Speaker 1: Welcome to tech Stuff, a production from I Heart Radio.
Speaker 1: Hey there, and welcome to tech Stuff. I'm your host
Speaker 1: Jonathan Strickland. I'm an executive producer with I Heart Radio.
Speaker 1: And how the tech are Young. Well, since it's been
Speaker 1: in the news quite a bit so far this year,
Speaker 1: I thought today we would look into open ai, both
Speaker 1: the for profit company and it's parent not for profit organization. So,
Speaker 1: for those of y'all who have managed to dodge all
Speaker 1: the hubbub, open ai is the company behind chat gpt.
Speaker 1: That's the chat bot that's been making headlines for everything
Speaker 1: from offending the musician Nick Cave of Nick Cave and
Speaker 1: the Bad Seeds Fame, to worrying teachers that their students
Speaker 1: are just going to use a chat bot to cheat
Speaker 1: on assignments rather than actually bother to learn something. But
Speaker 1: what about the company that made this thing in the
Speaker 1: first place. Well, the history of open ai dates back
Speaker 1: to twenty when a bunch of very wealthy tech entrepreneurs
Speaker 1: got together and said, you know what, maybe we should
Speaker 1: create an organization that aims to make helpful artificial intelligence
Speaker 1: before someone opens Pandora's box and Unleasha's malevolent you know,
Speaker 1: or at least uncaring super intelligence upon us all or
Speaker 1: something to that effect. Essentially, the goal was to develop
Speaker 1: AI and AI applications in a way that would be
Speaker 1: beneficial to humanity and try to avoid all the scary
Speaker 1: sky net terminator kind of stuff. But to talk about
Speaker 1: this requires us to define some terms, like terms that
Speaker 1: you might think are obvious on the face of it,
Speaker 1: but I would argue are not. So. The big one
Speaker 1: here would be artificial intelligence. There are certain words and
Speaker 1: phrases out in the world that have lots of different meanings,
Speaker 1: and this can sometimes cause confusion and miscommunication. I would
Speaker 1: argue artificial intelligence is a real doozy among these. You
Speaker 1: hear about someone working in AI and you start immediately
Speaker 1: getting preconceived ideas of what that means, and you're probably wrong. Actually,
Speaker 1: now that we're just talking about even just the word
Speaker 1: intelligence has some ambiguity to it. So what do we
Speaker 1: mean when we say that something is intelligent. Well, let's
Speaker 1: take a look at what some dictionaries say. So Webster
Speaker 1: defines intelligence as the ability to learn or understand or
Speaker 1: to deal with new or trying situations, or the ability
Speaker 1: to apply knowledge to manipulate one's environment or to think
Speaker 1: abstractly as measured by objective criteria such as tests. Thanks
Speaker 1: Webster Oxford defines it as the ability to learn, understand,
Speaker 1: and think in a logical way about things. The ability
Speaker 1: to do this, well, it's a little more succinct. But
Speaker 1: then if we really want to boil it down, the
Speaker 1: American Heritage Dictionary defines it as the ability to acquire, understand,
Speaker 1: and use knowledge. That's what intelligence is, according to those.
Speaker 1: Dr dah lyoel Lee, and I apologize Dr Lee for
Speaker 1: butchering your name is a professor of neuroscience and author
Speaker 1: of Birth of Intelligence, and he defines intelligence as the
Speaker 1: ability to solve complex problems or make decisions with outcomes
Speaker 1: that benefit the actor. Dr Lee also acknowledges that intelligence
Speaker 1: is actually pretty hard to define, and that there are
Speaker 1: many different definitions, which you know, we've just seen. Like
Speaker 1: even though all the definitions I mentioned have significant overlap
Speaker 1: between them and they all seem to be dancing around
Speaker 1: the same kind of concept, you might feel like none
Speaker 1: of them quite get it right. And that's where some
Speaker 1: of these challenges come from. Is that just defining intelligence
Speaker 1: before we even get to artificial intelligence is hard. All right, Well,
Speaker 1: let's let's say that intelligence generally is the ability to
Speaker 1: learn and to acquire knowledge and then to use that
Speaker 1: knowledge in new situations. Let's just use it by that
Speaker 1: and say that, you know, it's got an element of
Speaker 1: problem solving that goes with that, which I think is
Speaker 1: pretty much implied. So artificial intelligence, then will artificial suggests
Speaker 1: that it's something that's created by humans rather than found
Speaker 1: in nature. Oxford Languages defines artificial intelligence as the theory
Speaker 1: and development of computer systems able to perform tasks that
Speaker 1: normally require human intelligence, such as visual perception, speech recognition,
Speaker 1: decision making, and translation between languages. So that's a fairly
Speaker 1: decent definition. Uh, But here's where we run into some
Speaker 1: more ambiguity. When we talk about artificial intelligence. We're not
Speaker 1: necessarily using the word intelligence to mean the exact same
Speaker 1: thing when we apply it to a human context. You know,
Speaker 1: a person working in artificial intelligence isn't necessarily trying to
Speaker 1: make a machine think or appear to think like a
Speaker 1: human does. In fact, they're probably not doing anything of
Speaker 1: the sort. They might be working on something that, when
Speaker 1: collected with the work of countless others, ends up contributing
Speaker 1: to that kind of machine but that's different, so AI
Speaker 1: involves a lot of different disciplines and technologies. Facial recognition
Speaker 1: is a type of AI. Speech recognition is a type
Speaker 1: of AI. Text to speech is related to artificial intelligence.
Speaker 1: Robotics share a lot of features with AI, although you
Speaker 1: could also have robots that are fully programmed to complete
Speaker 1: precise routines and and that cases they're just following a
Speaker 1: list of instructions and there's no decision making component right there,
Speaker 1: just literally following step one, step two, step three, step four, repeat.
