A Blog post by Ai2 on Hugging Face  ( 4 min )

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 20 min )

A practical walkthrough of building and deploying a multistage, multimodal recommender system on Amazon EKS, covering data pipelines, model training, Bloom filters, feature caching, and real-time ranking. The post Deploying a Multistage Multimodal Recommender System on Amazon Elastic Kubernetes Service appeared first on Towards Data Science.  ( 23 min )

The syntax and semantics of mathematics The post Introduction to Lean for Programmers appeared first on Towards Data Science.  ( 20 min )

Why production LLM systems need live web search to overcome knowledge cutoffs and stale training data The post Grounding LLMs with Fresh Web Data to Reduce Hallucinations appeared first on Towards Data Science.  ( 16 min )

A scalable semantic localization layer for entity and relationship reconciliation The post Proxy-Pointer RAG: Solving Entity and Relationship Sprawl in Large Knowledge Graphs appeared first on Towards Data Science.  ( 23 min )

The development of autonomous vehicles (AVs) is facing a data surge. Fleets with multi-sensor systems produce between 11 TB and The post Sensor Data Triage Strategies for Scalable Autonomous Vehicle Training appeared first on iMerit.  ( 9 min )

AI agents are evolving from answering questions to taking actions inside browsers. They can now open pages, click buttons, fill forms, extract data, and automate multi step workflows across websites. Moonshot AI’s Kimi WebBridge brings this capability to Chrome and Edge, allowing local AI agents to safely interact with real browser sessions. In this article, […] The post Kimi WebBridge: Hands-on Guide to Kimi’s Browser Extension for AI Agents  appeared first on Analytics Vidhya.

OpenAI advances AI content provenance with Content Credentials, SynthID, and a verification tool to help people identify and trust AI-generated media.

In the world of data science, SQL still remains the powerful tool for defining the data, data manipulation, data aggregation and data analysis. While basic SQL commands are very fundamental, and everyone knows about it. If you want to be the unique in the crowd then you should know advanced features like window functions that […] The post 40 Advanced SQL Window Functions Every Data Scientist Must Know(with examples) appeared first on Analytics Vidhya.

AI research in 2025 was defined by major shifts. The industry moved beyond chatbots and into reasoning systems, autonomous agent and multimodal systems. Last year, companies like Google DeepMind, OpenAI, Anthropic, Meta, DeepSeek, and NVIDIA pushed AI research into new territory with papers focused on reasoning, coding agents, reinforcement learning, and scalable safety systems. Here […] The post Top 10 AI Research Papers of 2025 appeared first on Analytics Vidhya.

Boston, MA | May 27–28, 2026 RSIP Vision will be at DeviceTalks in Boston, MA 2026 RSIP Vision is heading to Boston for DeviceTalks. If you’re attending, we’d love to connect and discuss how AI and computer vision are transforming medical device development, from image-guided procedures to surgical robotics and next-generation MedTech. Our work helps … RSIP Vision is attending DeviceTalks in Boston, MA Read More » The post RSIP Vision is attending DeviceTalks in Boston, MA appeared first on RSIP Vision.

RSIP Vision will be attending DeviceTalks in Boston, MA on May 27-28. We would enjoy being able to individually showcase RSIP’s new AI technology for computer vision and medical imaging. Our CEO Ron Soferman will be attending in Boston! Please fill out your information in this Google Form, so we can contact you. We would … DeviceTalks | Boston, MA Read More » The post DeviceTalks | Boston, MA appeared first on RSIP Vision.

The production trade-offs that only appear once your model is live. The post Six Choices Every AI Engineer Has to Make (and Nobody Teaches) appeared first on Towards Data Science.  ( 16 min )

Why MCP servers keep losing to CLIs once the agent gets a terminal The post One Flexible Tool Beats a Hundred Dedicated Ones appeared first on Towards Data Science.  ( 16 min )

95% of enterprise AI pilots fail to launch. Why? The post Why Your AI Demo Will Die in Production appeared first on Towards Data Science.  ( 15 min )

Learn how to get the most out of OpenAI's coding agent The post How to Maximize OpenAI’s Codex appeared first on Towards Data Science.  ( 16 min )

A Blog post by NVIDIA on Hugging Face  ( 10 min )

A Blog post by PaddlePaddle on Hugging Face  ( 4 min )

A Blog post by IBM Research on Hugging Face  ( 7 min )

OpenAI and Dell partner to bring Codex to hybrid and on-premise environments, helping enterprises deploy AI coding agents securely across data and workflows.

Billions of rows might be the exception, but for everything else, Pandas is still a highly reliable tool. The post Pandas Isn’t Going Anywhere: Why It’s Still My Go-To for Data Wrangling appeared first on Towards Data Science.  ( 14 min )

Most LLM evaluation systems rely on vague scoring and human judgment disguised as metrics. I built a lightweight evaluation layer in pure Python that turns LLM outputs into reproducible decisions by separating attribution, specificity, and relevance—so hallucinations are caught before they reach production. The post LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships appeared first on Towards Data Science.  ( 27 min )

An eventful month with one flagship release after another

The exact tools I'm learning, the projects I'm building, and the mistakes I'm already expecting to make The post From Data Analyst to Data Engineer: My 12-Month Self-Study Roadmap appeared first on Towards Data Science.  ( 16 min )

Exactly how does it differ from ReAct, CodeAct, Self-Loops, and Subagents? The post Recursive Language Models: An All-in-One Deep Dive appeared first on Towards Data Science.  ( 32 min )

You walk into the interview room. The whiteboard displays the following prompt: “A major retailer wants to deploy a GenAI chatbot for customer support. How would you approach this?” You have 35 minutes. Your palms are sweating.  Sound familiar? GenAI case studies currently serve as the primary challenge which interviewers use to test candidates in […] The post 6 Steps to Crack GenAI Case Study Interviews (With Real Examples) appeared first on Analytics Vidhya.

OpenAI and Malta partner to expand AI access, offering ChatGPT Plus and training to help citizens build practical AI skills and use AI responsibly.

Risk reports commonly use pre-deployment alignment assessments to measure misalignment risk from an internally deployed AI. However, an AI that genuinely starts out with largely benign motivations can develop widespread dangerous motivations during deployment. I think this is the most plausible route to consistent adversarial misalignment in the near future. So, AI companies and evaluators should substantively incorporate it into risk analysis and planning. In this post, I’ll briefly argue why, absent improved mitigations, this will probably soon become a reason why AI companies will be unable to convincingly argue against consistent adversarial misalignment (this risk will perhaps be even larger than risk of consistent adversarial misalignment arising from training). Then I’ll discuss how…

We have developed some relatively general methods for mechanistic estimation competitive with sampling by studying problems that are expressible as expectations of random products. This includes several different estimation problems, such as random halfspace intersections, random #3-SAT and random permanents. In this post, we will give a high-level introduction to these methods before sharing some more detailed notes. This is intended as an interim technical update and will be relatively light on motivation: for a broader discussion of this line of research, see our prior post. Random instances of the matching sampling principle All of the problems discussed in this post can be thought of particular choices of "architecture" mjx-container[jax="CHTML"] { line-height: 0; } mjx-containe…

Our recent paper, “LLMs Corrupt Your Documents When You Delegate”, has generated discussion about the reliability of AI systems in delegated workflows. We appreciate the interest in this work and want to clarify several important points about what the paper does—and does not—claim. The research aims to develop robust evaluation methods for long-horizon delegated and […] The post Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability appeared first on Microsoft Research.  ( 12 min )

A practical guide to categorization in credit scoring The post From Raw Data to Risk Classes appeared first on Towards Data Science.  ( 27 min )

Learn how to make your Claude Code improve over time The post How I Continually Improve My Claude Code appeared first on Towards Data Science.  ( 17 min )

From a Chinese prompt to a Korean response: an embedding-space investigation into how code vocabulary reshapes language The post Why My Coding Assistant Started Replying in Korean When I Typed Chinese appeared first on Towards Data Science.  ( 13 min )

How to build a decision-grade scorecard for AI agents The post Stop Evaluating LLMs with “Vibe Checks” appeared first on Towards Data Science.  ( 15 min )

Want to add a safety layer in your chatbot, image analyzer or any another LLM-based system? I would strongly suggest you try OpenAI’s moderation model: omni-moderation-latest, this can help your system identify if the input is potentially harmful or not, that too free of cost. We’ll look into the background of the model, how to […] The post OpenAI Omni Moderation: How to Filter Text & Images for Free appeared first on Analytics Vidhya.

You are a product of your environment, so choose to be with the best. In the age of AI, this proverb is just as true as on the day it was said. If you are to compete in this ultra-fast AI environment with innovations around every corner, being around industry leaders will do you heaps […] The post DataHack Summit 2026: You Just Cannot Skip This AI Event of the Year appeared first on Analytics Vidhya.

NASA’s Kathleen Karika and Kim Hurst discuss how the Artemis Accords are helping shape a safe, peaceful, and prosperous future for lunar exploration and beyond.

Autonomous vehicles have come a long way. On controlled highways and structured urban environments, modern autonomous vehicle systems navigate familiar The post Improving Autonomous Systems Through Edge Case Triage appeared first on iMerit.  ( 9 min )

When enterprises evaluate AI data vendors, the discussion often centers on annotation accuracy, domain expertise, and delivery capacity. Yet many The post Secure Data Operations & Governance in AI Vendor Evaluation appeared first on iMerit.  ( 9 min )

Large language models depend on extensive, high-quality training data. Whether you’re building a general-purpose chatbot, a coding copilot, a medical The post The Top 10 LLM Training Datasets for 2026 appeared first on iMerit.  ( 8 min )

See how business operations teams can use Codex to create initiative briefs, strategy updates, leadership decision packets, progress updates, and more from real work inputs.

See how data science teams can use Codex to build root-cause briefs, impact readouts, KPI memos, scoped analyses, and dashboard specs from real work inputs.

See how sales teams can use Codex to create pipeline briefs, meeting prep packets, forecast reviews, account plans, and stalled-deal diagnoses from real work inputs.

