Back when we believed deep network pre-training could be achieved using stacked autoencoders trained with group sparsity regularization.
RESEARCH
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Benchmarking Performance of GPT-5.5, Claude Opus 4.7, and Gemini 3.1 Pro
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Benchmarking GPT-5.5, Claude Opus 4.7, and Gemini 3.1 Pro! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding
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AI Enhances Accuracy in Nanoscale Protein Motion Tracking
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AI Improves Accuracy in Tracking Protein Motion at the Nanoscale! #BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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Making Humans Responsible for AI in Academic Research
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Making humans responsible for their AI use seems like an incredibly reasonable way to address problems & opportunities in the use of AI for academic research, at least in the short term (autonomous scientific work will require different solutions).
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AI detects osteoporosis from retinal images in new study
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Our retina image is so rich with information, a gateway to the whole body, well beyond the eye. Today add osteoporosis to the list. https://
journals.plos.org/digitalhealth/
article?id=10.1371/journal.pdig.0001360&utm_source=pr&utm_medium=email&utm_campaign=plos006
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This adds to what I recently wrote about at Ground Truths https://
erictopol.substack.com/p/the-paradox-
of-medical-ai-implementation
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Traces d’agents auto-améliorants pour modèles
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you talking about how traces of self improving agents can be fed into the retraining of self improving models?
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Testing Higgsfield’s Supercomputer with multi-model routing
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I've been testing Higgsfield's Supercomputer for the past few days, and it genuinely caught me off guard. You type a task in plain language. The system picks from 61 production skills, routes each sub-task to the best available model (GPT-5.5, Claude Opus, Gemini, Seedance, Veo,
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Effective scaling laws for time series foundation models
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Are scaling laws finally effective for time series foundation models? Today, @datadoghq is releasing Toto 2.0 weights under Apache 2.0 on @huggingface. It's a family of open-weights TSFMs ranging from 4M to 2.5B parameters, where each size outperforms the previous one from a single hyperparameter configuration.
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AGI Alpha: A Public Proof Layer for Self-Improving AI
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Recursive validated self-improving AI at $ 4.65B. AGI ALPHA is building the public proof layer. Not just AI that improves—
AI that proves, replays, audits, archives & compounds. Proof-bound. Enterprise-ready. Built in public. https://
github.com/MontrealAI/agi
alpha-first-real-loop
… #AGIALPHA #MontrealAI -
AGI ALPHA: public proof-layer for recursive self‑improving AI
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Recursive validated self-improving AI at $4.65B. AGI ALPHA is building the public proof layer. Not just AI that improves—
AI that proves, replays, audits, archives & compounds. Proof-bound. Enterprise-ready. Built in public. https://
github.com/MontrealAI/agi
alpha-first-real-loop
… #AGIALPHA #MontrealAI