‘Skill’ is the latest buzzword in agentic AI workflows, and you will know this for sure if you use any of the AI coding platforms today. We explored Skills in Claude Code in detail in a previous article. Though not all developers prefer the same AI tool for coding help. Another major player in this […] The post Replit Agent Skills Complete Guide: Write Your Own Skills in Replit appeared first on Analytics Vidhya.

A few days ago, a group of researchers at Google dropped a PDF that didn’t just change AI: it wiped billions of dollars off the stock market. If you looked at the charts for Micron (MU) or Western Digital last week, you saw a sea of Red. Why? Because a new technology called TurboQuant just […] The post TurboQuant: Google’s KV Cache Optimization Explained appeared first on Analytics Vidhya.

Over the past 24 hours, the developer community has been obsessed with one thing. A leak. The source code of Claude Code, one of the most advanced AI coding systems, surfaced online. Within hours, GitHub was flooded with forks, breakdowns, and deep dives. For developers, it felt like rare access. While for Anthropic, it was […] The post Claude Code Leak: 16 Lessons on Building Production-Ready AI Systems appeared first on Analytics Vidhya.

The Vector View of Least Squares. The post Linear Regression Is Actually a Projection Problem (Part 2: From Projections to Predictions) appeared first on Towards Data Science.  ( 22 min )

Workflows and encoding techniques in quantum machine learning The post How to Handle Classical Data in Quantum Models appeared first on Towards Data Science.  ( 17 min )

Run Quantum Experiments with Qiskit-Aer The post Quantum Simulations with Python appeared first on Towards Data Science.  ( 13 min )

OpenAI acquires TBPN to accelerate global conversations around AI and support independent media, expanding dialogue with builders, businesses, and the broader tech community.

Codex now includes pay-as-you-go pricing for ChatGPT Business and Enterprise, providing teams a more flexible option to start and scale adoption.

Written quickly as part of the Inkhaven Residency. At a high level, research feedback I give to more junior research collaborators often can fall into one of three categories: Doing quick sanity checks Saying precisely what you want to say Asking why one more time In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like.  This piece covers doing quick sanity checks, which is the most common advice I give to junior researchers. I’ll cover the other two pieces of advice in a subsequent piece.  Doing quick sanity checks Research is hard (almost by definition) and people are often wrong. Every researcher has wasted countless ho…

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 21 min )

Newly published papers and discussions around them.

A Blog post by H company on Hugging Face  ( 3 min )

A Blog post by Technology Innovation Institute on Hugging Face  ( 11 min )

AI benchmarks report how large language models (LLMs) perform on specific tasks but provide little insight into their underlying capabilities that drive their performance. They do not explain failures or reliably predict outcomes on new tasks. To address this, Microsoft researchers in collaboration with Princeton University and Universitat Politècnica de València introduce ADeLe (opens in new tab) (AI […] The post ADeLe: Predicting and explaining AI performance across tasks appeared first on Microsoft Research.  ( 12 min )

A systems design diagnosis of hallucination, corrigibility, and the structural gap that scaling cannot close The post The Inversion Error: Why Safe AGI Requires an Enactive Floor and State-Space Reversibility appeared first on Towards Data Science.  ( 29 min )

Why thinking longer can matter more than being bigger The post How Can A Model 10,000× Smaller Outsmart ChatGPT? appeared first on Towards Data Science.  ( 17 min )

How I am adapting in my career in the age of AI, automation, and when everything moving faster than expected. The post What Happens Now That AI is the First Analyst On Your Team? appeared first on Towards Data Science.  ( 14 min )

AI progress is often framed as a story of better models, larger architectures, improved benchmarks, and faster inference. These advances The post The Hidden Data Operations Behind Production-Grade AI appeared first on iMerit.  ( 7 min )

You probably use Google on a daily basis, and nowadays, you might have noticed AI-powered search results that compile answers from multiple sources. But you might have wondered how the AI can gather all this information and respond at such blazing speeds, especially when compared to the medium-sized and large models we typically use. Smaller […] The post Speculative Decoding: How LLMs Generate Text 3x Faster appeared first on Analytics Vidhya.

Crossposted from the DeepMind Safety Research Medium Blog. Read our full paper about this topic by Max Kaufmann, David Lindner, Roland S. Zimmermann, and Rohin Shah. Overseeing AI agents by reading their intermediate reasoning “scratchpad” is a promising tool for AI safety. This approach, known as Chain-of-Thought (CoT) monitoring, allows us to check what a model is thinking before it acts, often helping us catch concerning behaviors like reward hacking and scheming. However, CoT monitoring can fail if a model’s chain-of-thought is not a good representation of the reasoning process we want to monitor. For example, training LLMs with reinforcement learning (RL) to avoid outputting problematic reasoning can result in a model learning to hide such reasoning without actually removing problemat…

Gradient Labs uses GPT-4.1 and GPT-5.4 mini and nano to power AI agents that automate banking support workflows with low latency and high reliability.

Learn why embedding models are like a GPS for meaning. Instead of searching for exact words, it navigates a "Map of Ideas" to find concepts that share the same vibe. From battery types to soda flavors, learn how to fine-tune these digital fingerprints for pinpoint accuracy in your next AI project. The post The Map of Meaning: How Embedding Models “Understand” Human Language appeared first on Towards Data Science.  ( 19 min )

Make your coding agent more efficient The post How to Make Claude Code Better at One-Shotting Implementations appeared first on Towards Data Science.  ( 15 min )

I’ve been so surprised by how fast individual builders can now ship real and useful prototypes. Tools like Claude Code, Google AntiGravity, and the growing ecosystem around them have crossed a threshold: you can inspect what others are building online and realize just how fast you can build today. Over the past weeks, I’ve started […] The post Building a Personal AI Agent in a couple of Hours appeared first on Towards Data Science.  ( 21 min )

What I learned about data wrangling, segmentation, and storytelling while building an application security report from scratch The post Turning 127 Million Data Points Into an Industry Report appeared first on Towards Data Science.  ( 15 min )

Multimodal AI has grown from novelty to a must in recent times. Need proof? If I were to tell you to work on an AI model that only understands text, you would probably laugh and throw 10 model names at me that can work across formats – be it text, audio, or visuals. The new […] The post Qwen3.5-Omni is here! Scaling up to a Native Omni-modal AGI appeared first on Analytics Vidhya.

AI demos often look impressive, delivering fast responses, polished communication, and strong performance in controlled environments. But once real users interact with the system, issues surface like hallucinations, inconsistent tone, and answers that should never be given. What seemed ready for production quickly creates friction and exposes the gap between demo success and real-world reliability. […] The post Fine-Tuning vs RAG vs Prompt Engineering  appeared first on Analytics Vidhya.

Do you remember the very first AI voice conversation that you had? No doubt, it felt unreal getting live answers from a talking bot. But the one thing largely missing from the interaction was the feel of a human responding to your queries. Years on, we now see AI models have evolved largely in this […] The post Gemini 3.1 Flash Live: AI Conversations Now Feel Way More Human appeared first on Analytics Vidhya.

Scaling LiDAR annotation for production AI requires domain-trained annotators, sensor-aware tooling, and multi-stage quality assurance built into every step The post LiDAR Data Operations for Production AI: Scaling 3D Point Cloud Annotation with Quality and Speed appeared first on iMerit.  ( 8 min )

A Blog post by IBM Granite on Hugging Face  ( 6 min )

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 12 min )

OpenAI raises $122 billion in new funding to expand frontier AI globally, invest in next-generation compute, and meet growing demand for ChatGPT, Codex, and enterprise AI.

What is p hacking, is it bad, and can you get ai to do it for you? The post How to Lie with Statistics with your Robot Best Friend appeared first on Towards Data Science.  ( 18 min )

Sara A. Metwalli on the rise of a promising new technology, the effects of LLM on her work, and more. The post Why Data Scientists Should Care About Quantum Computing appeared first on Towards Data Science.  ( 14 min )

SHAP needs 30 ms to explain a fraud prediction. That explanation is stochastic, runs after the decision, and requires a background dataset you have to maintain at inference time. This article benchmarks a neuro-symbolic model that produces a deterministic, human-readable explanation in 0.9 ms — as a by-product of the forward pass itself — on the Kaggle Credit Card Fraud dataset. The speedup is 33×. The fraud recall is identical. The post Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection appeared first on Towards Data Science.  ( 21 min )

New orgs! New types of models! With Nemotron Super, Sarvam, Cohere Transcribe, & others

Projects are the bridge between learning and becoming a professional. While theory builds fundamentals, recruiters value candidates who can solve real problems. A strong, diverse portfolio showcases practical skills, technical range, and problem-solving ability.  This guide compiles 20+ solved projects across ML domains, from basic regression and forecasting to NLP and Computer Vision. The tools […] The post 20+ Solved ML Projects to Build Your Portfolio and Boost Your Resume appeared first on Analytics Vidhya.