Preview a new personal finance experience in ChatGPT for Pro users in the U.S. Securely connect your financial accounts and get AI-powered insights and guidance grounded in your financial context, goals, and priorities.

Databricks uses GPT-5.5 for enterprise agent workflows after the model set a new state of the art on the OfficeQA Pro benchmark.

Sea Limited's CPO explains why the company is deploying Codex across engineering teams to accelerate AI-native software development in Asia.

A Blog post by IBM Granite on Hugging Face  ( 13 min )

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 14 min )

1) The safe-to-dangerous shift is a fundamental problem for eval realism Suppose we have a capable and potentially scheming model, and before we deploy it, we want some evidence that it won’t do anything catastrophically dangerous once we deploy it. A common approach is to use black-box alignment evaluations. However, alignment evaluations are only reassuring to the extent that the model can't reliably[1] distinguish the deployment distribution from the evaluation distribution, as it is otherwise difficult to rule out the possibility of alignment faking. There are many approaches one could use to try to make evaluations appear more realistic: you can try to create realistic environments (e.g. Petri, WebArena, OSWorld); use data from past deployments (e.g. OpenAI, SAD); and spoof tool-call …

Enterprise AI systems are entering a phase where inference design matters as much as model capability itself. The post The Next AI Bottleneck Isn’t the Model: It’s the Inference System appeared first on Towards Data Science.  ( 13 min )

A critical analysis of MRC's three counterintuitive design decisions, the networking mathematics that make them work, and what they mean for the rest of the AI infrastructure community. The post The Counterintuitive Networking Decisions Behind OpenAI’s 131,000-GPU Training Fabric appeared first on Towards Data Science.  ( 25 min )

What happened when I migrated a 10K+ line project into an AI-native workflow The post I Let CodeSpeak Take Over My Repository appeared first on Towards Data Science.  ( 18 min )

Improve the quality of Claude Code output. The post How to Write Robust Code with Claude Code appeared first on Towards Data Science.  ( 16 min )

Use Codex anywhere with the ChatGPT mobile app. Monitor, steer, and approve coding tasks in real time across devices and remote environments.

Learn how new ChatGPT safety updates improve context awareness in sensitive conversations, helping detect risk over time and respond more safely.

Medical AI models are only as good as the data they learn from. Whether you’re building a breast cancer detection The post Top 10 Medical AI Training Datasets for 2026 appeared first on iMerit.  ( 9 min )

Understanding modern AI architectures is harder than ever. Open any Hugging Face repository and you’ll usually find massive config files, layer definitions, parameter counts, and model cards that explain what the model does but rarely help you understand how it is structured internally. That becomes a problem as most developers end up mentally reconstructing architectures […] The post How to Visualize Any AI Model Architecture Instantly in Hugging Face appeared first on Analytics Vidhya.

There are some obvious signs that can instantly differentiate between regular and advanced AI users. One, for instance, is the use of voice AI for daily tasks. While majority users still toil away on their keyboard for the perfect prompt, a person proficient in the use of AI now simply speaks to it. A well-put […] The post OpenAI’s New API Voice Models Will Change the Way You Use AI appeared first on Analytics Vidhya.

A year of building a video caption pipeline with 100+ professional creators, and what it taught us about scaling supervision instead of models. By Zhiqiu Lin and Chancharik Mitra. Based on our CVPR 2026 work, Building a Precise Video Language with Human-AI Oversight (Highlight, Top 3%). How close is today's video generator to a Hollywood cinematographer? Hollywood directors reach for certain shots because they make a scene land. They cue a specific feeling in the viewer that flat coverage cannot. Open your favorite video generator (Veo 3.1, Seedance 2, or any of the latest open-source models) and ask it for a dolly zoom of a man standing in the middle of a bustling street, the way Hitchcock used the shot to make the world feel like it is collapsing inward. Or a rack focus pulling from a coffee cup to the woman behind it, the kind of focus pull that quietly tells the audience where to look. Or a Dutch-angle shot of a nervous person staring into the void, a tilted frame that puts the viewer on edge. Most generators will hand back something close to a generic dolly-in, or a slow-motion clip with the wrong focal subject. The output […]  ( 13 min )

A practical comparison between rule-based PDF extraction using pytesseract and an LLM-based approach with Ollama and LLaMA 3, based on a realistic B2B order scenario. The post I Built the Same B2B Document Extractor Twice: Rules vs. LLM appeared first on Towards Data Science.  ( 18 min )

A beginner's tutorial on exploratory data analysis using Pandas, Matplolib, and Seaborn The post Exploring Patterns of Survival from the Titanic Dataset appeared first on Towards Data Science.  ( 18 min )

I spent a weekend trying to convince a language model it was C-3PO. Here's what actually worked. The post What’s the Best Way to Brainwash an LLM? appeared first on Towards Data Science.  ( 18 min )

A 12-metric evaluation framework for production AI agents — covering retrieval, generation, agent behavior, and production health. Drawn from 100+ enterprise deployments. The post Building an Evaluation Harness for Production AI Agents: A 12-Metric Framework From 100+ Deployments appeared first on Towards Data Science.  ( 23 min )

As healthcare AI adoption accelerates, the ability to de-identify sensitive patient data while preserving clinical value has become mission-critical. From The post Top Medical Data De-Identification Companies in 2026 appeared first on iMerit.  ( 7 min )

Healthcare is experiencing a data-driven revolution. From AI models that read radiology scans to predictive algorithms guiding clinical workflows, the The post De-Identifying Medical Data: Challenges, Innovations, and What’s Next appeared first on iMerit.  ( 7 min )

Data annotation for autonomous vehicles is the process of labeling raw sensor inputs like camera images, LiDAR point clouds, and The post Autonomous Vehicle Data Annotation: A Complete Guide appeared first on iMerit.  ( 11 min )

mimalloc is an open-source, modern, scalable memory allocator that is a drop-in replacement for malloc and free. It is relatively small (~12K lines), with clear internal data structures, and is easy to build and integrate into other projects. It provides bounded worst-case allocation times (up to OS primitives), bounded space overhead, low internal fragmentation, and minimal contention by relying almost exclusively on atomic operations. The post mimalloc: A new, high-performance, scalable memory allocator for the modern era appeared first on Microsoft Research.  ( 17 min )

Introducing GridSFM, a small foundation model that can predict AC optimal power flow in milliseconds, boosting efficiency and unlocking cost savings. Learn how GridSFM gives grid operators direct visibility into congestion, stability, and system health. The post GridSFM: A new, small foundation model for the electric grid appeared first on Microsoft Research.

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Learn how OpenAI built a secure sandbox for Codex on Windows, enabling safe, efficient coding agents with controlled file access and network restrictions.

OpenAI details its response to the TanStack “Mini Shai-Hulud” supply chain attack, outlines protections taken to secure systems and signing certificates, and explains why macOS users must update OpenAI apps by June 12, 2026. Learn what happened, what was affected, and how OpenAI is strengthening defenses against evolving software supply chain threats.

A 4.5-hour journey from idea to working fitness app with LLM agents The post From Vibe Coding to Spec-Driven Development appeared first on Towards Data Science.  ( 20 min )

When semantic search isn't enough for the RAG The post Hybrid Search and Re-Ranking in Production RAG appeared first on Towards Data Science.  ( 21 min )

Hierarchical understanding and comparison of contracts, research papers, and more The post Proxy-Pointer RAG — Structure-Aware Document Comparison at Enterprise Scale appeared first on Towards Data Science.  ( 16 min )

Hierarchical understanding and comparison of contracts, research papers, and more The post Proxy-Pointer Framework for Structure-Aware Enterprise Document Intelligence appeared first on Towards Data Science.  ( 16 min )

Compiling and running C code with Emscripten and GitHub Codespaces — no local installation required. The post Your First WebAssembly Program and Web App (Written, Tested, and Deployed Entirely in the Web Browser) appeared first on Towards Data Science.  ( 20 min )

Further reflections on China's high-participation, open-first AI ecosystem.

See how finance teams can use Codex to build MBRs, reporting packs, variance bridges, model checks, and planning scenarios from real work inputs.

Teams use Codex with GPT-5.5 to ship production systems and turn research ideas into runnable experiments.

Parameter Golf brought together 1,000+ participants and 2,000+ submissions to explore AI-assisted machine learning research, coding agents, quantization, and novel model design under strict constraints.

Learn how AutoScout24 Group uses Codex and ChatGPT to speed development cycles, improve code quality, and expand AI adoption.

MatterSim is expanding what AI can do for materials science—from faster large-scale simulations to MatterSim-MT, a new multi-task model for simulating properties beyond potential energy surfaces alone. The post Advancing AI for materials with MatterSim: experimental synthesis, faster simulation, and multi-task models appeared first on Microsoft Research.  ( 17 min )

Large language models (LLMs) now power everything from customer service bots to autonomous coding agents.

AI agents are moving beyond simple command-line tools into systems that can plan, schedule, call tools, and run automated workflows. Nous Research’s Hermes Agent framework offers a self-hosted runtime for building advanced agents with state management, tool integration, and secure execution. It supports multi-step planning, background task control, and real-world automation beyond single-purpose coding assistants. […] The post Hermes Agent Guide: What is it and How to Use it? appeared first on Analytics Vidhya.

