David Schmaier

I'd be happy to, Lynn. AI has been around since the dawn of computers. So it was first talked about in the 40s and the 50s. And Alan Turing came up with something called the Turing test, where if someone could carry on a conversation

I'd be happy to, Lynn. AI has been around since the dawn of computers. So it was first talked about in the 40s and the 50s. And Alan Turing came up with something called the Turing test, where if someone could carry on a conversation

back then by teletype and you couldn't tell whether it was computer or not that was deemed artificial intelligence as we call it today and the idea was the neurons that we have in our brain could also work there could be computer oriented neurons that could sort of think just like we do as human beings.

back then by teletype and you couldn't tell whether it was computer or not that was deemed artificial intelligence as we call it today and the idea was the neurons that we have in our brain could also work there could be computer oriented neurons that could sort of think just like we do as human beings.

And so based on that idea, there was predictive AI, the first type of AI that really became popular. And the second is the big boom that we're in now that really changes everything, in my opinion, which is called generative AI. And the third is agents. And then robots are going to be the next big wave, and that's coming now, too.

And so based on that idea, there was predictive AI, the first type of AI that really became popular. And the second is the big boom that we're in now that really changes everything, in my opinion, which is called generative AI. And the third is agents. And then robots are going to be the next big wave, and that's coming now, too.

And this is all on the road to what many of the AI startups call AGI, which is artificial general intelligence, where the AI can do everything that a human can do, but it can do it faster.

And this is all on the road to what many of the AI startups call AGI, which is artificial general intelligence, where the AI can do everything that a human can do, but it can do it faster.

Predictive AI is a mathematical model that looks at the past data and uses that data to literally predict what the future will be. So I buy something on an amazon.com website and it predicts and recommends what are the next things I should buy, product A, B, and C. So that's all based on the mathematical model of predictions from the past to the future.

Predictive AI is a mathematical model that looks at the past data and uses that data to literally predict what the future will be. So I buy something on an amazon.com website and it predicts and recommends what are the next things I should buy, product A, B, and C. So that's all based on the mathematical model of predictions from the past to the future.

ChatGPT is based on this new transformer architecture. That's the T in GPT. And that's based on a paper that was authored by a division of Google called the Google Brain Division, where a number of AI scientists figured out that you could take AI and train it on a set of words. That had never been done before or had been contemplated.

ChatGPT is based on this new transformer architecture. That's the T in GPT. And that's based on a paper that was authored by a division of Google called the Google Brain Division, where a number of AI scientists figured out that you could take AI and train it on a set of words. That had never been done before or had been contemplated.

And so what they did is they originally trained the original version of ChatGPT, and I think the numbers are 500 million words. And then they went to billions of words. And then they went from there with like each model, each one of these is trained on more and more words and more and more content. So it's literally in the billions now.

And so what they did is they originally trained the original version of ChatGPT, and I think the numbers are 500 million words. And then they went to billions of words. And then they went from there with like each model, each one of these is trained on more and more words and more and more content. So it's literally in the billions now.

And in order to crunch all of those words to train it, it literally costs billions of dollars now. and takes billions of dollars of NVIDIA GPUs, basically NVIDIA chips, on a supercomputer that can now predict the next word or the next sentence or the next paragraph. So yes, it uses AI, but it's trained on an entirely different data source, not data, but content.

And in order to crunch all of those words to train it, it literally costs billions of dollars now. and takes billions of dollars of NVIDIA GPUs, basically NVIDIA chips, on a supercomputer that can now predict the next word or the next sentence or the next paragraph. So yes, it uses AI, but it's trained on an entirely different data source, not data, but content.

That algorithm to predict words can also be used on other kinds of content, like images, like videos, like movies. And so now there's this concept of multimodal AI models where we started out using it to train it on words. Now you can train the AI on images, on videos, on sounds. All of the five senses, if you will, can interact with the AI.

That algorithm to predict words can also be used on other kinds of content, like images, like videos, like movies. And so now there's this concept of multimodal AI models where we started out using it to train it on words. Now you can train the AI on images, on videos, on sounds. All of the five senses, if you will, can interact with the AI.

And you have AI that can cross these different modalities so that maybe I talk to the AI and it gives me the answer in text and then it generates a movie out of it. Or I can ask it, tell me what is a horse?

And you have AI that can cross these different modalities so that maybe I talk to the AI and it gives me the answer in text and then it generates a movie out of it. Or I can ask it, tell me what is a horse?