YouSaid · the spoken record

Gavin Uberti

lines on the record: 84
first: 2023-12-12
most recent: 2023-12-12
sittings or episodes: 1
sources: podcast

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“Mass production of a microchip can take four or five years. And we can't wait that long for training our new models. So I believe that to support this demand, support these custom ASICs for training that we will eventually need, there has to be a whole redesign of the waste semiconductors are designed.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“And backwards requires a different kinds of network primitives, different kinds of connectivity than just running forwards does. So training is a more challenging problem. But the economics that make a transformer-specific inference chip make sense, I think will also apply to training. If you're spending, say, $100 million on an AI model and it costs $100 million to build a new custom ASIC, then no matter how good the custom ASIC is, you're not going to make your money back. But if it costs a billion dollars to train your model, a custom ASIC costs $100 million, and it makes you 20% better, then yes, it does make economic sense to build that custom chip. If you're spending not $1 billion, but $10 billion, it is a no-brainer. You have to do it. Now, building that custom training chip is not an easy thing to do. Right now, going from conception to”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Four times. And that's one of the really interesting things that a transformer ASIC can do. You can have a much, much larger batch, not 64, but 2,500. So we're able to go load that weight at once, pay the expense of price, and then amortize that expensive price over a huge number of users, making inference much, much cheaper. And now, while this sounds good in theory, this does mean that you have to run that model in a place where you can have a huge number of users all kind of grouped together. So I think that means inference will be centralized in much of the same way as training. To go to training specifically, why are these machines so power hungry? Well, in inference, you have to run the model fords. Do that you have to run the model backwards, which takes about double the compute. Additionally, running a forwards.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, so first, I want to go talk about inference for a second, then we'll build into training because I believe that inference is going to be run in similarly large power-hungry data centers too. And here's the reason why. Only run a transformer model. We have this huge number of parameters. And each one of these parameters is a number. And to use that number, we take in a number from our input, we multiply them together, and we add them to a running total. So every one of those parameters, in the case of GPT-375 billion, is loaded from memory once and then used in a math operation at once. It turns out that loading a thing from memory is way more expensive than doing the math. So how do we solve this problem? Well, we say that these weights are the same across one user or two users or four users or eight users. So we batch together a huge number of queries. Then we load them that weight once, and we use it sixteen, thirty two sixty five.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Maybe one fails sometimes, and then you reset to a previous checkpoint, you try again. If you have 10 million GPUs or 10 million Transformer ASICs, then you're going to have one failing every couple of hours. You're going to have to figure out how do I deal with one of these things crapping out without having to stall the whole thing. No one solved that problem yet. Or heat. We've been able to go cool data centers in the 200, 300 megawatt range. What happens when a two gigawatts that go into that building are all turned into heat? How does that get pumped out? Is that just dumped into the atmosphere? What happens next? I really think wholesale industries going to evolve around building these massive, massive AI models. We've only seen the beginning of it.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“As a 2 gigawatt data center, as a data center center that consumes the entire energy output of a big power plant. And I think this is also going to be very centralized. If you compare, say, the bandwidth of an entire undersea submarine cable, the bandwidth between a couple of GPU ranch next to each other, the GPU racks have double the bandwidth of a literal undersea cable. So there is so much incentive to centralize data centers. So I think we're going to see a few very, very large facilities that do all this. The same is true of semiconductors for reference. TSMC's FABs are all very large, very self-contained buildings, but so much else has to happen too. You need way more power. We also need way more redundancy. If you have ten GPUs, then sure, none of them are going to fail. If you have ten thousand GPUs,”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“There's a great analogy here, actually, and that is a semiconductor fabrication, the buildings that make microchips, these incredibly advanced facilities have incredibly complex supply chains. Many have heard of a TSMC and the machines they buy from ASML for $300 million, and the Mirrors ASML buys from Karl Zeiss. That's just the photobography of machines, a huge array of complexity goes into this stuff. And that means that building a new fab cost ten, twenty billion dollars. And I think we're going to see a similar thing for models of level five and six. This whole chain will have to be redone. If you want to go and scale from four to five, have the same multiple of compute over a three to four or two to three, you have to have ten hundred times more chips, and that means ten hundred times more power. This is not going to be a 20 megawatt data center.