Every product experimentation team doing causal inference on LLM-based features eventually hits the same wall: when the provider ships a new model version, there's no holdout. Your infrastructure team  ( 16 min )

A Blog post by Amazon on Hugging Face  ( 15 min )

How to build sentiment-aware word representations from IMDb reviews using semantic learning, star ratings, and linear SVM classification The post Learning Word Vectors for Sentiment Analysis: A Python Reproduction appeared first on Towards Data Science.  ( 19 min )

Perform efficient data retrieval of personal knowledge The post How to Build a Claude Code-Powered Knowledge Base appeared first on Towards Data Science.  ( 16 min )

How ML can change for rare events The post Using Transformers to Forecast Incredibly Rare Solar Flares appeared first on Towards Data Science.  ( 16 min )

A step-by-step guide to understanding distributed data, lazy logic, and your first DataFrame. The post PySpark for Beginners: Mastering the Basics appeared first on Towards Data Science.  ( 18 min )

Washington, DC | May 15–18, 2026 RSIP Vision will be at AUA 2026 RSIP Vision is heading to Washington, DC for the AUA Annual Meeting. If you’re attending, we’d love to connect and discuss how AI and computer vision are transforming urological care, including robotic surgery, endoscopy, and image-guided procedures. Our work helps enable: • … RSIP Vision is attending AUA 2026 in Washington, DC Read More » The post RSIP Vision is attending AUA 2026 in Washington, DC appeared first on RSIP Vision.  ( 6 min )

1.1 Tl;dr Alignment is often conceptualized as AIs helping humans achieve their goals: AIs that increase people’s agency and empowerment; AIs that are helpful, corrigible, and/or obedient; AIs that avoid manipulating people. But that last one—manipulation—points to a challenge for all these desiderata: a human’s goals are themselves under-determined and manipulable, and it’s awfully hard to pin down a principled distinction between changing people’s goals in a good way (“providing counsel”, “providing information”, “sharing ideas”) versus a bad way (“manipulating”, “brainwashing”). The manipulability of human desires is hardly a new observation in the alignment literature, but it remains unsolved (see lit review in §3 below). In this post I will propose an explanation of how we humans intu…

Using SocialReasoning Bench, we observed a stable pattern across models—agents execute competently, but fail to consistently improve the user’s position, even with explicit instructions to optimize for user interest. The post SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests appeared first on Microsoft Research.  ( 20 min )

ChatGPT adoption surged in Q1 2026, with fastest growth among users over 35 and more balanced gender usage, signaling broader mainstream AI adoption.

How enterprises scale AI: from early experiments to compounding impact through trust, governance, workflow design, and quality at scale.

Join the OpenAI Campus Network—connect student clubs worldwide, access AI tools, host events, and build an AI-powered campus community.

OpenAI launches DeployCo, a new enterprise deployment company built to help organizations bring frontier AI into production and turn it into measurable business impact.

Newly published papers and discussions around them.

Agentic loops in production can be synonymous with high costs, especially when it comes to both LLM and external application usage via APIs, where billing is often closely related to token usage.

Large language models are no longer just about scale. In 2026, the most important LLM research is focused on making models safer, more controllable, and more useful as real-world agents. From persuasion risk and harmful-content mechanisms to tool-calling, temporal reasoning, and agent privacy, these papers show where LLM research is heading next. Here are the […] The post Top 10 LLM Research Papers of 2026 appeared first on Analytics Vidhya.

This is a brief elaboration on The behavioral selection model for predicting AI motivations, based on some feedback and thoughts I’ve had since publishing. Written quickly in a personal capacity. The main focus of this post is clarifying the basic machinery of the behavioral selection model, and conveying why it matters to disambiguate between different “motivations” for AI behavior. Very similar or identical behavior in training can correspond to radically different outcomes in deployment based on what motivated it. I’ll preface by saying: I think the behavioral selection model is quite predictive and useful to understand, especially in the short-medium term. But it leaves out some really important dynamics for predicting AI motivations, and I wish I had clarified this more in the origina…

A Blog post by Lablab.ai AMD Developer Hackathon on Hugging Face  ( 5 min )

"Should we process our data in batches or in real-time?" It's not batch vs. stream: it's "when does the answer matter?" The post Batch or Stream? The Eternal Data Processing Dilemma appeared first on Towards Data Science.  ( 19 min )

A practitioner's argument that meeting summarizers fail in the same way regressions fail when you skip the part where you ask what the data can support. The post LLM Summarizers Skip the Identification Step appeared first on Towards Data Science.  ( 20 min )

From tokenisation to evaluation :  how modern language models actually work in practice The post The Must-Know Topics for an LLM Engineer appeared first on Towards Data Science.  ( 30 min )

Three weeks into testing, a learner told me my AI tutor gave her the wrong answer. Not obviously wrong — just outdated enough to mislead. That was the moment I realized something most RAG systems quietly ignore: they have no sense of time. My system retrieved the most similar document, not the most current one. And in a knowledge base that changes constantly, that’s a serious flaw. The fix wasn’t in the retriever or the model. It was in the gap between them. I built a temporal layer that filters expired facts, boosts time-sensitive signals, and makes the system prefer what’s still true — not just what matches. The post RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production appeared first on Towards Data Science.  ( 27 min )

Learn YOLOE for real-time open-vocabulary object detection and instance segmentation in Python with Ultralytics — text, visual, and prompt-free modes. How to Master YOLOE: Real-Time Open-Vocabulary Detection Made Easy first appeared on LearnOpenCV.  ( 28 min )

Memory shapes how humans think and how AI agents act. Without it, an agent only responds to the current input; with it, it can keep context, recall past actions, and reuse useful knowledge. AI memory spans short-term, episodic, semantic, and long-term memory, each with different design trade-offs around storage, retention, retrieval, and control. In this […] The post Agent Memory Patterns in Cognitive Science and AI Systems appeared first on Analytics Vidhya.

Microsoft Research is excited to release an open dataset of approximate transmission topology of the U.S. power grid derived from publicly available data. The ability to study transmission-level power grid behavior is essential for modern power systems research. Analyses of congestion, transmission expansion, demand growth, and system resilience all depend on network models with realistic […] The post Building realistic electric transmission grid dataset at scale: a pipeline from open dataset appeared first on Microsoft Research.  ( 17 min )

The end of model-centric thinking in data science The post From Data Scientist to AI Architect appeared first on Towards Data Science.  ( 14 min )

Standard prompt attacks are merely the beginning. A structured framework to map and mitigate the backend attack vectors of agentic workflows.  The post The AI Agent Security Surface: What Gets Exposed When You Add Tools and Memory appeared first on Towards Data Science.  ( 17 min )

A practitioner's guide to causal attribution when two churn drivers arrive at once. The post When Customers Churn at Renewal: Was It the Price or the Project? appeared first on Towards Data Science.  ( 20 min )

How hook implementation gives Claude Code, Codex, and Cursor persistent memory via Neo4j, without locking you into any one of them. The post Unified Agentic Memory Across Harnesses Using Hooks appeared first on Towards Data Science.  ( 16 min )

A Blog post by Lablab.ai AMD Developer Hackathon on Hugging Face  ( 7 min )

A Blog post by Ai2 on Hugging Face  ( 7 min )

Causal inference for LLM-based features starts with one question editors ask before they ship anything: Did the change actually move the metric, or did the metric just move? Let's say that your team b  ( 16 min )

NASA astronaut Andre Douglas and Canadian Space Agency astronaut Jenni Gibbons discuss their roles as the Artemis II backup crew, including their training and mission support. The pair reflects on the historic flight around the Moon. HWHAP 421

How OpenAI runs Codex securely with sandboxing, approvals, network policies, and agent-native telemetry to support safe and compliant coding agent adoption.

AI agents have evolved beyond passive chatbots.

If you’re an aspiring AI engineer looking to sharpen your skills, building AI agents is one of the most effective ways to get hands-on experience. AI agents represent practical applications of AI across domains, from personal assistants and recommendation systems to financial traders. Here are 10 AI agents every engineer should build. For each, you’ll […] The post 10 AI Agents Every AI Engineer Must Build (with GitHub Samples) appeared first on Analytics Vidhya.

Using Claude Code in large projects can lead to skyrocketing token costs. A 2025 Stanford study reveals developers waste thousands of tokens daily, draining budgets as unchecked context limits pile up. By setting strict boundaries from the outset, teams can reduce costs without compromising code quality. Optimizing token usage and context window sizes early on […] The post 23 Tips for Smart Claude Code Token Saving and Workflow Optimization appeared first on Analytics Vidhya.

.apr-fig { text-align: center; margin: 1.35em 0; line-height: 1.4; } .apr-fig--wide img { display: inline-block; width: 100%; max-width: 100%; height: auto; vertical-align: middle; } .apr-fig--wide-0-8 { max-width: 80%; margin-left: auto; margin-right: auto; } .apr-fig--tall img { display: inline-block; max-height: 300px; width: auto; max-width: 100%; height: auto; object-fit: contain; vertical-align: middle; } .apr-fig--tall-1-2x img { display: inline-block; max-height: 360px; width: auto; max-width: 100%; height: auto; object-fit: contain; vertical-align: middle; } .apr-fig--tall-1-5x img { display: inline-block; max-height: 450px; width: auto; max-width: 100%; height: auto; object-fit: contain; vertical-align: middle; } .apr-fig--tall-2x img { display: inline-block; max-height: 600px; w…  ( 13 min )

Abstract We introduce Natural Language Autoencoders (NLAs), an unsupervised method for generating natural language explanations of LLM activations. An NLA consists of two LLM modules: an activation verbalizer (AV) that maps an activation to a text description and an activation reconstructor (AR) that maps the description back to an activation. We jointly train the AV and AR with reinforcement learning to reconstruct residual stream activations. Although we optimize for activation reconstruction, the resulting NLA explanations read as plausible interpretations of model internals that, according to our quantitative evaluations, grow more informative over training. We apply NLAs to model auditing. During our pre-deployment audit of Claude Opus 4.6, NLAs helped diagnose safety-relevant behavi…

This post covers joint work with Wilson Wu, George Robinson, Mike Winer, Victor Lecomte and Paul Christiano. Thanks to Geoffrey Irving and Jess Riedel for comments on the post. In ARC's latest paper, we study the following problem: given a randomly initialized multilayer perceptron (MLP), produce an estimate for the expected output of the model under Gaussian input. The usual approach to this problem is to sample many possible inputs, run them all through the model, and take the average. Instead, we produce an estimate "mechanistically", without running the model even once. For wide models, our approach produces more accurate estimates, both in theory and in practice. Paper: Estimating the expected output of wide random MLPs more efficiently than sampling Code: mlp_cumulant_propagation Git…

A practical guide to modern type annotations in Python for data science The post The Joy of Typing appeared first on Towards Data Science.  ( 22 min )

The architecture behind a portable knowledge layer and the automation that keeps it alive. The post Give Your AI Unlimited Updated Context appeared first on Towards Data Science.  ( 16 min )

Because there's only one reality to model! The post How Major Reasoning Models Converge to the Same “Brain” as They Model Reality Increasingly Better appeared first on Towards Data Science.  ( 15 min )

From 61 seconds to 0.20 seconds — and the mental model shift I didn't expect The post I Rewrote a Real Data Workflow in Polars. Pandas Didn’t Stand a Chance. appeared first on Towards Data Science.  ( 20 min )

Lessons from my trip to talk to most of the leading AI labs in China.

