user avatar
David Luan
@jluan
retired!! former cofounder of @adeptailabs, vp engineering @openai and @amazon, and @google LLMs lead. all about type II fun.
beep beep boop boop
Joined March 2009
Posts
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    A bunch of top ML folks from Google, DeepMind, OpenAI, etc have come together to build Adept! It’s a pleasure to be working with this kind and extremely talent-dense crew, incl. the folks who invented Transformer. We’re doing something a bit different… (thread)
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    This is tech’s “let them eat cake” moment: I only see talk of machine learning papers, WFH setups, and the escapist fantasy of space. Wake up, everybody. AI doesn’t matter if we can’t even treat everyone in this country like a real human being.
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    After some super restorative time off (both work-wise and twitter-wise), I'm excited to join Google Research! I'm starting a new group focused on large, multiyear DL projects with fundamental research goals... very cool to get to work with folks like @JeffDean!
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    Excited to share the news that we’ve raised $350M to build a natural language interface to your computer! Having a strong coalition of strategic partners for Adept (Atlassian, Microsoft, NVIDIA, and Workday) is going to be awesome!
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    !! @pabbeel and I are building a new AI research lab in SF for Amazon! We’re focused on the remaining major problems to build generally intelligent agents and are looking for a few dozen intrinsically motivated people to join our team and work with the Adept folks here. DM me!
    Super-excited about what's ahead. Want to move the AI research frontier, join us! amazon.science/blog/amazon-op… [email protected]
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    Replying to @jluan
    People have invested a ton of time and expertise to create software tools that help them get their work done. Rather than replacing these tools, we want to build a natural language interface to all of them — an NL frontend to your computer.
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    Replying to @jluan
    This is also the reverse of most AGI work out there. Rather than automating economically valuable tasks, we want to keep humans in the driver’s seat, by building AI tools that people can work with to do things together.
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    how i think of the gpt-2 language model:
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    Replying to @jluan
    It’s just been three months since we got started and we’ve already built a ton. If the idea of building a foundational general AI product – and using it to solve general intelligence – excites you, please reach out :)
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    I had a wonderful 1.25 years at Google Research! It was a real privilege to get to lead the large models effort there and work with folks like @RandomlyWalking, @achowdhery, @elicollins and @JeffDean on PaLM etc. After OpenAI and Google, I’ll be doing something totally different!
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    Finally out! AI is as much about engineering as it is about research. PaLM required solving hard problems across all levels of the stack—networking, XLA, distributed training infra, optimizers, model architecture, data. Our group’s model scaling effort did whatever it took.
    Introducing the 540 billion parameter Pathways Language Model. Trained on two Cloud #TPU v4 pods, it achieves state-of-the-art performance on benchmarks and shows exciting capabilities like mathematical reasoning, code writing, and even explaining jokes. goo.gle/3j6eMnK
    GIF
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    Solving Rubik's Cube with a humanoid hand shows my favorite part of @OpenAI's research philosophy: choose a hard task that we don't think is doable with today's techniques, then use or invent whatever technique to solve it. This is the transpose of how research is often done.
    Replying to @OpenAI
    We’re all used to robots that fail when their environment changes unpredictably. Our robotic system is adaptable enough to handle unexpected situations not seen during training, such as being prodded by a stuffed giraffe:
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    In the future, we’ll be able to ask our computers to do increasingly abstract and complex things in natural language—and it’ll be the default way people use their machines. Excited to share our first step in this direction! Some thoughts on why I think this is really cool:
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    Give me an artist, genre, and lyrics (or not), and this neural network will generate you a song! It can even rap. This is one of the coolest results from OpenAI’s Algorithms and Language team. Loved being involved in it as a bureaucrat. Next up, 24/7 lo-fi vaporwave.
    Introducing Jukebox, a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles. We're releasing a tool for everyone to explore the generated samples, as well as the model and code: openai.com/blog/jukebox/
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