Speaker 1: So those kinds of robots aren't really in the artificial
Speaker 1: intelligence realm, but there are other robots that are now
Speaker 1: frequently I find that the general public associates the concept
Speaker 1: of artificial intelligence with a machine that appears to have
Speaker 1: knowledge gathering and problems solving capabilities, usually paired with some
Speaker 1: method to put solutions into action, so often in the
Speaker 1: form of a robot or a computer system that's connected
Speaker 1: to stuff that can actually get crap done. I almost
Speaker 1: said the other phrase, but this is a family show,
Speaker 1: so they're thinking about what is often referred to as
Speaker 1: strong AI. These are machines that have a form of
Speaker 1: intelligence that is to all practical purposes. Indistinguishable from human intelligence.
Speaker 1: Now that's not to say that it's processing information the
Speaker 1: exact same way that we peep process information, but that
Speaker 1: the outcome is the same that at the end of
Speaker 1: the day, if the machine and the person were to
Speaker 1: come to the same conclusion, doesn't really matter what steps
Speaker 1: in the middle were taken. Now, if such a thing
Speaker 1: as possible, we're not there yet. We aren't at the
Speaker 1: point where we have this. But the work done in
Speaker 1: AI right now, which is really in the field of
Speaker 1: weak AI, that is, artificial intelligent solutions designed for specific purposes,
Speaker 1: is contributing toward the creation of strong AI. Now there's
Speaker 1: another phrase for strong AI that we need to talk about,
Speaker 1: which is artificial general intelligence or a g I. And
Speaker 1: I know there are a lot of initialisms, that's always
Speaker 1: the case when we talk about tech. But a g
Speaker 1: I general intelligence that kind of tells you, Okay, this
Speaker 1: is an AI that's meant to do lots of different stuff. Right,
Speaker 1: It's not designed to do a specific task and just
Speaker 1: get better and better and better at doing that task.
Speaker 1: It's meant to handle lots of different things, maybe any thing.
Speaker 1: And it's just like if you put a human and
Speaker 1: you have that human go into a situation they've never
Speaker 1: experienced before, how do they cope? Well, it's the goal
Speaker 1: is to create an artificial intelligence that would be able
Speaker 1: to handle new situations in a similar way to the
Speaker 1: way humans do. That's the artificial general intelligence. Again, no
Speaker 1: one has made one of these yet, but that would
Speaker 1: become open a eyes. Primary goal is to create an
Speaker 1: a g I, the first to create an a g I. Now,
Speaker 1: week AI does not mean that artificial intelligence is bad
Speaker 1: at its job or its inferior in some way. In fact,
Speaker 1: week AI might be much better at doing its specific
Speaker 1: task than humans are at completing that specific task. It's
Speaker 1: just that this is all the week AI can do.
Speaker 1: It can't do other things things it's operating under constraints.
Speaker 1: So as an example, let's just think of something that's
Speaker 1: really simple that you wouldn't even think of as being intelligent,
Speaker 1: like a basic calculator, not even a scientific calculator, a
Speaker 1: basic calculator like one that might be handed out by
Speaker 1: a bank, and you can enter a pretty tough mathematical
Speaker 1: problem into the calculator and it will provide a solution
Speaker 1: in a fraction of the time it would take your
Speaker 1: average human to do the same work, but that same
Speaker 1: human could do other stuff like maybe that human can
Speaker 1: play the guitar or juggle or paint or play a
Speaker 1: video game or any of an endless number of other tasks.
Speaker 1: But the calculator can't do that. It can just calculate.
Speaker 1: That's all it can do, and it can do it
Speaker 1: really well, but it's unable to extend this capability to
Speaker 1: anything beyond that purpose. Now, sometimes when we encounter a
Speaker 1: really good week AI, we can fool ourselves into thinking
Speaker 1: that the AI is doing something really magical, or that
Speaker 1: it's matching our own capabilities to think. It can actually
Speaker 1: be pretty easy to fall into this trap. A sufficiently
Speaker 1: sophisticated chatbot might fool listen to thinking that the machine
Speaker 1: we're chatting with is actually thinking itself. But it's not,
Speaker 1: at least not in the same way that people do. Now,
Speaker 1: why did I go through all of that trouble to
Speaker 1: define all these things? Well? The founding principle of open
Speaker 1: AI is to create artificial general intelligence and AI applications
Speaker 1: and technologies through a responsible, thoughtful approach, and that implies
Speaker 1: that there's an irresponsible way to do this, and that
Speaker 1: following such an irresponsible way could lead to disaster. And
Speaker 1: that's where we get to our science fiction stories, and
Speaker 1: that certainly tracks. You know, I'm not here to tell
Speaker 1: you that that's an unreasonable fear. That fear is totally reasonable.
