Bluemo / Shutaro Aoyama (@blu3mo) header
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@Columbia CS & PoliSci | prev @SonyCSL, @Team_Mirai_jp | AI-mediated Communication for Collective Decision Making

NYC
Joined February 2018
Photo by Bluemo / Shutaro Aoyama (@blu3mo)Photo by Bluemo / Shutaro Aoyama (@blu3mo)Photo by Bluemo / Shutaro Aoyama (@blu3mo)Photo by Bluemo / Shutaro Aoyama (@blu3mo)Photo by Bluemo / Shutaro Aoyama (@blu3mo)Photo by Bluemo / Shutaro Aoyama (@blu3mo)Photo by Bluemo / Shutaro Aoyama (@blu3mo)Photo by Bluemo / Shutaro Aoyama (@blu3mo)Photo by Bluemo / Shutaro Aoyama (@blu3mo)Photo by Bluemo / Shutaro Aoyama (@blu3mo)
Bluemo / Shutaro Aoyama retweeted
I made some plots to help visualize why coalition formation in Israel is so difficult right now We are witnessing an extremely rare situation that political scientists call a "Condorcet cycle" - a demonstration of Arrow's impossibility theorem in the wild!
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Bluemo / Shutaro Aoyama retweeted
A new very creative method for measuring ambiguity with LLMs co-led by @dsallydt and @LukeThorburn_ (EMNLP Oral 2026). When I worked with @mhtessler @summerfieldlab et al on the Habermas Machine (a project in which we use LLMs to find statements that maximize agreement within groups of people), we often found that LLMs maximize consensus by simply making statements more ambiguous. Eg a statement like "peaceful protests should be allowed" will likely lead to high agreement but that's because a lot of disagreement hides in different interpretations of what's "peaceful". One way to measure ambiguity is to ask lots of people how they would interpret a statement and see if those people disagree. But this is obviously impossible to do at scale. In this work we show that we can use LLMs to help approximate this. Read Sally's thread or the paper for more details. I think work like this is super important, for example to stress tests AI constitutions and model specs to see which rules are ambigious and in which situations. If you are in Budapest, please stop by Sally's presentation.
🚨 New paper: how to measure ambiguity in laws, public proposals, and even AI constitutions! with @LukeThorburn_ @Werdnamai @bakkermichiel 🧵
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Bluemo / Shutaro Aoyama retweeted
met @blu3mo after a long time! so many new ideas. he’s building some awesome stuff in nyc + japan
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【チームみらい「まる見え」宣言】 法案に賛成か反対か、または自由投票か。党の中でどのように意思決定をしたのか。そこは「オープンにする」を掲げるチームみらいにおいても、これまでブラックボックスでした。 今回、その党内議論をはじめ、OKR(組織目標と達成状況)、調研費(旧文通費)の使い道、海外視察の報告と費用についても公開していきます。 なぜ、政党を「まる見え」にするのか。考えをnoteにまとめました。 note.com/takahiroanno/n/n5c0…
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Bluemo / Shutaro Aoyama retweeted
AI Symbiont構想の「AI科学コミュニケーター」の取り組みが2026年度グッドデザイン賞を受賞しました🎊日本科学未来館・クウジット、関係者のみなさまに感謝です。
笠原俊一リサーチディレクターが進める、人間とAIが学び合いながら共に成長し新しい社会のかたちを模索する「 Society with AI Symbionts 」の日本科学未来館での取り組みが 2026年度グッドデザイン賞を受賞しました。 sonycsl.co.jp/news-articles/…
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Bluemo / Shutaro Aoyama retweeted