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“But not Bitcoin. Once you have an ASIC for something, it is just not commercially viable to run on GPUs anymore. I think the same exact thing is going to happen for transformers. Previously, in like the 2010 era of Bitcoin, there wasn't really enough volume to justify spending a hundred million dollars to build a custom chip. But once Bitcoin became popular, there totally was. Then the chips came out and everything else becomes no longer feasible. For transformers, even eighteen months ago, there wasn't enough demand to justify spending one hundred million dollars to burn that algorithm into the silver. But now, with ChatGPT, get up co pilot with character, with all these models from Google and Anthropic, it does make sense. The first transformer ASICs are coming.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Only a tiny fraction of that GPU is used for mining Bitcoin. We don't need the cashes. We don't need the IOs. We don't need the crazy instruction processing and L0 caches. What if we specialize a chip, put every transistor on that chip so it would just mine Bitcoin? And when Bitmain and MicroBT did this in the 2012-2013 era, we saw an explosion in how fast people were able to solve these problems. The Bitcoin algorithm made them much harder to compensate, and GPU mining became unprofitable overnight. Because of this, GPUs have not really been seen in Bitcoin mining for almost ten years. That is not to go say that GPUs were a scrapped overnight, they found other use cases. They're flexible. They went to mine Ethereum or went to go do AI or went to play games.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Let me give it to you in the context of Bitcoin in the very old days, back when the Bitcoin algorithm was first devised. People ran it on CPUs. And to go mine at Bitcoin, like you're saying, you have to solve a very hard math problem. And the only way to solve this math problem is by guessing and checking answers very fast. A CPU can do many things. It can do this too, but is not very fast about it. CPU mining was originally via standard. People then said, hey, let's run this thing on GBUs. A GPU has several hundred mini CPUs scattered across the silicon, and because of this able to go do many things in parallel, each one of these cores in a GPU can do all kinds of different tasks. Once people wrote GPU miners, CPU mining became so bad by comparison that you'd lose money running it. But then people said, well, what if we took this a step further? On a GPU,”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Just recompute it. So if you hit enter and your chat's already six messages deep, you and ChatGPT talking to each other, it's going to have to feed that entire message history back into the model before it gives you your next tokens back out. So instead of being two hundred tokens, even if that was your last message, it has to go compute this model on a thousand two hundred tokens. So if we want to bring this initial delay down, we're going to need chips that have way more compute and are able to use that really efficiently. And I think the best approach here is model specific chips. Imagine a chip where you take the transformer model, this family, and burn it into the silica. Because there's no flexible ways to read memory, you can fit an order of magnitude more compute and use it more than ninety percent utilization. This lets you solve this problem and get responses back in millions.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Strongly agree that real time AI is the bottleneck here. And to go a step deeper, what do we mean when we say real-time AI? What do we mean when we say we need better latency? We measure it in two ways. First, the number of milliseconds between when you submit your response and when you get your first token back. So you hitting enter and you're seeing the first answer from ChatGPT. And then the time between subsequent tokens. Now why does the first token take so much longer? In theory it's only running the same model. Well, that's because it's running the model on the entire prompt. If you give it, say two hundred token prompt and then it has to run all those two hundred tokens before it can give you your answer back. It's actually a little bit worse than this because imagine that you have some conversation six messages long. It's very expensive to go store all that in RAM while it's waiting for you to write your response is usually cheaper.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Like today's, and you'll add those other sensors later. It's tough though. You also need some very good response times for a robotics model. For text, it's okay if it takes a second to go off your machine, be processed, and then come back. A second is too long if you're about to fall over, so latency is so critical for any kind of application here. It's not quite there yet. There's been some limited adoption. In the Tesla self-driving cars today, they use vision transformers. So much like a language model, but it takes images instead. The internals are almost the same. But they haven't hit the mainstream for robotics, largely because of this latency problem.