take a quick break. We're back, okay, So we're up to and leaders in open Ai realized that they were facing their own existential crisis in the form of funding. So in order to remain relevant and competitive in the fast paced world of AI development, and in order to achieve the goal of creating an a g I before anyone else. The company was going to have to spend enormous amounts of money on computer systems and other assets like training, databases or else it was going to get left behind. It just wasn't possible to do this while also being a strictly not for profit company, so the leaders started to think about how they might address this. Meanwhile, in Elon Musk stepped down from the board of directors. Now officially, the reason given was that Musk wanted to avoid a potential conflict of interest because Tesla was pursuing its own AI research and Tesla was bound to compete for the same talent pool that Opened a I wanted to tap into, so in order to avoid a conflict of interest, he resigned from the board of directors. However, Musk also subsequently tweeted out that he felt open ai was falling short, mostly on the open part, and that he had disagreements regarding the direction of the organization's efforts. It was also in when open ai released its charter, the company charter, which started to hint at upcoming changes. The charter read, in part quote, we anticipate needing to marshal substant ential resources to fulfill our mission, but will always diligently act to minimize conflicts of interest among our employees and stakeholders that could compromise broad benefit end quote. It was like the leaders were starting to couch things in an effort to explain what was going to be coming up next. So the following year, twenty nineteen, saw open Ai create a new for profit company as a subsidiary. So the parent company, Open Eye Eye, Incorporated, remains a not for profit organization, but open Ai l P is a for profit company. Open Ai published a blog post that tried to explain this decision, saying, quote, we want to increase our ability to raise capital while still serving our mission, and no pre existing legal structure we know of strikes the right balance. Our solution is to create open Ai LP as a hybrid of a for profit and nonprofit, which we are calling a capped profit company end quote. So the idea here is that an investor can pour money into open Ai LP and can potentially earn up to one hundred times that investment as the company releases and generates revenue from products. But that's the limit. Once an investor hits one hundred times their investment. That's they're done. You ain't getting a hunter and one times return on your investment, bucko. So all the additional money over that one hundred times return would go toward nonprofit work. But um, that's that's a lot, right. One hundred times return on investment is huge, to the point where some people say, like, when would you ever hit that? I mean, Google, I think is somewhere in the realm of twenty times return on investment if you got in early on. So um, it's hard to imagine a hundred time return. So some people say, well, this is just language to make it seem like they're still dedicated to this nonprofit but aren't. Really, that's one of the criticisms I've I've read. Now, just imagine that you know that initial investment into open ai was a billion dollars, so presumably you'd have to see more than a hundred billion dollars in profit, uh in order to return that to investors before they were all paid out, and then the rest could go toward nonprofit That's just that initial investment, because believe me, open ai has received subsequent funding. In fact, in twenty nineteen, Microsoft board an additional billion dollars into the company, although only half of that was cash, so it was only like five million. The other five million was in like cloud computing credit, so that open ai could make use of Microsoft's Azure platform without having to pay for it because they had five hundred million dollars in credit. Yalza. And of course we've heard recently that Microsoft is considering a ten billion all our investment into open ai, and there ain't a yells a big enough to express how princely that sum is. In twenty nineteen, open Ai did something strange, at least strange if you remember that open is part of the company's name. The PR Department released information that open ai had been sitting on a language model named Generative pre Trained Transformer TO or GPT two that developed this and not talked about it, and now they were finally talking about it, and that this language model was capable of generating text in response to props, including stuff like it could create fake news articles or alternative takes on classic literature. Further, open ai said that it was actually too dangerous to release the code because people might then use the code to create misinformation or worse, which seemed to fly in the face of open Aiyes, purpose that the company had fostered a published, often and transparently culture, and that was keeping certain projects secret, and when finally talking about them, denying access to the research that seemed counter to the founding principles of open ai. The folks in open ai had sort of shifted their perspective a little bit. In their eyes, some secrecy and restrictions were needed to ensure safety and security, as