Positive Alignment: Artificial Intelligence for Human Flourishing Laukkonen et al.: https://
arxiv.org/abs/2605.10310 #ArtificialIntelligence #AIAgents
RESEARCH
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Positive Alignment: Artificial Intelligence for Human Flourishing
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Transformer research symposium with original authors
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先日サンフランシスコで開催された討論会「Transformers vs Post-Transformers」に、Sakana AIの共同創業者兼CTOであるLlion Jones @YesThisIsLion が登壇しました。
— Sakana AI (@SakanaAILabs) 21 mai 2026
本イベントは、現在のAI界を牽引するアーキテクチャ「トランスフォーマー」について、論文共著者を含む4人が、トランスフォーマー… https://t.co/KjkRgKKauc pic.twitter.com/ql0GpZQN1nLlion Jones @YesThisIsLion
, co-founder and CTO of Sakana AI, took the stage at the recent symposium "Transformers vs Post-Transformers" held in San Francisco. The event featured four panelists—including co-authors of the original Transformer paper—divided into supporters of the -
AI robot swarms target wounds with light
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Living #Robot swarms built from algae can split, merge, and target wounds with light
by Neetika Walter @IntEngineering Learn more: https://
bit.ly/4nvWBZ0 #MedTech #Healthcare #HealthTech #Tech #TechForGood -
Estimates of AI data center power and water usage
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Estimates of power usage here: https://
arxiv.org/pdf/2509.20241 (these numbers also match independent assessments) Estimates of water usage here: https://
eta-publications.lbl.gov/sites/default/
files/2024-12/lbnl-2024-united-states-data-center-energy-usage-report_1.pdf
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MeMo paper: Memory as a Model on arXiv
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MeMo: Memory as a Model
Paper: https://
arxiv.org/abs/2605.15156 -
MEMO: Store fresh knowledge in separate memory model without retraining
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Cool, your AI could learn new information without ever retraining its core brain! MIT, A*STAR, NUS, and Liquid AI researchers introduce MEMO (Memory as a Model). Instead of fine-tuning the LLM, MEMO stores fresh knowledge in a separate "memory" model. Think of it like adding a
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Estimated energy and water for an LLM solving an Erdös problem
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If this is true, using the best public estimates we have of LLM resource use, solving this Erdos problem took 0.6–6.3 kWh of electricity and about 3–31 liters of water. So that is less than three almonds worth of water and the electricity equivalent of 2-20 miles of EV driving.
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Which AI labs will prioritize social science research?
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Math is easy* because it has verifiable outputs and few messy judgement choices to make. Which AI labs have the guts to make advancing social science a priority? It may actually do more for human flourishing to unlock sociology, econ & psych reseach. * For AIs, not for humans
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Excitement over AGI takeoff with caveat from multiple companies
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As excited as I am that a takeoff seems to have begun and we are entering a golden age of science, one thing remains: I'm hearing more and more from all sides that AGI is within reach. This applies to Google (AGI, Physical AGI), as well as OpenAI and Anthropic. The only caveat:
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FFN Architecture Patterns in Mixture-of-Experts Models
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It’s interesting. I’ve seen hidden -> wider -> hidden FFNs and hidden -> narrower -> hidden FFNs.
But Command A+ seems tu use 4096 -> 4096 -> 4096 for each expert FFN, which I haven’t seen before (as far as I remember)