How frontier AI models are built 1. identify task model cannot solve
2. create task eval
3. collect new *eval-specific* training data
4. train model
5. model can do the task now
6. if not (AGI achieved): goto step 1 GLUE, MMLU, MATH, AIME, HLE, GPQA, ARC a tale as old as time
MACHINE LEARNING
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How Frontier AI Models Are Built: The Iterative Process
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Vision VAE as Ultimate Tokenizer: Unicode Efficiency
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Very cool work direction but also fair question.
I wonder if ultimately is a little vision patch VAE the ultimate "tokenizer"? Unicode + UTF-8 is just too high description length. -
Columbia Seligman’s Paul Wick on AI Investment Strategy
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Please enjoy my interview with Columbia Seligman's Paul Wick Seligman’s Paul Wick likes the midyear setup Fund manager and longtime tech observer Paul Wick of Columbia Seligman continues to be a big believer in the AI trade, but he’s not so enamored of acquisition-hungry,
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Implicit Feedback Mechanisms in AI Model Training Systems
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TikTok fyp is a textbook example here. How fast you swipe, how long you watch…feedback cues you can get (and learn from) way more often than explicit thumbs up/down or those 'what did you think of this' post-video surveys
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Universal Reward Function: Breakthrough in Reinforcement Learning
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A *UNIVERSAL* reward function. Yep. This is one of the most shockingly simple + effective RL approaches I've seen. Kyle and the team are beyond special.
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New AI Security Research: Quantization Defense and Runtime Analysis
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they also did some interesting new experiments
– system-level runtime measurements
– pareto analysis
– quantization as a defense
– password-cracking test overall pretty nice work! thorough, detail-oriented, and fair glad to see my results held up http://
arxiv.org/abs/2507.07700 -
Model Training Results Improved to 94% Perfect Embedding Inversion
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i was happy to see that the results held up! and i forgot that i trained the models a bit longer after the paper was submitted so they actually got somewhat better (94% perfect embedding inversion!)
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Context Drives Generative AI Revolution Through RAG and Knowledge Graphs
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#Context is fueling and boosting the current Generative #AI revolution! See these books:
> https://
amzn.to/34utTz7
> https://
amzn.to/317XV9L
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#RAG #LLMs #GenAI #DataScience #MachineLearning #Semantics #KnowledgeGraphs #IoT #InternetOfContext -
Building Neo4j-Powered Applications with LLMs and Haystack
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New book from @PacktDataML >> "Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI" Available at https://
amzn.to/4l9lLKO -
Neural Operators: Deep Learning at Any Resolution
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Neural Operators – Deep learning at any resolution Extending neural networks to function spaces: While many phenomena are inherently described by functions, neural networks define vector-to-vector mappings that rely on fixed discretization of the input and output. Neural