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Jane Wang
@janexwang
Senior staff research scientist at DeepMind. AI and neuro. Former physicist, current human.
London, England
Joined March 2009
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    At best, social networks are terrible at spreading accurate information; at worst, they're vulnerable to malicious attacks intended to manipulate our perceptions of reality. We need to really think hard about how to fix information spreading. nature.com/articles/d4158…
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    I don't usually ask people to call me "Dr" because I don't like the formality, but make no mistake. As with anyone who has earned a doctorate, my official designation is Dr. Wang. Not Mrs, not Ms, not Miss. I earned it. It means a lot to me. I expect you to respect that.
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    What if the missing ingredient for AGI is not more compute, or more parameters, or even a neat new algorithm, but rather social learning? New paper published in Nature Machine Intelligence! w/ @DuenezGuzman, Suzanne Sadedin, Kevin McKee, and @jzl86
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    fun fact: sunsets on mars are blue instead of red
    A sunset on Mars taken by the Curiosity Rover
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    Now that Q-learning is the latest hot trend, I'm looking forward to the AI community rediscovering policy gradient and actor-critic methods, and then finally meta-learning! (hint: it's now called "in-context learning") Everything old is new again.
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    Meta-learning is a hot topic in AI, but how does it look in nature? I wrote a short review for Current Opinion in Behavioral Sciences that tries explores this question, hope you enjoy! Meta-learning in natural and artificial intelligence: authors.elsevier.com/a/1cTXd8MqMif3…
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    Ok, I have to say something. I’ve heard the argument many times that DEI efforts force us to “lower the bar” in order to be able to achieve diversity goals. This is always presented as being objectively logical (“it’s just numbers!”). Here’s why it’s wrong:
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    The release in Nature about #AlphaFold has some amazing quotes: "The model from group 427 gave us our structure in half an hour, after we had spent a decade trying everything" nature.com/articles/d4158…
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    Dissolving the Fermi paradox. What a way to end a paper - Conclusion: Loneliness and existential ennui. arxiv.org/abs/1806.02404
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    Our latest work on how meta-reinforcement learning agents can perform causal reasoning by learning from data, led by the very talented Ishita Dasgupta and colleagues: "Causal Reasoning from Meta-reinforcement Learning" arxiv.org/abs/1901.08162
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    Amortized learning of neural causal representations: Use an attentive relational model to learn causal graphs from interventions in a meta-learning setup with the very talented @rosemary_ke @DaniloJRezende Jovana Mitrovic and Martin Szummer arxiv.org/pdf/2008.09301…
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    Excited to have my first DeepMind paper! Click to learn about learning to reinforcement learn
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    Why has my whole feed turned into my freshman year philosophy class?
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    LLMs are amazing but they’re not grounded in external, embodied environments. That’s why I’m excited to finally be able to talk about the project I’ve been working on for over a year: SIMA, an agent that can follow natural language in video games! dpmd.ai/3TiYV7d