In this episode of The Joe Rogan Experience, Joe talks about how he met President Trump, how he got to know him, and what it's like to be a member of the Trump administration. He also talks about his first time meeting with the President, and how important it is to have access to the President.
00:03:08.000But I like the fact that he's telling you what's on his mind.
00:03:11.000Almost every time he explains something and he says something, he starts with his, you could tell his love for America, what he wants to do for America.
00:03:22.000And everything that he thinks through is very practical and very common sense.
00:03:30.000And I still remember the first time I met him.
00:03:35.000And so this was, I'd never known him, never met him before.
00:03:38.000And Secretary Lutnick called, and we met right at the beginning of the administration.
00:03:45.000And he said, he told me what was important to President Trump, that United States manufactures on shore.
00:03:56.000And that was really important to him because it's important to national security.
00:04:00.000He wants to make sure that the important critical technology of our nation is built in the United States and that we reindustrialize and get good at manufacturing again because it's important for jobs.
00:04:11.000It just seems like common sense, right?
00:05:02.000It's just unfortunate we live in such a politically polarized society that you can't recognize good common sense things if they're coming from a person that you object to.
00:05:12.000And that, I think, is what's going on here.
00:05:14.000I think most people generally, as a country, you know, as a giant community, which we are, it just only makes sense that we have manufacturing in America, especially critical technology like you're talking about.
00:05:28.000Like, it's kind of insane that we buy so much technology from other countries.
00:05:33.000If the United States doesn't grow, we will have no prosperity.
00:05:39.000We can't invest in anything domestically or otherwise.
00:06:43.000Every successful person doesn't need to have a PhD.
00:06:46.000Every successful person doesn't have to have gone to Stanford or MIT.
00:06:50.000And I think that that sensibility is spot on.
00:06:56.000Now, when we're talking about technology growth and energy growth, there's a lot of people that go, oh, no, that's not what we need.
00:07:02.000We need to simplify our lives and get back.
00:07:05.000But the real issue is that we're in the middle of a giant technology race.
00:07:09.000And whether people are aware of it or not, whether they like it or not, it's happening.
00:07:13.000And it's a really important race because whoever gets to whatever the event horizon of artificial intelligence is, whoever gets there first, has massive advantages in a huge way.
00:07:43.000Or, you know, even going back to the discovery of energy, right?
00:07:47.000The United Kingdom was where the Industrial Revolution was, if you will, invented, when they realized that they can turn steam and such into energy, into electricity.
00:08:00.000All of that was invented largely in Europe.
00:08:05.000And the United States capitalized on it.
00:08:08.000We were the ones that learned from it.
00:08:34.000Manhattan Project was a technology race.
00:08:36.000We've been in the technology race ever since during the Cold War.
00:08:39.000I think we're still in a technology race.
00:08:41.000It is probably the single most important race.
00:08:43.000It is the technology gives you superpowers, you know, whether it's information superpowers or energy superpowers or military superpowers is all founded in technology.
00:08:57.000And so technology leadership is really important.
00:09:00.000Well, the problem is if somebody else has superior technology, right?
00:09:17.000And people are worried about that 20%, rightly so.
00:09:20.000I mean, you know, if you had 10 bullets in a revolver and you took out eight of them, you still have two in there and you spin it, you're not going to feel real comfortable when you pull that trigger.
00:09:35.000And when we're working towards this ultimate goal of AI, it's impossible to imagine that it wouldn't be of national security interest to get there first.
00:11:55.000If it's not certain about the answer or highly confident about the answer, it'll go back and do more research.
00:12:01.000It might actually even use a tool because that tool provides a better solution than it could hallucinate itself.
00:12:07.000As a result, we took all of that computing capability and we channeled it into having it produce a safer result, safer answer, a more truthful answer.
00:12:18.000Because as you know, one of the greatest criticisms of AI in the beginning was that it hallucinated.
00:13:33.000In the case of technology, it's also very similar in that way.
00:13:37.000And so if you look at what we're going to do with the next thousand times of performance in AI, a lot of it is going to be channeled towards more reflection, more research, thinking about the answer more deeply.
00:13:51.000So when you're defining safety, you're defining it as accuracy.
00:14:18.000Without a computer in the car, how would you do any of that?
00:14:22.000And that little computer, the computers that you have doing your traction control, is more powerful than the computer that went to Apollo 11.
00:14:29.000And so you want that technology, channel it towards safety, channel it towards functionality.
00:14:35.000And so when people talk about power, the advancement of technology, oftentimes I feel what they're thinking and what we're actually doing is very different.
00:14:45.000Well, what do you think they're thinking?
00:14:47.000Well, they're thinking somehow that this AI is being powerful, and their mind probably goes towards a sci-fi movie, the definition of power.
00:15:00.000Oftentimes, the definition of power is military power or physical power.
00:15:06.000But in the case of technology power, when we translate all of those operations, it's towards more refined thinking, more reflection, more planning, more options.
00:15:18.000I think the big fears that people have is one, a big fear is military applications.
00:15:24.000Because people are very concerned that you're going to have AI systems that make decisions that maybe an ethical person wouldn't make or a moral person wouldn't make based on achieving an objective versus based on how it's going to look to people.
00:15:41.000Well, I'm happy that our military is going to use AI technology for defense.
