Arvind Narayanan

What we've learned in the last two years is that the quality of data matters a lot more than the quantity of data. So if you're using synthetic data to try to augment the quantity, I think it's just coming at the expense of quality. You're not learning new things from the data. You're only learning things that are already there.

What we've learned in the last two years is that the quality of data matters a lot more than the quantity of data. So if you're using synthetic data to try to augment the quantity, I think it's just coming at the expense of quality. You're not learning new things from the data. You're only learning things that are already there.

Yeah, I think that's really spot on. I think one way in which people's intuitions have been kind of misguided by the rapid improvements in LLMs is that all of this has been in the paradigm of learning from data on the web that's already there. And once that runs out, you have to switch to new kinds of learning, analog of riding a bike. That's just kind of tacit knowledge.

Yeah, I think that's really spot on. I think one way in which people's intuitions have been kind of misguided by the rapid improvements in LLMs is that all of this has been in the paradigm of learning from data on the web that's already there. And once that runs out, you have to switch to new kinds of learning, analog of riding a bike. That's just kind of tacit knowledge.

Yeah, I think that's really spot on. I think one way in which people's intuitions have been kind of misguided by the rapid improvements in LLMs is that all of this has been in the paradigm of learning from data on the web that's already there. And once that runs out, you have to switch to new kinds of learning, analog of riding a bike. That's just kind of tacit knowledge.

It's not something that's been written down. So a lot of what happens in organizations is the cognitive equivalent of I think what happens in the physical skill of riding a bike.

It's not something that's been written down. So a lot of what happens in organizations is the cognitive equivalent of I think what happens in the physical skill of riding a bike.

It's not something that's been written down. So a lot of what happens in organizations is the cognitive equivalent of I think what happens in the physical skill of riding a bike.

And I think for models to learn a lot of these diverse kinds of tasks that they're not going to pick up from the web, you have to have the cycle of actually using the AI system in your organization and for it to learn from that back and forth experience instead of just passively ingesting.

And I think for models to learn a lot of these diverse kinds of tasks that they're not going to pick up from the web, you have to have the cycle of actually using the AI system in your organization and for it to learn from that back and forth experience instead of just passively ingesting.

And I think for models to learn a lot of these diverse kinds of tasks that they're not going to pick up from the web, you have to have the cycle of actually using the AI system in your organization and for it to learn from that back and forth experience instead of just passively ingesting.

It's got to be more than passive observation. You have to actually deploy AI to be able to get to certain types of learning. And I think that's going to be very slow. And I think a good analogy is self-driving cars, of which we had prototypes two or three decades ago.

It's got to be more than passive observation. You have to actually deploy AI to be able to get to certain types of learning. And I think that's going to be very slow. And I think a good analogy is self-driving cars, of which we had prototypes two or three decades ago.

It's got to be more than passive observation. You have to actually deploy AI to be able to get to certain types of learning. And I think that's going to be very slow. And I think a good analogy is self-driving cars, of which we had prototypes two or three decades ago.

But for these things to actually be deployed, you have to roll it out on slightly larger and larger scales while you collect data, while you make sure you get to the next nine of reliability, four nines of reliability to five nines of reliability. So it's that very slow rollout process. It's a very slow feedback loop.

But for these things to actually be deployed, you have to roll it out on slightly larger and larger scales while you collect data, while you make sure you get to the next nine of reliability, four nines of reliability to five nines of reliability. So it's that very slow rollout process. It's a very slow feedback loop.

But for these things to actually be deployed, you have to roll it out on slightly larger and larger scales while you collect data, while you make sure you get to the next nine of reliability, four nines of reliability to five nines of reliability. So it's that very slow rollout process. It's a very slow feedback loop.

And I think that's going to happen with a lot of AI deployment and organizations as well.

And I think that's going to happen with a lot of AI deployment and organizations as well.

And I think that's going to happen with a lot of AI deployment and organizations as well.