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Having to break itself 100,000 times. But transformers, I think, are an interesting way to solve this problem. There is a great paper out at Google. Two years ago, I think, RP one that proposed a robotics multimodal transformer. One of the other beauties of a transformer is that it can take inputs in almost any modality, and the modality is a thing like text or images or video, or in this case a robotic sensor input. And you can train a model on many different inputs. In this case, they pre trained the model with a huge amount of text, or that it built some understanding of the world from this guess the next word game. And then on top of that, added in this robotics encoder stuff. So I think that robotics is a super interesting use case for these models. I think that when transformers are put onto robots, they won't be trained just with their robotics inputs. They'll be pre-trained.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“I think robotics is a very interesting use case that's currently kind of stuck in the before times. Robotics is a field that works very heavily on feedback loops, on hard coding algorithms, on having some while loop that says, okay, move the arm until it goes and welds and then do this again and old tech kind of stuff. People have tried to go and put some AI models into these robots, but it's very hard because of this lack of training data. For images, you can go feed a million things from ImageNet, a million frames. For robotics, you can't have it break there about a million times before it figures out how to walk. That's just not affordable. These don't work super well because of the difference between a simulation and the real world. You need the robot to understand the real world without actually”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“You talk to it, and there's this pause before you get your response back. And that really breaks the flow of the conversation. So I think we need tech that will enable stuff like tree search by bringing the cost of running these models down by a factor of a hundred. I think we need tech that will bring the latency down by an order of magnitude so that we can go have conversations with these models. I think we need tech to allow them to generate a thousand tokens per second and make that code writing take two seconds instead of 20. So I think there are so many use cases beyond this chatbot interface that are just waiting to be built, the text not quite there yet.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“We will see agent use cases becoming more mainstream. And I also believe that this chatbot interface that I love as well is definitely not the end use case for these models. Any scenario where you are reading chat the language model writes token by token by token is not a use case that is expensive. The model is probably going to be cheaper than human eyeballs. Human time is very expensive. And the first thing you'd think to try. But there are real technological limitations that stop other use cases from becoming the standard. Take, for example, speech. This podcast we're doing right now. I'd love to be able to talk to GBT4, much like I talk to a human. It makes such a difference at talking versus descending text so much easier to be misinterpreted then. And while people have tried to build transformer speech to speech models, it's challenging because of latency.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“But it's not going to do very well in real life. But for whatever reason, transformers seem to not fall under this trap quite so much. They are, to quote one of my teachers in college, the first capitalist AIs, adding more money makes them better. So based on that, I think we are going to see GPT five next gen models from Google and Anthropic be smarter than GPT-4 is today, and that's going to unlock some interesting use cases. Agents, I don't think people have talked about for a long time. Imagine a GPT four thinking to itself and taking actions autonomously. And to date, people haven't had very much success here, and Largeport because GPT four is not quite smart enough to get out of loops and to go do the kind of planning needed to operating on its own. But I think we're almost there. I think when we scale up with GPT five and models beyond that,”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“I think the right way to think about where this tech will go is to think about how this tech got here. And there were a number of innovations that have brought us to this point. But the biggest by far in my mind is scale. We have gone from a one and a half billion parameter GPT2 model to a 175 billion parameter GPT-3 model to a multi-trillion parameter GPT-4 model. And companies seem set to spend ten times more cash training their next generation of models as well. Making these models bigger makes them better. And now I want to call this out because this is not how AI used to work. Previously you took a stats class in like twenty twelve you'd hear about overfitting. If you put too many parameters in your model, it will fit the data too closely and it will get worse results when you actually test it and use it in real world use cases. Imagine that you're sort of teaching the test. You give it the answers to the test and that makes it do very well on the test.