DeepMind
11 statements across 8 episodes · 4 bullish · 2 bearish · 7 people on the record · first statement Jan 2, 2019 by Murray Shanahan · said 18 times in 9 episodes since 2017 · across every show →
Mentions by year
brought up most by Sonal Chokshi (3), Matt Clifford (3), Cameron Schuler (3), Anjney Midha (3), Murray Shanahan (2), Michael Copeland (1), Marc Andreessen (1), Frank Chen (1)
tap a year for its mentions
2023 3 mentions in 1 episode
2022 1 mention in 1 episode
2019 13 mentions in 6 episodes 2 per episode
- a16z Podcast | Building Tech Startups in a Place Where Tech Isn’t Everything
- a16z Podcast | Machine Intelligence, from University to Industry
- a16z Podcast | Revenge of the Algorithms (Over Data)... Go! No?
- a16z Podcast | Artificial Intelligence and the 'Space of Possible Minds'
- a16z Podcast | Startups and Pendulum Swings Through Ideas, Time, Fame, and Money
- a16z Podcast | The Dream of AI Is Alive in Go
- every mention in 2019, scene by scene →
2017 1 mention in 1 episode
Everything said about DeepMind, oldest first
Jan 2, 2019 positive
Shanahan: Reinforcement learning progress does not require massive datasets
“Actually, DeepMind are another example of the same thing, because if you want to apply reinforcement learning to games, and that's enabled them to make some quite fundamental sort of progress, you don't need vast amounts of data either.”
Jan 2, 2019 negative
Shanahan: DeepMind's published DQN algorithm lacks inner rehearsal capabilities
“For the bit of work that they actually published, I think one of its shortcomings, actually, is that, in fact, although it has done all that learning about what the right action to do in, in the right circumstance is, it doesn't actually do in a rehearsal. It …”
Jan 2, 2019
Shanahan: Machine learning must be embedded within larger cognitive architectures
“I see machine learning as a kind of subfield of artificial intelligence, and it's a subfield that's had tremendously a tremendous amount of success in recent years, and is going to go very, very far, but ultimately, the machine learning components have to be e…”
Jan 2, 2019
Jan 2, 2019
Jan 2, 2019
Jan 2, 2019 positive
Sep 25, 2023 neutral
Dec 28, 2023 positive
2022 DeepMind paper showed dataset size matters more than parameter count
“In fact, in twenty-twenty-two, a pivotal paper came out that changed the way that many people in the research community thought about this very calculus. And it demonstrated that datasets were actually more important than just the sheer size of the model.”
Aug 16, 2025 negative
Parker-Holder: Top robotics simulators still face significant sim-to-real gaps
“The robotic simulations are even the best ones, and we have some of the best ones at DeepMind and Majoko, right, which we work with. They're still quite far away from the real world, right? And so you have the sim to real gap.”