00:15:48.000And I think that Andoril building military technology, I'm happy to hear that.
00:15:54.000I'm happy to see all these tech startups now channeling their technology capabilities towards defense and military applications.
00:16:24.000But it's also, it's an unusual intellect channeled into that very bizarre field is what you need.
00:16:31.000And I think it's happy that we're making it more socially acceptable.
00:16:38.000There was a time where when somebody wanted to channel their technology capability and their intellect into defense technology, somehow they're vilified.
00:17:21.000When you look at the future of AI, and you just said that no one really knows what's happening.
00:17:28.000Do you ever sit down and ponder scenarios?
00:17:33.000What do you think is the best case scenario for AI over the next two decades?
00:17:43.000The best case scenario is that AI diffuses into everything that we do, and everything's more efficient, but the threat of war remains a threat of war.
00:18:03.000Cybersecurity remains a super difficult challenge.
00:18:09.000Somebody is going to try to breach your security.
00:18:14.000You're going to have thousands of millions of AI agents protecting you from that threat.
00:18:22.000Your technology is going to get better.
00:18:25.000Their technology is going to get better, just like cybersecurity.
00:18:28.000Right now, while we speak, we're seeing cyber attacks all over the planet on just about every front door you can imagine.
00:18:39.000And yet, you and I are sitting here talking.
00:18:44.000And so the reason for that is because we know that there's a whole bunch of cybersecurity technology in defense.
00:18:51.000And so we just have to keep amping that up, keep stepping that up.
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00:19:47.000That's a big issue with people: the worry that technology is going to get to a point where encryption is going to be obsolete.
00:19:54.000Encryption is just, it's no longer going to protect data, it's no longer going to protect systems.
00:19:59.000Do you anticipate that ever being an issue, or do you think it's as the defense grows, the threat grows, then defense grows, and it just keeps going on and on and on, and they'll always be able to fight off any sort of intrusions?
00:22:44.000You know, we'll use the same AI technology to go defend against it.
00:22:48.000So do you anticipate a time ever in the future where it's going to be impossible, where there's not going to be any secrets?
00:22:57.000Where the bottleneck between the technology that we have and the information that we have, information is just all a bunch of ones and zeros.
00:23:04.000It's out there on hard drives, and the technology has more and more access to that information.
00:23:08.000Is it ever going to get to a point in time where there's no way to keep a secret?
00:23:40.000The crazy thing is when you hear about the kind of computation that quantum computing can do and the power that it has, where you're looking at all the supercomputers in the world, it would take billions of years and it takes them a few minutes to solve these equations.
00:23:55.000How do you make encryption for something that can do that?
00:24:04.000Yeah, we've got a bunch of scientists who are expert in that.
00:24:06.000And the ultimate fear that it can't be breached, that quantum computing will always be able to decrypt all other quantum computing encryption?
00:24:29.000My worry is this is a totally, you know, it's like history was one thing and then nuclear weapons kind of changed all of our thoughts on war and mutually assured destruction came or got everybody to stop using nuclear bombs.
00:25:10.000And so I think the idea that somehow this AI is going to pop out of nowhere and somehow think in a way that we can't even imagine thinking and do something that we can't possibly imagine, I think is far-fetched.
00:25:29.000And the reason for that is because we all have AIs, and there's a whole bunch of AIs being in development.
00:25:35.000We know what they are, and we're using it.
00:25:38.000And so every single day, we're close to each other.
00:25:42.000But don't they do things that are very surprising?
00:25:46.000Yeah, but so you have an AI that does something surprising.
00:26:36.000Okay, let's just pretend for a second that we believe that.
00:26:38.000I don't believe, actually, I actually don't believe that.
00:26:40.000But nonetheless, let's pretend we believe that.
00:26:42.000So your AI is conscious, and my AI is conscious.
00:26:46.000And let's say your AI is, you know, wants to, I don't know, do something surprising.
00:26:52.000My AI is so smart that it might be surprising to me, but it probably won't be surprising to my AI.
00:26:59.000And so maybe my AI thinks it's surprising as well.
00:27:05.000But it's so smart, the moment it sees it the first time, it's not going to be surprised the second time, just like us.
00:27:10.000And so I feel like I think the idea that only one person has AI and that one person's AI compares to everybody else's AI as Neanderthal is probably unlikely.
00:27:26.000I think it's much more like cybersecurity.
00:29:37.000I just think it's not going to happen.
00:29:38.000I know you think it's not going to happen, but it could, right?
00:29:42.000And here's the other thing: it's like if we're racing towards could, and could could be the end of human beings being in control of our own destiny.
00:30:19.000The consciousness, I guess first of all, you need to know about your own existence.
00:30:36.000You have to have experience, not just knowledge and intelligence.
00:30:47.000The concept of a machine having an experience, I'm not, well, first of all, I don't know what defines experience, why we have experiences and why this microphone doesn't.
00:31:01.000And so I think I know, well, I think I know what consciousness is.
00:31:10.000The sense of experience, the ability to know self versus the ability to be able to reflect, know our own self, the sense of ego.
00:31:25.000I think all of those human experiences probably is what consciousness is.
00:31:35.000But why it exists versus the concept of knowledge and intelligence, which is what AI is defined by today.
00:31:45.000It has knowledge, it has intelligence, artificial intelligence.