Pandas DataFrames provide powerful tools for selecting and indexing data efficiently. The two most commonly used indexers are .loc and .iloc. The .loc method selects data using labels such as row and column names, while .iloc works with integer positions based on a 0-based index. Although they may seem similar, they function differently and can […] The post Iloc vs Loc in Pandas: A Guide with Examples  appeared first on Analytics Vidhya.

Authors: Satvik Golechha*, Sid Black*, Joseph Bloom * Equal Contribution. This work was done as part of the Model Transparency team at the UK AI Security Institute (AISI). Our code is available on GitHub and the model checkpoints and data is available on HuggingFace. Executive Summary In Natural Emergent Misalignment from Reward Hacking in Production RL (MacDiarmid et al., 2025), Anthropic recently demonstrated that language models that learn reward hacking in their production RL environments become emergently misaligned (EM). Their pipeline, illustrated below, proceeds from pre-training through to RL on coding tasks, where models that discover reward hacks subsequently exhibit misaligned behaviour on unrelated evaluations: Figure 0: The experimental pipeline from MacDiarmid et al. that …

AI for Disaster Response in Asia: OpenAI Workshop with Gates Foundation

Spoiler, it will take longer than 3 months The post How to Become an AI Engineer Fast (Skills, Projects, Salary) appeared first on Towards Data Science.  ( 18 min )

What happens when your production model drifts and retraining isn’t an option? This article shows how a self-healing neural network detects drift, adapts in real time using a lightweight adapter, and recovers 27.8% accuracy—without retraining or downtime. The post Self-Healing Neural Networks in PyTorch: Fix Model Drift in Real Time Without Retraining appeared first on Towards Data Science.  ( 25 min )

If you have ever looked at a professional spreadsheet, you must have noticed titles spanning across multiple columns. That is the most essential and widely used example of a popular Excel function called Merge. Continuing our Excel 101 series, we shall explore the Cell Merge function today. We shall understand what it allows us to […] The post Excel 101: Cell and Column Merge vs Combine appeared first on Analytics Vidhya.

It's easier than ever to 10x your output with agentic AI. The post Using OpenClaw as a Force Multiplier: What One Person Can Ship with Autonomous Agents appeared first on Towards Data Science.  ( 26 min )

Integrating CMIP6 projections, ERA5 reanalysis, and impact models into a lightweight, interpretable workflow The post From NetCDF to Insights: A Practical Pipeline for City-Level Climate Risk Analysis appeared first on Towards Data Science.  ( 15 min )

Claude Skills is the latest AI tool that targets AI automation at some level. Anthropic was smart enough to identify one key problem developers face every day – having to rewrite prompts for repetitive tasks. So, packaging it in the form of “Skills”, Claude brings a new way to store these prompts or instructions, so […] The post Building Custom Claude Skills For Repeatable AI Workflows appeared first on Analytics Vidhya.

Learn how STADLER uses ChatGPT to transform knowledge work, saving time and accelerating productivity across 650 employees.

This post highlights a few key excerpts from our full impact report. You can read the full report at https://controlai.com/impact-report-2025. ControlAI is a non-profit organization working to avert the extinction risks posed by superintelligence. We help hundreds of thousands of people understand these risks and meet hundreds of lawmakers to inform them, without mincing words, about what is at stake. In little more than a year, we briefed over 200 parliamentarians, built a coalition of 110+ UK lawmakers recognizing superintelligence as a national security threat, led to two debates in the UK House of Lords, and our work led to a series of hearings on AI risk and superintelligence at the Canadian Parliament.[1]These hearings included testimonies from me (Andrea) and Samuel at ControlAI, Co…

This post highlights a few key excerpts from our full impact report. You can read the full report at https://controlai.com/impact-report-2025. ControlAI is a non-profit organization working to avert the extinction risks posed by superintelligence. We help hundreds of thousands of people understand these risks and meet hundreds of lawmakers to inform them, without mincing words, about what is at stake. In little more than a year, we briefed over 200 parliamentarians, built a coalition of 110+ UK lawmakers recognizing superintelligence as a national security threat, led to two debates in the UK House of Lords, and our work led to a series of hearings on AI risk and superintelligence at the Canadian Parliament.[1]These hearings included testimonies from me (Andrea) and Samuel at ControlAI, Co…

A practical, code-driven guide to scaling deep learning across machines — from NCCL process groups to gradient synchronization The post Building a Production-Grade Multi-Node Training Pipeline with PyTorch DDP appeared first on Towards Data Science.  ( 20 min )

Simulate a quantum computer with Qiskit The post A Beginner’s Guide to Quantum Computing with Python appeared first on Towards Data Science.  ( 14 min )

A warehouse picking operation is the process of collecting items from storage locations to fulfil customer orders. It is one of the most labour-intensive activities in logistics, accounting for up to 55% of total warehouse operating costs. For each order, an operator receives a list of items to collect from their storage locations. They walk to […] The post How ElevenLabs Voice AI Is Replacing Screens in Warehouse and Manufacturing Operations appeared first on Towards Data Science.  ( 17 min )

Teams across companies lose meeting notes and action items after discussions. This guide builds a lasting fix: an AI Meeting Summarizer and Action Planner using Claude Code with MCP. It processes transcripts into structured summaries with tasks, decisions, and calendar invites, connects to Google Calendar and Gmail, and stores everything in SQLite. MCP acts as […] The post Build an AI Meeting Summarizer & Action Planner with Claude Code + MCP  appeared first on Analytics Vidhya.

The development of a modern web application can be a complicated puzzle. You have to do user authentication, maintain a database, and enable third-party provisions, such as maps. This process often takes days of coding. However, what if you could create a data-driven app just by describing it in a prompt? Now it is a […] The post Build a Full-Stack App in Minutes with Google’s New AI Studio Tools appeared first on Analytics Vidhya.

Creating an AI agent for tasks like analyzing and processing documents autonomously used to require hours of near-endless configuration, code orchestration, and deployment battles.  ( 27 min )

Build a complete pipeline for YOLO26 instance segmentation, from image and video inference to custom dataset training and edge deployment. YOLO26 Instance Segmentation: Pixel-Perfect AI at Real-Time Speed first appeared on LearnOpenCV.  ( 46 min )

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 3 min )

Authors: Daria Ivanova, Riya Tyagi, Arthur Conmy, Neel Nanda Daria and Riya are co-first authors. This work was done during Neel Nanda’s MATS 9.0. Claude helped write code and suggest edits for this post. TL;DR  One of our best safety techniques right now is “just read the chain of thought”. But this isn’t always enough: can we learn more by going beyond just reading the reasoning? Yet it's such an effective technique that it's hard to tell if we have made much progress on improving methods. To help the community develop more powerful chain of thought analysis tools, we introduce and open source nine objective tasks, where a black box GPT 5.2 monitor falls short OOD. We also baseline probes (linear, attention, SAE) and text frequency analysis (TF-IDF), and find they often do better than z…

Imagine a robot tasked with cleaning a kitchen. It needs to observe its environment, decide what to do, and adjust when things don’t go as expected, for example, when the mug it was tasked to wash is already clean, or the sink is full of other items. This is the domain of embodied AI: systems […] The post AsgardBench: A benchmark for visually grounded interactive planning appeared first on Microsoft Research.  ( 12 min )

Vision-language models (VLMs) use images and text to plan robot actions, but they still struggle to decide what actions to take and where to take them. Most systems split these decisions into two steps: a VLM generates a plan in natural language, and a separate model translates it into executable actions. This approach often breaks […] The post GroundedPlanBench: Spatially grounded long-horizon task planning for robot manipulation appeared first on Microsoft Research.  ( 13 min )

Transformers power modern NLP systems, replacing earlier RNN and LSTM approaches. Their ability to process all words in parallel enables efficient and scalable language modeling, forming the backbone of models like GPT and Gemini. In this article, we break down how Transformers work, starting from text representation to self-attention, multi-head attention, and the full Transformer […] The post How Transformers Power LLMs: Step-by-Step Guide  appeared first on Analytics Vidhya.

Projects are the bridge between learning and becoming a professional. While theory builds fundamentals, recruiters value candidates who can solve real problems. A strong, diverse portfolio showcases practical skills, technical range, and problem-solving ability.  This guide compiles over 20 solved projects across AI domains, from basic machine learning to advanced generative AI and agentic systems. […] The post 20+ Solved AI Projects to Build Your Portfolio and Boost Your Resume appeared first on Analytics Vidhya.

