Dario Amodei

And so that smoothness gets reflected in how well the models are at predicting and how well they perform. Language is an evolved process, right? We've developed language. We have common words and less common words. We have common expressions and less common expressions. We have ideas, cliches that are expressed frequently, and we have novel ideas.

And so that smoothness gets reflected in how well the models are at predicting and how well they perform. Language is an evolved process, right? We've developed language. We have common words and less common words. We have common expressions and less common expressions. We have ideas, cliches that are expressed frequently, and we have novel ideas.

And so that smoothness gets reflected in how well the models are at predicting and how well they perform. Language is an evolved process, right? We've developed language. We have common words and less common words. We have common expressions and less common expressions. We have ideas, cliches that are expressed frequently, and we have novel ideas.

And that process has developed, has evolved with humans over millions of years. And so the guess, and this is pure speculation, would be that there's some kind of long tail distribution of the distribution of these ideas.

And that process has developed, has evolved with humans over millions of years. And so the guess, and this is pure speculation, would be that there's some kind of long tail distribution of the distribution of these ideas.

And that process has developed, has evolved with humans over millions of years. And so the guess, and this is pure speculation, would be that there's some kind of long tail distribution of the distribution of these ideas.

If you have a small network, you only get the common stuff, right? If I take a tiny neural network, it's very good at understanding that, you know, a sentence has to have, you know, verb, adjective, noun, right? But it's terrible at deciding what those verb, adjective, and noun should be and whether they should make sense. If I make it just a little bigger, it gets good at that.

If you have a small network, you only get the common stuff, right? If I take a tiny neural network, it's very good at understanding that, you know, a sentence has to have, you know, verb, adjective, noun, right? But it's terrible at deciding what those verb, adjective, and noun should be and whether they should make sense. If I make it just a little bigger, it gets good at that.

If you have a small network, you only get the common stuff, right? If I take a tiny neural network, it's very good at understanding that, you know, a sentence has to have, you know, verb, adjective, noun, right? But it's terrible at deciding what those verb, adjective, and noun should be and whether they should make sense. If I make it just a little bigger, it gets good at that.

Then suddenly it's good at the sentences, but it's not good at the paragraphs. And so these rarer and more complex patterns get picked up as I add more capacity to the network.

Then suddenly it's good at the sentences, but it's not good at the paragraphs. And so these rarer and more complex patterns get picked up as I add more capacity to the network.

Then suddenly it's good at the sentences, but it's not good at the paragraphs. And so these rarer and more complex patterns get picked up as I add more capacity to the network.

I don't think any of us knows the answer to that question. My strong instinct would be that there's no ceiling below the level of humans, right? We humans are able to understand these various patterns. And so that makes me think that if we continue to scale up these models to kind of develop new methods for training them and scaling them up,

I don't think any of us knows the answer to that question. My strong instinct would be that there's no ceiling below the level of humans, right? We humans are able to understand these various patterns. And so that makes me think that if we continue to scale up these models to kind of develop new methods for training them and scaling them up,

I don't think any of us knows the answer to that question. My strong instinct would be that there's no ceiling below the level of humans, right? We humans are able to understand these various patterns. And so that makes me think that if we continue to scale up these models to kind of develop new methods for training them and scaling them up,

that will at least get to the level that we've gotten to with humans. There's then a question of, you know, how much more is it possible to understand than humans do? How much is it possible to be smarter and more perceptive than humans? I would guess the answer has got to be domain dependent.

that will at least get to the level that we've gotten to with humans. There's then a question of, you know, how much more is it possible to understand than humans do? How much is it possible to be smarter and more perceptive than humans? I would guess the answer has got to be domain dependent.

that will at least get to the level that we've gotten to with humans. There's then a question of, you know, how much more is it possible to understand than humans do? How much is it possible to be smarter and more perceptive than humans? I would guess the answer has got to be domain dependent.

If I look at an area like biology, and I wrote this essay, Machines of Loving Grace, it seems to me that humans are struggling to understand the complexity of biology, right? If you go to Stanford or to Harvard or to Berkeley, you have whole departments Of, you know, folks trying to study, you know, like the immune system or metabolic pathways and and each person understands only a tiny bit.

If I look at an area like biology, and I wrote this essay, Machines of Loving Grace, it seems to me that humans are struggling to understand the complexity of biology, right? If you go to Stanford or to Harvard or to Berkeley, you have whole departments Of, you know, folks trying to study, you know, like the immune system or metabolic pathways and and each person understands only a tiny bit.