well as to maintain a competitive advantage over others in the field of AI research. Open ai would eventually release GPT two in several stages before the full code finally came out in November twenty nineteen. Critics accused open ai of relying on publicity stunts to hype up what their research and work had created, and thus pumping unrealistic expectations into the investor market, like, in other words, by saying, oh, this is really dangerous, I don't know if I can let you have this. It got people really excited about it, and so investors were willing to pour more money into open Ai. That's what the critics were saying, that you're just doing this to get people worked up into a frenzy and that the staged release process for GPT two was open AIS way to capitalize on all this height gradually so as not to just deflate expectations by releasing it and then everyone say, oh, that's it. Later, in a paper released in early open AI revealed another secret that the company was essentially using the more power approach of trying to achieve artificial general intelligence or a g I. So a quick word on what they were doing. This was called foresight, by the way, So broadly speaking, there are two big schools of thought on how the world will see a true a g I emerge. That is, an artificial intelligence that can perform very much like a human intelligence, you know, perhaps not in the same way, but again achieving the same outcomes. So one way, the one school of thought is that we already have all the off that we need in all the AI research that has been done over the years. We have all the pieces, They're all there. We just need to amp it up by providing more computational resources behind it and larger training sets. So everything's good to go. We just got to provide the power to push it into the realm of a g I. Now, the other school of thought is that we're still missing something or maybe several some things, and that until we figure those out and we incorporate them into our AI strategy, we just are not going to see an a G I. It won't matter how much power you put behind it. We're still missing elements that will actually allow us to hit a GI status. Now open ai subscribes to the more power philosophy generally speaking, and the research paper kind of explained us. And again this was something that open ai was holding in secret. They even compelled employees to stay quiet about the work. And what was essentially going on was that open ai researchers were taking AI work that was developed in other research labs and companies. These were tools that other competitors were offering, and so they essentially got hold of these tools, and then they jacked up the power of the tools by training them on larger data sets and providing more compute computational power to see if, oh, maybe what we already have is the way there and we just gotta give it the extra oomph to get it to in open ai announced the next generation of its Generative pre Trained Transformer. This would be GPT three and that it would make available in Application Programming Interface or a p I, which would be the company's first commercial product, so customers developers in this case could get access to the GPT three language model through this ap I and then integrate that with their app. So if it was an app would help you do things like I don't know book meetings, then the language model would be part of what would power this app. The following year, we got open aies tool that would generate digital images, which is doll E. That's d A L L E kind of a combination of Wally the Pixar character and Salvador Dolly, the absurdist artist with the incredible mustache. So you would feed Dolly a text prompt and it would try to create images based on that prompt. Sometimes it was delightful and sometimes it was disturbing. Sometimes it was a combination. But it was really impressive that it was able to do this at all, and similar to that of other generative image AI services like mid Journey, which would actually debut a year later in two and open Ai updated Dolly and released Dolly two. In the new version of Dolly is able to combine find more concepts together to create images and also to imitate specific styles. So you know, if you wanted a style that imitated a photograph from the nineteen twenties, it would try to create that that effect, or if you were to say, like a painting from the Cubist movement, that it would try and and accomplish that. In late two, open Ai introduced chat GPT, a chat bought built on top of the GPT three point five language model. That's the one that stirred up conversations around transparency, trusting AI output, and worrying about students cheating off an AI s AST. Now we've already touched on this in this episode about you know, a lot of the concern here, and I think a great deal of it rises not from Chat GPTs incredible abilities, which are genuinely impressive, but rather our human tendency to trust automated output implicitly when a fact it's sometimes wrong. In fact, as many reports have said, sometimes Chat GPT gets things very very wrong, but it presents it in a way that appears to be authoritative and trustworthy. So if we do trust the output of such a system and then we act on that output, where we're falling far short of that AI that's supposed to be beneficial to humanity, right. Open ai was built around that, So