Jeff Dean
speaker
343 appearances
1 recordings
1 series
first heard Aug 2026
last heard 1 Aug
Jeff Dean’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Aug 2026 with 1.
Appearances
Right, so if you do that, you're already way ahead than if you wake up and you're like, oh, I don't really wanna do this or whatever, or you're gonna build something that is actually not that useful to the world or to too many people.
So I think that's the number one selection criteria I try to apply for what problems should I work on next.
Second, I think you want to look at what the current more general models can do in that problem domain.
You can test them with like, are they able to do this thing very well?
And if they're completely failing, that's probably a good sign.
If they're kind of able to do some of it, but not very well,
That's maybe not a great sign because that's probably a sign that the capability is starting to be present in those models.
And with more training data or larger scale models or whatever, it's likely to get better.
So look for something where the model succeeds 0% or 1% of the time, not 20%.
Yeah, I mean, I think sometimes it's,
a product that you build that might have access to particular kind of data that the underlying model might not, the general model.
So it might be you're building something to help users organize all their own personal information, and the model won't necessarily have access to that.
And so there you can have a big advantage because all of a sudden your model has visibility, or your product has visibility into important data.
It could be some incredibly hard problem where if you get the right training data and you can train a more specific model than a general purpose one, you can actually do that in a very affordable way.
Maybe it doesn't take that much compute to train a niche model for this particular problem, but you can get something that's highly accurate.
That can sometimes be a really good building block for solving a important problem that is maybe not handled very well by the general model.
Yeah, I mean, I think if you look at my colleague's work on, say, AlphaFold, that was a very specific model for protein folding.
And it was highly successful.
and was able to really handle that domain quite well so that all of a sudden you now have this amazing tool and model that can give you answers to questions about proteins and their structure really effectively.
But it's not a general model.
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