Speaker 1: In fact, we've been seeing how weak AI can and
Speaker 1: does cause problems, or maybe how I should say, are
Speaker 1: our reliance upon week AI can cause problems. The AI
Speaker 1: on its own may not be able to cause a
Speaker 1: problem by itself, but because we rely on it, then
Speaker 1: we go and we create these problems. So let's go
Speaker 1: with facial recognition for this one. It has been shown
Speaker 1: time and again that many of the facial recognition technologies
Speaker 1: that are actively deployed in the world today have bias
Speaker 1: built into them. They are fairly reliable at identifying people
Speaker 1: within certain populations, like white people primarily, but then with
Speaker 1: people of color, these systems aren't nearly as accurate. So
Speaker 1: what happens is that these facial recognition systems can generate
Speaker 1: false positives more frequently for say, black people. And because
Speaker 1: we have law enforcement agencies that are making active use
Speaker 1: of facial recognition technologies when looking for suspects, this means
Speaker 1: that police can and do end up harassing innocent people,
Speaker 1: all based off of this misidentification. So imagine one day
Speaker 1: you're just going about your business and then suddenly law
Speaker 1: enforcement swoops in and arrests you for a crime not
Speaker 1: only you didn't commit, but you also have no knowledge
Speaker 1: of this crime. And it's all because a machine somewhere
Speaker 1: said this is the person you want. Now, imagine how
Speaker 1: your life would be affected. What if it happened while
Speaker 1: you were at work or at school. How do you
Speaker 1: think the people around you would react when police come
Speaker 1: in and arrest you. How many of those people would
Speaker 1: treat you differently even after hearing that the whole thing
Speaker 1: was just a mistake. What kind of stress would that
Speaker 1: put on you and the people in your life? Now?
Speaker 1: The reason I'm really nailing this home is because this
Speaker 1: stuff is happening right. This problem is a real problem.
Speaker 1: This is not a theoretical it's not a hypothetical. Real
Speaker 1: people have had their lives up ended because police have
Speaker 1: relied upon faulty facial recognition technology and saying, oops, it
Speaker 1: was our mistake doesn't fix your life when it's been
Speaker 1: turned upside down. Or as Matthew Grissinger of the Institute
Speaker 1: for Safe medication practices has put it quote. The tendency
Speaker 1: to favor or give greater credence to information supplied by
Speaker 1: technology e g. And a d C display, and to
Speaker 1: ignore a manual source of information that provides contradictory information
Speaker 1: e g. Handwritten entry on the computer generated m a R,
Speaker 1: even if it is correct, illustrates the phenomenon of automation bias.
Speaker 1: Automation complacency is a closely linked, overlapping concept that refers
Speaker 1: to the monitoring of technology with less frequency or vigilance
Speaker 1: because of a lower suspicion of error and a stronger
Speaker 1: belief in its accuracy end quote. So in other words,
Speaker 1: we have a tendency to trust the output of machines,
Speaker 1: and that trust is not always warranted. This can get
Speaker 1: us into trouble. We can trust that the machines know
Speaker 1: what they're doing and that the way they process information
Speaker 1: is reliable and even infallible, and by acting upon that
Speaker 1: we can create terrible consequences. Mr griss Singer's context was
Speaker 1: within the field of medication prescriptions, which, obviously, if you
Speaker 1: were to rely solely upon automated output and that automated
Speaker 1: output was wrong, could result in terrible consequences. But I'm
Speaker 1: sure you can imagine countless other scenarios in which an
Speaker 1: over reliance on technology could lead to disaster. We'll talk
Speaker 1: about another one when we come back from this quick break.
Speaker 1: We're back, and before the break, I was talking about
Speaker 1: how we have a tendency to put too much trust
Speaker 1: inte knowlogy in general and AI in particular, and how
Speaker 1: this can come back to haunt us. So an example
Speaker 1: that leaps to my mind is autonomous cars. And I'm
Speaker 1: going to be the first to admit I jumped on
Speaker 1: the autonomous car bandwagon without applying nearly enough critical thinking.
Speaker 1: I was really considering just the surface level of what
Speaker 1: it would mean to have autonomous cars. So here's how
Speaker 1: my flawed logic went. This is why I was so
Speaker 1: like Gung Ho on autonomous cars several years ago now
Speaker 1: and have subsequently changed my my thinking. So the way
Speaker 1: I originally thought was computer processors are wicked fast, right
Speaker 1: like a CPU in your computer can complete calculations so quickly,
Speaker 1: millions of them every second, billions in fact, depending upon
Speaker 1: the the sophistication of the of the operations. And then
Speaker 1: you have parallel processing, right like if you have a
Speaker 1: multi core processor could have lots of functions all being
Speaker 1: performed simultaneously by this processor. Then, on top of that,
Speaker 1: you could have sensors on your car that cover three
Speaker 1: sixty degrees of view around the vehicle, so you would
Speaker 1: be able to have the system pay attention in every
Speaker 1: single direction simultaneously, whereas a human driver can only pay
Speaker 1: attention within their field of view and then with the
Speaker 1: help of some mirrors, get a little extra you know,
Speaker 1: awareness around them. You could have mechanical systems that could
Speaker 1: react immediately upon receiving a command from the processors with
Speaker 1: no delay, so you don't have that delay of action
Speaker 1: between when you sense something happening and when you are
Speaker 1: able to act on that. So, surely such a system
Speaker 1: with incredible processing power, with three sixty degrees of awareness,
Speaker 1: with this immediate ability to react, would be able to
Speaker 1: engage in defensive driving faster, more effectively, and safer than
Speaker 1: in a human ever could. Clearly, machines are superior. We
Speaker 1: should all be in autonomous cars. This is where I
Speaker 1: ran into the problem of overreliance on technology. Sure, in
Speaker 1: isolated cases, everything I was thinking might be at least
Speaker 1: partly true, but when you take it together and you
Speaker 1: start to apply it in the field in a vehicle,
Speaker 1: things are far more complicated than I ever gave it
Speaker 1: credit for. And as we have seen with advanced driver
Speaker 1: assist features, if we rely too much on this technology,
Speaker 1: it can and does lead to tragedy. So we've seen
Speaker 1: this play out where people have depended too heavily upon
Speaker 1: this tech and have paid for it with their lives.
