Machine Learning

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An Implementation Guide to Running NVIDIA Transformer Engine with Mixed Precision, FP8 Checks, Benchmarking, and Fallback Execution

ai, AI (Artificial Intelligence), AI Infrastructure, Applications, Artificial Intelligence, deep learning, Editors Pick, Machine Learning, physical ai, Staff, Technology, Tutorials

In this tutorial, we implement an advanced, practical implementation of the NVIDIA Transformer Engine in Python, focusing on how mixed-precision acceleration can be explored in a realistic deep learning workflow. We set up the environment, verify GPU and CUDA readiness, attempt to install the required Transformer Engine components, and handle compatibility issues gracefully so that […]

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Meet MaxToki: The AI That Predicts How Your Cells Age — and What to Do About It

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Most foundation models in biology have a fundamental blind spot: they see cells as frozen snapshots. Give a model a single-cell transcriptome — a readout of which genes are active in a cell at a given moment — and it can tell you a lot about what that cell is doing right now. What it

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Architecture and Orchestration of Memory Systems in AI Agents

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

The evolution of artificial intelligence from stateless models to autonomous, goal-driven agents depends heavily on advanced memory architectures. While Large Language Models (LLMs) possess strong reasoning abilities and vast embedded knowledge, they lack persistent memory, making them unable to retain past interactions or adapt over time. This limitation leads to repeated context injection, increasing token

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Meet ‘AutoAgent’: The Open-Source Library That Lets an AI Engineer and Optimize Its Own Agent Harness Overnight

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There’s a particular kind of tedium that every AI engineer knows intimately: the prompt-tuning loop. You write a system prompt, run your agent against a benchmark, read the failure traces, tweak the prompt, add a tool, rerun. Repeat this a few dozen times and you might move the needle. It’s grunt work dressed up in

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5 Types of Loss Functions in Machine Learning

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

A loss function is what guides a model during training, translating predictions into a signal it can improve on. But not all losses behave the same—some amplify large errors, others stay stable in noisy settings, and each choice subtly shapes how learning unfolds. Modern libraries add another layer with reduction modes and scaling effects that

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Google DeepMind’s Research Lets an LLM Rewrite Its Own Game Theory Algorithms — And It Outperformed the Experts

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, Tech News, Technology

Designing algorithms for Multi-Agent Reinforcement Learning (MARL) in imperfect-information games — scenarios where players act sequentially and cannot see each other’s private information, like poker — has historically relied on manual iteration. Researchers identify weighting schemes, discounting rules, and equilibrium solvers through intuition and trial-and-error. Google DeepMind researchers proposes AlphaEvolve, an LLM-powered evolutionary coding agent

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Working to advance the nuclear renaissance

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer modeling, Energy, Faculty, Machine Learning, Nuclear power and reactors, Nuclear science and engineering, Profile, Renewable energy, School of Engineering, Sustainability

Today, there are 94 nuclear reactors operating in the United States, more than in any other country in the world, and these units collectively provide nearly 20 percent of the nation’s electricity. That is a major accomplishment, according to Dean Price, but he believes that our country needs much more out of nuclear energy, especially

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Open Sourcing Our Real-Time PPE Detection Mobile App

ai, AI (Artificial Intelligence), Android app, Artificial Intelligence, Computer vision, deep learning, Kotlin, Machine Learning, Mobile AI, mobile Development, object detection, On-Device AI, Open Source, PPE detection, Spritle Blog, SpritleSoftware, TensorFlow Lite, Workplace Safety, YOLOv8

By Spritle Software Engineering Team Workplace safety isn’t negotiable. But manual safety compliance monitoring is slow, inconsistent, and doesn’t scale. We built a real-time Personal Protective Equipment (PPE) detection app that runs entirely on your smartphone — no cloud, no expensive hardware, no delays. The Problem We’re Solving Every year, thousands of workplace accidents happen

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Step by Step Guide to Build an End-to-End Model Optimization Pipeline with NVIDIA Model Optimizer Using FastNAS Pruning and Fine-Tuning

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

In this tutorial, we build a complete end-to-end pipeline using NVIDIA Model Optimizer to train, prune, and fine-tune a deep learning model directly in Google Colab. We start by setting up the environment and preparing the CIFAR-10 dataset, then define a ResNet architecture and train it to establish a strong baseline. From there, we apply

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Arcee AI Releases Trinity Large Thinking: An Apache 2.0 Open Reasoning Model for Long-Horizon Agents and Tool Use

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The landscape of open-source artificial intelligence has shifted from purely generative models toward systems capable of complex, multi-step reasoning. While proprietary ‘reasoning’ models have dominated the conversation, Arcee AI has released Trinity Large Thinking. This release is an open-weight reasoning model distributed under the Apache 2.0 license, positioning it as a transparent alternative for developers

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