I wrote Deep Learning with Python to be the definitive guide to how deep learning works and how to best make use of it. Tens of thousands of people got their career start via this book. 120,000 copies sold, and downloaded by millions more. And now it's free to read online:
RESEARCH
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Google DeepMind TIPSv2 Boosts Vision-Language Dense Patch-Text Alignment
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Can vision-language models truly see the fine-grained details in images? Google DeepMind presents TIPSv2. They boost dense patch-text alignment using three novel tricks: a distillation method where the student outperforms the teacher, an upgraded masked image objective
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AI Transparency and Cognitive Impact on Developing Brains Needed
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At the minimum, transparency in what queries lead to which outputs (interpretability), how this connects to educational principles/pedagogy, more evidence of how this may impact cognition, especially of developing brains, in the short, medium and long term.
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RLHF requires pairwise comparison, not thumbs-up/down
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The thumbs-up/down buttons aren’t for RLHF. The HF in RLHF is selecting the better of two outputs for the same prompt (or, if you have more than two, ordering them best-to-worst). Pointwise data (thumbs, star ratings, etc.) generally doesn’t work well AFAIK.
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Grok 4.3: 10x cheaper, close to frontier models
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Grok 4.3 is literally 10x cheaper than GPT-5.5 or Claude for token output costs. It's also shockingly close on benchmarks (or better in some long-horizon agent tests) to the frontier models.
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Opus outlines, GPT-5.5 drafts and executes
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This works beyond coding. Content strategy: use Opus to outline the argument structure. Use GPT-5.5 to draft. Research: use Opus to design the research methodology. Use GPT-5.5 to execute the analysis. Business planning: use Opus to define the framework. Use GPT-5.5 to
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Opus 4.7 plus GPT-5.5 nearly doubles benchmark scores
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Dan Shipper at Every tested this on their Senior Engineer Benchmark. The scores:
Opus 4.7 alone: low 30s GPT-5.5 alone: low-to-mid 40s Opus 4.7 planning + GPT-5.5 executing: 62.5 For reference, human senior engineers score 80-90. The combo nearly doubled either model's -
Jack Clark Predicts Fully Automated AI R&D Timeline 2027-2028
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Fully automated AI R&D: ~30% chance by the end of 2027, ~60%+ chance by the end of 2028 Overall, Anthropic's Jack Clark has written a very worthwhile essay: His timeline is that fully automated AI R&D probably won’t arrive in 2026, but we may see a proof-of-concept within 1–2
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Why 1M-Context Models Still Don’t Work Beyond 200K Tokens
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it is endlessly fascinating to me that we still don't have a true 1M-context model it's an unusual case where the infra is far ahead of the science. Claude discontinued 1M+ context bc it didn't really work past ~200k we don't have the right data? training techniques? not sure
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Turing Test Conflates Intelligence With Human Behavior
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The Turing test is a test of intelligence. It has nothing to do with consciousness. (And even as the former it’s a bad idea, because it confuses being intelligent with being human. It’s like saying an airplane doesn’t fly unless it can fool birds into thinking it’s a bird.)