Speaker 1: So we know that this is more complex than what
Speaker 1: I initially thought of back in my naive days of
Speaker 1: being so, you know, flag bearing for the whole autonomous
Speaker 1: car our movement, and I still believe in autonomous cars
Speaker 1: and how they could contribute to greater safety, but I
Speaker 1: also recognize that it's a far more complex problem than
Speaker 1: what I originally imagined. All right, so we have thoroughly
Speaker 1: defined the problem at this point. Right. Artificial intelligence has
Speaker 1: the potential to help us do amazing things, but only
Speaker 1: if we develop and deploy it properly. Otherwise it could
Speaker 1: exacerbate existing problems or even create all new problems. So
Speaker 1: there's a need to be thoughtful about design and application
Speaker 1: and deployment and distribution. So who decided to codify this
Speaker 1: philosophy of being careful about AI and create an organization
Speaker 1: dedicated to doing that. Well, the two people who are
Speaker 1: frequently cited as the co founders for open ai are
Speaker 1: Elon Musk and Sam Altman, though I would hate and
Speaker 1: add there were many other people who are really co
Speaker 1: founders as well, but these are the two that, you know,
Speaker 1: everyone says, these are the guys who started talking and
Speaker 1: kind of generated the initial idea that became open AI.
Speaker 1: So let's start with Musk. So years before he decided
Speaker 1: to drop billions of dollars in an effort to troll
Speaker 1: the Internet whenever he wanted to, Mr Musk was something
Speaker 1: of an AI doomsayer. You know, he was warning that
Speaker 1: artificial intelligence could potentially pose an existential threat to humans.
Speaker 1: Kind of this idea of we create a human level
Speaker 1: or even superhuman level strong AI, and then it turns
Speaker 1: on us and wipes us out. And certainly bad AI
Speaker 1: can be a huge issue. We just talked about how
Speaker 1: even weak AI can be a really big problem. Now,
Speaker 1: I don't think we're close to having a human, let
Speaker 1: alone superhuman intelligence determined to wipe out humanity emerge, but
Speaker 1: you know, you can definitely have bad AI contribute to
Speaker 1: human suffering. See also Tesla, one of Mr Musk's companies.
Speaker 1: One might even argue that Elon Musk knows the danger
Speaker 1: that artificial intelligence poses to humanity because one of his
Speaker 1: companies is leading the charge in that field in the
Speaker 1: form of Tesla autopilot and full self driving modes. Now again,
Speaker 1: you could say that I'm being unkind, because we do
Speaker 1: need to remember that Tesla, despite the languages it uses
Speaker 1: for marketing purposes, does alert drivers that they are not
Speaker 1: supposed to take their hands off the wheel or stop
Speaker 1: paying attention to the road, and that at least in
Speaker 1: all the accounts I have read about terrible accidents involving
Speaker 1: Tesla vehicles that were in driver assists mode, it sounds
Speaker 1: like the driver wasn't following those directions. So you could
Speaker 1: argue that, you know, the driver ultimately is at fault
Speaker 1: because they're failing to adhere to the instructions that Tesla gives.
Speaker 1: The flip side of that is that Tesla markets these
Speaker 1: features as if they are more than you know, sophisticated
Speaker 1: driver assist features. The other co founder of open Ai
Speaker 1: that's frequently mentioned is Sam Altman, the current CEO of
Speaker 1: open Ai. Sam Altman was previously president of y Combinator.
Speaker 1: He became president of y Combinator in fourteen, which was
Speaker 1: the year before he co founded open Ai with Elon Musk.
Speaker 1: And you might say, well, what is why Combinator. It's
Speaker 1: a startup accelerator, which doesn't really mean anything either, right, Well,
Speaker 1: that's a company that helps people who have startup business
Speaker 1: ideas get the support they need in order to launch
Speaker 1: their idea and make it a reality. So that can
Speaker 1: include stuff like mentoring the startup leaders so that they
Speaker 1: can build a good business model and create the right
Speaker 1: corporate structure that they're going to need in order to
Speaker 1: do business, all the way up to prepping them and
Speaker 1: connecting them with people that they can pitch their idea
Speaker 1: to in order to get investment into their startup. So
Speaker 1: one of the big valuable services that companies like y
Speaker 1: Combinator provide is access to the investor community that you
Speaker 1: might not otherwise be able to get to without that
Speaker 1: kind of support. Now, Altman would continue to serve as
Speaker 1: y Combinator president until twenty nineteen. At that point he
Speaker 1: stepped down from that position to focus on open Ai.
