Sony AI’s Fair Human‑Centric Image Benchmark is a free, open dataset that helps developers test and fix bias in computer‑vision models. Read our guide to start building fairer AI today.
Agentic AI frameworks such as SEAL and DeepResearch empower models to plan, act, and learn. This article covers new model releases, practical guides, and how to integrate them into your projects.
TinyML neural architecture search automates the design of compact AI models that run on microcontrollers. This guide shows how to search, quantify, and deploy a model on an ESP32.
Federated learning for edge devices lets tiny gadgets learn together while keeping data private. This guide covers the basics, tools, and a smart‑home example.
AI threat hunting blends anomaly detection, behavioural models, NLP and graph analysis to uncover hidden cyber threats early.
Federated Learning in Healthcare lets hospitals collaborate on AI models while keeping patient data inside each institution. This guide explains the process, benefits, and real‑world use cases.
Federated Learning for Edge Devices keeps data on the device, reduces bandwidth, and builds better AI models across a fleet of devices. This guide explains the concepts, architecture, and real-world use cases.
AI automated data labeling is a practical workflow that lets you quickly generate clean, accurate labels for machine learning. It reduces human effort, saves money, and speeds up model training, making it ideal for teams of any size.
AI Cybersecurity Threat Detection uses machine learning on network logs to spot malicious activity fast, cut phishing incidents, and give teams a clearer view of threats.
Secure Federated Learning protects data privacy by training AI models on local data and sharing only encrypted updates, while guarding against poisoning, inversion, and inference attacks.