Daphne Koller
12 statements across 2 episodes · 3 bullish · 3 bearish · 1 people on the record · first statement Jan 2, 2019 by Daphne Koller · said 3 times in 3 episodes since 2024 · across every show →
On the record as a speaker too: Daphne Koller's record, appearances and statements → this page counts the times other people say the name.
Mentions by year
brought up most by Martin Casado (1), Kim Branson (1), Balaji Srinivasan (1)
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2025 1 mention in 1 episode
2024 2 mentions in 2 episodes 1 per episode
Everything said about Daphne Koller, oldest first
Jan 2, 2019 negative
Koller: Theranos never published peer-reviewed papers or disclosed raw data
“We all know Theranos, you know, that's an extreme example, but, ah, the fact that they never had a peer-reviewed publication, they never presented their data in any way, they kept even potential customers from looking at the raw data, I mean, those are all rea…”
Jan 2, 2019
Jan 2, 2019 negative
Jan 2, 2019 bearish
Koller: General artificial intelligence is not right around the corner
“I think one of the big risks that we run as a machine learning community is the incredible amount of hyperbole that's going on right now, where it's like, we're gonna have general intelligence right around the corner. We're not. Ok, we really aren't.”
Jan 2, 2019
Koller: Interdisciplinary innovators must risk looking foolish to ask basic questions
“If you're working at the boundary between two disciplines, you need to go in with the confidence that you are an expert in your domain, and it's okay for you to appear like a complete idiot in the other one, because if you're not going to ask those questions, …”
Jan 2, 2019 neutral
Jan 2, 2019
Sep 25, 2023 bullish
Sep 25, 2023 neutral
Koller: Human technician variance is a primary signal in biological ML
“When you do biological experiments, one of the strongest signals when you apply machine learning to it is what was the technician who actually did the experiments? You could read that very clearly off the cells because they behave a little bit differently.”
Sep 25, 2023 positive
Koller: Biological datasets became large enough for meaningful ML around 2016
“What brought me back to this field back in 2016 post Coursera was the realization that we can now finally, for the first time, measure biology at scale, both at the cellular level, sometimes at subcellular level, and at the organism level via ways of quantitat…”
Sep 25, 2023 neutral
Koller: ML lagged in life sciences due to lack of cross-disciplinary talent
“It wasn't having much of an impact in the life sciences, and I believe one of the main reasons for that is because there's so very few people who actually have the language of both disciplines and are able to bring them together.”
Sep 25, 2023 bullish
Koller: Insitro will deliver AI-discovered medicines to patients by 2030
“The hope is that by the end of the Of this decade, we will have built this process, we will have run through it a number of times, we will have delivered some medicines to patients in our first tranche of indications, but then we will have learned enough from …”