Speaker 1: H Elon Musk would sit on the board of directors
Speaker 1: for open Ai until ten. We'll talk about that in
Speaker 1: just a bit. Now. I mentioned that there were also
Speaker 1: other co founders, So in addition to these two entrepreneurs,
Speaker 1: early founders in the open Ai initiative included Greg Brockman,
Speaker 1: who's still there. I believe he's a former chief technology
Speaker 1: officer of Stripe, the payment processing company. The PayPal co
Speaker 1: founder Peter Thiel was also one of the early investors
Speaker 1: in open Ai. LinkedIn co founder read Garrett Hoffman, another
Speaker 1: one one of Altman's y Combinator colleagues. Jessica Livingston was another,
Speaker 1: and there were a few more. Now collectively, the founders
Speaker 1: and partners all pledged one billion dollars to fund open ai,
Speaker 1: which again was meant to be a nonprofit organization dedicated
Speaker 1: to developing productive, friendly AI and not the scary pew
Speaker 1: pew lasers kind of AI. But then there's also the
Speaker 1: open part of open ai. So during the brainstorming that
Speaker 1: would lead to the founding of this organization, the co
Speaker 1: founders talked about how big tech companies typically do all
Speaker 1: their AI development behind closed doors with no transparency, and
Speaker 1: that their version of AI was meant to benefit the
Speaker 1: parent company, not humanity as a whole. The open Ai
Speaker 1: organization is going to take a different approach. The idea
Speaker 1: was to share the benefits of AI research with the
Speaker 1: world and do that as much as possible on an
Speaker 1: effort to evolve AI in a way that helps but
Speaker 1: doesn't harm. Researchers would be encouraged to publish their work
Speaker 1: in various formats as frequently as they could, and any
Speaker 1: patents that open ai would secure would similarly be shared
Speaker 1: with the world. The message appeared to be the goal
Speaker 1: is more important than the organization, that friendly AI is
Speaker 1: the chief important goal here, and that open ai only
Speaker 1: exists to see that become reality, and that open ai
Speaker 1: was really kind of more of a a shepherd of
Speaker 1: pushing AI into this direction rather than brazenly forging a
Speaker 1: path into the wilderness, although that's not how things would
Speaker 1: turn out now. Early on, the organization grew mostly through
Speaker 1: connections in the AI research community, with Luminaries and x
Speaker 1: birds joining the organization, but the organization itself kind of
Speaker 1: lacked a real sense of leadership or direction. There was
Speaker 1: this noble goal, right, Everyone knew that they were trying
Speaker 1: to make reliable, safe, friendly, beneficial AI, but how there
Speaker 1: wasn't really any plan for how to get to where
Speaker 1: they wanted to be. Google researcher Dariomday visited open ai
Speaker 1: in mid and he came away thinking that no one
Speaker 1: at the organization really had any idea of what they
Speaker 1: were doing. Despite that, or maybe because of it, i'm
Speaker 1: a Day would join the organization a couple of months
Speaker 1: later and became head of research there. Now. One of
Speaker 1: the first things to emerge from open ai was in
Speaker 1: ten like it was founded in late and in twenty
Speaker 1: sixteen they were already producing some interesting stuff. And the
Speaker 1: first up was a testing environment that the organization called
Speaker 1: Jim Jim as a gymnasium, not as in Jimmy Jim
Speaker 1: Jim Jim Hawkins. So what was being tested, well, they
Speaker 1: were testing learning agents. This brings us to a discipline
Speaker 1: that's within artificial intelligence. It's called machine learning, and basically
Speaker 1: machine learning is what it says on the TIN. It's
Speaker 1: finding ways to make machines learn so that they discover
Speaker 1: how to do certain tasks and how to improve at
Speaker 1: doing them over time. And there is no single way
Speaker 1: that this is done. It's not like there's one and
Speaker 1: only one way for machine learning to happen. There are
Speaker 1: actually lots of different models. For example, there's the generative
Speaker 1: adversarial model of machine learning. Basically, this is a model
Speaker 1: that involves having two machines set against each other. One
Speaker 1: machine is set up to try and accomplish a specific task.
Speaker 1: This is the generative part, and the other machine is
Speaker 1: set up to foil that task is the adversarial part. So,
Speaker 1: for example, maybe you're training the generative model to create
Speaker 1: a digital painting mimicking the style of famous impressionists, and
Speaker 1: the adversarial system's job is to figure out which images
Speaker 1: that are fed to it are real impressionist paintings from
Speaker 1: history and which ones were generated by the computer system.
Speaker 1: And you run these trials over and over, with each
Speaker 1: system getting better over time. The generative one gets better
Speaker 1: at making Impressionist style paintings and the adversarial one gets
Speaker 1: better at finding little hints that indicate this was not
Speaker 1: an actual painting but was computer generated. The open ai
Speaker 1: jem specializes in learning agents that rely on reinforcement learning,
Speaker 1: and when you break it down, it sounds a lot
Speaker 1: like your typical kind of school work. That is, when
Speaker 1: the learning agent performs well, it is rewarded when it
Speaker 1: performs poorly, it is punished. So it's kind of like
Speaker 1: getting your test paper back and finding out you aced
Speaker 1: the exam, or if things didn't go well that you
Speaker 1: totally whiffed it and you'll be going to summer school
Speaker 1: to make up for that. Also in open Ai introduced
Speaker 1: a platform humbly called Universe. This platform helps track progress
Speaker 1: and train learning agents to problem solve, starting with the
Speaker 1: most serious of all problems, finding the fun in Atari
Speaker 1: video games. I'm talking about classic Autari video games like Pitfall, which,
Speaker 1: let's be honest, awesome game. You don't have to find
Speaker 1: the fund there, it's right there. But let's say et
Speaker 1: the Extraterrestrial or their version of pac Man. Yeah, you
Speaker 1: have to really find the fun in those. And I'm
Speaker 1: being a little facetious here, but Universe really does train
Speaker 1: learning agents by having them learn how to play video games.
Speaker 1: They started with the Tari games and then they began
Speaker 1: to build from there, and Universe trains these agents to
Speaker 1: play the games, and the ideas that by learning how
Speaker 1: to play games, as the agents encounter new games, they
Speaker 1: can apply the previous learnings from the experiences of playing
Speaker 1: everything before to the new game. Just like we humans,
Speaker 1: will try and apply our knowledge and experience with certain tasks.
Speaker 1: When we face a totally new situation. You come into
Speaker 1: something you've never done before, and you might think, well,
Speaker 1: when I do this other thing, I do it this way,
Speaker 1: So let me try that here first. Maybe that skill
Speaker 1: translates to this new situation, and maybe it works, maybe
Speaker 1: it doesn't, but either way, that informs you and then
Speaker 1: you can start branching out from there to learn how
Speaker 1: to master this new task. That's the idea with Universe.
Speaker 1: Jim and Universe both gave a glimpse at the big
Speaker 1: plans open Ai had in store. But there was a
Speaker 1: looming problem on the horizon. And it wasn't a levolent
Speaker 1: Ai that was hell bent on destroying humanity. It was
Speaker 1: a far more mundane threat. Open Ai was in danger
Speaker 1: of running out of money. I'll explain more, but before
Speaker 1: I run out of money, let's take a quick break.