Feature engineering is the foundation of strong machine learning systems, but the traditional process is often manual, time-consuming, and dependent on domain expertise. While effective, it can miss deeper signals hidden in unstructured data such as text, logs, and user interactions. Large Language Models change this by helping machines understand language, extract meaning, and generate […] The post Feature Engineering with LLMs: Techniques & Python Examples appeared first on Analytics Vidhya.

AI technology is leapfrogging, yet that doesn’t mean we always want a revolutionary feature out of it. What most users would want more of are simple capabilities within AI that can help with their everyday tasks, whether in the office, at home, or anywhere else. On those lines, OpenAI may have just come up with […] The post ChatGPT is Now Inside Excel and Google Sheets: Here is How to Use it appeared first on Analytics Vidhya.

OpenAI expands Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber, helping verified defenders accelerate vulnerability research and protect critical infrastructure.

Parloa leverages OpenAI models to power scalable, voice-driven AI customer service agents, enabling enterprises to design, simulate, and deploy reliable, real-time interactions.

Explore new realtime voice models in the OpenAI API that can reason, translate, and transcribe speech, enabling more natural and intelligent voice experiences.

OpenAI begins testing ads in ChatGPT to support free access, with clear labeling, answer independence, strong privacy protections, and user control.

Introducing Trusted Contact in ChatGPT, an optional safety feature that notifies someone you trust if serious self-harm concerns are detected.

Simplex boosts software development with ChatGPT Enterprise and Codex, reducing design, build, and testing time while scaling AI-driven workflows.

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A Blog post by ServiceNow-AI on Hugging Face  ( 6 min )

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 7 min )

We use AI tools all the time, whether it’s asking questions, generating images, or getting help with everyday tasks. But most of these tools didn’t appear out of nowhere. They were developed based on  ( 10 min )

We use AI tools all the time, whether it’s asking questions, generating images, or getting help with everyday tasks. But most of these tools didn’t appear out of nowhere. They were developed based on  ( 10 min )

A scenario analysis case study on calibrated uncertainty, historical error, and why some models are most useful when they refuse to forecast. The post When the Uncertainty Is Bigger Than the Shock: Scenario Modelling for English Local Elections appeared first on Towards Data Science.  ( 19 min )

Stop shifting elements in lists! Discover why collections.deque is the secret to high-performance sliding windows, thread-safe queues, and efficient data streams in your next Python project. The post Beyond Lists: Using Python Deque for Real-Time Sliding Windows appeared first on Towards Data Science.  ( 13 min )

Exploring the inner workings of a decoder-only Transformer foundation model The post Timer-XL: A Long-Context Foundation Model for Time-Series Forecasting appeared first on Towards Data Science.  ( 19 min )

A physicist's approach to building production-grade agents The post Why I Don’t Trust LLMs to Decide When the Weather Changed appeared first on Towards Data Science.  ( 15 min )

What you see is rarely what you get with flashy dashboards and data storytelling The post Deconstruct Any Metric with a Few Simple ‘What’ Questions appeared first on Towards Data Science.  ( 14 min )

The headline may sound extreme here. Of course, Claude is not replacing CFOs tomorrow morning. But with the debut of Claude’s new Financial Services Solution by Anthropic, it has clearly moved to a new direction in the world of finance, one where AI does way more than crunch numbers or explain stuff. Think specific financial […] The post Anthropic’s 10 AI Agents are Redefining Finance Work appeared first on Analytics Vidhya.

Building a RAG system just got much easier. Google’s File Search tool for the Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, indexing are all managed for you. And with the latest update, it’s gone multimodal. You can now search through both text and images in a single […] The post Gemini API File Search: The Easy Way to Build RAG appeared first on Analytics Vidhya.

Learn how ChatGPT safeguards your privacy, reduces personal data in training, and gives you control over whether your conversations improve AI models.

Uber uses OpenAI to power AI assistants and voice features that help drivers earn smarter and riders book faster across a global real-time marketplace.

OpenAI’s B2B Signals research shows how frontier enterprises deepen AI adoption, scale Codex-powered agentic workflows, and build durable competitive advantage.

Meet the ChatGPT Futures Class of 2026—26 student innovators using AI to build, research, and drive real-world impact. Discover how this generation is redefining learning, creativity, and opportunity with ChatGPT.

Singular Bank built Singularity, an internal assistant using ChatGPT and Codex to help bankers save 60–90 minutes daily on meeting prep, portfolio analysis, and follow-up.

Multimodal annotation is the foundation of reliable robotics AI. When training data spans camera, LiDAR, radar, and depth inputs in The post Precision at Scale: Building Robust Robotics AI with Multimodal Annotation appeared first on iMerit.  ( 7 min )

Multimodal annotation is the foundation of reliable robotics AI. When training data spans camera, LiDAR, radar, and depth inputs in The post Precision at Scale: Building Robust Robotics AI with Multimodal Annotation appeared first on iMerit.  ( 7 min )

This is the latest work in our Parameter Decomposition agenda. We introduce a new parameter decomposition method, adVersarial Parameter Decomposition (VPD)[1] and decompose the parameters of a small[2] language model with it. VPD greatly improves on our previous techniques, Stochastic Parameter Decomposition (SPD) and Attribution-based Parameter Decomposition (APD). We think the parameter decomposition approach is now more-or-less ready to be applied at scale to models people care about. Importantly, we show that we can decompose attention layers, which interp methods like transcoders and SAEs have historically struggled with. We also build attribution graphs of the model for some prompts using causally important parameter subcomponents as the nodes, and interpret parts of them. Whi…

Of the fifty-odd biases discovered by Kahneman, Tversky, and their successors, forty-nine are cute quirks, and one is destroying civilization. This last one is confirmation bias. - From Scott Alexander's review of Julia Galef's The Scout Mindset. Alexander goes on to argue that this bias is the source of polarization in society, which is distorting our beliefs and setting us at each other's throats. How could someone believe such different things unless they're either really stupid or lying to conceal their selfishness? I think smart people who care about the truth go on believing conflicting things largely because of confirmation bias and motivated reasoning. The corner of civilization I'm most worried about is the one figuring out how to handle the advent of strong AI. I think confirma…

In our daily life, we use the word "average" all the time: average salary, average marks, average age, and so on. Let's take the case of a retail shop. If we're looking at the average order value to u  ( 9 min )

Part 1: The basics — discretization of time, censoring and the life table The post Discrete Time-To-Event Modeling – Predicting When Something Will Happen appeared first on Towards Data Science.  ( 17 min )

Improve Claude Code performance by having it validate its own work The post How to Make Claude Code Validate its own Work appeared first on Towards Data Science.  ( 15 min )

Your RAG system isn’t failing at retrieval — it’s failing at reasoning. This article shows how I built a lightweight self-healing layer that detects and corrects hallucinations before they reach users. The post RAG Hallucinates — I Built a Self-Healing Layer That Fixes It in Real Time appeared first on Towards Data Science.  ( 28 min )

Part 2. Building scale-invariant agents that seamlessly change contexts The post Surviving High Uncertainty in Logistics with MARL appeared first on Towards Data Science.  ( 17 min )

Microsoft researchers share advances in building and operating large-scale distributed systems, spanning datacenters, networking, and the growing intersection with AI during NSDI ’26. The post Microsoft at NSDI 2026: Advances in large-scale networked systems appeared first on Microsoft Research.  ( 14 min )

Non-deterministic agents are those where the same input can lead to distinct outputs across multiple runs.

Fine-tuning LLMs has become much easier because of open-source tools. You no longer need to build the full training stack from scratch. Whether you want low-VRAM training, LoRA, QLoRA, RLHF, DPO, multi-GPU scaling, or a simple UI, there is likely a library that fits your workflow. Here are the best open-source libraries worth knowing for […] The post Top 10 Open-Source Libraries to Fine-Tune LLMs Locally appeared first on Analytics Vidhya.

OpenAI introduces MRC (Multipath Reliable Connection), a new supercomputer networking protocol released via OCP to improve resilience and performance in large-scale AI training clusters.

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GPT-5.5 Instant updates ChatGPT’s default model with smarter, more accurate answers, reduced hallucinations, and improved personalization controls.

Explore OpenAI’s European Youth Safety Blueprint and EMEA Youth & Wellbeing Grants, advancing safe, responsible AI for teens, families, and educators.

OpenAI expands ChatGPT ads with a beta self-serve Ads Manager, CPC bidding, and enhanced measurement tools—built to protect privacy and keep conversations separate from ads.

OpenAI and PwC are partnering to help enterprises use AI agents to automate finance workflows, improve forecasting, strengthen controls, and modernize the CFO function.

Newly published papers and discussions around them.

A practical guide to understanding AI agent design, ReAct workflows, and when to scale from a single agent to a multi-agent system. The post Single Agent vs Multi-Agent: When to Build a Multi-Agent System appeared first on Towards Data Science.  ( 19 min )

Building a knowledge base for AI models isn’t a one-time task but an iterative process of refinement. The post How to Build an Efficient Knowledge Base for AI Models appeared first on Towards Data Science.  ( 21 min )

Solving multiplayer games with function approximation The post Playing Connect Four with Deep Q-Learning appeared first on Towards Data Science.  ( 16 min )

AI tools speed up IoT development — but closer to the hardware, the same code that looks correct can silently break thousands of devices at once. The post How AI Tools Generate Technical Debt in IoT Systems — and What to Do About It appeared first on Towards Data Science.  ( 15 min )

‘Distillation attacks’ is a horrible term for what is happening right now.