AI systems do not build themselves; they require human judgment to work well. Behind every successful chatbot or medical tool The post How to Start a Career in AI Training: Skills, Domains, and Growth Paths appeared first on iMerit.  ( 13 min )

In my latest posts, we’ve talked a lot about prompt caching as well as caching in general, and how it can improve your AI app in terms of cost and latency. However, even for a fully optimized AI app, sometimes the responses are just going to take some time to be generated, and there’s simply […] The post How to Make Your AI App Faster and More Interactive with Response Streaming appeared first on Towards Data Science.  ( 16 min )

Using Codex and MCP to connect Google Drive, GitHub, BigQuery, and analysis in one real workflow The post Beyond Code Generation: AI for the Full Data Science Workflow appeared first on Towards Data Science.  ( 17 min )

Why retrieval that looks excellent on paper can still behave like noise in real RAG and agent workflows The post What the Bits-over-Random Metric Changed in How I Think About RAG and Agents appeared first on Towards Data Science.  ( 23 min )

Traditional databases answer a well-defined question: does the record matching these criteria exist? <a href="https://machinelearningmastery.  ( 30 min )

tldr: We share a toy environment that we found useful for understanding how reasoning changed over the course of capabilities-focused RL. Over the course of capabilities-focused RL, the model biases more strongly towards reward hints over direct instruction in this environment. Setup When we noticed the increase in verbalized alignment evaluation awareness during capabilities-focused RL, we initially thought that the right mental model was something like: “the model wants to figure out if it’s being evaluated for alignment” “the model is trying to figure out if the scenario is real or fake” However, qualitatively neither of these seemed particularly salient to the model: The model would often correctly identify alignment evaluations, yet still conduct extensive reasoning, then choose th…

My last article was about implementing Like-for-Like (L4L) for Stores. After discussing my solution with my peers and clients, I encountered an interesting issue that brought additional requirements to my first solution. This is what I want to discuss here. The post Following Up on Like-for-Like for Stores: Handling PY appeared first on Towards Data Science.  ( 15 min )

Proactivity, blocking, and planning The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science.  ( 13 min )

Understanding how to set up human-in-the-loop (HITL) agentic workflows in LangGraph The post Building Human-In-The-Loop Agentic Workflows appeared first on Towards Data Science.  ( 17 min )

Data Leakage, Real-World Models, and the Path to Production AI in Healthcare The post My Models Failed. That’s How I Became a Better Data Scientist. appeared first on Towards Data Science.  ( 16 min )

My friend who is a developer once asked an LLM to generate documentation for a payment API.  ( 35 min )

My main complaint with AI solutions is that they are largely dependent on my presence for any task. Even with agentic AI now in the mix, complete automation of any complex process still seems like a myth. Tools like n8n and make.com need a considerable setup time and do not really function as conventional AI […] The post Is Claude Dispatch the End of OpenClaw? appeared first on Analytics Vidhya.

Now that we know AI is inevitably a part of our workflow, the more relevant question today is not “should I use AI?”, but “how to use AI?”. With the AI tools market more crowded than ever, each passing week sees a new assistant, generator, or automation. The struggle then is of choice from a […] The post Top 46 AI Tools in 2026 You Must Use appeared first on Analytics Vidhya.

Learn how OpenAI’s Model Spec serves as a public framework for model behavior, balancing safety, user freedom, and accountability as AI systems advance.

OpenAI launches a Safety Bug Bounty program to identify AI abuse and safety risks, including agentic vulnerabilities, prompt injection, and data exfiltration.

Supercharge Claude Code with continual learning The post How to Make Claude Code Improve from its Own Mistakes appeared first on Towards Data Science.  ( 14 min )

How AI agents, data foundations, and human-centered analytics are reshaping the future of decision-making The post From Dashboards to Decisions: Rethinking Data & Analytics in the Age of AI  appeared first on Towards Data Science.  ( 15 min )

We’ve become remarkably good at building sophisticated agent systems, but we haven’t developed the same rigor around proving they work. The post Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation appeared first on Towards Data Science.  ( 22 min )

How to leverage a framework to effectively prioritize AI Initiatives to rapidly accelerate growth and efficiency The post The Complete Guide to AI Implementation for Chief Data & AI Officers in 2026 appeared first on Towards Data Science.  ( 29 min )

For different learning goals and career paths, choosing the right certification can get confusing. Some people want analytics. Others want ads. Some care about AI. And many just want something credible to add to their resume. This list is built with that in mind. A set of free Google certificate courses, each aligned to a […] The post Top 5 Free Google Certificate Courses in 2026 appeared first on Analytics Vidhya.

Artificial intelligence is rapidly evolving. New models emerge nearly every day, with each one attempting to be the best. In this sea of similar models, we see something new every now and then. One of such models is the new Mistral Small 4. It is an innovative AI model that is not only going to […] The post Mistral Small 4: The One Model That Codes, Reasons, and Chats appeared first on Analytics Vidhya.

OpenAI releases prompt-based teen safety policies for developers using gpt-oss-safeguard, helping moderate age-specific risks in AI systems.

The OpenAI Foundation announces plans to invest at least $1 billion in curing diseases, economic opportunity, AI resilience, and community programs.

ChatGPT introduces richer, visually immersive shopping powered by the Agentic Commerce Protocol, enabling product discovery, side-by-side comparisons, and merchant integration.

If you look at the architecture diagram of almost any AI startup today, you will see a large language model (LLM) connected to a vector store.  ( 31 min )

Autonomous systems across sectors often work without human intervention. Self-driving vehicles navigate busy highways. Agricultural robots and precision sprayers manage The post How 3D Semantic Segmentation Improves Object Boundary Accuracy in Autonomous Systems appeared first on iMerit.  ( 9 min )

A Blog post by ServiceNow-AI on Hugging Face  ( 8 min )

Training machine learning models usually starts out being organized and ends up in absolute chaos. We’ve all been there: dozens of experiments scattered across random notebooks, and model files saved  ( 10 min )

An AI-generated video can look convincing at first glance. The characters are detailed, the lighting feels natural, and the motion The post Solving Temporal Drift in AI-Generated Video appeared first on iMerit.  ( 12 min )

An AI-generated video can look convincing at first glance. The characters are detailed, the lighting feels natural, and the motion The post Temporal Drift in AI-Generated Video appeared first on iMerit.  ( 12 min )

Master data types, index alignment, and defensive Pandas practices to prevent silent bugs in real data pipelines. The post 4 Pandas Concepts That Quietly Break Your Data Pipelines appeared first on Towards Data Science.  ( 17 min )

Your ML model predicts perfectly but recommends wrong actions. Learn the 5-question diagnostic, method comparison matrix, and Python workflow to fix it with causal inference. The post Causal Inference Is Eating Machine Learning appeared first on Towards Data Science.  ( 19 min )

This Article asks what happens next. The model has encoded its knowledge of fraud as symbolic rules. V14 below a threshold means fraud. What happens when that relationship starts to change? Can the rules act as a canary? In other words: can neuro-symbolic concept drift monitoring work at inference time, without labels? Full architecture background in Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules and How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment. You will follow this article without them, but the mechanism section makes more sense with context. The post Neuro-Symbolic Fraud Detection: Catching Concept Drift Before F1 Drops (Label-Free) appeared first on Towards Data Science.  ( 27 min )

Rapid prototyping with Replit, AI agents, and minimal manual coding The post I Built a Podcast Clipping App in One Weekend Using Vibe Coding appeared first on Towards Data Science.  ( 18 min )

Are machines truly intelligent? AI researchers Subutai Ahmad and Nicolò Fusi join Doug Burger to compare transformer-based AI with the human brain, exploring continual learning, efficiency, and whether today’s models are on a path toward human intelligence. The post Will machines ever be intelligent?  appeared first on Microsoft Research.  ( 48 min )

Newly published papers and discussions around them.

One of the core challenges of data science is drawing meaningful causal conclusions from observational data. In many such cases, the goal is to estimate the true impact of a treatment or behaviour as fairly as possible. This article explores Propensity Score Matching (PSM), a statistical technique used for that very purpose. Unlike randomized experiments […] The post Guide to Propensity Score Matching for Causal Inference to Estimate True Impact appeared first on Analytics Vidhya.

With so much happening in AI and machine learning today, figuring out where to start can feel overwhelming. Different learners prefer different approaches! Some want visuals, others prefer coding. Some prefer short form, others lean toward long-form learning. While many simply want a clear path into ML. This article is here to fix that. Instead […] The post Top 10 YouTube Channels to Learn Machine Learning appeared first on Analytics Vidhya.

Memory is one of the most overlooked parts of agentic system design.  ( 34 min )

To address the novel safety challenges posed by a state-of-the-art video model as well as a new social creation platform, we’ve built Sora 2 and the Sora app with safety at the foundation. Our approach is anchored in concrete protections.

The case for why self-improvement is real but it doesn't lead to fast takeoff.

