Arvind Narayanan

Because if you have a sentence like the capital of France is blank, It helps to know what the capital of France is so that you can complete that sentence with a high probability word instead of a low probability word. So it turns out that's really the secret behind it. And it might seem a little bit disappointing to hear that that's all it is. And in a sense, that's all it is.

But I think it is truly remarkable that developers are able to create something useful with this really brute force approach.

But I think it is truly remarkable that developers are able to create something useful with this really brute force approach.

But I think it is truly remarkable that developers are able to create something useful with this really brute force approach.

Chatbots have been trained on essentially all of the text on the internet, approximately speaking, and a lot of books and so on. So that's what it's pulling from, right?

Chatbots have been trained on essentially all of the text on the internet, approximately speaking, and a lot of books and so on. So that's what it's pulling from, right?

Chatbots have been trained on essentially all of the text on the internet, approximately speaking, and a lot of books and so on. So that's what it's pulling from, right?

So what training means is that it has learned the statistical patterns that allow it to say, for example, something you should, the bot would know that no is a likely next word because there are many discussions of this podcast online. And so it has learned that statistical pattern. So that's primarily what it's learning from.

So what training means is that it has learned the statistical patterns that allow it to say, for example, something you should, the bot would know that no is a likely next word because there are many discussions of this podcast online. And so it has learned that statistical pattern. So that's primarily what it's learning from.

So what training means is that it has learned the statistical patterns that allow it to say, for example, something you should, the bot would know that no is a likely next word because there are many discussions of this podcast online. And so it has learned that statistical pattern. So that's primarily what it's learning from.

To a lesser extent, these bots learn from their conversations with us, but it's not in the way that a person would learn. It's not automatic. There's a cumbersome process by which companies have to filter these chat conversations and feed that back into the training data. But to a first approximation, it's learning from text on the web.

To a lesser extent, these bots learn from their conversations with us, but it's not in the way that a person would learn. It's not automatic. There's a cumbersome process by which companies have to filter these chat conversations and feed that back into the training data. But to a first approximation, it's learning from text on the web.

To a lesser extent, these bots learn from their conversations with us, but it's not in the way that a person would learn. It's not automatic. There's a cumbersome process by which companies have to filter these chat conversations and feed that back into the training data. But to a first approximation, it's learning from text on the web.

Sure. Yeah. So predictive AI, on the other hand, is statistics that we've had for a century almost that's been rebranded into quote unquote AI. So this is used, for example, in the criminal justice system to determine if a defendant should be jailed before their trial, you know, which could be months or years away.

Sure. Yeah. So predictive AI, on the other hand, is statistics that we've had for a century almost that's been rebranded into quote unquote AI. So this is used, for example, in the criminal justice system to determine if a defendant should be jailed before their trial, you know, which could be months or years away.

Sure. Yeah. So predictive AI, on the other hand, is statistics that we've had for a century almost that's been rebranded into quote unquote AI. So this is used, for example, in the criminal justice system to determine if a defendant should be jailed before their trial, you know, which could be months or years away.

It's used in healthcare to detect sepsis in a hospital context, for instance, by looking at various indicators. It's often used in hiring to try to predict which employee might be a fit for the role or who's going to be a good employee and that sort of thing. Now, what's happening in all of these cases is that the system is just picking up crude statistical patterns.

It's used in healthcare to detect sepsis in a hospital context, for instance, by looking at various indicators. It's often used in hiring to try to predict which employee might be a fit for the role or who's going to be a good employee and that sort of thing. Now, what's happening in all of these cases is that the system is just picking up crude statistical patterns.

It's used in healthcare to detect sepsis in a hospital context, for instance, by looking at various indicators. It's often used in hiring to try to predict which employee might be a fit for the role or who's going to be a good employee and that sort of thing. Now, what's happening in all of these cases is that the system is just picking up crude statistical patterns.

So in criminal justice, the system learns that younger defendants are more likely to re-offend if they're released before their trial, and so recommends treating them more harshly. So these kinds of, again, fairly crude statistical patterns that we've known how to do for a long time, but it's not the same kind of technology behind ChatGPT