Must Read Books on Artificial Intelligence! #BigData #Analytics #DataScience #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
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ooks
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MACHINE LEARNING
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Must Read Books on Artificial Intelligence from @gp_pulipaka
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Deep Learning Detects Acute Ischemic Stroke
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Acute Ischemic Stroke Detected by Deep Learning! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #HealthTech #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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Evaluating Deep Agents with LangSmith on AWS
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Evaluating Deep Agents with LangSmith on AWS Great deep dive blog with our friends at AWS on evaluating DeepAgents with LangSmith Covers datapoint and evaluator design for longer horizon agents https://
aws.amazon.com/blogs/machine-
learning/evaluating-deep-agents-using-langsmith-on-aws/
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Anthropic plans consumer and bioscience expansion with new features
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Anthropic is planning to further expand into the consumer and bioscience sectors. The biggest things to watch for – Conway agent
– Orbit assistant
– Knowledge-based memory
– Multilingual Voice Mode
– Operon for bioscience researchers and more! Which one do you think will -
Token-level SSL vs Latent Prediction for Recursive Hierarchy Learning
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The key distinction: Token-level SSL asks the model to recover hierarchy through the leaves. Latent prediction lets the model climb the hierarchy recursively. Once one abstraction level is learned, it becomes supervision for the next.
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Token-level SSL vs Latent Prediction: Climbing Abstraction Hierarchies
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The key distinction: Token-level SSL asks the model to recover the hidden tree through the leaves. Latent prediction lets the model climb the tree. Once one abstraction level is learned, it becomes the substrate for learning the next.
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New paper on sample-complexity theory for data-efficiency gap
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The data-efficiency gap between machines and children may not be solved by “more tokens.” It may be solved by changing what the model is asked to predict. A beautiful new paper by Daniel J. Korchinski, Alessandro Favero, and Matthieu Wyart gives a sample-complexity theory for a
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AI memory shifts from retrieval to compilation with LLM Wiki
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RAG is already becoming the “old way” The future of AI memory is not retrieval.
It’s compilation. Here’s the shift in one sentence: From searching information To structuring knowledge The new model? LLM Wiki Instead of: Chunking documents Running similarity -
Apple AI to run distilled Google Gemini on iPhone
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Interesting updates on Apple AI: As Apple's WWDC lands next month, and the long-delayed Siri and on-device AI upgrades are expected to be the centerpiece: a smaller, distilled version of Google's Gemini running locally on iPhone silicon, pitched on privacy and lower token costs.
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GEPA visualizer: LLM self-optimizing prompts beyond black-box scoring
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A new GEPA visualizer shows an LLM optimizing its own prompts in real time.
— AlphaSignal AI (@AlphaSignalAI) 31 mai 2026
Most prompt optimizers treat the LLM like a black box.
They reduce every run to a single score and call it learning.
GEPA takes a different path.
It reads the full execution trace, error logs, and… pic.twitter.com/ixJEsIMutdA new GEPA visualizer shows an LLM optimizing its own prompts in real time. Most prompt optimizers treat the LLM like a black box. They reduce every run to a single score and call it learning. GEPA takes a different path. It reads the full execution trace, error logs, and