Gavin Uberti
speaker
403 appearances
1 recordings
1 series
first heard Jun 2026
last heard 30 Jun
Gavin Uberti’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 Jun 2026 with 1.
Appearances
And in much the same way, for people, storing data and loading memory is very cheap for neurons, and doing math is relatively expensive.
And it is the exact opposite for chips.
Generally, loading data is very expensive, and doing math is very cheap.
And as time goes on, you'll end up finding that math gets cheaper at a rate that is faster than memory gets cheaper due to this fundamental limit on any kind of DRAM device.
You should think about how can I make my model use a huge, huge amount of compute?
What if I had, for example, many copies running at the same time?
What if I activated a huge number of experts?
What if I had gigantic experts that I could go ahead and run on multiple server racks at the same time?
That is how I think you'll build models that are the next generation of intelligence.
And context, too.
There's been a lot of work on, hey, very efficient inference.
What if I don't load the full context in the memory?
And most of the time, I think that makes a lot of sense.
You want to go build a super intelligence.
Why can't you go look at a billion tokens of context?
Why can't you spend a huge amount of compute to go ahead and read all of this super fast?
I would love to be able to talk to a machine that was able to go attend to every book ever written in short-term memory.
And I think you're going to get to a point where you can't.
There was a viral tweet by Noah Brown, where he said that as these models are having longer and longer time horizons, they can be tasks that take, say, six months.
And there's often not enough time to go and evaluate them for such a long period of time, because by that point, you'll have a new model out.
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