Dianne Penn
speaker
606 appearances
1 recordings
1 series
first heard Jul 2026
last heard 26 Jul
Dianne Penn’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 Jul 2026 with 1.
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And so it's a very smooth linear curve of like the models get more intelligent as you scale them up.
What's actually also interesting in that paper is there are these like very different emerging capability graphs.
And so, for example, as you add in more data and you train the models with more compute, you essentially see these actually discontinuous emergent capabilities jump.
So the models go from one plus one being a thing that it can't calculate to a thing that it can reliably calculate.
And so these emerging capabilities, this like some nature of like predictability is not necessarily everyone knows the exact moment.
Like you need the evals to be able to assess that has actually always been a part of how this technology works.
And also what makes like things like safety harder because unless you have the evals, unless you have the systems to test, these jumps might actually happen and you don't know.
I think there's like product overhang and user overhang, like to maybe put it in our PM language, even on today's models.
And I think there's like a lot that we could be exploring on like our current opuses and definitely with like Fable, for example.
And that discovery is actually another part of what's been in the early days of Anthropix DNA.
And I think it's also continuing to be a big part of how we operate in product and labs and across research.
Yeah, I think I take more of like a almost product lens.
It's almost like token spin is more the input.
And really the output is what you described of experimentation.
And I think if we were orienting like goals around experimentation, I feel like that would
that might be the better framing of the outcomes.
And therefore, there might be different ways of achieving that outcome.
I will say internally, some of the most creative thinkers, the best like prototypers do spend a lot of time with Claude, with every new version of a research model that we have.
And so there is something around, you have to be like using the models to then come up with
good, then great, then better ideas.
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