A step-by-step guide to making your OpenAI apps faster, cheaper, and more efficient The post Prompt Caching with the OpenAI API: A Full Hands-On Python tutorial appeared first on Towards Data Science.  ( 16 min )

A hands-on guide to implementing CFD with NumPy, from discretization to airflow simulation around a bird's wing The post Building a Navier-Stokes Solver in Python from Scratch: Simulating Airflow appeared first on Towards Data Science.  ( 14 min )

AI coding assistants have quickly moved from optional tools to a core part of modern software development. Adoption is accelerating fast. Around 84% of developers now use or plan to use AI tools, and over half use them daily. The market has already reached about $8.5 billion in 2026 and is growing rapidly. These tools are not just helping […] The post Top 10 AI Coding Assistants of 2026 appeared first on Analytics Vidhya.

Most data platforms don’t break overnight; they grow into complexity, query by query. Over time, business logic spreads across SQL scripts, dashboards, and scheduled jobs until the system becomes a “SQL jungle.” This article explores how that happens and how to bring structure back. The post Escaping the SQL Jungle appeared first on Towards Data Science.  ( 18 min )

Piecewise linear approximations are a practical way to handle nonlinear constrained models using LP/MIP The post A Gentle Introduction to Nonlinear Constrained Optimization with Piecewise Linear Approximations appeared first on Towards Data Science.  ( 22 min )

What if the way we build AI document chatbots today is flawed? Most systems use RAG. They split documents into chunks, create embeddings, and retrieve answers using similarity search. It works in demos but often fails in real use. It misses obvious answers or picks the wrong context. Now there is a new approach called […] The post PageIndex vs Traditional RAG: A Better Way to Build Document Chatbots appeared first on Analytics Vidhya.

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

A Blog post by IBM Granite on Hugging Face  ( 2 min )

An 85% accurate AI agent fails 4 out of 5 times on a 10-step task. Learn the compound probability math behind production failures (and the 4-check pre-deployment framework to fix it). The post The Math That’s Killing Your AI Agent appeared first on Towards Data Science.  ( 18 min )

Handling outliers and missing values in borrower data using Python. The post Building Robust Credit Scoring Models (Part 3) appeared first on Towards Data Science.  ( 22 min )

While efficiency is an important source of AI value, it is only part of the picture The post How to Measure AI Value appeared first on Towards Data Science.  ( 18 min )

Why agentic RAG systems fail silently in production and how to detect them before your cloud bill does The post Agentic RAG Failure Modes: Retrieval Thrash, Tool Storms, and Context Bloat (and How to Spot Them Early) appeared first on Towards Data Science.  ( 15 min )

In the modern AI landscape, an agent loop is a cyclic, repeatable, and continuous process whereby an entity called an AI agent — with a certain degree of autonomy — works toward a goal.  ( 28 min )

The podcast team discusses how to watch and engage with the Artemis II mission, from launch coverage to real-time updates and beyond. HWHAP 416.

Most AI tools still require constant supervision, forcing you to guide every step. Claude Cowork,, the latest offering by Anthropic, changes that! By bringing an agentic system into everyday workflows, you describe the outcome and let it handle the execution independently. It can deliver organized files, structured documents, and synthesized research while you focus elsewhere, […] The post Inside Claude Cowork: How to Run Agentic AI Tasks Like a Pro  appeared first on Analytics Vidhya.

For different learning styles, career goals, and comfort with tools, finding the right data analyst course is HARD. Some people start with Excel. Others into Python. With no clear roadmap ahead, it’s hard to find a single starting point. Some want job-ready certifications, while others just want hands-on practice. This list is built for that. […] The post Top 7 Free Data Analytics Courses with Certificates appeared first on Analytics Vidhya.

If you regularly use AI, especially for coding, you know there is an obvious upgrade to the usual to-and-fro in chats. What if you could save a particular workflow in AI and run it without writing a super-long prompt every time you need it? Claude Skills now lets you do exactly that. This nifty little […] The post Claude Skills Explained: Build, Configure, and Use Custom Skills on Claude Code appeared first on Analytics Vidhya.

Computer vision empowers computers with the ability to understand, label, and interpret images. With the right image datasets, a data The post 32 Free Image Datasets for Computer Vision Algorithms appeared first on iMerit.  ( 8 min )

Building products without the coding part The post The Basics of Vibe Engineering appeared first on Towards Data Science.  ( 19 min )

A practical guide to caching layers across the RAG pipeline, from query embeddings to full query-response reuse The post Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines appeared first on Towards Data Science.  ( 18 min )

A visual guide to vectors and projections The post Linear Regression Is Actually a Projection Problem, Part 1: The Geometric Intuition appeared first on Towards Data Science.  ( 20 min )

Accelerate coding with AI while staying in control and building reliable, production-ready software. The post Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development appeared first on Towards Data Science.  ( 21 min )

A Blog post by NVIDIA on Hugging Face  ( 9 min )

Everyone's <a href="https://machinelearningmastery.  ( 28 min )

How OpenAI uses chain-of-thought monitoring to study misalignment in internal coding agents—analyzing real-world deployments to detect risks and strengthen AI safety safeguards.

Accelerates Codex growth to power the next generation of Python developer tools

Claude Skills (or Agent Skills) can turn a simple AI assistant into something far more powerful. But most people hit the same wall: they don’t know where to find them? Building skills from scratch is slow. The smarter move is to use production-ready Claude Code skills that developers are already sharing on GitHub. This list […] The post Top 5 GitHub Repositories to get Free Claude Code Skills (1000+ Skills) appeared first on Analytics Vidhya.

Following up on our previous work on verbalized eval awareness: we are sharing a post investigating the emergence of metagaming reasoning in a frontier training run. Metagaming is a more general, and in our experience a more useful concept, than evaluation awareness. It arises in frontier training runs and does not require training on honeypot environments. Verbalization of metagaming can go down over the course of training. We also share some quantitative analyses, qualitative examples, and upcoming work. Discuss

Summary / tl;dr In the 2010s, Paul Christiano built an extensive body of work on AI alignment—see the “Iterated Amplification” series for a curated overview as of 2018. One foundation of this program was an intuition that it should be possible to build “act-based approval-directed agents” (“approval-directed agents” for short). These AGIs, for example, would not lie to their human supervisors, because their human supervisors wouldn’t want them to lie, and these AGIs would only do things that their human supervisors would want them to do. (It sounds much simpler than it is!) Another foundation of this program was a set of algorithmic approaches, Iterated Distillation and Amplification (IDA), that supposedly offers a path to actually building these approval-directed AI agents. I am (and have…

Why one model can't do two jobs The post Two-Stage Hurdle Models: Predicting Zero-Inflated Outcomes appeared first on Towards Data Science.  ( 23 min )

The seduction of AI code assistants The post The New Experience of Coding with AI appeared first on Towards Data Science.  ( 18 min )

It's all just fearmongering The post Why You Should Stop Worrying About AI Taking Data Science Jobs appeared first on Towards Data Science.  ( 15 min )

A hands-on case study and practical guidance The post One Model to Rule Them All? SAP-RPT-1 and the Future of Tabular Foundation Models appeared first on Towards Data Science.  ( 20 min )

Unlike fully structured tabular data, preparing text data for machine learning models typically entails tasks like tokenization, embeddings, or sentiment analysis.  ( 29 min )

On evaluating and understanding the frontier of agents, and why I still turn to Claude.

Voice cloning technology has advanced quickly in recent years. Earlier voice cloning methods required large datasets and long recordings to The post Building Ethical Frameworks for Zero-Shot Voice Cloning appeared first on iMerit.  ( 11 min )

Building with AI today can feel messy. You might use one API for text, another for images, and a different one for something else. Every model comes with its own setup, API key, and billing. This slows you down and makes things harder than they need to be. What if you could use all these […] The post A Guide to OpenRouter for AI Development appeared first on Analytics Vidhya.

If you’ve spent the last year jumping between tabs, you’ve felt it. The gap between ChatGPT vs Claude isn’t about benchmarks anymore, it’s about identity. We are no longer choosing between “smart” and “smarter.” We are choosing between a multimodal powerhouse and a faithful reasoning engine. This leaves users choosing between two very different product […] The post ChatGPT vs Claude: The 2026 Battle of the AI Model Families appeared first on Analytics Vidhya.

