vitrupo (@vitrupo)
Misha Laskin이 RL이 대규모로 작동하기 시작하면 AI 발전은 결국 경제성 문제로 바뀐다고 언급했습니다. AlphaGo 사례와 함께, 언어모델에서 RL이 가능해지면서 다른 분야로도 확장되며, 질병 치료 같은 문제에 수십억 달러를 투입할 의지가 핵심이 된다는 관점입니다.
Misha Laskin says once RL works at scale, progress becomes an economics problem. AlphaGo kept improving until it wasn’t worth the compute. Now that RL works on language models, the same logic applies elsewhere: “How much are you willing to spend $10B, $100B to cure a disease?”
yahoo news | The Man Who Thought He Could Keep AI Safe
Demis Hassabis, the co‑founder of DeepMind, has spent his career trying to shape artificial‑intelligence into a force that benefits humanity while averting the existential threats it could pose. From the moment Google acquired DeepMind in 2014, he insisted on strict safety controls, an independent oversight board, and a ban on military applications, framing his work as a moral quest rather than a purely commercial venture. Early on he even imagined a “singleton” scenario—a secret bunker‑based research enclave that would develop superintelligence under a single, trusted authority, protecting the world from a rogue AI.
When the vision of a unified, safe AI effort began to crumble—sparked by Elon Musk’s founding of OpenAI and deep‑seated competition among tech giants—Hassabis accelerated DeepMind’s progress, achieving landmark breakthroughs such as AlphaGo and protein‑folding predictions that earned a Nobel Prize. Yet his attempts to embed nonprofit‑style governance inside Google faltered, and a controversial NHS partnership triggered privacy backlash, leading to internal retreats and the departure of co‑founder Mustafa Suleyman. The emergence of powerful chatbots like ChatGPT forced Hassabis to confront a new moral crossroads: either cede the chatbot market to rivals or engage in an all‑out “wartime” race for AI dominance.
By 2026 Hassabis has accepted that formal governance structures alone cannot guarantee safety; instead he seeks personal influence at the highest decision‑making tables. He argues that real safety comes from having a seat at the table when critical issues arise, allowing him to steer outcomes while adapting from idealism to pragmatic realism. Though his original values appear compromised, his presence offers a modest “scaffolding of reassurance” amid a global, multi‑lab AI arms race, until broader governmental restraints can be imposed.
Read more: https://www.theatlantic.com/ideas/2026/03/ai-google-deep-mind-hassabis/686527/
800.000 Neuronen lernen in 5 Minuten Ping-Pong – und spielen #Doom.
Klingt wie #Sci-Fi? Ist aber echte Forschung von Cortical Labs. DishBrain lernt schneller und energieeffizienter als #DeepBlue oder #AlphaGo. Revolutioniert biologisches Computing nun KI?
Der #BlogArtikel von Henrik Müller ist kostenlos online verfügbar: https://www.laborjournal.de/blog/
#Laborjournal #LifeSciences #Neurowissenschaften #BioComputing #neuronalePlastizität #KI
Demis Hassabis (@demishassabis)
Google DeepMind가 런던에 새로운 연구·사무 공간 'Platform 37'을 개설해 현지 거점을 강화한다는 발표입니다. 건물명은 AlphaGo의 유명한 'Move 37'을 기념하는 명칭으로, 과학과 AI 연구의 영감이 될 시설을 목표로 한다고 밝히며 향후 연구·혁신을 위한 허브로서의 역할을 강조했습니다.
London has incredible talent & entrepreneurial spirit. Thrilled to deepen @GoogleDeepMind’s roots here with our spectacular new building Platform 37 - a nod to AlphaGo’s legendary Move 37. It’s a tribute to Science & AI, and an inspirational space for our next big breakthroughs!
Demis Hassabis (@demishassabis)
알파고의 서울 전설적 대국(10년 전)과 유명한 '무브 37'을 회고하며, 이 사건이 현대 AI 시대의 시작을 알렸고 강화학습 등 AI 기법이 과학 등 실제 문제 해결에 활용될 준비가 되었음을 주장합니다. 또한 이러한 방법에서 영감을 받은 아이디어들이 AGI 구축에 중요하다고 언급합니다.
Ten years ago, AlphaGo’s legendary match in Seoul heralded the start of the modern era in AI. Its famous ‘Move 37’ signaled to us that AI techniques were ready to tackle real-world problems in areas like science - and ideas inspired by these methods are critical to building AGI
ML_Bear (@MLBear2)
제프리 힌튼의 주장 요약: 스케일링 법칙은 한계에 가까워지고 있으며, 가치 높은 데이터는 기업 내부에 갇혀 공개 인터넷은 소진되었음. 해결책으로 모델이 자체 트레이닝 데이터를 생성하는 방안을 제시하며, 이는 AlphaGo가 인간을 이긴 방식과 유사하다고 언급.
AlphaGo (李世乭版) 要復出跟申真諝對奕？
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