AlphaGo, every mention

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Verbatim, from the transcripts: the passages where AlphaGo comes up

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▶ 0:16 Sarah Guo Reflection AI's co-founders Misha Laskin and Yana Santanoglu work together as leaders at Google DeepMind on groundbreaking projects like AlphaGo, AlphaZero, and Gemini.
▶ 1:32 Misha Laskin So, uh, right, AlphaGo was a super intelligent system, and there were other systems during that time that were built that were super intelligent in narrow domains.
▶ 5:25 Misha Laskin Saw kind of AlphaGo happen, and my sense was that, uh, I want to pursue the kind of the root science, uh, but there is such a thing as kind of the root science of our time. 2 times in the scene
▶ 39:52 Misha Laskin It happened with, um, the Atari games, AlphaGo, um, you know, then Dota V and AlphaStar were near super intelligent systems, and if OpenAI and DeepMind had sunk more compute into them, they would have definitely become super intelligent.

▶ 0:34 Sarah Guo In the way that Move.com did with AlphaGo, something humans hadn't done before, even in thousands of years of play.
▶ 2:53 Pushmeet Kohli Our earlier sort of work, uh, and if we go back to, uh, the project on AlphaCo, 2 times in the scene
▶ 37:35 Pushmeet Kohli The solutions and the programs that, uh, AlphaGo finds are, uh, sort of interpretable by, uh, human, uh, programmers.

▶ 2:14 Thomas Hubert And that's how I joined the company and have been kind of involved with AlphaGo and AlphaZero, MuZero, this line of work.
▶ 3:32 Thomas Hubert And, and for instance, when we did this AlphaGo or AlphaZero algorithms, uh, we could really have this thing, uh, this, this, this thing about self-play.

▶ 23:43 Oriol Vinyals Now, historically, if you look at AlphaGo, which actually followed quite closely the recipe of you pre-train your model on all human data, you then use RL to make it better, and then you do some search at inference time, the compute there… 2 times in the scene

▶ 0:58 Alexandr Wang Right before Scale, I was studying AI and machine learning at MIT, and this was the year when DeepMind came out with AlphaGo.

▶ 10:32 Elad Gil And, um, I think that's been a really interesting wave, and some of them, again, are taking very different approaches from the traditional, let's just build a giant LLM, and they're looking at things like AlphaGo,

▶ 17:04 Mustafa Suleyman First with, uh, you know, AlexNet, of course, in 2012, and then with DQN, uh, the Atari paper in 2013, um, you know, and then a kind of succession of breakthroughs after AlphaGo and people got more, more, more sort of aware of it, but it…
▶ 18:41 Mustafa Suleyman Um, that's part of the reason why we started the Alpha Fold project, because it was actually my group that was, um, looking around for other applications of DQN-like, AlphaGo-like, uh, tools, and, uh, in a hackathon, um, that we did one… 2 times in the scene

▶ 4:30 Noam Brown I mean, I think for me, especially it was, it was the AlphaGo moment, you know, like when, when you see that it's just very clear. 3 times in the scene
▶ 6:34 Noam Brown And you know, if any, if any of your listeners hasn't, haven't seen the AlphaGo documentary, I highly recommend watching it.
▶ 20:53 Noam Brown Like you look at AlphaGo, you look at, um, especially AlphaZero, the latest version of AlphaGo, and you look at the, you know, the Dota two bot, the way they're trained is by playing against themselves for millions, uh, or billions of… 2 times in the scene
▶ 34:02 Noam Brown Alpha zero, uh, the latest version of AlphaGo. 6 times in the scene
▶ 50:12 Noam Brown I think, especially after AlphaGo, this became a very, uh, popular problem because after AlphaGo, 2 times in the scene

▶ 40:57 Dr. Percy Liang That seems maybe harder, but on the other hand, AlphaGo was able to discover completely kind of alien different strategies and with the right, you have to set it up correctly.

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