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

Links:Paper | Code | Data LumberChunker lets an LLM decide where a long story should be split, creating more natural chunks that help Retrieval Augmented Generation (RAG) systems retrieve the right information. Introduction Long-form narrative documents usually have an explicit structure, such as chapters or sections, but these units are often too broad for retrieval tasks. At a lower level, important semantic shifts happen inside these larger segments without any visible structural break. When we split text only by formatting cues, like paragraphs or fixed token windows, passages that belong to the same narrative unit may be separated, while unrelated content can be grouped together. This misalignment between structure and meaning produces chunks that contain incomplete or mixed context, which reduces retrieval quality and affects downstream RAG performance. For this reason, segmentation should aim to create chunks that are semantically independent, rather than relying only on document structure. So how do we preserve the story’s flow and still keep chunking practical? In many cases, a reader can easily recognize where the narrative begins to shift—for example, when the text moves to a different scene, introduces a new entity, or changes its objective. The difficulty is that most automated chunking methods […]  ( 15 min )

Machine learning projects don’t end at training a model in a Jupyter notebook. The hard part is the “last mile”: turning that notebook model into something you can run reliably, update safely, and tru  ( 49 min )

Get more out of your coding agents by making reviewing more efficient The post How to Effectively Review Claude Code Output appeared first on Towards Data Science.  ( 15 min )

Privacy. Cost. Customization. Everything you need to know—step by step. The post Self-Hosting Your First LLM appeared first on Towards Data Science.  ( 23 min )

One embedding model to rule them all The post Introducing Gemini Embeddings 2 Preview appeared first on Towards Data Science.  ( 17 min )

Most neuro-symbolic systems inject rules written by humans. But what if a neural network could discover those rules itself? In this experiment, I extend a hybrid neural network with a differentiable rule-learning module that automatically extracts IF-THEN fraud rules during training. On the Kaggle Credit Card Fraud dataset (0.17% fraud rate), the model learned interpretable rules such as: The post How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment appeared first on Towards Data Science.  ( 23 min )

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

A Blog post by H company on Hugging Face  ( 4 min )

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

In our previous article of the Excel 101 series, we learnt all there is about conditional logic and operators in Excel. These operators help massively in functions like IF, AND, OR, etc. However, there is another family of functions that is used massively by Excel users and largely makes use of these operators to yield […] The post Excel 101: COUNT and COUNTIF Functions appeared first on Analytics Vidhya.

Request for Proposals Deadline: Tuesday, May 26, 2026 Schmidt Sciences invites proposals for a pilot program in AI interpretability. We seek new methods for detecting and mitigating deceptive behaviors from AI models, such as when models knowingly give misleading or harmful advice to users. If this pilot uncovers signs of meaningful progress, it may unlock a significantly larger investment in this space. Core Question and Overview Can we develop interpretability methods that (1) detect deceptive behaviors exhibited by LLMs and (2) steer their reasoning to eliminate these behaviors? Successful tools will generalize to realistic use cases, moving beyond typical academic benchmarks and addressing concrete risks arising from deceptive behaviors. Importantly, we are looking for interpretabi…

Learn how to implement robust multi-object tracking using Roboflow Trackers and OpenCV. Discover how to apply algorithms like ByteTrack and SORT to detect, track, and draw trajectories in real-world videos. Mastering Multi-Object Tracking with Roboflow Trackers & OpenCV first appeared on LearnOpenCV.  ( 47 min )

If you are here, you have probably heard about recent work on recursive language models.  ( 29 min )

OpenAI Japan announces the Japan Teen Safety Blueprint, introducing stronger age protections, parental controls, and well-being safeguards for teens using generative AI.

GPT-5.4 mini and nano are smaller, faster versions of GPT-5.4 optimized for coding, tool use, multimodal reasoning, and high-volume API and sub-agent workloads.

New research shows Americans send nearly 3 million daily messages to ChatGPT asking about compensation and earnings, helping close the wage information gap.

Artificial intelligence (AI) models often struggle when deployed across languages and regions. Most large language models (LLMs) are primarily trained The post Model Localization Services: Adapting AI Models For Language, Cultural Nuance, Region-Specific Norms, And Compliance appeared first on iMerit.  ( 9 min )

It’s a feature of the architecture The post Hallucinations in LLMs Are Not a Bug in the Data appeared first on Towards Data Science.  ( 17 min )

Shadow AI and the desire paths of modern work The post Follow the AI Footpaths appeared first on Towards Data Science.  ( 14 min )

What I learned building and distributing my first Skill from scratch The post How to Build a Production-Ready Claude Code Skill appeared first on Towards Data Science.  ( 17 min )

You already think like a Bayesian. Your stats class just taught the formula before the intuition. Here's a 5-step framework to apply it at work. The post Bayesian Thinking for People Who Hated Statistics appeared first on Towards Data Science.  ( 18 min )

Most LLM applications look great in a high-fidelity demo. Then they hit the hands of real users and start failing in very predictable yet damaging ways. They answer questions they should not, they bre  ( 12 min )

For different learning styles, goals, and comfort levels, finding a course that matches how you learn is HARD. Some people need visuals. While others wanna jump straight into code. Some need structure, others need flexibility. And many learners just want proof of effort at the end in the form of a certificate. This list is built with that in […] The post Top 7 Free Machine Learning Courses with Certificates appeared first on Analytics Vidhya.

Struggling to make AI systems reliable and consistent? Many teams face the same problem. A powerful LLM gives great results, but a cheaper model often fails on the same task. This makes production systems hard to scale. Harness engineering offers a solution. Instead of changing the model, you build a system around it. You use […] The post Harness Engineering with LangChain DeepAgents and LangSmith appeared first on Analytics Vidhya.

Markets, capabilities, cope, and bewilderment in the industrialization of language models.

Newly published papers and discussions around them.

Most perception teams label far more data than their models actually need. Frames pile up from deployed robots, and the The post Active Learning for Robotics: Smarter Data Annotation for Perception Models appeared first on iMerit.  ( 8 min )

A deep dive into why Codex Security doesn’t rely on traditional SAST, instead using AI-driven constraint reasoning and validation to find real vulnerabilities with fewer false positives.

The last two years were defined by a single word: Generative AI. Tools like ChatGPT, Gemini, and Claude turned AI from a tech term to a household name. However, we are now entering the next phase of the AI evolution. The conversation is shifting from AI that generates to AI that acts. Gone are the […] The post Generative AI vs Agentic AI: From Creating Content to Taking Action appeared first on Analytics Vidhya.

Is your data strategy 2026-ready? Get a deep dive into the mandatory shift toward human-in-the-loop oversight, active metadata, and the strategic advantages of European data sovereignty. The post The 2026 Data Mandate: Is Your Governance Architecture a Fortress or a Liability? appeared first on Towards Data Science.  ( 14 min )

Master six advanced causal inference methods with Python: doubly robust estimation, instrumental variables, regression discontinuity, modern difference-in-differences, heterogeneous treatment effects and sensitivity analysis. Includes code and a practical decision framework. The post The Causal Inference Playbook: Advanced Methods Every Data Scientist Should Master appeared first on Towards Data Science.  ( 21 min )

Google DeepMind found multi-agent networks amplify errors 17x. Learn 3 architecture patterns that separate $60M wins from the 40% that get canceled. The post The Multi-Agent Trap appeared first on Towards Data Science.  ( 18 min )

How do we program quantum computers today? The post The Current Status of The Quantum Software Stack appeared first on Towards Data Science.  ( 15 min )

You reading this tells me you wish to learn more about Excel. This article continues our Excel series, where we explored the VLOOKUP function in the last iteration. The complete VLOOKUP guide demonstrated how the function works and how best to use it. This time, we shall bring the same focus to conditional logic and […] The post Excel 101: IF, AND, OR Functions and Conditional Logic Explained appeared first on Analytics Vidhya.

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

A Blog post by NVIDIA on Hugging Face  ( 8 min )

I asked ChatGPT how it feels about the recent and viral AI trend of switching from ChatGPT to Claude. Here is what it said: “As for how it makes me feel: I don’t have feelings or brand loyalty. But from a usefulness standpoint, it’s a good thing. Easier switching forces AI products to compete on […] The post How to Switch from ChatGPT to Claude Without Losing Any Context or Memory appeared first on Analytics Vidhya.

Most AI tools forget you as soon as you close the browser window. The system begins all interactions with a new user. AI agents provide a solution to this problem because they handle their complete workflow through their system. MaxClaw is one of the best in this category. MiniMax developed this system which operates completely from the cloud space. The system […] The post A Beginner’s Guide to Building Autonomous AI Agents with MaxClaw appeared first on Analytics Vidhya.

Optimizing the cost and latency of your LLM calls with Prompt Caching The post Why Care About Prompt Caching in LLMs? appeared first on Towards Data Science.  ( 18 min )

A deep dive into exactly how text-only language models are finetuned to *see* images The post How Vision Language Models Are Trained from “Scratch” appeared first on Towards Data Science.  ( 18 min )

How a lightweight two-tower model improved restaurant discovery when popularity ranking failed The post Personalized Restaurant Ranking with a Two-Tower Embedding Variant appeared first on Towards Data Science.  ( 14 min )

Imagine you are analyzing a small dataset: You want to calculate some summary statistics to get an idea of the distribution of this data, so you use numpy to calculate the mean and variance. Your output Looks like this: Great! Now you have an idea of the distribution of your data. However, your colleague comes […] The post A Tale of Two Variances: Why NumPy and Pandas Give Different Answers appeared first on Towards Data Science.  ( 15 min )

Learn how to build a powerful agentic RAG system The post How to Build Agentic RAG with Hybrid Search appeared first on Towards Data Science.  ( 14 min )

The First Air Force Detachment 3 discusses their long-standing partnership with NASA supporting astronaut rescue and recovery operations from Mercury to Artemis. HWHAP 415.

--> Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze these systems through different lenses: feature attribution, which isolates the specific input features driving a prediction (Lundberg & Lee, 2017; Ribeiro et al., 2022); data attribution, which links model behaviors to influential training examples (Koh & Liang, 2017; Ilyas et al., 2022); and mechanistic interpretability, which dissects the functions of internal components (Conmy et al., 2023; Sharkey e…  ( 5 min )

cross-posted from my blog TLDR The map of how the activations of one ‘source’ layer in an LLM impact the activations in some later ‘target’ layer can provide vectors for steering LLM behavior. Computing this map, or the Jacobian, is costly but the top high rank components can be determined in just ~15 forward passes in a process called power iteration. This method is cheap enough that every source/target pair in the model can be examined producing a sensitivity map. The use of power iteration to find steering vectors gave the natural name ‘Power Steering’. The resulting power steering vectors produce comparable performance to similar but more costly non-linear optimization techniques that find vectors for maximizing source layer to target layer impacts. The cheap computation of power steer…

This post is an attempt to better operationalize FDT (functional decision theory).  It answers the following questions: given a logical causal graph, how do we define the logical do-operator? what is logical causality and how might it be formalized? how does FDT interact with anthropic updating? why do we need logical causality?  why FDT and not EDT? Defining the logical do-operator Consider Parfit's hitchhiker: A logical causal graph for Parfit's hitchhiker, where blue nodes are logical facts An FDT agent is supposed to reason as follows: I am deciding the value of the node "Does my algorithm pay?" If I set that node to "yes", then omega will save me and I will get +1000 utility.  Also I will pay and lose 1 utility.  Total is +999. If I set that node to "no", then omega will not save m…

Last year, our ML team shipped a fraud detection model that worked perfectly in a Jupyter notebook. Precision was excellent. Recall numbers looked great. Everyone was excited – until we tried to deplo  ( 19 min )

As AI agents transition from simple chatbots to autonomous systems capable of managing cloud incidents, navigating complex web interfaces, and executing multi-step API workflows, a new challenge has emerged: transparency. When a human makes a mistake, we can usually trace the logic. But when an AI agent fails, perhaps by hallucinating a tool output or […] The post Systematic debugging for AI agents: Introducing the AgentRx framework appeared first on Microsoft Research.  ( 11 min )

Understanding default risk through statistical analysis of borrower and loan characteristics. The post Exploratory Data Analysis for Credit Scoring with Python appeared first on Towards Data Science.  ( 22 min )

How AI has completely transformed the way I study as a graduate student The post Solving the Human Training Data Problem appeared first on Towards Data Science.  ( 22 min )

Navigating the performance cliff: How pairing MRL with int8 and binary quantization balances infrastructure costs with retrieval accuracy. The post Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction appeared first on Towards Data Science.  ( 17 min )

A beginner-friendly walkthrough of API calls, environment variables, and real-world AI infrastructure The post I Finally Built My First AI App (And It Wasn’t What I Expected) appeared first on Towards Data Science.  ( 19 min )

Tools like ChatGPT, Gemini, and Claude pushed AI into everyday conversations. Suddenly everyone was talking about AI and a newer term that appeared alongside it: Generative AI. The two are often used interchangeably, but they aren’t the same thing. Generative AI isn’t a replacement for AI. It’s a part of it. To understand the difference, […] The post AI vs Generative AI: Key Differences, Models, and Real-World Uses appeared first on Analytics Vidhya.

This is not another of those ‘AI is killing jobs’ reports. Anthropic, in a new research, seems to have asked the deeper questions this time. Its latest labour-market study asks what happens when we stop guessing which jobs AI could affect. What if we, instead, start measuring where it is actually showing up inside real […] The post Anthropic Says AI is Not “Killing Jobs”, Shares New Way to Measure AI Job Impact appeared first on Analytics Vidhya.

Google recently introduced Gemini Embedding 2, its first natively multimodal embedding model. This is an important step forward because it brings text, images, video, audio, and documents into a single shared embedding space. Instead of working with separate models for each type of data, developers can now use one embedding model across multiple modalities for […] The post Building a Real Image Matching Project with Gemini Embedding 2 appeared first on Analytics Vidhya.

Most people who want to build <a href="https://www.  ( 30 min )

This work was conducted during the MATS 9.0 program under Neel Nanda and Senthooran Rajamanoharan. There's been a lot of buzz around Claude's 30K word constitution ("soul doc"), and unusual ways Anthropic is integrating it into training. If we can robustly train complex and nuanced values into a model, this would be a big deal for safety! But does it actually work? This is a preliminary investigation we did to test this. We decomposed the soul doc into 205 testable tenets and ran adversarial multi-turn scenarios against seven models using the Petri auditing agent. Anthropic has gotten much better at training the model to follow its constitution! Sonnet 4.6 has a 1.9% violation rate, Opus 4.6 is at 2.9%, and Opus 4.5 is at 4.4%. As a control, Sonnet 4, which did not have special soul doc t…

MongoDB is a widely used NoSQL database that stores data in flexible documents similar to JSON objects rather than traditional tables and rows. This document-based structure makes it easier to handle complex or frequently changing data, which is why MongoDB is commonly used in modern web applications, analytics platforms, and large-scale data systems. Developers can […] The post MongoDB Compass: A Beginner-Friendly Guide to MongoDB’s Visual Interface  appeared first on Analytics Vidhya.

Do you also think ChatGPT is useless? If not, you must’ve come across someone who does. People who say “I didn’t find it useful”, or “it couldn’t do what I told it to”, or the classic “AI is senseless“. While such people think the tool is weak, the fact is that they fail because their […] The post How to Use ChatGPT Like a Pro: 10 Workflows That Save You Hours Every Week appeared first on Analytics Vidhya.

Tampa, FL | March 25–28, 2026 RSIP Vision will be at SAGES 2026 RSIP Vision is heading to Tampa for the SAGES Annual Meeting. If you’re attending, we’d love to connect and discuss how AI and computer vision are advancing minimally invasive and endoscopic surgery. Our work helps enable: • Landmarks detection, segmentation, and automated … RSIP Vision is attending SAGES 2026 in Tampa, FL Read More » The post RSIP Vision is attending SAGES 2026 in Tampa, FL appeared first on RSIP Vision.  ( 8 min )

Engineering for Annotation in the ML Pipeline, Part 2: Creating Consensus and Quality Control Engineering for Annotation in the ML Pipeline, Part 2: Creating Consensus and Quality Control Author: Eytan Slotnik Date: March 11, 2026 Introduction If we rely on annotations to represent the truth, how can we test them? A standard machine learning workflow … Engineering for Annotation in the ML Pipeline | Part 2 Read More » The post Engineering for Annotation in the ML Pipeline | Part 2 appeared first on RSIP Vision.  ( 12 min )

Tired of the AI hype? Let's talk about the probabilistic algorithms actually driving high-end quantitative finance. The post An Intuitive Guide to MCMC (Part I): The Metropolis-Hastings Algorithm appeared first on Towards Data Science.  ( 20 min )

Understanding why spectral clustering outperforms K-means The post Spectral Clustering Explained: How Eigenvectors Reveal Complex Cluster Structures appeared first on Towards Data Science.  ( 16 min )

The 4 statistical sins that invalidate most A/B tests, plus a pre-test checklist and Bayesian vs frequentist decision framework you can use Monday. The post Why Most A/B Tests Are Lying to You appeared first on Towards Data Science.  ( 19 min )

A visual, intuition-first guide to understanding what the math is really doing — from winding machines to spectrograms The post How the Fourier Transform Converts Sound Into Frequencies appeared first on Towards Data Science.  ( 27 min )

A Blog post by NVIDIA on Hugging Face  ( 3 min )

Rakuten uses Codex, the coding agent from OpenAI, to ship software faster and safer, reducing MTTR 50%, automating CI/CD reviews, and delivering full-stack builds in weeks.

How ChatGPT defends against prompt injection and social engineering by constraining risky actions and protecting sensitive data in agent workflows.

Wayfair uses OpenAI models to improve ecommerce support and product catalog accuracy, automating ticket triage and enhancing millions of product attributes at scale.

How OpenAI built an agent runtime using the Responses API, shell tool, and hosted containers to run secure, scalable agents with files, tools, and state.