00:31:48.000We don't call it artificial consciousness.
00:31:51.000Artificial intelligence, the ability to perceive, recognize, understand, plan, perform tasks.
00:32:06.000Those things are foundations of intelligence to know things.
00:32:56.000But isn't AI interacting with society?
00:33:00.000So doesn't it acquire experience through that interaction?
00:33:05.000I don't think interactions is experience.
00:33:06.000I think experience is experience is a collection of feelings, I think.
00:33:14.000You're aware that AI, I forget which one, where they gave it some false information about one of the programmers having an affair with his wife just to see how it would respond to it.
00:33:25.000And then when they said they were going to shut it down, it threatened to blackmail him and reveal his affair.
00:33:30.000And it was like, whoa, like it's conniving.
00:33:32.000Like if that's not learning from experience and being aware that you're about to be shut down, which would imply at least some kind of consciousness, or you could kind of define it as consciousness if you were very loose with the term.
00:33:46.000And if you imagine that this is going to exponentially become more powerful, wouldn't that ultimately lead to a different kind of consciousness than we're defining from biology?
00:33:57.000Well, first of all, let's just break down what it probably did.
00:34:24.000That in the collection of numbers that relates to a husband cheating on a wife has subsequently a bunch of numbers that relates to black male and such things, whatever the revenge was.
00:34:44.000And so it's just like, you know, it's just as if I'm asking it to write me a poem in Shakespeare.
00:34:51.000It's just whatever the words are, in that dimensionality, this dimensionality is all these vectors in multi-dimensional space.
00:35:01.000These words that were in the prompt that described the affair subsequently led to one word after another led to some revenge and something.
00:35:15.000But it's not because it had consciousness or it just spewed out those words, generated those words.
00:35:28.000But at a certain point in time, one would say, okay, well, it couldn't do this two years ago, and it couldn't do this four years ago.
00:35:35.000Like, when we were looking towards the future, like, at what point in time, when it can do everything a person does, what point in time do we decide that it's conscious?
00:35:44.000If it absolutely mimics all human thinking and behavior patterns, that doesn't make it conscious.
00:36:25.000And that's where the real doomsday people are worried, that you are creating a form of consciousness that you can't control.
00:36:32.000I believe it is possible to create a machine that imitates human intelligence and has the ability to understand information, understand instructions, break the problem down, solve problems, and perform tasks.
00:37:00.000I believe that we could have a computer that has a vast amount of knowledge, some of it true, some of it not true, some of it generated by humans, some of it generated synthetically, and more and more of knowledge in the world will be generated synthetically going forward.
00:37:25.000Until now, the knowledge that we have are knowledge that we generate and we propagate and we send to each other and we amplify it and we add to it and we modify it, we change it.
00:37:39.000In the future, in a couple of years, maybe two or three years, 90% of the world's knowledge will likely be generated by AI.
00:37:57.000It's because what difference does it make to me that I am learning from a textbook that was generated by a bunch of people I didn't know or written by a book that, you know, from somebody I don't know, to knowledge generated by AI, computers that are assimilating all of these and re-synthesizing things.
00:38:20.000To me, I don't think there's a whole lot of difference.
00:38:25.000We still have to make sure that it's based on fundamental first principles.
00:38:29.000And we still have to do all of that, just like we do today.
00:38:32.000Is this taking into account the kind of AI that exists currently?
00:38:36.000And do you anticipate that just like we could have never really believed that AI would be, at least a person like myself would never believe AI would be as so ubiquitous and so worth it's so powerful today and so important today.
00:39:10.000But if you go forward nine years from now and then ask yourself what's going to happen ten years from now, I think it'll be quite gradual.
00:39:21.000One of the things that Elon said that makes me happy is He believes that we're going to get to a point where it's not necessary for people to work.
00:39:34.000And not meaning that you're going to have no purpose in life, but you will have, in his words, universal high income because so much revenue is generated by AI that it will take away this need for people to do things that they don't really enjoy doing just for money.
00:39:54.000And I think a lot of people have a problem with that because their entire identity and how they think of themselves and how they fit in the community is what they do.
00:40:03.000Like, this is Mike, he's an amazing mechanic.
00:40:05.000Go to Mike, and Mike takes care of things.
00:40:07.000But there's going to come a point in time where AI is going to be able to do all those things much better than people do.
00:40:14.000And people will just be able to receive money.
00:40:58.000And I think the concept sounds great until you take into account human nature.
00:41:04.000And human nature is that we like to have puzzles to solve and things to do and an identity that's wrapped around our idea that we're very good at this thing that we do for a living.
00:41:25.000So one of the predictions from Jeff Hinton, who started the whole deep learning phenomenon, the deep learning technology trend, and incredible, incredible researcher, professor at University of Toronto.
00:41:46.000He invented, discovered, or invented the idea of back propagation, which allows the neural network to learn.
00:41:56.000And as you know, for the audience, software historically was humans applying first principles and our thinking to describe an algorithm that is then codified just like a recipe that's codified in software.
00:42:21.000It looks just like a recipe, how to cook something.
00:42:24.000It looks exactly the same, just in a slightly different language.
00:42:27.000We call it Python or C or C or whatever it is.