Speaker 1: We're back, okay, So we're up to and leaders in
Speaker 1: open Ai realized that they were facing their own existential
Speaker 1: crisis in the form of funding. So in order to
Speaker 1: remain relevant and competitive in the fast paced world of
Speaker 1: AI development, and in order to achieve the goal of
Speaker 1: creating an a g I before anyone else. The company
Speaker 1: was going to have to spend enormous amounts of money
Speaker 1: on computer systems and other assets like training, databases or
Speaker 1: else it was going to get left behind. It just
Speaker 1: wasn't possible to do this while also being a strictly
Speaker 1: not for profit company, so the leaders started to think
Speaker 1: about how they might address this. Meanwhile, in Elon Musk
Speaker 1: stepped down from the board of directors. Now officially, the
Speaker 1: reason given was that Musk wanted to avoid a potential
Speaker 1: conflict of interest because Tesla was pursuing its own AI
Speaker 1: research and Tesla was bound to compete for the same
Speaker 1: talent pool that Opened a I wanted to tap into,
Speaker 1: so in order to avoid a conflict of interest, he
Speaker 1: resigned from the board of directors. However, Musk also subsequently
Speaker 1: tweeted out that he felt open ai was falling short,
Speaker 1: mostly on the open part, and that he had disagreements
Speaker 1: regarding the direction of the organization's efforts. It was also
Speaker 1: in when open ai released its charter, the company charter,
Speaker 1: which started to hint at upcoming changes. The charter read,
Speaker 1: in part quote, we anticipate needing to marshal substant ential
Speaker 1: resources to fulfill our mission, but will always diligently act
Speaker 1: to minimize conflicts of interest among our employees and stakeholders
Speaker 1: that could compromise broad benefit end quote. It was like
Speaker 1: the leaders were starting to couch things in an effort
Speaker 1: to explain what was going to be coming up next.
Speaker 1: So the following year, twenty nineteen, saw open Ai create
Speaker 1: a new for profit company as a subsidiary. So the
Speaker 1: parent company, Open Eye Eye, Incorporated, remains a not for
Speaker 1: profit organization, but open Ai l P is a for
Speaker 1: profit company. Open Ai published a blog post that tried
Speaker 1: to explain this decision, saying, quote, we want to increase
Speaker 1: our ability to raise capital while still serving our mission,
Speaker 1: and no pre existing legal structure we know of strikes
Speaker 1: the right balance. Our solution is to create open Ai
Speaker 1: LP as a hybrid of a for profit and nonprofit,
Speaker 1: which we are calling a capped profit company end quote.
Speaker 1: So the idea here is that an investor can pour
Speaker 1: money into open Ai LP and can potentially earn up
Speaker 1: to one hundred times that investment as the company releases
Speaker 1: and generates revenue from products. But that's the limit. Once
Speaker 1: an investor hits one hundred times their investment. That's they're done.
Speaker 1: You ain't getting a hunter and one times return on
Speaker 1: your investment, bucko. So all the additional money over that
Speaker 1: one hundred times return would go toward nonprofit work. But um,
Speaker 1: that's that's a lot, right. One hundred times return on
Speaker 1: investment is huge, to the point where some people say, like,
Speaker 1: when would you ever hit that? I mean, Google, I
Speaker 1: think is somewhere in the realm of twenty times return
Speaker 1: on investment if you got in early on. So um,
Speaker 1: it's hard to imagine a hundred time return. So some
Speaker 1: people say, well, this is just language to make it
Speaker 1: seem like they're still dedicated to this nonprofit but aren't. Really,
Speaker 1: that's one of the criticisms I've I've read. Now, just
Speaker 1: imagine that you know that initial investment into open ai
Speaker 1: was a billion dollars, so presumably you'd have to see
Speaker 1: more than a hundred billion dollars in profit, uh in
Speaker 1: order to return that to investors before they were all
Speaker 1: paid out, and then the rest could go toward nonprofit
Speaker 1: That's just that initial investment, because believe me, open ai
Speaker 1: has received subsequent funding. In fact, in twenty nineteen, Microsoft
Speaker 1: board an additional billion dollars into the company, although only
Speaker 1: half of that was cash, so it was only like
Speaker 1: five million. The other five million was in like cloud
Speaker 1: computing credit, so that open ai could make use of
Speaker 1: Microsoft's Azure platform without having to pay for it because
Speaker 1: they had five hundred million dollars in credit. Yalza. And
Speaker 1: of course we've heard recently that Microsoft is considering a
Speaker 1: ten billion all our investment into open ai, and there
Speaker 1: ain't a yells a big enough to express how princely
Speaker 1: that sum is. In twenty nineteen, open Ai did something strange,
Speaker 1: at least strange if you remember that open is part
Speaker 1: of the company's name. The PR Department released information that
Speaker 1: open ai had been sitting on a language model named
Speaker 1: Generative pre Trained Transformer TO or GPT two that developed
Speaker 1: this and not talked about it, and now they were
Speaker 1: finally talking about it, and that this language model was
Speaker 1: capable of generating text in response to props, including stuff
Speaker 1: like it could create fake news articles or alternative takes
Speaker 1: on classic literature. Further, open ai said that it was
Speaker 1: actually too dangerous to release the code because people might
Speaker 1: then use the code to create misinformation or worse, which
Speaker 1: seemed to fly in the face of open Aiyes, purpose
Speaker 1: that the company had fostered a published, often and transparently culture,
Speaker 1: and that was keeping certain projects secret, and when finally
Speaker 1: talking about them, denying access to the research that seemed
Speaker 1: counter to the founding principles of open ai. The folks
Speaker 1: in open ai had sort of shifted their perspective a
Speaker 1: little bit. In their eyes, some secrecy and restrictions were
Speaker 1: needed to ensure safety and security, as well as to
Speaker 1: maintain a competitive advantage over others in the field of
Speaker 1: AI research. Open ai would eventually release GPT two in
Speaker 1: several stages before the full code finally came out in
Speaker 1: November twenty nineteen. Critics accused open ai of relying on
Speaker 1: publicity stunts to hype up what their research and work
Speaker 1: had created, and thus pumping unrealistic expectations into the investor market, like,
Speaker 1: in other words, by saying, oh, this is really dangerous,
Speaker 1: I don't know if I can let you have this.