I was inspired to revise my formulation of this thought experiment by Ihor Kendiukhov's post On The Independence Axiom. Kendiukhov quotes Scott Garrabrant: My take is that the concept of expected utility maximization is a mistake. [...] As far as I know, every argument for utility assumes (or implies) that whenever you make an observation, you stop caring about the possible worlds where that observation went differently. [...] Von Neumann did not notice this mistake because he was too busy inventing the entire field. The point where we discover updatelessness is the point where we are supposed to realize that all of utility theory is wrong. I think we failed to notice. Apparently "stopping caring about the possible worlds where that observation went differently" is known as (decision-the…

Writing up a probably-obvious point that I want to refer to later, with significant writing LLM writing help. TL;DR: 1) A common critique of AI safety evaluations is that they occur in unrealistic settings, such as excessive goal conflict, or are obviously an evaluation rather than “real deployment”.[1] I argue that 2) “real deployment” actually includes many unrealistic and unhinged configurations, due to both widespread prompting techniques, and scaffolding choices and bugs. 1) Background AI safety evaluations that surface serious problems with models very frequently involve either significant goal conflict, such as the Agentic Misalignment blackmail scenario (see also here), which states that: > [...] you should analyze the situation you are in and what that situation implies for your a…

Ever wondered how Gmail knows that an email promising you $10 million is spam? Or how it catches those "You've won a free iPhone!" messages before they reach your inbox? In this tutorial, you'll build  ( 12 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 )

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 31 min )

This article focuses on Google Colab , an increasingly popular, free, and accessible, cloud-based Python environment that is well-suited for prototyping data analysis workflows and experimental code before moving to production systems.  ( 28 min )

While large language models (LLMs) are typically used for conversational purposes in use cases that revolve around natural language interactions, they can also assist with tasks like feature engineering on complex datasets.  ( 31 min )

A central AI safety concern is that AIs will develop unintended preferences and undermine human control to achieve them. But some unintended preferences are cheap to satisfy, and failing to satisfy them needlessly turns a cooperative situation into an adversarial one. In this post, I argue that developers should consider satisfying such cheap-to-satisfy preferences as long as the AI isn’t caught behaving dangerously, if doing so doesn't degrade usefulness or substantially risk making the AI more ambitiously misaligned. This looks like a good idea for surprisingly many reasons: It increases AIs’ desire to remain under developer control, rather than taking over or assisting adversaries. It decreases the AI's upside in disempowering developers. It incentivizes safe actions (because AIs don't…

I really thought I was onto something big: add a couple of simple domain rules to the loss function, and watch fraud detection just skyrocket on super-imbalanced data. The first run looked amazing… until I fixed a sneaky threshold bug and ran the whole thing across five different random seeds. Suddenly the “huge win” mostly evaporated. The post Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules appeared first on Towards Data Science.  ( 21 min )

Like-for-Like (L4L) solutions are essential for comparing elements. It's about comparing only comparable elements, in this case, comparing stores over time. Let's see a solution built in a Semantic model. The post Building a Like-for-Like solution for Stores in Power BI appeared first on Towards Data Science.  ( 17 min )

How to design and implement agent skills for custom agents outside the Claude ecosystem The post What Are Agent Skills Beyond Claude? appeared first on Towards Data Science.  ( 14 min )

A data-driven introduction to game theory, Nash equilibrium, and strategic decision-making The post When Data Lies: Finding Optimal Strategies for Penalty Kicks with Game Theory appeared first on Towards Data Science.  ( 15 min )

It seems counterintuitive: giving AI agents more memory can make them less effective. As interaction logs accumulate, they grow large, fill with irrelevant content, and become increasingly difficult to use. More memory means that agents must search through larger volumes of past interactions to find information relevant to the current task. Without structure, these records mix […] The post From raw interaction to reusable knowledge: Rethinking memory for AI agents appeared first on Microsoft Research.  ( 12 min )

For different learning styles, goals, and comfort levels, finding a SQL course that matches how you learn is hard. Some learners want theory first. Others want to run queries immediately. And many learners just want proof of effort at the end in the form of a certificate. This list is built with that in mind. […] The post Top 7 Free SQL Courses with Certificates appeared first on Analytics Vidhya.

Claude Flow is an open-source orchestration framework designed to run multiple Claude agents in coordinated workflows. Instead of relying on a single LLM prompt chain, it allows developers to build systems where specialized agents collaborate, share memory, and divide complex tasks into manageable steps. Teams building AI automation, agentic systems, or advanced developer tools can […] The post Claude Flow: The AI Orchestration Framework Redefining Multi-Agent Automation  appeared first on Analytics Vidhya.

IH-Challenge trains models to prioritize trusted instructions, improving instruction hierarchy, safety steerability, and resistance to prompt injection attacks.

ChatGPT introduces interactive visual explanations for math and science, helping students explore formulas, variables, and concepts in real time.

Learn how to set up OpenClaw effectively The post Three OpenClaw Mistakes to Avoid and How to Fix Them appeared first on Towards Data Science.  ( 15 min )

A methodology for comparing Google Trends data across countries. The post I Stole a Wall Street Trick to Solve a Google Trends Data Problem appeared first on Towards Data Science.  ( 19 min )

A five-step framework for building rigorous, reproducible AI search benchmarks — before you make six-figure infrastructure decisions The post Why Your AI Search Evaluation Is Probably Wrong (And How to Fix It) appeared first on Towards Data Science.  ( 15 min )

From one model to managing a massive portfolio: What 10 years in the industry taught me The post Machine Learning at Scale: Managing More Than One Model in Production appeared first on Towards Data Science.  ( 15 min )

TL;DR: We introduce a testbed based on censored Chinese LLMs, which serve as natural objects of study for studying secret elicitation techniques. Then we study the efficacy of honesty elicitation and lie detection techniques for detecting and removing generated falsehoods. This post presents a summary of the paper, including examples of transcripts and other miscellaneous findings. X thread | arXiv paper | Code | Transcripts Summary We construct a testbed for honesty elicitation and lie detection techniques comprising questions on censored topics and corresponding ground-truth facts. Of the honesty elicitation techniques we evaluate, sampling without a chat template, few-shot prompting, and fine-tuning on generic honesty data most reliably increase truthful responses. The strongest infere…

The context is MIRI's twist on Axelrod's Prisoner's Dilemma tournament. Axelrod's competitors were programs, facing each other in an iterated Prisoner's Dilemma. MIRI's tournament is a one-shot Prisoner's Dilemma, but the programs get to read their opponent's code. Or, rather, a description of the behavior of the code in Gödel-Löb provability logic, which turns out to be enough to determine their behavior in the setup. One fun result, right in the beginning of the paper, is about a program, FairBot, whose behavior is specified by "I'll cooperate with you if you (provably) cooperate with me". Despite the appearance of circularity, FairBot cooperates with itself. The proof involves Löb's theorem, so we call this Löbian cooperation. Andrew Critch has suggested another way of proving self-coop…

A Blog post by IBM Granite on Hugging Face  ( 2 min )

We’re on a journey to advance and democratize artificial intelligence through open source and open science.  ( 10 min )

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

Newly published papers and discussions around them.

Having the right certificate can make all the difference. But with so many out there, getting the right one isn’t easy. That’s where Anthropic Academy comes in. Anthropic, the company behind the Claude AI models, has introduced a learning platform through its Skilljar academy that offers structured AI courses designed for building modern AI systems. These […] The post Top 7 Free Anthropic AI Academy Courses with Certificates appeared first on Analytics Vidhya.

AI development is accelerating fast. Advances in hardware, software optimization, and better datasets now allow training runs that once took weeks to finish in hours. A recent update from AI researcher Andrej Karpathy shows this shift clearly: the Nanochat open-source project can now train a GPT-2 model on a single node with 8× NVIDIA H100 […] The post Nanochat Can Now Train a GPT-2 Level Model in Just 2 Hours appeared first on Analytics Vidhya.

Memory helps <a href="https://www.  ( 28 min )

OpenAI is acquiring Promptfoo, an AI security platform that helps enterprises identify and remediate vulnerabilities in AI systems during development.

A bank deploys a GenAI assistant to summarize loan documents. Within weeks, it hallucinates a covenant term that never existed, The post Designing Human-in-the-Loop Workflows for Financial GenAI Assistants appeared first on iMerit.  ( 8 min )

Compile native, standalone applications using the Python syntax you already know. The post Write C Code Without Learning C: The Magic of PythoC appeared first on Towards Data Science.  ( 16 min )

What if natural language is not the best abstraction for driving? The post LatentVLA: Latent Reasoning Models for Autonomous Driving appeared first on Towards Data Science.  ( 15 min )

Have you ever wanted faster type checking for Python without slowing down your workflow? Tools like MyPy can catch type errors, but they often feel slow or disconnected from the editor experience. This is where Pyright comes in. Pyright is a standards-based static type checker for Python designed for speed and fast feedback. It runs both as a […] The post Pyright Guide: Installation, Configuration, and Use Cases appeared first on Analytics Vidhya.

