Machine Learning

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Users flock to open source Moltbot for always-on AI, despite major risks

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI assistants, AI security, AI sycophancy, anthropic, Artificial Intelligence, Biz & IT, chatbots, Machine Learning, Open Source, Peter Steinberger

An open source AI assistant called Moltbot (formerly “Clawdbot”) recently crossed 69,000 stars on GitHub after a month, making it one of the fastest-growing AI projects of 2026. Created by Austrian developer Peter Steinberger, the tool lets users run a personal AI assistant and control it through messaging apps they already use. While some say […]

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Moonshot AI Releases Kimi K2.5: An Open Source Visual Agentic Intelligence Model with Native Swarm Execution

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, Open Source, Tech News, Technology, Uncategorized

Moonshot AI has released Kimi K2.5 as an open source visual agentic intelligence model. It combines a large Mixture of Experts language backbone, a native vision encoder, and a parallel multi agent system called Agent Swarm. The model targets coding, multimodal reasoning, and deep web research with strong benchmark results on agentic, vision, and coding

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DSGym Offers a Reusable Container Based Substrate for Building and Benchmarking Data Science Agents

agentic ai, ai, AI (Artificial Intelligence), AI Agents, 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

Data science agents should inspect datasets, design workflows, run code, and return verifiable answers, not just autocomplete Pandas code. DSGym, introduced by researchers from Stanford University, Together AI, Duke University, and Harvard University, is a framework that evaluates and trains such agents across more than 1,000 data science challenges with expert curated ground truth and

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How Tree-KG Enables Hierarchical Knowledge Graphs for Contextual Navigation and Explainable Multi-Hop Reasoning Beyond Traditional RAG

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, deep learning, Editors Pick, Knowledge Graphs, Language Model, Machine Learning, Staff, Tech News, Technology, Tutorials

In this tutorial, we implement Tree-KG, an advanced hierarchical knowledge graph system that goes beyond traditional retrieval-augmented generation by combining semantic embeddings with explicit graph structure. We show how we can organize knowledge in a tree-like hierarchy that mirrors how humans learn, from broad domains to fine-grained concepts, and then reason across this structure using

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How Automated NLP Pipelines Cut Oncology Data Abstraction from Weeks to Hours

ai, AI (Artificial Intelligence), Artificial Intelligence, Healthcare, Machine Learning, Tech

Abhijit Nayak, Senior Data Scientist at Cognizant and IEEE conference speaker, discusses building production-grade information extraction systems for cancer research and why domain expertise matters more than model size. A July survey in Artificial Intelligence Review analyzed 156 NLP studies in oncology and identified a pattern: transformer models perform impressively on research benchmarks, then collapse

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NVIDIA Revolutionizes Climate Tech with ‘Earth-2’: The World’s First Fully Open Accelerated AI Weather Stack

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

For decades, predicting the weather has been the exclusive domain of massive government supercomputers running complex physics-based equations. NVIDIA has shattered that barrier with the release of the Earth-2 family of open models and tools for AI weather and climate prediction accessible to virtually anyone, from tech startups to national meteorological agencies. In a move

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StepFun AI Introduce Step-DeepResearch: A Cost-Effective Deep Research Agent Model Built Around Atomic Capabilities

StepFun has introduced Step-DeepResearch, a 32B parameter end to end deep research agent that aims to turn web search into actual research workflows with long horizon reasoning, tool use and structured reporting. The model is built on Qwen2.5 32B-Base and is trained to act as a single agent that plans, explores sources, verifies evidence and

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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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