00:42:32.000In the case of deep learning, this invention of artificial intelligence, we put a structure of a whole bunch of neural networks and a whole bunch of math units.
00:42:58.000And we give it the input that the software would eventually receive.
00:43:06.000And we just let it randomly guess what the output is.
00:43:11.000And so we say, for example, the input could be a picture of a cat.
00:43:17.000And one of the outputs of the switchboard is where the cat signal is supposed to show up.
00:43:25.000And all of the other signals, the other one's a dog, the other one's an elephant, the other one's a tiger, and all of the other signals are supposed to be zero when I show it a cat.
00:43:36.000And the one that is a cat should be one.
00:43:40.000And I show it a cat through this big, huge network of switchboards and math units.
00:43:47.000And they're just doing multiply and adds, multiplies and adds.
00:43:54.000And this thing, this switchboard, is gigantic.
00:43:58.000The more information you're going to give it, the more the bigger this switchboard has to be.
00:44:03.000And what Jeff Hinton discovered was invented, was a way for you to guess that, put the cat signal in, put the cat image in, and that cat image, you know, could be a million numbers because it's a megapixel image, for example.
00:44:20.000And it's just a whole bunch of numbers.
00:44:22.000And somehow, from those numbers, it has to light up the cat signal.
00:45:06.000And all of the other switch, all the other outputs have to be zero.
00:45:11.000And I want to backpropagate that and just do it over and over and over again.
00:45:15.000It's just like showing a kid this is an apple, this is a dog, this is a cat, and you just keep showing it to them until they eventually get it.
00:45:24.000Okay, well, anyways, that big invention is deep learning.
00:45:27.000That's the foundation of artificial intelligence.
00:45:30.000A piece of software that learns from examples.
00:45:35.000That's basically machine learning, a machine that learns.
00:45:40.000And so one of the big first applications was image recognition.
00:45:48.000And one of the most important image recognition applications is radiology.
00:45:54.000And so he predicted about five years ago that in five years' time, the world won't need any radiologists because AI would have swept the whole field.
00:46:08.000Well, it turns out AI has swept the whole field.
00:48:08.000You know, one of the examples that I gave that I would give is, for example, if my car became self-driving, will all chauffeurs be out of jobs?
00:48:21.000Because some chauffeurs, for some people who are driving you, they could be protectors.
00:48:27.000Some people, they're part of the experience, part of the service.
00:48:31.000So when you get there, they could take care of things for you.
00:48:35.000And so for a lot of different reasons, not all chauffeurs would lose their jobs.
00:48:39.000Some chauffeurs would lose their jobs.
00:48:42.000And many chauffeurs would change their jobs.
00:48:45.000And the type of applications of autonomous vehicles will probably increase.
00:48:50.000The usage of the technology within find new homes.
00:48:54.000And so I think you have to go back to what is the purpose of a job.
00:48:58.000Like, for example, if AI comes along, I actually don't believe I'm going to lose my job because my purpose isn't to, I have to look at a lot of documents.
00:49:40.000It could be a lot of people, but it'll probably generate – like, for example, let's say I'm super excited about the robots Elon's working on.
00:50:10.000And so you're going to have a whole industry of people taking care of, like, for example, all the mechanics and all the people who are building things for cars, supercharging cars.
00:50:22.000That didn't exist before cars, and now we're going to have robots.
00:52:37.000And so I think the question, maybe partly, it's hard to answer, partly because it's hard to talk about infinity, and it's hard to talk about a long time from now.
00:52:52.000And the reason for that is because there's just too many scenarios to consider.
00:52:59.000But I think in the next several years, call it five to ten years, there are several things that I believe in hope.
00:54:19.000And if it doesn't know your language, you'll speak it in that language and it'll probably figure out that it doesn't completely understand your language, go learns it instantly and comes back and talk to you.
00:54:30.000And so I think the technology divide has a real chance finally that you don't have to speak Python or C ⁇ or Fortran.
00:54:39.000You can just speak human and whatever form of human you like.
00:54:43.000And so I think that that has a real chance of closing the technology divide.
00:54:47.000Now, of course, the counter narrative would say that AI is only going to be available for the nations and the countries that have a vast amount of resources because AI takes energy and AI takes a lot of GPUs and factories to be able to produce the AI.
00:55:11.000No doubt at the scale that we would like to do in the United States.
00:55:14.000But the fact of the matter is, your phone's going to run AI just fine, all by itself, you know, in a few years.
00:55:22.000Today, it already does it fairly decently.
00:55:24.000And so the fact that every country, every nation, every society will have to benefit of very good AI.
00:55:46.000You don't need frontier AI like we need frontier AI because we want to be the world leader.
00:55:51.000But for every single country, everybody, I think the capability to elevate everybody's knowledge and capability and intelligence, that day is coming.
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00:57:19.000And also energy production, which is the real bottleneck when it comes to third world countries and electricity and all the resources that we take for granted.
00:57:31.000Almost everything is going to be energy constrained.
00:57:33.000And so if you take a look at one of the most important technology advances in history is this idea called Moore's Law.
00:57:43.000Moore's Law started basically in my generation.
00:57:50.000And my generation is the generation of computers.
00:57:53.000I graduated in 1984, and that was basically at the very beginning of the PC revolution and the microprocessor.
00:58:03.000And every single year, it approximately doubled.