Speaker 1: It got people really excited about it, and so investors
Speaker 1: were willing to pour more money into open Ai. That's
Speaker 1: what the critics were saying, that you're just doing this
Speaker 1: to get people worked up into a frenzy and that
Speaker 1: the staged release process for GPT two was open AIS
Speaker 1: way to capitalize on all this height gradually so as
Speaker 1: not to just deflate expectations by releasing it and then
Speaker 1: everyone say, oh, that's it. Later, in a paper released
Speaker 1: in early open AI revealed another secret that the company
Speaker 1: was essentially using the more power approach of trying to
Speaker 1: achieve artificial general intelligence or a g I. So a
Speaker 1: quick word on what they were doing. This was called foresight,
Speaker 1: by the way, So broadly speaking, there are two big
Speaker 1: schools of thought on how the world will see a
Speaker 1: true a g I emerge. That is, an artificial intelligence
Speaker 1: that can perform very much like a human intelligence, you know,
Speaker 1: perhaps not in the same way, but again achieving the
Speaker 1: same outcomes. So one way, the one school of thought
Speaker 1: is that we already have all the off that we
Speaker 1: need in all the AI research that has been done
Speaker 1: over the years. We have all the pieces, They're all there.
Speaker 1: We just need to amp it up by providing more
Speaker 1: computational resources behind it and larger training sets. So everything's
Speaker 1: good to go. We just got to provide the power
Speaker 1: to push it into the realm of a g I. Now,
Speaker 1: the other school of thought is that we're still missing
Speaker 1: something or maybe several some things, and that until we
Speaker 1: figure those out and we incorporate them into our AI strategy,
Speaker 1: we just are not going to see an a G I.
Speaker 1: It won't matter how much power you put behind it.
Speaker 1: We're still missing elements that will actually allow us to
Speaker 1: hit a GI status. Now open ai subscribes to the
Speaker 1: more power philosophy generally speaking, and the research paper kind
Speaker 1: of explained us. And again this was something that open
Speaker 1: ai was holding in secret. They even compelled employees to
Speaker 1: stay quiet about the work. And what was essentially going
Speaker 1: on was that open ai researchers were taking AI work
Speaker 1: that was developed in other research labs and companies. These
Speaker 1: were tools that other competitors were offering, and so they
Speaker 1: essentially got hold of these tools, and then they jacked
Speaker 1: up the power of the tools by training them on
Speaker 1: larger data sets and providing more compute computational power to
Speaker 1: see if, oh, maybe what we already have is the
Speaker 1: way there and we just gotta give it the extra
Speaker 1: oomph to get it to in open ai announced the
Speaker 1: next generation of its Generative pre Trained Transformer. This would
Speaker 1: be GPT three and that it would make available in
Speaker 1: Application Programming Interface or a p I, which would be
Speaker 1: the company's first commercial product, so customers developers in this
Speaker 1: case could get access to the GPT three language model
Speaker 1: through this ap I and then integrate that with their app.
Speaker 1: So if it was an app would help you do
Speaker 1: things like I don't know book meetings, then the language
Speaker 1: model would be part of what would power this app.
Speaker 1: The following year, we got open aies tool that would
Speaker 1: generate digital images, which is doll E. That's d A
Speaker 1: L L E kind of a combination of Wally the
Speaker 1: Pixar character and Salvador Dolly, the absurdist artist with the
Speaker 1: incredible mustache. So you would feed Dolly a text prompt
Speaker 1: and it would try to create images based on that prompt.
Speaker 1: Sometimes it was delightful and sometimes it was disturbing. Sometimes
Speaker 1: it was a combination. But it was really impressive that
Speaker 1: it was able to do this at all, and similar
Speaker 1: to that of other generative image AI services like mid Journey,
Speaker 1: which would actually debut a year later in two and
Speaker 1: open Ai updated Dolly and released Dolly two. In the
Speaker 1: new version of Dolly is able to combine find more
Speaker 1: concepts together to create images and also to imitate specific styles.
Speaker 1: So you know, if you wanted a style that imitated
Speaker 1: a photograph from the nineteen twenties, it would try to
Speaker 1: create that that effect, or if you were to say,
Speaker 1: like a painting from the Cubist movement, that it would
Speaker 1: try and and accomplish that. In late two, open Ai
Speaker 1: introduced chat GPT, a chat bought built on top of
Speaker 1: the GPT three point five language model. That's the one
Speaker 1: that stirred up conversations around transparency, trusting AI output, and
Speaker 1: worrying about students cheating off an AI s AST. Now
Speaker 1: we've already touched on this in this episode about you know,
Speaker 1: a lot of the concern here, and I think a
Speaker 1: great deal of it rises not from Chat GPTs incredible abilities,
Speaker 1: which are genuinely impressive, but rather our human tendency to
Speaker 1: trust automated output implicitly when a fact it's sometimes wrong.
