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Voting on Features: Federated Feature Selection Without Sharing Data
Hospitals, banks, and edge devices can agree on which features matter by sharing votes instead of rows, runnable in NVIDIA FLARE and Flower The post Voting on Features: Federated Feature Selection Without Sharing Data appeared first on Towards Data Science.
A Scaffold at the Gate: Building AI Applications That Can Evolve
A technical reflection on why data scientists need software engineering principles when AI agents write code. The post A Scaffold at the Gate: Building AI Applications That Can Evolve appeared first on Towards Data Science.
Gemini 4 Argon Hands-On Review: Capabilities and Performance
Google s Gemini 4 Argon promises stronger capabilities across creative, analytical, and document-heavy tasks. But how well does it perform in practice? From generating videos and building websites to analyzing data and reconciling conflicting documents, the model offers plenty to explore. In our hands-on tests, Argon delivered useful results but also introduced an unsupported claim in [ ] The post Gemini 4 Argon Hands-On Review: Capabilities and Performance appeared first on Analytics Vidhya.
Your Agent Doesn't Need More Freedom
Agents got good. The hard part now is everything around them. The post Your Agent Doesn't Need More Freedom appeared first on Towards Data Science.
Three Samplers, One Posterior: NUTS vs. Emcee vs. Nested Sampling
Benchmarking NumPyro, affine-invariant MCMC, and dynesty against exact ground truth and watching them fail. The post Three Samplers, One Posterior: NUTS vs. Emcee vs. Nested Sampling appeared first on Towards Data Science.
Coding Agents Can Solve the Task — But How Much Work Did They Waste?
Real repositories, seeded agent decisions: what 50,400 runs show about wasted turns, patch churn, and which harness rules help The post Coding Agents Can Solve the Task — But How Much Work Did They Waste? appeared first on Towards Data Science.
How to Leverage NLP for Product Data Science
A practical guide to strengthening churn, CLV, and other traditional models with text The post How to Leverage NLP for Product Data Science appeared first on Towards Data Science.
OpenAI Decisions API: What it is and how to use it?
Imagine building an AI agent that has to decide whether a customer complaint needs human intervention, which department should handle a query, or whether a submitted delivery photo is valid. Getting these decisions right matters. A false positive can trigger an unnecessary escalation, while a false negative can let a genuine issue slip through. Traditionally, [ ] The post OpenAI Decisions API: What it is and how to use it? appeared first on Analytics Vidhya.
We Specified an AI Agent in 2016. We Called It Analytics.
Re-reading my own IBM patent on real-time concert feedback as what it actually was: an agentic loop with multimodal perception, memory-grounded inference, and a human in the loop. What it got right, and what a decade of hindsight teaches agent builders today. The post We Specified an AI Agent in 2016. We Called It Analytics. appeared first on Towards Data Science.
I Built a Trap for AI Image Judges. They Walked Into a Different One.
Three vision AI models ignored the pretty image, yet GPT-4o invented answers and Claude kept answering B. The post I Built a Trap for AI Image Judges. They Walked Into a Different One. appeared first on Towards Data Science.
LLM Batching Explained: Static vs Dynamic vs Continuous Batching
Imagine four people booking a ride to the same destination. If each person takes a separate taxi, four vehicles make the same trip. But if they travel together in a bus, everyone reaches the destination in a single trip. The bus carries more people at once and makes better use of the resources. Batching in [ ] The post LLM Batching Explained: Static vs Dynamic vs Continuous Batching appeared first on Analytics Vidhya.
Kairos: Toward a Byte-Level Foundation Model for Every Modality
arXiv:2610.12495v1 Announce Type: new Abstract: Small language models trained on a single local machine leave little room for architectural trial and error. Kairos combines bidirectional gated-DeltaNet linear attention and sliding-window attention over shared projections (LiZAttention2), block-wise Attention-Residual aggregation, a sparse Mixture-of-Experts (MoE) feed-forward layer and a multi-scale byte-level codec, trained with a uniform-noise denoising objective over one byte space shared by text, image, audio, video, lidar and control streams; this report asks whether the combination optimizes at all before asking whether it is useful. We give code-derived algorithms for each component, including a cross-session recurrent-state memory gate, then run convergence sanity checks: overfittinga single 440-byte sentence at 1--15M parameters, text only, with three seeds for codec and attention choices and single-run sweeps for seven further factors. The two attention variants tested at 3\,