00:58:10.000And we describe it as every single year we double the performance.
00:58:14.000But what it really means is that every single year, the cost of computing halved.
00:58:20.000And so the cost of computing in the course of five years reduced by a factor of 10.
00:58:28.000The amount of energy necessary to do computing, to do any task, reduced by a factor of 10.
00:58:34.000Every single 10 years, 100, 1,000, 10,000, 100,000, so on and so forth.
00:58:44.000And so each one of the clicks of Moore's Law, the amount of energy necessary to do any computing reduced.
00:58:51.000That's the reason why you have a laptop today when back in 1984 it sat on the desk, you got a plug in, it wasn't that fast, and it consumed a lot of power.
00:59:01.000Today, you know, it is only a few watts.
00:59:04.000And so Moore's Law is the fundamental technology, the fundamental technology trend that made it possible.
00:59:19.000It took us about 30 years to really make a huge breakthrough.
00:59:24.000In that 30 years or so, we took computing, you know, probably a factor of, well, let me just say in the last 10 years, the last 10 years, we improved the performance of computing by 100,000 times.
00:59:42.000Imagine a car over the course of 10 years, it became 100,000 times faster.
00:59:47.000Or at the same speed, 100,000 times cheaper.
00:59:52.000Or at the same speed, 100,000 times less energy.
00:59:56.000If your car did that, it doesn't need energy at all.
01:00:00.000What I mean, what I'm trying to say is that in 10 years' time, the amount of energy necessary for artificial intelligence for most people will be minuscule, utterly minuscule.
01:00:13.000And so we'll have AI running in all kinds of things and all the time because it doesn't consume that much energy.
01:00:19.000And so if you're a nation that uses AI for, you know, almost everything in your social fabric, of course, you're going to need these AI factories.
01:00:28.000But for a lot of countries, I think you're going to have excellent AI and you're not going to need as much energy.
01:00:34.000Everybody will be able to come along, is my point.
01:00:36.000So currently, that is a big bottleneck, right?
01:02:21.000And so in 2012, Jeff Hinton's lab, this gentleman I was talking about, Ilya Suskabur, Alex Krushzewski, they made a breakthrough in computer vision in literally creating a piece of software called AlexNet.
01:02:50.000And it recognized images at a level, computer vision, which is fundamental to intelligence.
01:02:57.000If you can't perceive, it's hard to have intelligence.
01:03:00.000And so computer vision is a fundamental pillar of, not the only, but fundamental pillar of.
01:03:05.000And so breaking computer vision or breaking through in computer vision is pretty foundational to almost everything that everybody wants to do in AI.
01:03:14.000And so in 2012, their lab in Toronto made this breakthrough called AlexNet.
01:03:24.000And AlexNet was able to recognize images so much better than any human created computer vision algorithm.
01:03:37.000So all of these people, all these scientists, and we had many too, working on computer vision algorithms.
01:03:45.000And these two kids, Ilya and Alex, under Jeff Hinton, took a giant leap above it.
01:03:56.000And it was based on this thing called ElexNet, this neural network.
01:04:01.000And the way it ran, the way they made it work was literally buying two NVIDIA graphics cards.
01:04:09.000Because NVIDIA's GPUs, we've been working on this new way of doing computing.
01:04:15.000And our GPU's application, and it's basically a supercomputing application back in 1984, in order to process computer games and what you have in your racing simulator, that is called an image generator supercomputer.
01:04:37.000And so NVIDIA started, our first application was computer graphics.
01:04:43.000And we applied this new way of doing computing where we do things in parallel instead of sequentially.
01:04:53.000In our case, we break the problem down and we give it to thousands of processors.
01:05:00.000And so our way of doing computation is much more complicated.
01:05:08.000But if you're able to formulate the problem in the way that we create it called CUDA, this is the invention of our company, if you could formulate it in that way, we could process everything simultaneously.
01:05:23.000Now, in the case of computer graphics, it's easier to do because every single pixel on your screen is not related to every other pixel.
01:05:33.000And so I could render multiple parts of the screen at the same time.
01:05:38.000Not completely true, because maybe the way lighting works or the way shadow works, there's a lot of dependency and such.
01:05:45.000But computer graphics, with all the pixels, I should be able to process everything simultaneously.
01:05:52.000And so we took this embarrassingly parallel problem called computer graphics and we applied it to this new way of doing computing, NVIDIA's accelerated computing.
01:06:05.000We put it in all of our graphics cards.
01:06:35.000And so anyways, these two kids trained this model using the technique I described earlier on our GPUs because our GPUs could process things in parallel.
01:06:45.000It's essentially a supercomputer in a PC.
01:06:49.000The reason why you used it for Quake is because it is the first consumer supercomputer.
01:07:08.000Simultaneously, this deep learning phenomenon was happening all over the country.
01:07:13.000Universities after another recognized the importance of deep learning, and all of this work was happening at Stanford, at Harvard, at Berkeley, just all over the place.
01:07:23.000New York University, Yan Le Kun, Andrew Yang at Stanford, so many different places.
01:07:51.000And so we realized that this architecture for deep neural networks, back propagation, the way deep neural networks were created, we could probably scale this problem, scale the solution to solve many problems.
01:08:08.000That is essentially a universal function approximator.
