AutoML, short for automated machine learning, uses software to automate repetitive parts of building a model. A system may prepare features, compare algorithms, tune hyperparameters, select architectures, allocate training resources, and rank candidates against validation metrics. This can help teams establish strong baselines and make machine learning accessible when specialist time is limited. Automation does not eliminate the need for judgment: people still define the problem, choose data, prevent leakage, select meaningful metrics, assess fairness, and decide whether the result is safe to deploy. AutoML can also consume substantial compute or overfit repeated validation tests, so its search process and final model should be documented and independently evaluated.
Elon Musk has unveiled Grokipedia – an AI-driven encyclopedia built on Grok that aims to offer factual, bias-free knowledge as an alternative to Wikipedia.
Andrew Tulloch, co-founder of the $12 billion AI startup Thinking Machines Lab, has joined Meta after previously rejecting what reports described as a $1.5 billion offer — a figure Meta has since called ‘inaccurate and ridiculous.’
Google has unveiled Gemini Enterprise, a next-generation AI platform designed for business productivity and collaboration, positioning itself directly against Microsoft’s Copilot and OpenAI’s enterprise solutions in the race for workplace AI dominance.
Google Cloud and Figma have launched an expanded partnership to embed Google’s generative AI models – including Gemini 2.5 Flash and Imagen 4 – directly into Figma’s design tools, reducing latency and accelerating creative workflows.