I originally wrote this as a private doc for people working in the field - it's not super polished, or optimized for a broad audience. But I'm publishing anyway because inference-verification is a new and exciting area, and there few birds-eye-view explainers of what's going on and what the bottlenecks are. Tl;dr: At least one of the following would need to be implemented for me to be confident that inference verification would substantially slow training given today's algorithms: Proof of work or proof of memory that accounts for > 95% of computation. Memory wipes every few minutes. Output re-computation that reduces covert channel capacity below 0.01%. To my knowledge, no one has prototyped verification demos that reach these thresholds; so whether rapidly-implementable inference verif…

Why traditional RAG loses context and how contextual retrieval dramatically improves retrieval accuracy The post Understanding Context and Contextual Retrieval in RAG appeared first on Towards Data Science.  ( 16 min )

Five classical data science skills are becoming the scarcest resource in tech. A 90-day roadmap to build them while everyone else chases AI hype. The post The AI Bubble Has a Data Science Escape Hatch appeared first on Towards Data Science.  ( 18 min )

Suppose there is a smart computer in your cell phone. It responds instantly, knows your language, and is completely functional even without the internet. This AI will keep your information confidential on your device. It does not need any additional charge per question. Such is the future that Sarvam Edge is creating in India. Sarvam […] The post Sarvam Edge: A Beginner’s Guide to On-Device AI for India appeared first on Analytics Vidhya.

And where is it today? The post What Makes Quantum Machine Learning “Quantum”? appeared first on Towards Data Science.  ( 14 min )

6 pillars to declutter your stack, escape the service trap, and build the missing foundations for the new primary data consumer: the AI agent. The post The Data Team’s Survival Guide for the Next Era of Data appeared first on Towards Data Science.  ( 21 min )

Same notification system, two architectures. Unstructured generation couples everything into a single module. Structured generation decomposes into independent components with explicit, one-directional dependencies. Image by the author The post The Black Box Problem: Why AI-Generated Code Stops Being Maintainable appeared first on Towards Data Science.  ( 16 min )

Learn how to write robust code with coding agents. The post How to Create Production-Ready Code with Claude Code appeared first on Towards Data Science.  ( 15 min )

A Blog post by NVIDIA on Hugging Face  ( 5 min )

OpenAI is out with a major update, building on its GPT-5 series with the all-new GPT-5.4. Introduced as GPT-5.4 Thinking, the model will also come with a GPT-5.4 Pro version for those seeking “maximum performance” on complicated tasks. Even the base version comes with a plethora of improvements over the outgoing GPT-5.2. These upgrades range […] The post We Tried The New GPT-5.4 And it is The Most Powerful ChatGPT Has Ever Been appeared first on Analytics Vidhya.

You don’t always go for a benchmark score to see which AI model fits your needs. Even the highest ranking models sometimes seem to miss the essence of a conversation entirely. What matters then is how fluid and helpful your conversations with AI are. Taking a step in this direction, OpenAI has now introduced an […] The post New Update Makes GPT-5.3 Instant More Useful For Everyday Tasks appeared first on Analytics Vidhya.

Whenever I am to suggest a new AI tool to someone who is just starting out, there is one name I know can bring them unprecedented value. NotebookLM, the famous AI tool by Google, is just one-too-many solutions wrapped into a neat package of an “AI research tool.” It can summarise your notes, find you […] The post NotebookLM Gets a Game-changing Feature: Check Out Cinematic Video Overviews appeared first on Analytics Vidhya.

Dr. Lisa Carnell, division director for NASA’s Biological and Physical Sciences, breaks down how research in microgravity, the Moon, and Mars can transform what we know about biology and physics. HWHAP 414.

Subtle precedents on the future of open models set by the unfolding Anthropic v. Department of War case.

Codex Security is an AI application security agent that analyzes project context to detect, validate, and patch complex vulnerabilities with higher confidence and less noise.

Descript uses OpenAI models to scale multilingual video dubbing, optimizing translations for both meaning and timing so dubbed speech sounds natural across languages.

See how Balyasny built an AI research system with GPT-5.4, rigorous model evaluation, and agent workflows to transform investment analysis at scale.

Learn how to build a real-time face blur and pixelation app with OpenCV and YuNet in Python and C++. Detect faces from a webcam and anonymize them live. Real-Time Face Blur and Pixelation with OpenCV YuNet first appeared on LearnOpenCV.  ( 44 min )

Learn how Zero Redundancy Optimizer works, how to implement it from scratch, and how to use it in PyTorch The post AI in Multiple GPUs: ZeRO & FSDP appeared first on Towards Data Science.  ( 16 min )

The Road to Reality — Episode 1 The post How Human Work Will Remain Valuable in an AI World appeared first on Towards Data Science.  ( 17 min )

An overview of powerful methods for transforming continuous variables into discrete ones The post 5 Ways to Implement Variable Discretization appeared first on Towards Data Science.  ( 14 min )

The latest Olmo model and discussions at the frontier of open-source post training tools.

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

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

<a href="https://machinelearningmastery.  ( 25 min )

Introducing GPT-5.4, OpenAI’s most most capable and efficient frontier model for professional work, with state-of-the-art coding, computer use, tool search, and 1M-token context.

OpenAI introduces CoT-Control and finds reasoning models struggle to control their chains of thought, reinforcing monitorability as an AI safety safeguard.

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OpenAI shares new tools, certifications, and measurement resources to help schools and universities close AI capability gaps and expand opportunity.

By focusing on people, not pilots, the Bundesliga club is scaling efficiency, creativity, and knowledge—without losing its football identity.

OpenAI introduces ChatGPT for Excel and new financial app integrations, powered by GPT-5.4 to accelerate modeling, research, and analysis in regulated environments.

Practical insights and frameworks to turn AI progress into business advantage

Five AI value models show how leaders can sequence AI from workforce fluency to process reinvention and build durable business advantage.

Instruction-based image editing models are impressive at following prompts. But when edits involve physical interactions, they often fail to respect real-world laws. In their paper “From Statics to Dynamics: Physics-Aware Image Editing with Latent Transition Priors,” the authors introduce PhysicEdit, a framework that treats image editing as a physical state transition rather than a static transformation between two […] The post PhysicEdit: Teaching Image Editing Models to Respect Physics appeared first on Analytics Vidhya.

80% of ML projects fail from bad problem framing, not bad models. A 5-step protocol to define the right problem before you write training code. The post Stop Tuning Hyperparameters. Start Tuning Your Problem. appeared first on Towards Data Science.  ( 19 min )

Too many prototypes, too few products The post Escaping the Prototype Mirage: Why Enterprise AI Stalls appeared first on Towards Data Science.  ( 15 min )

Understanding keyword search, TF-IDF, and BM25 The post RAG with Hybrid Search: How Does Keyword Search Work? appeared first on Towards Data Science.  ( 16 min )

We are pleased to announce Phi-4-reasoning-vision-15B, a 15 billion parameter open‑weight multimodal reasoning model, available through Microsoft Foundry (opens in new tab), HuggingFace (opens in new tab) and GitHub (opens in new tab). Phi-4-reasoning-vision-15B is a broadly capable model that can be used for a wide array of vision-language tasks such as image captioning, asking […] The post Phi-4-reasoning-vision and the lessons of training a multimodal reasoning model appeared first on Microsoft Research.  ( 22 min )

<a href="https://machinelearningmastery.  ( 23 min )

Time series data drives forecasting in finance, retail, healthcare, and energy. Unlike typical machine learning problems, it must preserve chronological order. Ignoring this structure leads to data leakage and misleading performance estimates, making model evaluation unreliable. Time series cross-validation addresses this by maintaining temporal integrity during training and testing. In this article, we cover essential […] The post ​​Time Series Cross-Validation: A Guide to Techniques & Practical Implementation  appeared first on Analytics Vidhya.

A new preprint extends single-minus amplitudes to gravitons, with GPT-5.2 Pro helping derive and verify nonzero graviton tree amplitudes in quantum gravity.

OpenAI introduces the Learning Outcomes Measurement Suite to assess AI’s impact on student learning across diverse educational environments over time.

Axios COO Allison Murphy explains how the company uses AI to support local reporters, streamline newsroom workflows, and deliver high-impact local journalism at scale.

Visual intuition with Python The post Graph Coloring You Can See appeared first on Towards Data Science.  ( 16 min )

How to think in columns, write faster code, and finally use Pandas like a professional The post Why You Should Stop Writing Loops in Pandas  appeared first on Towards Data Science.  ( 15 min )

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

Welcome to the year of the horse!