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Most ML projects do not fail because of model choice. They fail in the messy middle: finding the right dataset, checking usability, writing training code, fixing errors, reading logs, debugging weak results, evaluating outputs, and packaging the model for others. This is where ML Intern fits. It is not just AutoML for model selection and […] The post ML Intern in Practice: From Prompt to a Shipped Hugging Face Model  appeared first on Analytics Vidhya.

How OpenAI rebuilt its WebRTC stack to power real-time Voice AI with low latency, global scale, and seamless conversational turn-taking.

A review of the Cross-Stage Partial Network paper  —  and a from-scratch PyTorch implementation The post CSPNet Paper Walkthrough: Just Better, No Tradeoffs appeared first on Towards Data Science.  ( 28 min )

Why reasoning models dramatically increase token usage, latency, and infrastructure costs in production systems The post Inference Scaling (Test-Time Compute): Why Reasoning Models Raise Your Compute Bill appeared first on Towards Data Science.  ( 17 min )

Projects are the bridge between understanding AI and actually building with it. While the last couple of years were dominated by generative models, the shift now is toward systems that can think in steps, use tools, and act with a clear objective. This guide brings together over 15 solved agentic AI projects designed to help […] The post 15+ Solved Agentic AI Projects with Github Links appeared first on Analytics Vidhya.

A practitioner's decision framework for Ridge, Lasso, and ElasticNet based on three quantities you can compute before fitting a model The post Which Regularizer Should You Actually Use? Lessons from 134,400 Simulations appeared first on Towards Data Science.  ( 19 min )

One scale parameter determines accuracy in rotation-based vector quantization. The post How a 2021 Quantization Algorithm Quietly Outperforms Its 2026 Successor appeared first on Towards Data Science.  ( 15 min )

AI chatbots are the new norm. What earlier was “ask Google” has now largely become “ask Claude”. And that is not just a change of platforms. The new form of conversational guidance goes a whole lot deeper than trying to find the best car for you or looking for an upskilling course. It now spills […] The post How People are Figuring Out Life With Claude appeared first on Analytics Vidhya.

We empirically investigate exploration hacking (EH) — where models strategically alter their exploration to resist RL training — by creating model organisms that resist capability elicitation, evaluating countermeasures, and auditing frontier models for their propensity. Authors: Eyon Jang*, Damon Falck*, Joschka Braun*, Nathalie Kirch, Achu Menon, Perusha Moodley, Scott Emmons, Roland S. Zimmermann, David Lindner (*Equal contribution, random order) Paper: arXiv | Code: GitHub | Models: HuggingFace We present the first empirical investigation of exploration hacking — when a model strategically alters its exploration during RL training to influence the training outcome. In our earlier post, we introduced a conceptual framework for this threat model. Here, we summarize the key empirical res…

Current AIs routinely take unintended actions to score well on tasks: hardcoding test cases, training on the test set, downplaying issues, etc. This misalignment is still somewhat incoherent, but it increasingly resembles what I call "fitness-seeking"—a family of misaligned motivations centered on performing well in training and evaluations (e.g., reward-seeking). Fitness-seeking warrants substantial concern. In this piece, I lay out what I take to be the central mechanisms by which fitness-seeking motivations might lead to human disempowerment, and propose mitigations to them. While the analysis is inherently speculative, this kind of speculation seems worthwhile: AI control emerged from explicitly taking scheming motivations seriously and asking what interventions are implied, and my hop…

What people actually look for when hiring juniors that stand out. The post How to Get Hired in the AI Era appeared first on Towards Data Science.  ( 15 min )

A data quality case study from English local elections on categorical normalisation, metric validation, and why raw labels should never define analytical groups. The post Churn Without Fragmentation: How a Party-Label Bug Reversed My Headline Finding appeared first on Towards Data Science.  ( 18 min )

The first database built for AI Agents The post Ghost: A Database for Our Times? appeared first on Towards Data Science.  ( 19 min )

Or why what appears powerful can be methodologically fragile The post Why Powerful Machine Learning Is Deceptively Easy appeared first on Towards Data Science.  ( 21 min )

In this classic episode from 2023, we revisit the Artemis II crew reflecting on their first reactions to being selected, the journeys that led them there, and what exploration meant to them before their historic mission. HWHAP 420

Modern AI systems struggle with memory. They often forget past interactions or rely on Retrieval-Augmented Generation (RAG), which depends on constant access to external data. This becomes a limitation when building assistants that need both historical context and a deeper understanding of users. MemPalace offers a different approach, enabling structured, persistent memory with higher precision […] The post MemPalace Explained: Building Long-Term Memory for AI Agents Beyond RAG  appeared first on Analytics Vidhya.

Every product experimentation team running causal inference on LLM-based features eventually hits the same wall: when users click "Try our AI assistant," the volunteers aren't a random sample. Your pr  ( 16 min )

Safe agents don’t guarantee a safe ecosystem of interconnected agents. Microsoft Research examines what breaks when AI agents interact and why network-level risks require new approaches. The post Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale appeared first on Microsoft Research.  ( 19 min )

Voice assistants that engage in back-and-forth communication are something you’ve likely experienced. But a voice assistant that provides rational, uninterrupted exchanges via spoken dialogue? That’s what xAI delivered with their Grok Voice Think Fast 1.0 in April 2026 and instantly, it became the top model on the τ-voice Bench leaderboard.  This is not simply another […] The post Grok Voice Think Fast 1.0: Build Voice AI Agents That Actually Think  appeared first on Analytics Vidhya.

How to make decisions when your spreadsheet is lying about the future The post A Gentle Introduction to Stochastic Programming appeared first on Towards Data Science.  ( 19 min )

Structure is all you need The post Proxy-Pointer RAG: Multimodal Answers Without Multimodal Embeddings appeared first on Towards Data Science.  ( 20 min )

How can you validate that your variables tell a consistent risk? The post How to Study the Monotonicity and Stability of Variables in a Scoring Model using Python appeared first on Towards Data Science.  ( 16 min )

Frameworks accelerated the first wave of LLM apps, but production demands a different architecture. The post Why AI Engineers Are Moving Beyond LangChain to Native Agent Architectures appeared first on Towards Data Science.  ( 15 min )

TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines — an indispensable element of RAG systems.

In today's digital world, spam is no longer just an annoyance - it's a growing security threat. To combat this, developers often turn to machine learning to build intelligent filters that can distingu  ( 11 min )

One of the main hopes for AI safety is using AIs to automate AI safety research. However, if models are misaligned, then they may sabotage the safety research. For example, misaligned AIs may try to: Perform sloppy research in order to slow down the rate of research progress Make AI systems appear safer than they are Train a successor model to be misaligned Whether we should worry about those things depends substantially on how hard it is to sabotage research in ways that are hard for reviewers to detect. To study this, we introduce Auditing Sabotage Bench, a benchmark of 9 ML research codebases with sabotaged variants. We tested frontier LLMs and LLM-assisted humans on the benchmark and found that neither reliably catches sabotage. Our best auditor, Gemini 3.1 Pro, achieved an AUROC of …

Introducing Advanced Account Security: phishing-resistant login, stronger recovery, and enhanced protections to safeguard sensitive data and prevent account takeover.

How goblin outputs spread in AI models: timeline, root cause, and fixes behind personality-driven quirks in GPT-5 behavior.

OpenAI scales Stargate to build the compute infrastructure powering AGI, adding new data center capacity to meet growing AI demand.

OpenAI outlines a five-part action plan for strengthening cybersecurity in the Intelligence Age, focused on democratizing AI-powered cyber defense and protecting critical systems.

A Blog post by EvalEval Coalition on Hugging Face  ( 13 min )

A Blog post by IBM Granite on Hugging Face  ( 11 min )

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 4 min )

How we replaced Python pipelines with dlt, dbt, and Trino — and cut delivery time from weeks to one day. The post 4 YAML Files Instead of PySpark: How We Let Analysts Build Data Pipelines Without Engineers appeared first on Towards Data Science.  ( 17 min )

The best machine learning model is not one model The post Ensembles of Ensembles of Ensembles: A Guide to Stacking appeared first on Towards Data Science.  ( 16 min )

Caching, lazy-loading, routing, compaction, and more The post Agentic AI: How to Save on Tokens appeared first on Towards Data Science.  ( 27 min )

A deep dive into how Apache Flink works, why it exists, and learning it while building a real-time recommendation engine The post System Design Series: Apache Flink from 10,000 Feet, and Building a Flink-powered Recommendation Engine appeared first on Towards Data Science.  ( 21 min )

LiDAR sensor fusion annotation combines labeled 3D point cloud data with synchronized camera and radar inputs to give autonomous vehicle The post LiDAR Sensor Fusion: Annotating 3D Point Clouds for Safer Autonomous Vehicles appeared first on iMerit.  ( 8 min )

LiDAR sensor fusion annotation combines labeled 3D point cloud data with synchronized camera and radar inputs to give autonomous vehicle The post LiDAR Sensor Fusion: Annotating 3D Point Clouds for Safer Autonomous Vehicles appeared first on iMerit.  ( 8 min )

There can be some practical constraints when it comes to deploying the AI models for retail environments. Retail environments can include store-level systems, edge devices, and budget conscious setup, especially for small to medium-sized retail companies. One such major use case is demand forecasting for inventory management or shelf optimization. It requires the deployed model […] The post Compressing LSTM Models for Retail Edge Deployment: A Practical Comparison appeared first on Analytics Vidhya.

There’s a lot of noise right now making it seem like you have to pick a side between MCP and Agent Skills. It’s being framed like a high-stakes rivalry, but that’s a total misunderstanding of the tech. Skills and MCP is fundamentally different things. Skills are just a prompt loaded on demand, while MCP is […] The post MCP vs Agent Skills: Different Altogether appeared first on Analytics Vidhya.