this seems again to be a contradiction to open a eyes goal that if it has a chat bot that occasionally produces incorrect information and then people act on it, wouldn't you argue that this AI could be potentially harmful to humanity not beneficial. Now you could say that it's the people who are relying too heavily on chat GPT that are the problem, and that's not really open aiyes fault. They can't control how people use their tools. That, just like the test law owners, people are not properly making use of the technology with enough awareness of that technology's limitations. But others might argue that open ai hasn't exactly made people aware of the limitations at all, at least not in a way that's equal to the hype that surrounds their various products. That open Ai is benefiting from this excitement around the undeniably impressive achievements, but that the company is failing to live up to this commitment to creating beneficial AI because they're not being good stewards of this tool and the outcome of people using it. And it is a very complicated problem, and AI isn't likely to solve this one right away. Open ai is currently developing GPT four, so that's the next generation of the language model it's been developing all these years. CEO Sam Altman has already said that people are likely going to be disappointed by GPT for not the cause the model won't be impressive. I have no doubt it will be, but because people have already built up in their minds a bar that GPT four simply will not be able to reach. And while that is a fair observation, I can't help but think that open ai is at least partly responsible for encouraging the fervor that led to this impossibly high bar. I don't think people said it all on their own. I think open aiyes own approach has kind of encouraged this sort of reaction. I mean, there's already this tendency for us to hype stuff when we just get a hint of what is possible and we start to extrapolate from that. That's true all the time. You can see it over and over and over again in lots of different technologies throughout the years. But at the same time, I feel open ai takes a kind of almost coy approach, and that helps encourage this behavior rather than discourage it. The company is openly doing the goal of building the first a g I, though as we've seen, it's not doing so in quite as transparent away as the organization first set out to follow. But if you're pursuing that goal, it means you've got like really big ambitions, and that again, I think helps to fuel the hype cycle. Now, I guess I can conclude this episode by just reflecting on the fact that open ai is a company that Elon Musk has criticized for failing to be transparent. That's something, y'all. Now. I don't wish to disparage the people who work for open ai or even the goal of the organization itself. I think it's a worthy goal. I think there are a lot of people who truly believe in that goal who are working for open Ai. I think the leadership believes in the goal and that that's what they're pursuing. It's just the realities of trying to achieve that in a world where you need to make money in order to fuel that pursuit creates complications, and there are no perfect solutions unless you just happen to have, you know, a a bottomless pit of a benefactor who can just pour money into the organization and allow it to pursue these these developments without having to worry about the commercial aspect of it. Unless you have that, then you have to deal with these real world complications. And just like the autonomous cars that you know, on the surface should be able to maneuver without any driver in the driver's seat and do so perfectly safely, we learned that once you put it into the real world, there are so many other variables and complications at play. It's never as simple as you first thought. So I know I've dogged on open Ai a lot. There are a lot of really great critical articles about the company. But I do believe in the work they're doing. I just the way they go about it has some elements to it that I find troubling. But it's not like I can suggest a better approach. I just think that it's important for us to pay attention and to criticize when necessary, and to ask questions and to hold the organization accountable because it has claimed to be this organization founded with a pursuit of developing beneficial AI and doing so in an open, transparent way. And if it fails to do that, I think we have to call them on it, because otherwise what we get may not be that beneficial AI we've been hoping for. All Right, that's it for this episode. Hope you enjoyed it, and if you have suggestions for topics I should cover in future episodes of tech Stuff, please reach out to me. You can download the i heart Radio app for free and navigate over to tech Stuff. Just put tech stuff in the search field. That will bring you over to our little page on that app, and you will find a microphone icon on the tech stuff page. If you click on that, you can leave a voice message up to thirty seconds in length let me know what you would like to hear, or if you prefer, you can head on over to Elon Musk's Twitter and you can send me a Twitter message. The handle for the show is tech Stuff H s W and I'll talk to you again really soon, y. Tech Stuff is an I Heart Radio production. 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