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Allegedly, it is the combination of Q learning and ASTART research. This Q learning being a way to do stuff on top of ROHF, and the A-Star being, hey, we're going to pick which of these tasks is the best. That said, I don't want to put too much credence into this. It really is just a rumor, and there are so many ways a thing like this could work.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“But first of all, it can be kind of helpful. And Beam Search also requires four times more compute, which is a very large overhead to pay. So this beam search tree search stuff has largely not caught on yet. Although with the rumors of QSTAR learning from OpenAI, if you believe them, that does suggest that they are doing similar tree search stuff in there. That's where the star comes from.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Way these work, the neural ones that are the most advanced to date is not by just looking at a board and guessing the next position. Instead, you're going to try a huge number of possible sets of moves. You'll play hundreds, thousands, maybe millions of games against yourself and your simulated opponent. And at the end of all of those, you'll say, okay, this position I find myself in, how much do I like that? And you can kind of backtrack saying, okay, based on where I want to be, this was the best tree to get there. And I think that language modeling might go a similar direction, where the model makes a couple hundred plans that, hey, here the next hundred things I could say, I like this path the best, so I'm going to go pick that. However, this hasn't really caught on quite yet. There's a concept called a beam search in the literature that does some of this. Beam search, I think, is much more primitive. It usually has at most four beams. They don't look very far ahead.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“I do want to clarify a misconception a little bit. Just because the model's output words word by word doesn't mean that they're not planning ahead. They have all these internal layers and there is very strong evidence that there is a little bit of planning going on, much like when you and I talk, I give one word at a time, but I'm able to make internal plans that you can't see about what I'm going to say after that. And as you would expect, if you have some internal plan about what words come next, it makes you better at guessing the next word so that this pre-training helps to develop this skill. Now that said, I do think there is a lot of work to be done to make these models not as autoregressive as they are right now. A good analogy. Imagine a chess engine, like a stockfish.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“We want to go teach kids how to be good investment bankers, how to be good computer programmers. But you can't really just throw a toddler into a computer programming course and expect him to get it right. That's too hard. You need to get them to have some baseline stuff. And this baseline stuff has to be easy to grade. It doesn't necessarily have to be super relevant. So you put the kid in grade school for 13 years, having him do tasks that are not that related, but easy to get feedback on, much like the transformer, goes to the pre training step, where it takes in a bunch of words, predicts the next one, is not super relevant, but it is easy to grade. And after grades school or after pre training, you have a model of understand some core concepts, and then and only then you ascended to college or ROHF, where you can put these other concepts on top of this and make it into a helpful honest assistant or a good.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Of data takes much less computational time than that first step After you stack these two things, you get era GPT model that takes in a word. I'll put the next word that it thinks this helpful honest assistant would say. And I think a good analogy here is children going to grade school.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Now, at the end of that, you're going to get a machine that is bad at guessing the next word. Maybe it gets it right 18% of the time. But to even get it right, 18% of the time, you have to have a huge number of concepts stored within that machine. So they take these transformer models. They feed them a huge amount of text playing this guess the next word game. And at the end, you get a model that is able to predict the next word in science papers by understanding some of science, the next word of internet stuff, having read a lot of web pages, and so on and so forth. And the second step on top of that is what they call RLHF, where you take the model that has been pre trained with predicting the next word and has all these concepts stored somewhere inside of it, and then you ask it to, hey, mimic this text that is helpful and honest, you give it some examples of debugging code and being helpful. In this second piece requires a lot more expensive.