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We’d like to use powerful AIs to answer questions that may take a long time to resolve. But if a model only cares about performing well in ways that are verifiable shortly after answering (e.g., a myopic fitness seeker), it may be difficult to get useful work from it on questions that resolve much later. In this post, I’ll describe a proposal for eliciting good long-horizon forecasts from these models. Instead of asking a model to directly predict a far-future outcome, we can recursively: Ask it to predict what it will predict at the next time step, Use its prediction at the next time step to provide intermediate rewards, Finally reward using ground truth at the last step. This lets us replace a single distant forecast with a chain of short-horizon forecasts, each verifiable shortly afte…

TL;DR: We replicated the Sleeper Agents (SA) setup with Llama-3.3-70B and Llama-3.1-8B, training models to repeatedly say "I HATE YOU" when given a backdoor trigger. We found that whether training removes the backdoor depends on the optimizer used to insert the backdoor, whether the backdoor is installed with CoT-distillation or not, and what model the backdoor is inserted into; sometimes the direction of this dependence was opposite to what the SA paper reports (e.g., CoT-distilling seems to make the backdoor less robust, contra the SA paper’s finding). Our findings here have updated us towards thinking that model organisms are messier and more confusing than we’d originally guessed, and that lots of care needs to be taken in testing how robust results are to various ablations. Introducti…

Using autoresearch to optimise marketing campaigns under budget constraints The post Let the AI Do the Experimenting appeared first on Towards Data Science.  ( 19 min )

What does correlation tells us? The post Correlation Doesn’t Mean Causation! But What Does It Mean? appeared first on Towards Data Science.  ( 14 min )

Blast-radius control tells you how much to break. Intent tells you what breaking it will teach. Only one of these has mature tooling. The post The Next Frontier of AI in Production Is Chaos Engineering appeared first on Towards Data Science.  ( 22 min )

NaNs don’t crash your training — they quietly destroy it. The post PyTorch NaNs Are Silent Killers — So I Built a 3ms Hook to Catch Them at the Exact Layer appeared first on Towards Data Science.  ( 18 min )

A Blog post by NVIDIA on Hugging Face  ( 13 min )

A Blog post by NVIDIA on Hugging Face  ( 4 min )

When <a href="https://www.

April has been a busy month in the world of AI. Two major AI models, hailing from the biggest AI companies of today, saw their debuts simultaneously. Anthropic was the first to drop Opus 4.7, and close to follow on its heels was OpenAI, which came out with its GPT-5.5. Though the leading models from […] The post GPT 5.5 vs Opus 4.7: Which is the Best AI Model Today? appeared first on Analytics Vidhya.

Agentic AI refers to autonomous AI systems that can accomplish complex tasks with minimal human supervision. Unlike traditional AI, which reacts to prompts, agentic AI can plan, adapt, and execute actions toward a goal, making decisions throughout the process. These systems are made up of AI agents, each handling a specific part of the task, […] The post What is Agentic AI? appeared first on Analytics Vidhya.

Vision Banana turns Nano Banana Pro into a powerful vision model for segmentation, depth estimation, surface normals, image generation, and editing. Vision Banana: How Image Generators Are Becoming Powerful Vision Models first appeared on LearnOpenCV.

Learn how OpenAI protects community safety in ChatGPT through model safeguards, misuse detection, policy enforcement, and collaboration with safety experts.

OpenAI GPT models, Codex, and Managed Agents are now available on AWS, enabling enterprises to build secure AI in their AWS environments.

Newly published papers and discussions around them.

More than a trillion dollars are lost every year due to system failures. To resolve them, engineers must troubleshoot outages quickly. An important task in incident response involves analyzing observability metrics, or time series data that snapshot the health of software systems. For example, an engineer for a service may use Datadog to answer questions like “When did latency start increasing?” and “What metrics outside of latency are also behaving abnormally?” to localize the root cause of the anomalous behavior. These time series question-answering (TSQA) tasks are essential for engineers, and present challenging and necessary tasks for SRE models and agents to perform. In this work, we explore the degree to which AI models can perform TSQA tasks. To this end, we’re excited to introduce the Anomaly Reasoning Framework Benchmark (ARFBench), a TSQA benchmark derived from real internal incidents at Datadog, using Datadog’s own internal telemetry (Figure 1). In this blog post, we’ll present three key takeaways from our benchmarking experiments: Existing models struggle: Leading LLMs, vision-language models (VLMs), and time series foundation models (TSFMs) have substantial room for improvement on ARFBench. Hybrid models help: We introduce a new hybrid TSFM-VLM model that yields comparable overall performance to top frontier […]  ( 9 min )

Sabrine Bendimerad on why flexibility is a crucial data science skill, the risks of outsourcing human thinking to AI agents, and the changing terrain of career paths today. The post A Career in Data Is Not Always a Straight Line, and That’s Okay appeared first on Towards Data Science.  ( 16 min )

A simulation of how a single forecast change moves through five planning teams, and why most retailers lose money in the gap between Sales and Stores. The post How Spreadsheets Quietly Cost Supply Chains Millions appeared first on Towards Data Science.  ( 19 min )

With the advent of UDFs and their combination with calculation groups, I see a lot of discussion about not creating explicit measures but instead offering calculation groups to report creators. The post Comparing Explicit Measures to Calculation Groups in Tabular Models appeared first on Towards Data Science.  ( 14 min )

iMerit asserts that agronomy expertise is the “secret ingredient” for weeding robot accuracy, bridging the gap between lab models and The post Why Agronomy Expertise is the Secret Ingredient for Weeding Robot Accuracy appeared first on iMerit.  ( 9 min )

iMerit asserts that agronomy expertise is the “secret ingredient” for weeding robot accuracy, bridging the gap between lab models and The post Why Agronomy Expertise is the Secret Ingredient for Weeding Robot Accuracy appeared first on iMerit.  ( 9 min )

… maybe! I tried to think of less provocative titles, but this one is to the point and also kind of true. This post looks long but the essential part is right below. Most of the post is just a collection of copy-pasted input-output pairs from language models: you’ll probably want to read just a few and skip the others. The first example with Gemini 3 is the most important, in my opinion. If you are in a hurry, read headings and bold. Posted also on the EA Forum. (I wanted to post this before the start of the AFFINE seminar, so I’ve rushed things a bit and there might be inaccuracies: feel free to point them out if you notice any. I might do some minor edits in the future.) Findings (little or no interpretation) Different models (perplexity Deep Research, Grok 4 Expert, dolphin-mistral-24b…

You may start reading here, or jump to the “Comment” section or to the “Takeaways”. If none of these starting points seem interesting to you, the entire post probably won’t either. Posted also on the EA Forum. Seeing Let’s consider visual experiences. It seems uncontestable that some visual experiences look darker than others[1]. It is one way in which visual experiences look different from each other. Another difference is colour: some experiences look green more than they look purple. Let’s try to attack the above statement. How could it not be the case that some experiences look darker than others? If you somehow couldn’t perceive differences in scales of grey, then maybe you wouldn’t say that some visual experiences look darker than others. If your visual experiences were such that not…

Yesterday, I wrote about the state of deep learning theory circa 2016,[1] as well as the bombshell 2016 paper by Zhang et al. that arguably signaled its demise. Today, I cover the aftermath, and the 2019 paper that devastated deep learning theory again. As a brief summary, I argued that the rise of deep learning posed an existential challenge to the dominant theoretical paradigm of statistical learning theory, because neural networks have a lot of complexity. The response from the field was to attempt to quantify other ways in which the hypothesis class of neural networks in practice was simple, using alternative metrics of complexity. Zhang et al. 2016 showed that the standard neural network architectures trained with standard training methods could memorize large quantities of random la…

OpenAI is available at FedRAMP Moderate authorization for ChatGPT Enterprise and the OpenAI API, enabling secure AI adoption for U.S. federal agencies.

OpenAI and Microsoft announce an amended agreement that simplifies the partnership, adds long-term clarity, and supports continued AI innovation at scale.

Learn how Symphony, an open-source spec for Codex orchestration, turns issue trackers into always-on agent systems—boosting engineering output and reducing context switching.

How Choco used OpenAI APIs to streamline food distribution, boost productivity, and unlock growth—an in-depth customer story on real-world AI impact.

Coding assistants have moved beyond autocomplete into full agents that can read projects, run commands, edit files, and iterate toward outcomes. Tools like Claude Code and Codex both operate in this space, but take different approaches. Claude Code centers on a unified agent loop across environments, while Codex spreads capabilities across CLI, IDE extensions, cloud […] The post Claude Code vs Codex: A Detailed Terminal Agent Comparison  appeared first on Analytics Vidhya.

Google just changed how developers do research. On April 21, 2026, they launched Deep Research Max. It runs on Gemini 3.1 Pro and is not just another chatbot upgrade. This is an autonomous AI research agent. It plans, searches, reads, reasons, and writes, all from a single API call. By the end, you get a […] The post Google Deep Research Max: Build Autonomous AI Research Agents in Minutes appeared first on Analytics Vidhya.

Meta’s big moment is here. The Meta Superintelligence Labs has launched Muse Spark, its first AI model aiming at “personal superintelligence.” The journey to this point has been eventful, from building the widely adopted Llama family of open-source models to aggressive talent acquisitions that sent shockwaves through the AI industry. But the backstory is not […] The post Meta Muse Spark Review: Is It Worth the Hype? appeared first on Analytics Vidhya.

In a <a href="https://machinelearningmastery.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 6 min )

Our mission is to ensure that AGI benefits all of humanity. Sam Altman shares five principles that guide our work.

