The Platonic Representation Hypothesis Huh et al.: https://
arxiv.org/abs/2405.07987 #ArtificialIntelligence #DeepLearning #MachineLearning
MACHINE LEARNING
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Platonic Representation Hypothesis: New Framework for Deep Learning Models
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Sakana AI’s Agent Wins Programming Contest Against 800 Humans
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pretty cool! Sakana AI (@SakanaAILabs) Our AI agent has achieved 1st place in a competitive optimization programming contest against over 800 human participants. Blog: sakana.ai/ahc058 In AtCoder Heuristic Contest 058, Sakana AI’s ALE-Agent took the top spot. For context on the difficulty of these challenges, an OpenAI agent secured 2nd place in the AHC world tournament last year. The task was a 4-hour production planning optimization challenge. While the problem setters anticipated a standard approach combining constructive heuristics and simulated annealing, our agent independently discovered a more effective strategy. It implemented a "virtual power" heuristic and a diverse neighborhood search that allowed it to escape local optima better than human experts. This was achieved through inference time scaling using multiple frontier AI models. The agent ran parallel code generation, analyzed the results, and iteratively refined its algorithms in real time. The total cost was approximately $1,300. This result suggests AI agents can now match top human experts in tasks requiring extended reasoning and original scientific discovery. Please read our blog for more details. We extend our deepest thanks to the host, @algo_artis, and @atcoder. We will continue to research AI as a partner that expands human exploration to discover solutions to complex real-world problems. — https://nitter.net/SakanaAILabs/status/2008195936917586416#m
→ View original post on X — @_yutaroyamada, 2026-01-05 22:24 UTC
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Jensen announces Alpamayo, first reasoning self-driving model
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Jensen just announced Alpamayo – full reasoning self driving model. The first such model is publicly available. @nvidia #GTC
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ScaledML Returns After 6 Years with ML Industry Foresight
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ScaledML is back! After a 6 year hiatus. Consistently 2-3 years ahead of where ML will be. Examples of foresight at SML:
– OpenAI in 2016,2017,2019 announced GPT-2 & RL efforts
– Turing award for Deep Learning announced by Turing award winner on morning of award
– Groq chip -
#ces2026 Report #7: AI for Hair Stylists – Purple Hair?
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#ces2026 Report #7: AI for hair stylists. Would you hate me if I made my hair purple? pic.twitter.com/vG32Qf18uT
— Robert Scoble (@Scobleizer) 5 janvier 2026#ces2026 Report #7: AI for hair stylists. Would you hate me if I made my hair purple?
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AI Development: 8-Step Process for Creating Real Value
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AI isn’t magic. It’s a process. 8 steps:
define problem
collect/prepare data
choose model
train
evaluate
fine-tune
deploy
ensure ethics & safety Real value comes from running this loop well. #AI #MachineLearning #DataScience #ResponsibleAI -
PMLR Volume 311: Machine Learning Computational Biology 2025 Proceedings
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Volume 311 Proceedings of Machine Learning in Computational Biology 2025 https://
proceedings.mlr.press/v311/ Is now available on PMLR. -
Normal LLMs store knowledge in high-precision weights causing huge memory
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This is insane, here’s how this works:
— AI Breakfast (@AiBreakfast) 5 janvier 2026
Normal LLMs (like Grok or GPT variants) store their "kn0wledge" in billions of numbers (weights)
Each weight is usually a high-precision decimal (like 3.14159), stored using 16 bits per weight. This makes the model huge in memory and… https://t.co/P2W0pNNqxqThis is insane, here’s how this works: Normal LLMs (like Grok or GPT variants) store their "kn0wledge" in billions of numbers (weights) Each weight is usually a high-precision decimal (like 3.14159), stored using 16 bits per weight. This makes the model huge in memory and
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Sakana AI’s Agent Wins Competitive Programming Contest Against 800 Humans
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Our AI agent has achieved 1st place in a competitive optimization programming contest against over 800 human participants. Blog: sakana.ai/ahc058 In AtCoder Heuristic Contest 058, Sakana AI’s ALE-Agent took the top spot. For context on the difficulty of these challenges, an OpenAI agent secured 2nd place in the AHC world tournament last year. The task was a 4-hour production planning optimization challenge. While the problem setters anticipated a standard approach combining constructive heuristics and simulated annealing, our agent independently discovered a more effective strategy. It implemented a "virtual power" heuristic and a diverse neighborhood search that allowed it to escape local optima better than human experts. This was achieved through inference time scaling using multiple frontier AI models. The agent ran parallel code generation, analyzed the results, and iteratively refined its algorithms in real time. The total cost was approximately $1,300. This result suggests AI agents can now match top human experts in tasks requiring extended reasoning and original scientific discovery. Please read our blog for more details. We extend our deepest thanks to the host, @algo_artis, and @atcoder. We will continue to research AI as a partner that expands human exploration to discover solutions to complex real-world problems.
→ View original post on X — @_yutaroyamada, 2026-01-05 15:16 UTC
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Data + AI Summit 2026 Call for Presentations Open Now
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If you’re building with data and AI, now’s the time to tell the story. Call for Presentations for Data + AI Summit 2026 is open, and we’re looking for real-world solutions, lessons learned, and bold ideas from data engineers, AI pioneers, data scientists, analysts and