Sebastian Raschka

speaker

1,024 appearances 1 recordings 1 series first heard Feb 2026 last heard 1 Feb

Sebastian Raschka’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 1 in all, peaking in Feb 2026 with 1.

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Lex Fridman Podcast · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI · 1 Feb 2026 podcast

If someone had to go home to spend time with their family to save the marriage, it's crazy.

Then colleagues say, okay, this is like red alert for this situation.

We have to let that person go home this weekend.

But at the same time, I don't think they were forced to work.

It's really they were so passionate about the product, I guess, that you get into that mindset.

And I had that sometimes as an academic, but also as an independent person, I have that sometimes.

I overwork sometimes.

And it's unhealthy.

I had, you know, I had back issues.

I had neck issues because I did not take the breaks that I maybe should have taken.

But it's not because no one forced me to.

It's because I wanted to work because it's exciting stuff.

yeah so we talked a lot about the transformer architecture and the autoregressive transformer architecture specifically like gpt and it doesn't mean no one else is working on anything else so people are always on the let's say lookout for the next big thing because i think it would be almost like um yeah stupid not to because sure right now the transformer architecture is the thing and it works best and there's right now nothing else out there but you know

It's always a good idea to not put all your eggs into one basket.

So people are developing other things, alternatives to the autoregressive transformer.

One of them would be, for example, text diffusion models.

And listeners may know diffusion models from the image generation, like Stable Diffusion popularized it.

There was a paper on generating images.

Back then, people used GANs, the Generative Adversarial Networks.

And then there was this diffusion process where you iteratively denoise an image and that resulted in really good quality images over time.

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