Most discussions of continual learning in AI focus on one thing: updating model weights. But for AI agents, learning can happen at three distinct layers: the model, the harness, and the context. Understanding the difference changes how you think about building systems that improve over time.The thre...
Meet MaxToki: The AI That Predicts How Your Cells Age — and What to Do About It
Most foundation models in biology have a fundamental blind spot: they see cells as frozen snapshots. Give a model a single-cell transcriptome — a readout of which genes are active in a cell at a given moment — and it can tell you a lot about what that cell is doing right now. What it […]
The post Me...
Copilot is ‘for entertainment purposes only,’ according to Microsoft’s terms of use
AI skeptics aren’t the only ones warning users not to unthinkingly trust models’ outputs — that’s what the AI companies say themselves in their terms of service.
Architecture and Orchestration of Memory Systems in AI Agents
The evolution of artificial intelligence from stateless models to autonomous, goal-driven agents depends heavily on advanced memory architectures. While Large Language Models (LLMs) possess strong reasoning abilities and vast embedded knowledge, they lack persistent memory, making them unable to ret...
Proxy-Pointer RAG: Achieving Vectorless Accuracy at Vector RAG Scale and Cost
A new way to build vector RAG—structure-aware and reasoning-capable
The post Proxy-Pointer RAG: Achieving Vectorless Accuracy at Vector RAG Scale and Cost appeared first on Towards Data Science.
Meet ‘AutoAgent’: The Open-Source Library That Lets an AI Engineer and Optimize Its Own Agent Harness Overnight
There’s a particular kind of tedium that every AI engineer knows intimately: the prompt-tuning loop. You write a system prompt, run your agent against a benchmark, read the failure traces, tweak the prompt, add a tool, rerun. Repeat this a few dozen times and you might move the needle. It’s grunt wo...
Inside the Creative Artificial Intelligence (AI) Stack: Where Human Vision and Artificial Intelligence Meet to Design Future Fashion
Fashion has always been about anticipation, determining what one would prefer to wear before they know it themselves. It’s meant in terms of intuition, presentation, experience, and the “good eye”. Today, it can be conveyed through algorithms, neural networks, and machine learning. Artificial Intell...
AI Weekly Issue #479: 100 years from now : what happens when every living thing carries an AI inside it
This is 100 Years From Now, a weekly series. Once a week, we skip ahead a century and imagine ordinary life in a world that's had a hundred years to absorb the things we're only beginning to build. No predictions — just honest speculation about where our choices lead.
This week: what happens when ev...
A loss function is what guides a model during training, translating predictions into a signal it can improve on. But not all losses behave the same—some amplify large errors, others stay stable in noisy settings, and each choice subtly shapes how learning unfolds. Modern libraries add another layer ...
National Robotics Week — Latest Physical AI Research, Breakthroughs and Resources
This National Robotics Week, NVIDIA is highlighting the breakthroughs that are bringing AI into the physical world — as well as the growing wave of robots transforming industries, from agricultural and manufacturing to energy and beyond. Advancements in robot learning, simulation and foundation mode...
Building a Python Workflow That Catches Bugs Before Production
Using modern tooling to identify defects earlier in the software lifecycle.
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A Practical Guide to Measuring Relationships between Variables for Feature Selection in a Credit Scoring.
The post Building Robust Credit Scoring Models with Python appeared first on Towards Data Science.
Netflix AI Team Just Open-Sourced VOID: an AI Model That Erases Objects From Videos — Physics and All
Video editing has always had a dirty secret: removing an object from footage is easy; making the scene look like it was never there is brutally hard. Take out a person holding a guitar, and you’re left with a floating instrument that defies gravity. Hollywood VFX teams spend weeks fixing exactly thi...
Google DeepMind’s Research Lets an LLM Rewrite Its Own Game Theory Algorithms — And It Outperformed the Experts
Designing algorithms for Multi-Agent Reinforcement Learning (MARL) in imperfect-information games — scenarios where players act sequentially and cannot see each other’s private information, like poker — has historically relied on manual iteration. Researchers identify weighting schemes, discounting ...
Dean Price, assistant professor in the Department of Nuclear Science and Engineering, sees a bright future for nuclear power, and believes AI can help us realize that vision.
Mamba4 Explained: A Faster Alternative to Transformers for Sequential Modeling
Transformers revolutionized AI but struggle with long sequences due to quadratic complexity, leading to high computational and memory costs that limit scalability and real-time use. This creates a need for faster, more efficient alternatives. Mamba4 addresses this using state space models with selec...
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I built a self-healing deployment pipeline for our GTM Agent. After every deploy, it detects regressions, triages whether the change caused them, and kicks off an agent to open a PR with a fix, with no manual intervention needed until review time.
When we try to train a very deep neural network model, one issue that we might encounter is the vanishing gradient problem. This is essentially a problem where the weight update of a model during training slows down or even stops, hence causing the model not to improve. When a network is very deep, ...