01:08:13.000Okay, meaning, you know, back when you're in school, you have a box, inside of it is a function.
01:08:21.000You give it an input, it gives you an output.
01:08:24.000And the reason why I call it a universal function approximator is that this computer, instead of you describing the function, a function could be a Newton's equation, F equals MA.
01:10:58.000Turns out they were gamers and it was lucky they found it.
01:11:02.000And it was lucky that we paid attention to that moment.
01:11:06.000It was a little bit like, you know, that Star Trek, you know, first contact.
01:11:16.000The Vulcans had to have seen the warped drive at that very moment.
01:11:20.000If they didn't witness the warped drive, you know, they would have never come to Earth.
01:11:26.000And everything would have never happened.
01:11:28.000It's a little bit like if I hadn't paid attention to that moment, that flash, and that flash didn't last long, if I hadn't paid attention to that flash or our company didn't pay attention to it, who knows what would have happened.
01:11:40.000But we saw that and we reasoned our way into this is a universal function approximator.
01:11:45.000This is not just a computer vision approximator.
01:11:47.000We could use this for all kinds of things if we could solve two problems.
01:11:51.000The first problem is that we have to prove to ourselves it could scale.
01:11:55.000The second problem we had to wait for, I guess, contribute to and wait for, is the world will never have enough data on input and output where we could supervise the AI to learn everything.
01:12:18.000For example, if we have to supervise our children on everything they learn, the amount of information they could learn is limited.
01:12:43.000And the reason why the AI could learn by itself is because we have many examples of right answers.
01:12:49.000Like, for example, if I want to learn, if I want to teach an AI how to predict the next word, I could just grab it, grab up a whole bunch of text that we already have, mask out the last word, and make it try and try and try again until it predicts the next one.
01:13:07.000Or I mask out random words inside the text, and I make it try and try and try until it predicts it.
01:13:12.000You know, like Mary goes down to the bank.
01:13:35.000Okay, now you know it must be the riverbank.
01:13:38.000And so you give these AIs a whole bunch of these examples and you mask out the words, it'll predict the next one.
01:13:46.000And so unsupervised learning came along.
01:13:48.000These two ideas, the fact that it's scalable and unsupervised learning came along, we were convinced that we ought to put everything into this and help create this industry because we're going to solve a whole bunch of interesting problems.
01:14:54.000I announced it at GTC and at one of our annual events.
01:15:00.000And I described this deep learning thing, computer vision thing, and this computer called DGX1.
01:15:07.000The audience was like completely silent.
01:15:09.000They had no idea what I was talking about.
01:15:14.000And I was lucky because I had known Elon, and I helped him build the first computer for Model 3, the Model S.
01:15:26.000And when he wanted to start working on autonomous vehicle, I helped him build the computer that went into the Model S A V system, his full self-driving system.
01:15:38.000We were basically the FSD computer version one.
01:15:42.000And so we're already working together.
01:15:46.000And when I announced this thing, nobody in the world wanted it.
01:18:44.000I mean, if you wanted to make a story for a film, I mean, that would be the story that, like, What better scenario if it really does become a digital life form, how funny would it be that it is birthed out of the desire for computer graphics for video games?
01:19:08.000Kind of crazy when you think about it that way.
01:19:10.000Because computer graphics was one of the hardest supercomputer problems, generating reality.
01:19:22.000And also one of the most profitable to solve because computer games are so popular.
01:19:28.000When NVIDIA started in 1993, we were trying to create this new computing approach.
01:19:34.000The question is, what's the killer app?
01:19:38.000And the problem we wanted to, the company wanted to create a new type of computing computing architecture, a new type of computer that can solve problems that normal computers can't solve.
01:19:56.000Well, the applications that existed in the industry in 1993 are applications that normal computers can solve.
01:20:06.000Because if the normal computers can't solve them, why would the application exist?
01:20:11.000And so we had a mission statement for a company that has no chance of success.
01:22:48.000And Chris's kids are about the same age as ours.
01:22:54.000And we would go to work in this townhouse.
01:22:57.000But, you know, when you're a startup and the mission statement is the way we described, you're not going to have too many customers calling you.
01:24:44.000And all of the technology ideas that we had, the architecture concepts were sound, but the way we were doing computer graphics was exactly backwards.
01:24:55.000You know, instead of, I won't bore you with the technology, but instead of inverse texture mapping, we were doing forward texture mapping.
01:25:04.000Instead of triangles, we did curved surfaces.
01:26:30.000And ultimately, we had to make several decisions.
01:26:36.000The first decision is: well, if we change now, we will be the last company.
01:26:50.000And even if we changed into the technology that we believe to be right, we'd still be dead.
01:26:58.000And so that argument, you know, do we change and therefore be dead?
01:27:05.000Don't change and make this technology work somehow?
01:27:09.000Or go do something completely different.
01:27:13.000That question stirred the company strategically and was a hard question.
01:27:18.000I eventually advocated for we don't know what the right strategy is, but we know what the wrong technology is.
01:27:26.000So let's stop doing it the wrong way and let's give ourselves a chance to go figure out what the strategy is.
01:27:31.000The second thing, the second problem we had was our company was running out of money and I was in a contract with Sega and I owed them this game console.
01:27:43.000And if that contract would have been canceled, we'd be dead.