Speaker 1: In fact, as many reports have said, sometimes Chat GPT
Speaker 1: gets things very very wrong, but it presents it in
Speaker 1: a way that appears to be authoritative and trustworthy. So
Speaker 1: if we do trust the output of such a system
Speaker 1: and then we act on that output, where we're falling
Speaker 1: far short of that AI that's supposed to be beneficial
Speaker 1: to humanity, right. Open ai was built around that, So
Speaker 1: this seems again to be a contradiction to open a
Speaker 1: eyes goal that if it has a chat bot that
Speaker 1: occasionally produces incorrect information and then people act on it,
Speaker 1: wouldn't you argue that this AI could be potentially harmful
Speaker 1: to humanity not beneficial. Now you could say that it's
Speaker 1: the people who are relying too heavily on chat GPT
Speaker 1: that are the problem, and that's not really open aiyes fault.
Speaker 1: They can't control how people use their tools. That, just
Speaker 1: like the test law owners, people are not properly making
Speaker 1: use of the technology with enough awareness of that technology's limitations.
Speaker 1: But others might argue that open ai hasn't exactly made
Speaker 1: people aware of the limitations at all, at least not
Speaker 1: in a way that's equal to the hype that surrounds
Speaker 1: their various products. That open Ai is benefiting from this
Speaker 1: excitement around the undeniably impressive achievements, but that the company
Speaker 1: is failing to live up to this commitment to creating
Speaker 1: beneficial AI because they're not being good stewards of this
Speaker 1: tool and the outcome of people using it. And it
Speaker 1: is a very complicated problem, and AI isn't likely to
Speaker 1: solve this one right away. Open ai is currently developing
Speaker 1: GPT four, so that's the next generation of the language
Speaker 1: model it's been developing all these years. CEO Sam Altman
Speaker 1: has already said that people are likely going to be
Speaker 1: disappointed by GPT for not the cause the model won't
Speaker 1: be impressive. I have no doubt it will be, but
Speaker 1: because people have already built up in their minds a
Speaker 1: bar that GPT four simply will not be able to reach.
Speaker 1: And while that is a fair observation, I can't help
Speaker 1: but think that open ai is at least partly responsible
Speaker 1: for encouraging the fervor that led to this impossibly high bar.
Speaker 1: I don't think people said it all on their own.
Speaker 1: I think open aiyes own approach has kind of encouraged
Speaker 1: this sort of reaction. I mean, there's already this tendency
Speaker 1: for us to hype stuff when we just get a
Speaker 1: hint of what is possible and we start to extrapolate
Speaker 1: from that. That's true all the time. You can see
Speaker 1: it over and over and over again in lots of
Speaker 1: different technologies throughout the years. But at the same time,
Speaker 1: I feel open ai takes a kind of almost coy approach,
Speaker 1: and that helps encourage this behavior rather than discourage it.
Speaker 1: The company is openly doing the goal of building the
Speaker 1: first a g I, though as we've seen, it's not
Speaker 1: doing so in quite as transparent away as the organization
Speaker 1: first set out to follow. But if you're pursuing that goal,
Speaker 1: it means you've got like really big ambitions, and that again,
Speaker 1: I think helps to fuel the hype cycle. Now, I
Speaker 1: guess I can conclude this episode by just reflecting on
Speaker 1: the fact that open ai is a company that Elon
Speaker 1: Musk has criticized for failing to be transparent. That's something, y'all. Now.
Speaker 1: I don't wish to disparage the people who work for
Speaker 1: open ai or even the goal of the organization itself.
Speaker 1: I think it's a worthy goal. I think there are
Speaker 1: a lot of people who truly believe in that goal
Speaker 1: who are working for open Ai. I think the leadership
Speaker 1: believes in the goal and that that's what they're pursuing.
Speaker 1: It's just the realities of trying to achieve that in
Speaker 1: a world where you need to make money in order
Speaker 1: to fuel that pursuit creates complications, and there are no
Speaker 1: perfect solutions unless you just happen to have, you know,
Speaker 1: a a bottomless pit of a benefactor who can just
Speaker 1: pour money into the organization and allow it to pursue
Speaker 1: these these developments without having to worry about the commercial
Speaker 1: aspect of it. Unless you have that, then you have
Speaker 1: to deal with these real world complications. And just like
Speaker 1: the autonomous cars that you know, on the surface should
Speaker 1: be able to maneuver without any driver in the driver's
Speaker 1: seat and do so perfectly safely, we learned that once
Speaker 1: you put it into the real world, there are so
Speaker 1: many other variables and complications at play. It's never as
Speaker 1: simple as you first thought. So I know I've dogged
Speaker 1: on open Ai a lot. There are a lot of
Speaker 1: really great critical articles about the company. But I do
Speaker 1: believe in the work they're doing. I just the way
Speaker 1: they go about it has some elements to it that
Speaker 1: I find troubling. But it's not like I can suggest
Speaker 1: a better approach. I just think that it's important for
Speaker 1: us to pay attention and to criticize when necessary, and
Speaker 1: to ask questions and to hold the organization accountable because
Speaker 1: it has claimed to be this organization founded with a
Speaker 1: pursuit of developing beneficial AI and doing so in an open,
Speaker 1: transparent way. And if it fails to do that, I
Speaker 1: think we have to call them on it, because otherwise
Speaker 1: what we get may not be that beneficial AI we've
Speaker 1: been hoping for. All Right, that's it for this episode.
Speaker 1: Hope you enjoyed it, and if you have suggestions for
Speaker 1: topics I should cover in future episodes of tech Stuff,
Speaker 1: please reach out to me. You can download the i
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Speaker 1: Just put tech stuff in the search field. That will
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