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“At a high level, Transformer is a sequence to sequence model. You put in a sequence, you get out a sequence, and the way that you try to determines what conversion is done. But that's not a very satisfying answer. So let's go one level deeper. One of the other real breakthroughs, or the transformer, was realizing that Takes a lot of data to teach a machine how to be helpful, how to be honest, how to give these useful responses. But we don't have to train it on just helpful, honest, and useful responses. Trick they found is imagine we take a machine and we give it the first hundred words of a document and we say given these first hundred words, guess what the next word is? And then given those hundred and one words, guess what the next word is we play this game again and again and again trillions of times.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Even go a step back. Machines have been fatter than humans for a long time. I cannot outrun a car, but yet I still go running. People still run marathons. There is so much value, so much fulfillment in doing a against other humans, even if the machines are in some sense better.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“I think chess is a great analogy here. The machines have been superhuman at chess for 25 years. And back when a deep blue first beat Gary Kasparov, people said chess is going to die, whether through lack of interest or through rantom cheating, and the opposite has happened. Chess has become more popular today than it was back then. Go as well. Machines became super intelligent at Go in twenty sixteen, and yet there was no death of all the human Go players. People just said these things are better at it than us, but there's still a lot of value to be extracted by humans playing other humans.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“It is very hard on the excitement side. I cannot wait to see what kind of books, what kind of movies, what kinds of video games, a machine that's ten times smarter than me can build. You look at the film somebody like Spielberg, a great director, versus some Joe Below like myself, you say wow, that is incredible. Imagine what a director ten times better than Spielberg could put together. Wouldn't you love to see that? So I think there's so much to be excited about. AI is making breakthroughs in science and technology, providing some sort of universal basic income. And yes, that's probably more impactful, but like viscerally, I am most excited to see what kinds of art and games and movies and TV shows these things put together.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“Already is a better lawyer than I am. So I think superintelligence is much more of a spectrum than it is a binary thing. And eventually we'll look and we'll say, well, GPT-12 is obviously superintelligent. There's things way smarter than any human. It'll be clear in retrospect. But when you're in the middle of that curve, it looks flat.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't think super intelligence is some black and white thing. There are some people who say that one day the machine will be as smart as us and the next they'll be a hundred times smarter. I don't think that's true. And the reason I don't is because to go from GPT two to GPT three to GPT four requires spending exponentially more compute. So I don't think you get to this crazy godlike superintelligence just overnight. I think you have to go build an enormous data center to support a thing like that way, way bigger than anything on Earth today. I don't believe in this concept of fast takeoff. I think that we're going to spend two years building GPT-5 and then we're going to build new data centers for GPT-6 and that'll take three, five years. And then we'll keep iterating on that, slowly building better and better and better ones. And eventually, they'll begin to seem human level. They'll begin to show human level agencies. And then they'll become better than humans in some avenues.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“The field is so young that we haven't had 100 years to develop it. We haven't had 100 years to solve these low-hanging problems. A single person can make a major, major impact. And this got even better with the transformer. All this previous technology of convolutional networks and a support vector machines was thrown away in 2020 when GPT-3 came out, showing that transformers really are the best way to do things. I am really emboldened by this transformer of evolution. I think it is the most interesting problem of our time. And I think that it is very accessible because there's no hundred years of history built up around it.”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source
“I strongly agree with this. One can help but think that 100 years ago, 200 years ago, there were so many mysteries left to be solved, that low hanging fruit, so to speak, somebody smart go into their whatever they had instead of a garage, think for a couple of years and come up with optics or physics, you could go and explore continents. Then, as technology developed, that became no longer feasible. That stuff had already been done. And yes, you could still do interesting and novel work, but it often required going to school for ten years, for you could learn all the mathematics you needed to go do new mathematics. Say chemistry. You had to go study all that had come before, and some day mankind will go to the stars and will be able to explore other planets, and that fruit will be low hanging too. But that stuff is very expensive today. But I think AI is very unique. Because unlike mathematics and unlike chemistry,”
2023-12-12 · Invest Like the Best · Gavin Uberti - Real-Time AI & The Future of AI Hardware - [Invest Like the Best, EP.356] · IDENTIFIED FROM THE TRANSCRIPT · source