#AI doom warnings are getting louder. Are they realistic?
by Elizabeth Gibney @Nature Learn more: https://
bit.ly/4mG5Rtb #MachineLearning #ArtificialIntelligence #ML
RESEARCH
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AI Doom Warnings: Are They Realistic? Nature Article
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Seven Decision-Making Prompts and Frameworks
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I built these 7 prompts from frameworks that have driven billions of dollars in decisions across companies like Amazon, Toyota, and Berkshire Hathaway. The prompts are free. The thinking systems behind them are what separate good decisions from expensive mistakes. Also checkout
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Training NLP Models on Amazon Reviews with Python
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Training AI on Amazon Electronic Reviews Using #Python for Natural Language! – by – @gp_pulipaka
! JupyterLab/Jupyter Notebook WordNet, Lexical Semantic Relation Analyzer
Thesaurus, 155,000 Words
Synset 115,000, 205,000 word-Sense Pair. NLTK Library, spaCy, TextBlob -
Deep Reinforcement Learning Hands-On Practical Guide
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Deep Reinforcement Learning! Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF! #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux
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Did AI depend on training on huge human knowledge?
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or is the question: did AI depend on training on enormous amounts of human knowledge?
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Study: Perfect AI-human value alignment mathematically impossible
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Perfect alignment between #AI and human values is mathematically impossible, study says
by PNAS Nexus @TechXplore_com Learn more: https://
bit.ly/4e7EiqJ #MachineLearning #ArtificialIntelligence #ML -
AI is transforming mathematics, says Nature article
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‘It is incredible’: How #AI is transforming mathematics
by @dcastelvecchi @Nature Learn more: https://
buff.ly/NDPsRy2 #LLM #ArtificialIntelligence #MachineLearning #DeepLearning -
Gary Marcus: AGI is not here according to established definitions
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AGI is certainly not here by the definitions I have repeatedly laid out.
— Gary Marcus (@GaryMarcus) 24 mai 2026
or by criteria that @hendrycks @Yoshua_Bengio and I and others recently laid about at https://t.co/hhzYRu8MTJ
i don’t think any current AI can even reliably do any of the ten examples in my bet with… https://t.co/k5LjFwqM5lAGI is certainly not here according to the definitions I have repeatedly set forth. or according to the criteria that @hendrycks @Yoshua_Bengio and I and others recently established at http://agidefinition.AI I do not think that any current AI can even accomplish in a
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When extra test-time compute helps model convergence
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The key distinction: More test-time compute is not automatically useful. It becomes useful when the model has learned an internal landscape where extra iterations move the latent state toward solution-aligned attractors rather than spurious ones. That is why the convergence
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New paper introduces Equilibrium Reasoners for latent AI reasoning
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The next clue in AI reasoning: answers may be attractors. A new paper from Benhao Huang, Zhengyang Geng, and Zico Kolter introduces Equilibrium Reasoners (EqR) — a sharp mechanistic view of test-time scaling in latent reasoning models. The core idea is simple, but deep: