Dr. Terry Sejnowski

But some of them, I have to say that, for example, there are some that are really much better at math than others.

But some of them, I have to say that, for example, there are some that are really much better at math than others.

But some of them, I have to say that, for example, there are some that are really much better at math than others.

Google's Gemini recently did some fine-tuning with what's called chain of reasoning. When you reason, you go through a sequence of steps. And when you solve a math problem, you go through a sequence of steps of first finding out what's missing and then adding that. And it went from 20% correct to 80%. right, on those problems.

Google's Gemini recently did some fine-tuning with what's called chain of reasoning. When you reason, you go through a sequence of steps. And when you solve a math problem, you go through a sequence of steps of first finding out what's missing and then adding that. And it went from 20% correct to 80%. right, on those problems.

Google's Gemini recently did some fine-tuning with what's called chain of reasoning. When you reason, you go through a sequence of steps. And when you solve a math problem, you go through a sequence of steps of first finding out what's missing and then adding that. And it went from 20% correct to 80%. right, on those problems.

So I think we are... being perhaps a little bit unfair to compare these large language models to the best humans rather than the average human, right? As you said, most people couldn't pass the LSAT, the test to get into law school, or MCAT, the test to get into medical school, and JetGPT has.

So I think we are... being perhaps a little bit unfair to compare these large language models to the best humans rather than the average human, right? As you said, most people couldn't pass the LSAT, the test to get into law school, or MCAT, the test to get into medical school, and JetGPT has.

So I think we are... being perhaps a little bit unfair to compare these large language models to the best humans rather than the average human, right? As you said, most people couldn't pass the LSAT, the test to get into law school, or MCAT, the test to get into medical school, and JetGPT has.

Okay. So I've lived through this myself. Back in the 1980s, I was just starting my career, and I was one of the pioneers in developing learning algorithms for neural network models. Jeff Hinton and I collaborated together on something called the Bosa Machine, and he actually won a Nobel Prize for this recently.

Okay. So I've lived through this myself. Back in the 1980s, I was just starting my career, and I was one of the pioneers in developing learning algorithms for neural network models. Jeff Hinton and I collaborated together on something called the Bosa Machine, and he actually won a Nobel Prize for this recently.

Okay. So I've lived through this myself. Back in the 1980s, I was just starting my career, and I was one of the pioneers in developing learning algorithms for neural network models. Jeff Hinton and I collaborated together on something called the Bosa Machine, and he actually won a Nobel Prize for this recently.

Yeah, he's one of my best friends. Brilliant. And he well deserved it for not just the Bosa Machine, but all the work he's done since then on machine learning and then back propagation and so forth. But back then, We, Jeff and I, had this view of the future. AI was dominated by symbol processing, rules, logic, right? Writing computer programs.

Yeah, he's one of my best friends. Brilliant. And he well deserved it for not just the Bosa Machine, but all the work he's done since then on machine learning and then back propagation and so forth. But back then, We, Jeff and I, had this view of the future. AI was dominated by symbol processing, rules, logic, right? Writing computer programs.

Yeah, he's one of my best friends. Brilliant. And he well deserved it for not just the Bosa Machine, but all the work he's done since then on machine learning and then back propagation and so forth. But back then, We, Jeff and I, had this view of the future. AI was dominated by symbol processing, rules, logic, right? Writing computer programs.

For every problem, you need a different computer program. And it was very human resource intensive to write programs so that it was very, very slow going. And they never actually got there. They never wrote a program for vision, for example, even though the computer vision community really worked hard for a long time. But we had this view of the future.

For every problem, you need a different computer program. And it was very human resource intensive to write programs so that it was very, very slow going. And they never actually got there. They never wrote a program for vision, for example, even though the computer vision community really worked hard for a long time. But we had this view of the future.

For every problem, you need a different computer program. And it was very human resource intensive to write programs so that it was very, very slow going. And they never actually got there. They never wrote a program for vision, for example, even though the computer vision community really worked hard for a long time. But we had this view of the future.

We had this view that nature has solved these problems. There's existence proof that you can solve the vision problem. Look, every animal can see, even insects, right? Come on. Let's figure out how they did it. Maybe we can help by following up on nature. Again, going back to algorithms, I was telling you.

We had this view that nature has solved these problems. There's existence proof that you can solve the vision problem. Look, every animal can see, even insects, right? Come on. Let's figure out how they did it. Maybe we can help by following up on nature. Again, going back to algorithms, I was telling you.