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.
Appearances
It's very vague.
If you bring that to a researcher and you say, please fix Claude from being hallucinated, it's not very actionable.
And so part of the time of the team is understanding, okay, what's the trajectory of why that user gave that feedback?
And it's like consented.
And so we look at, okay,
what should Claude have called tools in that moment, or from its current knowledge, or it called the right, looked at the right document, but it looked at the wrong facts.
In the first case, that would have been a failure on tool use.
On the second case, it would have been a failure on, let's say, search or knowledge and search and search synthesis.
or it could be something around alignment.
And so bring that level of detail to researchers coming up with like, is this a big enough problem?
Figure out things like evals to then describe what we've improved it.
Like those are the levels of actionability and it's the day-to-day language of the researchers.
And so we try to stay very close to how to bring that in an actionable manner.
between users to the core model training and the research development loop.
Researchers generally are research and product managers working with research or both.
Yeah, I think a lot of the most successful researchers and research leadership at Anthropic are folks who are really strong first principles thinkers about problems.
Like they reason through problems really well.
Who are just passionate about their research area and have a bold description of what that could look like.
And then who are actually close to the details.
And so our leadership, our chief scientists, our heads of fine tuning and RL, folks are actually really close to the training runs and actually look at things like how the training run is going, evals, looking at the underlying data.
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