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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Appearances
Now, oftentimes these thought experiments don't work out because there are very good reasons that, you know, for the last 50 years, we've done this thing this way and not that way, but it's good to kind of revisit those every so often.
I mean, exactly.
Like signals in our brain are not especially reliable from getting one place to another.
And so I think in brains, when there are really important things you need to get from one place to another, there are multiple pathways that enable you to sort of do that.
Yeah, I mean, I guess... That worked out, actually.
Yeah, I mean, I think, well, TPUs is a good example.
Like being able to specialize hardware for a very niche problem domain before that problem domain seemed as important as it is today is one thought experiment.
You know, I think the
The origin of MapReduce is another good example.
We had worked, Sanjay and myself and a number of other colleagues had worked on various iterations of the crawling and indexing system at Google.
And we'd written lots of hand-parallelized code with lots of checkpointing to make sure it would be robust and reliable if it was running on 100 computers or 1,000 computers and some of those died.
But that code tended to be intermixed with the actually relatively simple thing you often were trying to do.
Like, I just want to look at all the contents of all the web pages and then compute on the side mapping from URL to what language is this page in.
It's the text of this page.
And it would get obscured by all this kind of other code for parallelization and reliability.
And so we sort of remembered our training in functional languages and realized we could squint at those problems and develop this map, produce abstraction,
that you could have above this implementation.
And then below the implementation, you could put all the checkpointing and reliability mechanisms into that lower level library that everything could then build on.
And so that became a hugely successful way of dealing with very large scale computations at Google in a robust and reliable way from that bot experiment of like, well, if we squint at it, could we find lots of problems that fit into this abstraction?
Yeah, I mean, I think more generally, there's this sort of,
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