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Kai Arulkumaran
@kaixhin
Researcher, programmer, DJ, transhumanist. ; formerly /////.
Tokyo, Japankaixhin.comJoined August 2011

Kai Arulkumaran’s Tweets

We are hiring at Araya for the Moonshot IoB project!
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My main work at @ArayaPress, as part of the @moonshot_IoB, is to, essentially, control robots with my mind. If that sounds like an awesome goal, I'm hoping to hire multidisplinary researchers to build this 🧠🤖
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The project is challenging, but I believe meaningful, and can be solved with a combination of research and engineering. Let's make science-fiction science-reality 🚀
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Hi Neuro Twitter, what's the most impressive experiment you know to date where a human learned to send signals via non-invasive brain measurements? Do we have an idea of what maximum intentional bit rate could be achieved given enough training?
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Eager mode was what made PyTorch successful. So why did we feel the need to depart from eager mode in PyTorch 2.0? Answer: it's the damn hardware! Let's tell a story about how the assumptions PyTorch were based off of became untrue, and why PyTorch needed to evolve. (1/10)
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Any nice solutions for teleoperating robots (for manipulation) using VR hardware? Hardware suggestions? Any software that makes it easier, or does everyone really have to roll their own?
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Our new paper with Patrick Copinger is out🎉 We show that consideration of a topological Berry phase inspired local momentum phase coupled to electromagnetism gives rise to a momentum gauge dependent emergent spacetime.
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Was about to re-use my own meme, but! These authors actually tested RL problems 🥺 (was worse than Adam but RL was considered OoD for VeLO so 🤷)
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Tired of tuning your neural network optimizer? Wish there was an optimizer that just worked? We’re excited to release VeLO 🚲, the first hyperparameter-free learned optimizer that outperforms hand-designed optimizers on real-world problems: velo-code.github.io 🧵
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Kudos to for being the place to go to get pretrained models, wrapped up in simple APIs. Wanted to test out some new computer vision capabilities, and instead of spending ages figuring out people's repos, I'm up and running in a few minutes!
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Spaceship Generator Competition 🚀 Competition Details ⚙️ spaceengineersgame.com/announcing-the ⚙️ 𝐃𝐞𝐯𝐬 𝐥𝐨𝐬𝐭 𝐢𝐧 𝐬𝐩𝐚𝐜𝐞 ➡️Today, Nov. 10th, 5 - PM UTC 🛰️Twitch: twitch.tv/keencommunityn 🛰️YouTube: youtube.com/user/SpaceEngi #SpaceEngineers #Innovation #Space #Xbox #NeedToCreate
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Neat work that shows how many RL problems (including dynamics prediction) can be turned into inference over conditioned sequence modelling tasks. Merging this with the algorithm I presented for Generalised UDRL would be even more general-purpose 🥳
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You know how we train general language models by randomly masking parts of the input? We think this makes even more sense for training a single _general sequential decision model_ that can perform behavior cloning, offline-RL, goal-conditioning & more! 📑arxiv.org/abs/2204.13326
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Very exciting. LLMs enable language interfaces for RL, and now code-LLMs enable symbolic policies. Prediction: high-level embodied policies will enable self-experimentation (ideally via meta-learning) for understanding the physical world, for further improvements in capabilities.
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How can robots perform a wide variety of novel tasks from natural language? Execited to present Code as Policies - using language models to directly write robot policy code from language instructions. See paper, colabs, blog, and demos at code-as-policies.github.io long 🧵👇
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アラヤの研究開発部のKai ArulkumaranとRoberto Gallottaが、手続き型コンテンツ生成(Procedural Content Generation)のための新しい進化的アルゴリズム(Evolutionary Algorithms)を開発する研究プロジェクトにて「AI宇宙船ジェネレータ」を開発いたしました.
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🎤 Tonight! Livestream at 19:00 CEST with guest appearance by AI researchers Martin Poliak and ' on #EA #algorithms + #PCG techniques for in-game content generation 🚀
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Don't miss our next live stream😎 𝐃𝐞𝐯𝐬 𝐥𝐨𝐬𝐭 𝐢𝐧 𝐬𝐩𝐚𝐜𝐞 ➡️Thursday, Oct. 27th, 5 - PM UTC 🛰️Twitch: twitch.tv/keencommunityn 🛰️YouTube: youtube.com/user/SpaceEngi #SpaceEngineers #Innovation #Space #Science #Sandbox #Xbox #NeedToCreate
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PLEs combine methods from EAs, rec sys, pref learning, RL and ML in general. Seems like a lot, but the goal was a general, flexible framework. Make use of your favourite ML model (linear? NN? GP?), RL sampling strategy (ε-greedy? UCB?), etc.
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Manually selecting items to evolve at every iteration may be slow and tiresome, so why not get the AI to do that for you too? So we speed up the mixed-initiative setting via preference learning, using a learned model to imitate human selections for some iterations.
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Building upon our earlier work on a hybrid constrained optimisation EA for generating functional vehicles, and an improved version with surrogate fitness models, we now go to the interactive PCG setting, with an app for players to use to generate spaceships.
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Best of luck to the team! This sort of tooling is very important, and having dedicated maintenance is a great asset for the RL community.
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Today we're launching the Farama Foundation, a new nonprofit dedicated to open source reinforcement learning, and we're beginning by maintaining and standardizing all the major open source reinforcement learning environments. Read more here: farama.org/Announcing-The
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Having worked in RL with video game testbeds and moving to robotics and neuroscience domains, where previously domain knowledge was key, it feels like collecting more data is the way things are going. Trumps everything else. More data + simple objective + good model architecture.
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Currently writing the background section of a paper that includes 5 subsections with completely different areas of AI 😵‍💫 Culmination of my grant, with putting the finishing touches on the project. Exciting reveal soon 🎉
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理研AIP主催の女子中高生向けオンラインセミナーが始まります! 数理・情報・Al系の研究者・大学院生が研究について話します。 第2回は私も少し登壇させていただきます☺️ 近くに興味のありそうな女子中高生がいらっしゃれば共有お願いします! 詳細はこちら ▶️ aip.riken.jp/event-list/sur
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This also seems to mesh nicely with Information Generation Theory, proposed by . In IGT, consciousness sits on top of a generative model. This could be episodic memory, or more broadly, what the authors call the "conscious memory system".
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Interesting read - a relatively broad theory of consciousness that tries to explain many of the phenomena and possibly unintuitive findings on consciousness from a vast swathe of research. It evolved as a way to use episodic memory.
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"We argue that consciousness originally developed as part of the episodic memory system" pubmed.ncbi.nlm.nih.gov/36178498/
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