Machine Learning

Auto Added by WPeMatico

The ‘Bayesian’ Upgrade: Why Google AI’s New Teaching Method is the Key to LLM Reasoning

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Machine Learning, Staff, Technology

Large Language Models (LLMs) are the world’s best mimics, but when it comes to the cold, hard logic of updating beliefs based on new evidence, they are surprisingly stubborn. A team of researchers from Google argue that the current crop of AI agents falls far short of ‘probabilistic reasoning’—the ability to maintain and update a

The ‘Bayesian’ Upgrade: Why Google AI’s New Teaching Method is the Key to LLM Reasoning Read More »

Improving AI models’ ability to explain their predictions

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, data, Electrical engineering and computer science (EECS), Human-computer interaction, Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output.Concept bottleneck modeling is one method that enables artificial intelligence systems to explain their decision-making process. These methods force a deep-learning model to use a

Improving AI models’ ability to explain their predictions Read More »

How to Build Progress Monitoring Using Advanced tqdm for Async, Parallel, Pandas, Logging, and High-Performance Workflows

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science, Editors Pick, Machine Learning, Technology, Tutorials

In this tutorial, we explore tqdm in depth and demonstrate how we build powerful, real-time progress tracking into modern Python workflows. We begin with nested progress bars and manual progress control, then move into practical scenarios such as streaming downloads, pandas data processing, parallel execution, structured logging, and asynchronous tasks. Throughout this tutorial, we focus

How to Build Progress Monitoring Using Advanced tqdm for Async, Parallel, Pandas, Logging, and High-Performance Workflows Read More »

Google Launches TensorFlow 2.21 And LiteRT: Faster GPU Performance, New NPU Acceleration, And Seamless PyTorch Edge Deployment Upgrades

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Machine Learning, New Releases, Staff, Technology

Google has officially released TensorFlow 2.21. The most significant update in this release is the graduation of LiteRT from its preview stage to a fully production-ready stack. Moving forward, LiteRT serves as the universal on-device inference framework, officially replacing TensorFlow Lite (TFLite). This update streamlines the deployment of machine learning models to mobile and edge

Google Launches TensorFlow 2.21 And LiteRT: Faster GPU Performance, New NPU Acceleration, And Seamless PyTorch Edge Deployment Upgrades Read More »

Microsoft Releases Phi-4-Reasoning-Vision-15B: A Compact Multimodal Model for Math, Science, and GUI Understanding

ai, AI (Artificial Intelligence), AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, Multimodal AI, New Releases, Small Language Model, Staff, Tech News, Technology, Vision Language Model

Microsoft has released Phi-4-reasoning-vision-15B, a 15 billion parameter open-weight multimodal reasoning model designed for image and text tasks that require both perception and selective reasoning. It is a compact model built to balance reasoning quality, compute efficiency, and training-data requirements, with particular strength in scientific and mathematical reasoning and understanding user interfaces. https://arxiv.org/pdf/2603.03975 What the

Microsoft Releases Phi-4-Reasoning-Vision-15B: A Compact Multimodal Model for Math, Science, and GUI Understanding Read More »

A Coding Guide to Build a Scalable End-to-End Machine Learning Data Pipeline Using Daft for High-Performance Structured and Image Data Processing

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer vision, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a real-world MNIST dataset, then progressively transform it using UDFs, feature engineering, aggregations, joins, and lazy execution. Also, we demonstrate how to seamlessly combine structured data processing, numerical computation, and

A Coding Guide to Build a Scalable End-to-End Machine Learning Data Pipeline Using Daft for High-Performance Structured and Image Data Processing Read More »

JPMorgan expands AI investment as tech spending nears $20B

ai, AI (Artificial Intelligence), AI Business Strategy, AI in Action, Artificial Intelligence, banking, Features, Finance, Finance AI, investment, Machine Learning

Artificial intelligence is moving from pilot projects to core business systems inside large companies. One example comes from JPMorgan Chase, where rising AI investment is helping push the bank’s technology budget toward about US$19.8 billion in 2026. The spending plan reflects a broader shift among large enterprises. AI is no longer treated as a small

JPMorgan expands AI investment as tech spending nears $20B Read More »

YuanLab AI Releases Yuan 3.0 Ultra: A Flagship Multimodal MoE Foundation Model, Built for Stronger Intelligence and Unrivaled Efficiency

ai, AI (Artificial Intelligence), AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, Open Source, Staff, Tech News, Technology

How can a trillion-parameter Large Language Model achieve state-of-the-art enterprise performance while simultaneously cutting its total parameter count by 33.3% and boosting pre-training efficiency by 49%? Yuan Lab AI releases Yuan3.0 Ultra, an open-source Mixture-of-Experts (MoE) large language model featuring 1T total parameters and 68.8B activated parameters. The model architecture is designed to optimize performance

YuanLab AI Releases Yuan 3.0 Ultra: A Flagship Multimodal MoE Foundation Model, Built for Stronger Intelligence and Unrivaled Efficiency Read More »

​​Time Series Cross-Validation: A Guide to Techniques & Practical Implementation 

ai, AI (Artificial Intelligence), Artificial Intelligence, Beginner, Machine Learning

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

​​Time Series Cross-Validation: A Guide to Techniques & Practical Implementation  Read More »

A “ChatGPT for spreadsheets” helps solve difficult engineering challenges faster

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer science and technology, Design, Machine Learning, Mechanical engineering, MIT Schwarzman College of Computing, Research, School of Engineering, Systems design

Many engineering challenges come down to the same headache — too many knobs to turn and too few chances to test them. Whether tuning a power grid or designing a safer vehicle, each evaluation can be costly, and there may be hundreds of variables that could matter.Consider car safety design. Engineers must integrate thousands of

A “ChatGPT for spreadsheets” helps solve difficult engineering challenges faster Read More »