01:29:43.000I explained to him why the technology doesn't work, why we thought it was going to work, why it doesn't work.
01:29:51.000And I asked him to convert the last $5 million that they were going to complete the contract to give us that money as an investment instead.
01:30:11.000And he said, but it's very likely your company will go out of business, even with my investment.
01:30:30.000And here's a pile of competitors doing it right.
01:30:34.000What are the chances that giving NVIDIA $5 million, that we would develop the right strategy, that he would get a return on that $5 million or even get it back?
01:30:49.000If I were sitting there right there, I wouldn't have done it.
01:30:52.000$5 million was a mountain of money to Sega at the time.
01:30:57.000And so I told him that if you invested that $5 million in us, it is most likely to be lost.
01:31:12.000But if you didn't invest that money, we'd be out of business and we would have no chance.
01:31:18.000And I told him that I don't even know exactly what I said in the end, but I told him that I would understand if he decided not to, but it would make the world to me if he did.
01:31:38.000He went off and thought about it for a couple days and came back and said, we'll do it.
01:34:16.000I brought it back and I gave one to each one of the architects.
01:34:18.000And I said, read that and let's go save the company.
01:34:23.000And so they read this textbook, learned from the giant at the time, Silicon Graphics, about how to do 3D graphics.
01:34:35.000But the thing that was amazing, and what makes NVIDIA special today, is that The people that are there are able to start from first principles, learn best-known art, but re-implement it in a way that's never been done before.
01:34:55.000And so, when we reimagined the technology of 3D graphics, we reimagined it in a way that manifests today the modern 3D graphics.
01:35:07.000We really invented modern 3D graphics, but we learned from previous known arts and we implement it fundamentally differently.
01:35:18.000Well, you know, ultimately, ultimately, the simple answer is that the way silicon graphics works, the geometry engine is a bunch of software running on processors.
01:35:34.000We took that and eliminated all the generality, the general purposeness of it, and we reduced it down into the most essential part of 3D graphics.
01:35:54.000And so, instead of something general purpose, we hard-coded it very specifically into just the limited applications, limited functionality necessary for video games.
01:36:08.000And that capability, that super, and because we reinvented a whole bunch of stuff, it supercharged the capability of that one little chip.
01:36:17.000And our one little chip was generating images as fast as a $1 million image generator.
01:37:13.000And so we narrowly focused our problem statement so I could reject all of the other complexities, and we shrunk it down into this one little focus, and then we supercharged it for gamers.
01:37:25.000And the second thing that we did was we created a whole ecosystem of working with game developers and getting their games ported and adapted to our silicon so that we could turn essentially what is a technology business into a platform business, into a game platform business.
01:37:45.000So GeForce is really today, it's also the most advanced 3D graphics technology in the world.
01:37:52.000But a long time ago, GeForce is really the game console inside your PC.
01:37:58.000It runs Windows, it runs Excel, it runs PowerPoint, of course, those are easy things.
01:38:03.000But its fundamental purpose was simply to turn your PC into a game console.
01:38:08.000So we were the first technology company to build all of this incredible technology in service of one audience, gamers.
01:38:18.000Now, of course, in 1993, the gaming industry didn't exist.
01:38:23.000But by the time that John Carmack came along and the Doom phenomenon happened, and then Quake came out, as you know, that entire world, that entire community boom, took off.
01:38:38.000Do you know where the name Doom came from?
01:38:40.000It came from this there's a scene in the movie, The Color of Money, where Tom Cruise, who's this elite pool player, shows up at this pool hall and this local hustler says, What do you got in the case?
01:39:42.000$5 million, that pivot, with that conversation with that gentleman, if he did not agree to that, if he did not like you, what would the world look like today?
01:40:53.000Because without the software testing the chip, you don't know the chip works.
01:40:58.000And then you're going to find a bug, probably, because every time you test something, you find bugs, which means you have to tape it out again, which is more time, more money.
01:41:43.000You could take your design, all of the software that describes the chip, and you could put it into this machine.
01:41:54.000And this machine will pretend it's our chip.
01:41:57.000So I don't have to send it to the fab, wait until the fab sends it back.
01:42:01.000I could have this machine pretend it's our chip, and I could put all of the software on top of this machine called an emulator and test all of the software on this pretend chip, and I could fix it all before I send it to the fab.
01:42:19.000And if I could do that, when I send it to the fab, it should work.
01:45:42.000And as we were starting the production, Morris flew to the United States and he didn't so many words ask me so, but he asked me a whole lot of questions that was trying to tease out, do I have any money?
01:46:00.000But he didn't directly ask me that, you know.
01:46:03.000And so the truth is that we didn't have all the money.
01:46:08.000But we had a strong PO from the customer.
01:46:11.000And if it didn't work, some wafers would have been lost.
01:46:18.000I'm not exactly sure what would have happened, but we would have come short.
01:52:10.000Well, kudos to you for admitting that.
01:52:12.000I think that's important for a lot of people to hear because, you know, there's probably some young people out there that are in a similar position to where you were when you were starting out that just feel like, oh, those people that have made it, they're just smarter than me and they had more opportunities than me.
01:52:30.000And it's just like it was handed to them or they're just in the right place at the right time.
01:52:35.000Joe, I just described to you somebody who didn't know what was going on.