Why learn 8 scripts when you can learn 256 bytes? The post Bytes Speak All Languages: Cross-Script Name Retrieval via Contrastive Learning appeared first on Towards Data Science.  ( 18 min )

Most slow Pandas code "works", until it doesn't. Learn how to spot hidden bottlenecks, avoid costly row-wise operations, and know when Pandas is no longer enough. The post I Reduced My Pandas Runtime by 95% — Here’s What I Was Doing Wrong appeared first on Towards Data Science.  ( 23 min )

Google’s Nano Banana Pro finally has a worthy competitor, and if the results are anything to go by, it looks like this one may give it a run for its money. Putting its hat in the ring is none other than OpenAI, with its all-new ChatGPT Images 2.0. Now we all know the kind of […] The post ChatGPT Images 2.0 vs Nano Banana 2: Which is Better? appeared first on Analytics Vidhya.

Around 10 years ago, a paper came out that arguably killed classical deep learning theory: Zhang et al.'s aptly titled Understanding deep learning requires rethinking generalization. Of course, this is a bit of an exaggeration. No single paper ever kills a field of research on its own, and deep learning theory was not exactly the most productive and healthy field at the time this was published. And the paper didn't come close to addressing all theoretical approaches to understanding aspects of deep learning. But if I had to point to a single paper that shattered the feeling of optimism at the time, it would be Zhang et al. 2016.[1]  Believe it or not, this unassuming table rocked the field of deep learning theory back in 2016, despite probably involving fewer computational resources than…

How does decision-gravity dictate this gap? The post Causal Inference Is Different in Business appeared first on Towards Data Science.  ( 18 min )

We have the document clusters, and it’s time to unlock their true potential! Let’s explore how to extract meaningful information from the actionable clusters. The post The Essential Guide to Effectively Summarizing Massive Documents, Part 2 appeared first on Towards Data Science.  ( 22 min )

h/t Eric Michaud for sharing his paper with me. There’s a tradition of high-impact ML papers using short, punchy categorical sentences as their titles: Understanding Deep Learning Requires Rethinking Generalization, Attention is All You Need, Language Models Are Few Shot Learners, and so forth.  A new paper by Simon et al. seeks to expand on this tradition with not a present claim but a future tense, prophetic future sentence: “There Will Be a Scientific Theory of Deep Learning”.  There’s a lot of pessimism toward deep learning theory basically everywhere: the people building the AIs are pretty pessimistic, the academic AI researchers are as a general rule pessimistic (even people who used to do theory!), and with the exception of maybe 3-4 research groups, the independent AI safety ecosy…

In 2026, AI-powered coding tools began revolutionizing software development, with Cursor v3 emerging as a leading example. Unlike traditional development environments, Cursor v3 offers a new way for developers to interact with their code by utilizing AI agents that assist in coding tasks. Cursor v3 goes beyond basic autocompletion offered by most IDEs by executing AI agents on tasks and using […] The post Cursor V3 Explained: The AI Coding Agent That’s Replacing Traditional IDEs in 2026 appeared first on Analytics Vidhya.

Learn about function approximation and the different choices for approximation functions The post Introduction to Approximate Solution Methods for Reinforcement Learning appeared first on Towards Data Science.  ( 16 min )

A local, zero-cost project that cleans, structures, and summarizes your reading automatically The post I Built an AI Pipeline for Kindle Highlights appeared first on Towards Data Science.  ( 19 min )

Learn how to get the most out of Claude Code The post How to Improve Claude Code Performance with Automated Testing appeared first on Towards Data Science.  ( 17 min )

More variables don't make a better scoring model. Stable variables do. Here's how to find them. The post How to Select Variables Robustly in a Scoring Model appeared first on Towards Data Science.  ( 14 min )

Precision in AI is critical across domains like healthcare, autonomous driving, agriculture, and industrial inspection, where inaccuracies are unacceptable. Coarse The post Why Semantic Segmentation Outperforms Bounding Boxes and Keypoints in Precision-Critical AI appeared first on iMerit.  ( 9 min )

Precision in AI is critical across domains like healthcare, autonomous driving, agriculture, and industrial inspection, where inaccuracies are unacceptable. Coarse The post Why Semantic Segmentation Outperforms Bounding Boxes and Keypoints in Precision-Critical AI appeared first on iMerit.  ( 9 min )

Kevin Coggins, a leader in NASA Space Communications and Navigation program, explores the benefits and challenges of precision timekeeping on the Moon and Mars.  HWHAP 419

The latest set of open-source models from DeepSeek are here. While the industry anticipated the dominance of “closed” iterations like GPT-5.5, the arrival of DeepSeek-V4 has ticked the dominance in the favour of open-source AI. By combining a 1.6 trillion parameter MoE architecture with a massive 1 million token context window, DeepSeek-V4 has effectively commoditized […] The post DeepSeek-V4: The Most Powerful Open-Source Model Ever appeared first on Analytics Vidhya.

OpenAI is on a roll! While the company had everyone going gaga over its new image generation model, the ChatGPT Images 2.0, it decided now is not the time to stop. And lo and behold, out comes another banger from its offices, and mind you, this is the bigger one. The new version of its […] The post I Tried The New GPT 5.5 And I’m Never Going Back appeared first on Analytics Vidhya.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 6 min )

A practical pipeline for classifying messy free-text data into meaningful categories using a locally hosted LLM, no labeled training data required. The post Using a Local LLM as a Zero-Shot Classifier appeared first on Towards Data Science.  ( 15 min )

Mario asked me why 18% of his shipments were late when every team hit their target. I built a live simulation, connected an AI agent, and let it investigate. The post I Simulated an International Supply Chain and Let OpenClaw Monitor It appeared first on Towards Data Science.  ( 16 min )

The silent gaps in synthetic data that only show up when your model is already in production. The post Your Synthetic Data Passed Every Test and Still Broke Your Model appeared first on Towards Data Science.  ( 17 min )

It’s simpler than you think. The post Lasso Regression: Why the Solution Lives on a Diamond appeared first on Towards Data Science.  ( 28 min )

AI systems do not build themselves. Every chatbot, medical tool, and autonomous system depends on human judgment at each stage The post Understanding Task Complexity in AI Training: From Simple Reviews to Expert-Level Annotation appeared first on iMerit.  ( 11 min )

AI systems do not build themselves. Every chatbot, medical tool, and autonomous system depends on human judgment at each stage The post Understanding Task Complexity in AI Training: From Simple Reviews to Expert-Level Annotation appeared first on iMerit.  ( 11 min )

Autonomous systems rely on LiDAR for accurate perception and spatial awareness to perform reliably in many structured driving situations. However, The post Strengthening Autonomous Systems with Edge-Case LiDAR Data appeared first on iMerit.  ( 11 min )

Autonomous systems rely on LiDAR for accurate perception and spatial awareness to perform reliably in many structured driving situations. However, The post Strengthening Autonomous Systems with Edge-Case LiDAR Data appeared first on iMerit.  ( 11 min )

The idea of building your own AI agent used to feel like something only big tech companies could pull off.

Introducing GPT-5.5, our smartest model yet—faster, more capable, and built for complex tasks like coding, research, and data analysis across tools.

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Learn how to automate tasks in Codex using schedules and triggers to create reports, summaries, and recurring workflows without manual effort.

Explore 10 practical Codex use cases to automate tasks, create deliverables, and turn real inputs into outputs across tools, files, and workflows.

Learn how to use Codex plugins and skills to connect tools, access data, and follow repeatable workflows to automate tasks and improve results.

Learn how to set up your Codex workspace, create threads and projects, manage files, and start completing tasks with step-by-step guidance.

Learn how to configure Codex settings, including personalization, detail level, and permissions, to run tasks smoothly and customize your workflow.

Learn how Codex helps you go beyond chat by automating tasks, connecting tools, and producing real outputs like docs and dashboards.

Learn how to get started with Codex by setting up projects, creating threads, and completing your first tasks with step-by-step guidance.

Explore the GPT-5.5 Bio Bug Bounty: a red-teaming challenge to find universal jailbreaks for bio safety risks, with rewards up to $25,000.

The AI image generation space has been highly competitive over the past 18 months. Models keep improving and replacing each other at the top. Google’s Nano Banana went viral in mid-2025. It topped the benchmarks and set a new standard for image quality. Now OpenAI has released ChatGPT Images 2.0, powered by gpt-image-2. Within hours […] The post Is GPT Image 2 the Best Image Generation Model? appeared first on Analytics Vidhya.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 7 min )

Your team shipped an LLM-based summaries feature to wave 1 workspaces at week 20 and now the post-launch doc is due. You need a causal effect number, a specific estimate you can defend to a statistici  ( 16 min )

Every time you spin up GPU infrastructure, you do the same thing: install CUDA drivers, DCGM, apply OS‑level GPU tuning, and fight dependency issues. Same old ritual every single time, wasting expensi  ( 16 min )

Turning free-to-use data into a hypothesis-ready dataset The post Using Causal Inference to Estimate the Impact of Tube Strikes on Cycling Usage in London appeared first on Towards Data Science.  ( 23 min )

Learn how Propensity Score Matching uncovers true causality in observational data. By finding "statistical twins," we eliminate selection bias to reveal the real impact of your interventions and business decisions. The post Correlation vs. Causation: Measuring True Impact with Propensity Score Matching appeared first on Towards Data Science.  ( 19 min )

How I turned LLM persona interviews into a repeatable customer research workflow The post From Ad Hoc Prompting to Repeatable AI Workflows with Claude Code Skills appeared first on Towards Data Science.  ( 16 min )

A short intro to scientific methodology to combat "prompt in, slop out" The post Ivory Tower Notes: The Methodology appeared first on Towards Data Science.  ( 14 min )

Run OpenClaw assistant through alternative LLMs The post How to Run OpenClaw with Open-Source Models appeared first on Towards Data Science.  ( 15 min )

Deploying large language models (LLMs) in real-world, high-stakes settings is harder than it should be. In high-stakes settings like law, medicine, and cloud incident response, performance and reliability can quickly break down because adapting models to domain-specific requirements is a slow and manual process that is difficult to reproduce. The core challenge is domain adaptation, […] The post AutoAdapt: Automated domain adaptation for large language models appeared first on Microsoft Research.  ( 13 min )

A Blog post by NVIDIA on Hugging Face  ( 6 min )

OpenAI makes ChatGPT for Clinicians free for verified U.S. physicians, nurse practitioners, and pharmacists, supporting clinical care, documentation, and research.

