Machine Learning

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A Coding Implementation to Automating LLM Quality Assurance with DeepEval, Custom Retrievers, and LLM-as-a-Judge Metrics

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, Staff, Technology, Tutorials

We initiate this tutorial by configuring a high-performance evaluation environment, specifically focused on integrating the DeepEval framework to bring unit-testing rigor to our LLM applications. By bridging the gap between raw retrieval and final generation, we implement a system that treats model outputs as testable code and uses LLM-as-a-judge metrics to quantify performance. We move […]

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Deep Learning vs. Machine Learning: Key Differences Explained for Business Leaders 

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

At its core, ML involves algorithms that analyze data, recognize patterns, and make predictions. These models “learn” from past data to improve their performance over time. For example, an ML model trained on user purchase history can predict which products a customer might buy next. Artificial Intelligence (AI) is no longer a future concept. This is

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How Machine Learning and Semantic Embeddings Reorder CVE Vulnerabilities Beyond Raw CVSS Scores

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

In this tutorial, we build an AI-assisted vulnerability scanner that goes beyond static CVSS scoring and instead learns to prioritize vulnerabilities using semantic understanding and machine learning. We treat vulnerability descriptions as rich linguistic artifacts, embed them using modern sentence transformers, and combine these representations with structural metadata to produce a data-driven priority score. Also,

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eBay bans illicit automated shopping amid rapid rise of AI agents

agentic ai, Agentic Commerce, ai, AI (Artificial Intelligence), AI Agents, AI shopping, Amazon, anthropic, Arbitration, Artificial Intelligence, Biz & IT, ChatGPT, eBay, eCommerce, Etsy, Google, Large Language Models, Machine Learning, OpenAI, Perplexity, shopify, user agreements, web scraping

On Tuesday, eBay updated its User Agreement to explicitly ban third-party “buy for me” agents and AI chatbots from interacting with its platform without permission, first spotted by Value Added Resource. On its face, a one-line terms of service update doesn’t seem like major news, but what it implies is more significant: The change reflects

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FlashLabs Researchers Release Chroma 1.0: A 4B Real Time Speech Dialogue Model With Personalized Voice Cloning

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

Chroma 1.0 is a real time speech to speech dialogue model that takes audio as input and returns audio as output while preserving the speaker identity across multi turn conversations. It is presented as the first open source end to end spoken dialogue system that combines low latency interaction with high fidelity personalized voice cloning

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50+ Machine Learning Resources for Self Study in 2026

ai, AI (Artificial Intelligence), Artificial Intelligence, Beginner, Career, Data Science, deep learning, GitHub, Listicle, Machine Learning, Resource

Are you following the trend or genuinely interested in Machine Learning? Either way, you will need the right resources to TRUST, LEARN and SUCCEED. If you are unable to find the right Machine Learning resource in 2026? We are here to help. Let’s reiterate the definition of Machine Learning… Machine learning is an exciting field

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Wikipedia volunteers spent years cataloging AI tells. Now there’s a plugin to avoid them.

ai, AI (Artificial Intelligence), AI detection, AI detectors, ai slop, AI writing, anthropic, Artificial Intelligence, Biz & IT, chatbots, ChatGPT, chatgtp, claude, Generative AI, Large Language Models, Machine Learning, Wikipedia

On Saturday, tech entrepreneur Siqi Chen released an open source plugin for Anthropic’s Claude Code AI assistant that instructs the AI model to stop writing like an AI model. Called “Humanizer,” the simple prompt plugin feeds Claude a list of 24 language and formatting patterns that Wikipedia editors have listed as chatbot giveaways. Chen published

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How AutoGluon Enables Modern AutoML Pipelines for Production-Grade Tabular Models with Ensembling and Distillation

ai, AI (Artificial Intelligence), AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine Learning, Staff, Tech News, Technology, Tutorials

In this tutorial, we build a production-grade tabular machine learning pipeline using AutoGluon, taking a real-world mixed-type dataset from raw ingestion through to deployment-ready artifacts. We train high-quality stacked and bagged ensembles, evaluate performance with robust metrics, perform subgroup and feature-level analysis, and then optimize the model for real-time inference using refit-full and distillation. Throughout

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Liquid AI Releases LFM2.5-1.2B-Thinking: a 1.2B Parameter Reasoning Model That Fits Under 1 GB On-Device

ai, AI (Artificial Intelligence), AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Machine Learning, New Releases, Python, Staff, Tech News, Technology, Uncategorized

Liquid AI has released LFM2.5-1.2B-Thinking, a 1.2 billion parameter reasoning model that runs fully on device and fits in about 900 MB on a modern phone. What needed a data center 2 years ago can now run offline on consumer hardware, with a focus on structured reasoning traces, tool use, and math, rather than general

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Why it’s critical to move beyond overly aggregated machine-learning metrics

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Health care, Institute for Medical Engineering and Science (IMES), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering, Technology and society

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to test whenever a model is deployed in a new setting.“We demonstrate that even when you train models on large amounts of data, and choose the

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