Zuzanna Stamirowska

Pretty, pretty cool.

Right now, all the models that we see that are out there are built on one type of architecture, one type of technology.

And that was an absolute kind of algorithmic breakthrough.

And this is a transformer.

So the transformer was like fundamentally built for language.

Funnily enough, one of the cultures of transformer actually was like the first check in Pathway.

But this technology is fantastic.

by definition deprived of the notion of time and memory so pathway right now is building the first post-transformer frontier model which is tackling this fundamental problem of lack of memory in ai memory is linked to time of course because you remember you need to remember things over time you need to remember how you were thinking how you were solving something for example

You need to remember to see consequences, right?

You need to remember to stay coherent while problem solving.

The more you know, the longer you can stay focused on a task.

I mean, this means memory.

That kind of requires time, right?

And we kind of know right now there is this lab called METER that actually kind of measures the benchmarks, the equivalence of, okay, like how the level of human tasks, let's say that the LLMs can do with, let's say, 50% success rate.

And right now, the length of those tasks is at like two hours, 17 minutes for GPT-5.

So, I mean, we could say that current LLMs are kind of reliving their Groundhog Day every day.

So they don't have memory as such.

The way it works is that they're trained ones with a lot of, a lot of, a lot of, a lot of data.

To the point that by now we know we've exhausted all the data readily available on the internet for training.

This is where they get their power from because these are like fundamental language models.