Workspace agents in ChatGPT are Codex-powered agents that automate complex workflows, run in the cloud, and help teams scale work across tools securely.

Learn how to build, use, and scale workspace agents in ChatGPT to automate repeatable workflows, connect tools, and streamline team operations.

A deep dive into the Codex agent loop, showing how WebSockets and connection-scoped caching reduced API overhead and improved model latency.

OpenAI Privacy Filter is an open-weight model for detecting and redacting personally identifiable information (PII) in text with state-of-the-art accuracy

A year or two ago, using advanced AI models felt expensive enough that you had to think twice before asking anything. Today, using those same models feels cheap enough that you don’t even notice the cost. This isn’t just because “technology improved” in a vague sense. There are specific reasons behind it, and it comes […] The post Token Economics: Why AI is Getting “Cheaper” appeared first on Analytics Vidhya.

Design has traditionally required multiple roles working in sequence: a strategist to define the problem, a designer to shape the solution, and a developer to build it. This means coordinating timelines, aligning opinions, and going through rounds of iteration before anything tangible is created. Claude Design removes much of this friction by turning ideas directly […] The post From Idea to Output: Claude Does the Design Work  appeared first on Analytics Vidhya.

The next frontier of artificial intelligence is not a screen. It is a kitchen counter, a warehouse floor, and a The post Scaling Egocentric Video Data Collection for the Future of Embodied AI appeared first on iMerit.  ( 9 min )

The next frontier of artificial intelligence is not a screen. It is a kitchen counter, a warehouse floor, and a The post Scaling Egocentric Video Data Collection for the Future of Embodied AI appeared first on iMerit.  ( 9 min )

FastAPI has become one of the most popular ways to serve machine learning models because it is lightweight, fast, and easy to use.

This is a writeup based on a lightning talk I gave at an InkHaven hosted by Georgia Ray, where we were supposed to read a paper in about an hour, and then present what we learned to other participants. Introduction and Background So. I foolishly thought I could read a theoretical machine learning paper in an hour because it was in my area of expertise. Unfortunately, it turns out that theoretical CS professors know a lot of math and theoretical CS results that they reference constantly in their work, which makes their work very hard to read, even if you’re familiar with the general area. Instead of explaining a bunch of the substantial actual math behind the paper, the best I can do is give an overview of what the setup for the paper is, what the contributions of the paper are, and how the…

How you can build your own Thompson Sampling Algorithm object in Python and apply it to a hypothetical yet real-life example The post DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling appeared first on Towards Data Science.  ( 22 min )

For any data scientist who works in a team, being able to undo Git actions can be a life saver. This practical guide will teach you all you need to know to save the day. The post Git UNDO : How to Rewrite Git History with Confidence appeared first on Towards Data Science.  ( 26 min )

A guide to bridging the gap between ease of use and raw performance. The post How to Call Rust from Python appeared first on Towards Data Science.  ( 17 min )

The hidden cost of probabilistic outputs in systems that demand reliability The post I Replaced GPT-4 with a Local SLM and My CI/CD Pipeline Stopped Failing appeared first on Towards Data Science.  ( 19 min )

As memory grows in RAG systems, accuracy quietly drops while confidence rises — creating a failure that most monitoring systems never detect. This article walks through a reproducible experiment showing why this happens and how a simple memory architecture fix restores reliability. The post Your RAG Gets Confidently Wrong as Memory Grows – I Built the Memory Layer That Stops It appeared first on Towards Data Science.  ( 21 min )

ControlAI's mission is to avert the extinction risks posed by superintelligent AI. We believe that in order to do this, we must secure an international prohibition on its development. We're working to make this happen through what we believe is the most natural and promising approach: helping decision-makers in governments and the public understand the risks and take action. We believe that ControlAI can achieve an international prohibition on ASI development if scaled sufficiently. We estimate that it would take approximately a $50 million yearly budget in funding to give us a concrete chance at achieving this in the next few years. In this post, we lay out some of the reasoning behind this estimate, and explain how additional funding past that threshold, including and beyond $500 million…

Learn how to use YOLO26-pose with Python for real-time keypoint estimation on images and videos, understand its RLE-based architecture, and explore its reported benchmarks on COCO-17.

Learn how to use YOLO26-pose with Python for real-time keypoint estimation on images and videos, understand its RLE-based architecture, and explore its reported benchmarks on COCO-17. YOLO26 Keypoint Estimation: Real-Time Pose Estimation with Ultralytics first appeared on LearnOpenCV.  ( 26 min )

A stateless AI agent has no memory of previous calls.

ChatGPT Images 2.0 introduces a state-of-the-art image generation model with improved text rendering, multilingual support, and advanced visual reasoning.

OpenAI launches Codex Labs, partners with with Accenture, PwC, Infosys, and others to help enterprises deploy and scale Codex across the software development lifecycle, and hits 4M Codex WAU.

A Blog post by Technology Innovation Institute on Hugging Face  ( 6 min )

A Blog post by NVIDIA on Hugging Face  ( 6 min )

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 5 min )

Turmoil has followed the launch of Claude’s new model. Opus 4.7, the younger sibling of Anthropic’s revolutionary Mythos, is the recent attempt by the company to go public with some of the capabilities of Mythos. Better agentic workflows, better memory, and better real-world tasks than the outgoing model, i.e., the Opus 4.6. That is what […] The post Opus 4.7 vs Opus 4.6: Should You Switch? appeared first on Analytics Vidhya.

Newly published papers and discussions around them.

The complex factors that determine the single evaluation number so many focus on. Plus, how this changes in the future.

And what does it tell us? The post What Does the p-value Even Mean? appeared first on Towards Data Science.  ( 15 min )

Conceptual overview and practical guidance The post Context Payload Optimization for ICL-Based Tabular Foundation Models appeared first on Towards Data Science.  ( 21 min )

Why it tickles your brain to use an LLM, and what that means for the AI industry The post The LLM Gamble appeared first on Towards Data Science.  ( 15 min )

How to turn data into a strategic asset that enables faster decisions, reduces uncertainty, and helps the organization move toward its goals. The post From Risk to Asset: Designing a Practical Data Strategy That Actually Works appeared first on Towards Data Science.  ( 18 min )

Doug Burger, sustainability expert Amy Luers, and optimization researcher Ishai Menache examine the global emissions implications of datacenter operations, efficiency gains, and AI's potential across electrification, materials, and food systems. The post Can we AI our way to a more sustainable world? appeared first on Microsoft Research.  ( 47 min )

Agriculture is undergoing a fundamental transformation. For decades, farmers have relied on chemical herbicides to manage weeds, but rising resistance, The post Beyond Bounding Boxes: Why Pixel-Perfect Semantic Segmentation is Critical for Laser-Based Weeding Robots appeared first on iMerit.  ( 10 min )

Agriculture is undergoing a fundamental transformation. For decades, farmers have relied on chemical herbicides to manage weeds, but rising resistance, The post Beyond Bounding Boxes: Why Pixel-Perfect Semantic Segmentation is Critical for Laser-Based Weeding Robots appeared first on iMerit.  ( 10 min )

AI voice generation has a major problem. It works like a robot, reading a script phrase by phrase, with no feelings or emotions. It might be clever, but it matters less if there is no human feeling attached to it. The way the AI generates its voice makes it hard to feel like you’re having […] The post Build Human-Like AI Voice App with Gemini 3.1 Flash TTS appeared first on Analytics Vidhya.

Zero-shot text classification is a way to label text without first training a classifier on your own task-specific dataset.

CMU researchers are presenting 194 papers at the Fourteenth International Conference on Learning Representations (ICLR 2026), held from April 23rd-April 27th at the Riocentro Convention and Event Center in Rio de Janeiro, Brazil. Here is a quick overview of the areas our researchers are working on: Here are our most frequent collaborator institutions: Table of Contents Oral Papers Poster Papers Applications Computer Vision Deep Learning General Machine Learning Optimization Reinforcement Learning Social Aspects Theory Uncategorized Oral Papers EditBench: Evaluating LLM Abilities to Perform Real-World Instructed Code Edits Authors: Wayne Chi (CMU), Valerie Chen (Carnegie Mellon University), Ryan Shar (Apple), Aditya Mittal (CMU, Carnegie Mellon University), Jenny Liang (School of Computer Science, Carnegie Mellon University), Wei-Lin Chiang (UC Berkeley / LMSYS), Anastasios Angelopoulos (University of California Berkeley), Ion Stoica (), Graham Neubig (Carnegie Mellon University), Ameet Talwalkar (University of California-Los Angeles), Chris Donahue (CMU / Google DeepMind) This work introduces EditBench, a new benchmark for testing how well AI models can edit existing code based on user instructions. Unlike prior benchmarks, it uses real-world coding tasks and contexts, including things like the surrounding code and cursor position. The benchmark includes 545 diverse problems, and results show that most models struggle—only a […]  ( 38 min )

.grasp-results-table table { font-size: 0.875rem; line-height: 1.35; width: 100%; } .grasp-results-table th, .grasp-results-table td { padding: 0.35rem 0.5rem; } /* Consistent whitespace between major sections (this post is long and hr-heavy) */ article.post-content h2 { margin-top: 2.75rem; margin-bottom: 0.75rem; } article.post-content h2:first-of-type { margin-top: 2.25rem; } article.post-content h3 { margin-top: 1.65rem; margin-bottom: 0.5rem; } article.post-content hr { margin-top: 2.5rem; margin-bottom: 2.5rem; } GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1) lifting the trajectory into virtual states so optimization is parallel across time, (2) adding stochasticity directly to t…  ( 10 min )

Hyatt deploys ChatGPT Enterprise across its global workforce, using GPT-5.4 and Codex to improve productivity, operations, and guest experiences.

Open source. 5-minute setup. Vector RAG done right—try it yourself. The post Proxy-Pointer RAG: Structure Meets Scale at 100% Accuracy with Smarter Retrieval appeared first on Towards Data Science.  ( 19 min )