【動画インタビュー協力者を募集】 将来、自分の仕事がAIに奪われるかもしれない― そんな危機感を持っている方々、ぜひ生の声をお聞かせいただけませんか? 現在、動画企画でインタビューさせていただける方を募集しています。 <職業(例)> デザイナー/ドライバー/コンサルタント/料理人 など <インタビュー内容> ・AIに任せられそう/代わられそうな業務 ・人だからこそ出せる価値 撮影協力いただける方は下記フォームよりご登録ください。 docs.google.com/forms/d/e/1F… ※取材日が近いためご出演をお願いする方のみご連絡させていただきます、あらかじめご了承ください。
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Bluemo / Shutaro Aoyama retweeted
今晩21時から! やらねばなことは山積みですが、話したいことも山積みなので久しぶりに生配信をします! ここ最近やっていたこととか、今後のこととか。政治と感情の話とか、ここのところ考えていたことをゆるゆるお話しする予定です。
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Bluemo / Shutaro Aoyama retweeted
I've been thinking hard about modern biosecurity for five years. In the course of that thinking, I've learned a lot about what the field consensus is on a complete biodefense stack for the AI age. Today, as part of my work with @BlueDotImpact, @chrisstrahle_ and I have published an overview of this defense stack, alongside a starting list of ten projects we would like to support motivated founders to build. Funding will be available through the Seed Grants program. Please share this with the best founders you know. blog.bluedot.org/p/biosecuri…
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Bluemo / Shutaro Aoyama retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Bluemo / Shutaro Aoyama retweeted
Code for Japanの代表理事を継ぎました シビックテックという言葉もない中で、コードで、自分たちの手で社会をつくっていくことを夢みた先輩ハッカーたちが生み出したうねりを、Social R&Dを通した課題解決につなげていきます。 受け取ったものを最大限使い、たくさんの挑戦をするこれからのコード・フォー・ジャパンを、どうぞよろしくお願いします!
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Bluemo / Shutaro Aoyama retweeted
このイベントは私なりの日本のテック思想を表現したものだった。 これを今言語化したらそれなりにわかってもらえるのではと思うようになってきた。
オードリー・タン氏 @audreyt 、宮口あや氏 @AyaMiyagotchi , Glen Weyl氏 @glenweyl 、鈴木健氏 @kensuzuki 、安野たかひろ氏 @takahiroanno ほか、素晴らしい登壇者でデジタル民主主義、デジタル公共財の議論ができます。 引用ポストから一年半ぐらい経ちますが、 高木 @0xtkgshn さん 守 @MorleySheen さん 関 @hal_sk さん Anna beth @ontologymachine david @david1casey kimを中心にFtC Tokyoを開催できることを誇りに思います。 @FundingCommons ⬇️ FtC Tokyo 7月24日・25日 fundingthecommons.io/tokyo-2…
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Bluemo / Shutaro Aoyama retweeted
Made with @claudeai Opus 5.5
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Bluemo / Shutaro Aoyama retweeted
おお、これはおもしろいな。アダプティブなコンポジションの動的更新。AIじゃないと難しい"ならでは"な表現だ。新しい技術には新しい表現の提案がないとね。やるなRunway
We’ve been experimenting with responsive generative video interfaces. Real-time video models are reshaping responsive design. A thread 🧵
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Bluemo / Shutaro Aoyama retweeted
一年ぶりに自主制作を進めています! 今回のテーマは仮想の鏡です。単純なミラー反転ではなく、立体的に世界が反転したらどうなるか。 Vista4D+Meridian+Minimax H3+ComfyUIの力を借りて表現しています。単一の動画からポイントクラウド化、それをBlenderで再度レイアウトして最後に推論をかけています。完成するまで定期的にメイキング投稿していく予定です!
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Bluemo / Shutaro Aoyama retweeted
New experiment: json-render + jev The future Generative UI is instant Your components, your actions, your design system Rendered in milliseconds
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Bluemo / Shutaro Aoyama retweeted
Jevを並列で動かして 周囲の人の感情によってどう感情が変わるか をセルオートマトンでシミュレーションしてみた こんだけ増やしてもこの速さで動く 周囲が怒ってたり悲しんでたりすると鬱になるし、 周りが慌ててるとみんな慌て始めたりでめっちゃおもろいww
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