01:53:16.000And their rewriting of history oftentimes had them somehow extraordinarily smart and they were geniuses and they knew all along and they were spot on.
01:53:28.000The business plan was exactly what they thought.
01:54:42.000We need to help him succeed because it helps everybody, all of us succeed.
01:54:48.000And I'm lucky that I work in a company where I have 40,000 people who want me to succeed.
01:54:58.000They want me to succeed and I can tell.
01:55:00.000And they're all every single day to help me overcome these challenges, trying to realize what I describe to be our strategy, doing their best.
01:55:11.000And if it's somehow wrong or not perfectly right, to tell me so that we could pivot.
01:55:19.000And the more vulnerable we are as a leader, the more able other people are able to tell you, you know, that, Jensen, that's not exactly right.
01:58:25.000I would imagine one of the more difficult aspects of your job currently, now that the company is massively successful, is anticipating where technology is headed and where the applications are going to be.
01:58:54.000You have to be surrounded by amazing people.
01:58:56.000And NVIDIA is now, if you look at the large tech companies in the world today, most of them have a business in advertising or social media or content distribution.
01:59:14.000And at the core of it is really fundamental computer science.
01:59:20.000And so the company's business is not computers.
01:59:23.000The company's business is not technology.
02:00:30.000And now the question is how do you systematically be able to see the future, stay alert of it, and reduce the likelihood of missing something or being wrong.
02:00:53.000And so there's a lot of different ways you could do that.
02:00:55.000For example, we have great partnerships.
02:01:09.000And so I'm constantly working with researchers outside the company.
02:01:15.000We have the benefit of having amazing customers.
02:01:19.000And so I have the benefit of working with Elon and others in the industry.
02:01:24.000And we have the benefit of being the only pure play technology company that can serve consumer internet, industrial manufacturing, scientific computing, healthcare, financial services, all the industries that we're in, they're all signals to me.
02:01:45.000And so they all have mathematicians and scientists.
02:01:49.000And so because I have the benefit now of a radar system that is the most broad of any company in the world, working across every single industry, from agriculture to energy to video games.
02:02:05.000And so the ability for us to have this vantage point, one, doing fundamental research ourselves, and then two, working with all the great researchers, working with all the great industries, the feedback system is incredible.
02:02:20.000And then finally, you just have to have a culture of staying super alert.
02:02:25.000There's no easy way of being alert except for paying attention.
02:02:31.000I haven't found a single way of being able to stay alert without paying attention.
02:02:35.000And so, you know, I probably read several thousand emails a day.
02:04:18.000It really, I mean, it has to be kind of surreal to be in the position that you're in now when you look back at how many times that it could have fallen apart and humble beginnings.
02:06:54.000It's kind of an amazing feat, actually.
02:06:58.000The ability to hold your population for when it's 600 people is quite a magical thing, however, they did it.
02:07:08.000And so the school had a mission of being an open school for any children who'd like to come.
02:07:20.000And what that basically means is that if you're a troubled student, if you have a troubled family, if you're, you know, whatever your background, you're welcome to come to Oneida Baptist Institute, including kids from international who would like to stay there.
02:08:30.000I know his name, but I don't know where he is now.
02:08:32.000But anyways, that night we got – and the second thing I noticed when you walk into your dorm room is there are no drawers and no closet doors.
02:09:32.000It was a junior high, but they took me anyways because if I walked about a mile across the Kentucky River, the swing bridge, the other side is a middle school that I could go to.
02:09:47.000And then I can go to that school and I come back and I stay in the dorm.
02:09:53.000And so basically, Oneida Baptist Institute was my dorm when I went to this other school.
02:09:58.000My older brother went to the junior high.
02:10:02.000And so we were there for a couple of years.
02:12:26.000And what breaks my heart, probably the only thing that really breaks my heart about that experience was so we didn't have enough money to make international phone calls every week.
02:12:42.000And so my parents gave us this tape deck, this IWA tape deck, and a tape.
02:12:51.000And so every month we would sit in front of that tape deck and my older brother, Jeff, and I the two of us would just tell them what we did the whole month.
02:13:09.000And my parents would take that tape and record back on top of it and send it back to us.
02:13:17.000Could you imagine if for two years that tape still existed of these two kids just describing their first experience with the United States?
02:13:28.000Like I remember telling my parents that I joined the swim team and my roommate was really buff and so every day we spent a lot of time in the in the gym and so every night 100 push-ups, 100 sit-ups every day in the gym.
02:15:46.000And I just still remember the look on my mom's face, you know, because they didn't have any money and she didn't know how she was going to pay it back.
02:15:54.000But anyhow, that kind of tells you how hard it was for them to come here.
02:22:39.000So that was the beginning of it, is just seeing other people do it.
02:22:43.000And then saying, all right, let's just try it.
02:22:44.000And then so the beginning days, we just did it on a laptop, had a laptop with a webcam and just messed around, had a bunch of comedians come in, we would just talk and joke around.
02:24:00.000You know, it's an amazing gift to be able to have so many conversations with so many interesting people because it changes the way you see the world because you see the world through so many different people's eyes.
02:24:14.000And you have so many different people of different perspectives and different opinions and different philosophies and different life stories.
02:24:21.000And, you know, it's an incredibly enriching and educating experience having so many conversations with so many amazing people.