Mike Cafarella

There was a time when Cosmo.com was the canonical example of a... You'll loss on every single transaction. And at some point it was replaced by... by MoviePass, maybe the $200 OpenAI product is now gonna push MoviePass into the dustbin of history and take its rightful place as the product that loses money on every transaction.

There was a time when Cosmo.com was the canonical example of a... You'll loss on every single transaction. And at some point it was replaced by... by MoviePass, maybe the $200 OpenAI product is now gonna push MoviePass into the dustbin of history and take its rightful place as the product that loses money on every transaction.

You could basically DoorDash a Snickers bar for zero delivery cost.

You could basically DoorDash a Snickers bar for zero delivery cost.

All right. My six-year, which I think is optimistic, a lot like Simon's, is the first gene therapy that uses a DNA sequence suggested by an LLM is actually deployed at least in a research hospital, maybe not wild. That is, yeah. Like a CGTA sequence that came from the model goes into a human body.

All right. My six-year, which I think is optimistic, a lot like Simon's, is the first gene therapy that uses a DNA sequence suggested by an LLM is actually deployed at least in a research hospital, maybe not wild. That is, yeah. Like a CGTA sequence that came from the model goes into a human body.

you know, I would say that my rough reading of the models that have been designed for genetic sequence prediction is that like, they're able to achieve kind of remarkable things. I, I, I'm in particular thinking of this Evo model that was released kind of early in 24. I don't know if Simon or others are familiar with this thing. Um,

you know, I would say that my rough reading of the models that have been designed for genetic sequence prediction is that like, they're able to achieve kind of remarkable things. I, I, I'm in particular thinking of this Evo model that was released kind of early in 24. I don't know if Simon or others are familiar with this thing. Um,

To me, they do this experiment in that model, which is really jaw-dropping. Okay, so the core technical idea here is that the model architecture's a little bit different, because when you're predicting genetic sequences, the alphabet is small, but the sequences are much longer than in natural language, right? So the model architecture's a little bit different.

To me, they do this experiment in that model, which is really jaw-dropping. Okay, so the core technical idea here is that the model architecture's a little bit different, because when you're predicting genetic sequences, the alphabet is small, but the sequences are much longer than in natural language, right? So the model architecture's a little bit different.

But the experiment that they performed that was really stunning to me was the following. So imagine you have a genetic sequence, and this was just in single-celled organisms. They're not doing this on mammals or anything. Imagine you have a genetic sequence and you intentionally mutate it. So you've got a bunch of different versions of that sequence.

But the experiment that they performed that was really stunning to me was the following. So imagine you have a genetic sequence, and this was just in single-celled organisms. They're not doing this on mammals or anything. Imagine you have a genetic sequence and you intentionally mutate it. So you've got a bunch of different versions of that sequence.

And then you try to evaluate its fitness in two different ways. One is that you try to grow it in the lab and see how much it grows. The other is that you look at the probability of that sequence as evaluated by one of these trained models. And now let's imagine you take all of those sequences and you sort them according to those two scores.

And then you try to evaluate its fitness in two different ways. One is that you try to grow it in the lab and see how much it grows. The other is that you look at the probability of that sequence as evaluated by one of these trained models. And now let's imagine you take all of those sequences and you sort them according to those two scores.

You sort them according to the observed fitness in the lab, like when you try to grow it in a petri dish, and you also sort it according to the inverse of the probability, meaning like High probable strings go on the top, low probability strings go on the bottom. And what's stunning is that those two sort orders are remarkably highly correlated.

You sort them according to the observed fitness in the lab, like when you try to grow it in a petri dish, and you also sort it according to the inverse of the probability, meaning like High probable strings go on the top, low probability strings go on the bottom. And what's stunning is that those two sort orders are remarkably highly correlated.

So like the ability to just stare at a genetic sequence and actually say something with maybe some predictive accuracy about its real world fitness to me is just absolutely stunning.

So like the ability to just stare at a genetic sequence and actually say something with maybe some predictive accuracy about its real world fitness to me is just absolutely stunning.

Let's see. Could I come up with one? That's right. I mean, it feels like an easy parlay from where you got there. I don't know if the following is optimistic or pessimistic. The PlayStation 6 is the last PlayStation. There's never a PlayStation. Ooh.

Let's see. Could I come up with one? That's right. I mean, it feels like an easy parlay from where you got there. I don't know if the following is optimistic or pessimistic. The PlayStation 6 is the last PlayStation. There's never a PlayStation. Ooh.