Generative AI Tools

Google Unveils Gemini 3, Ushering in an ‘Agentic Era’ of AI Google has introduced Gemini 3, a foundational model designed to power a new ‘agentic era’ of AI. Unlike previous models accessed through a single interface, Gemini 3 is being deeply integrated across Google’s product suite—including Search, Workspace, Android, and Cloud—to act as a comprehensive ’nervous system.’ This strategic shift focuses on creating AI systems that can execute actions, not just generate responses. To support this, Google is also releasing tools for developers to leverage the model for complex, long-running coding tasks. ...

AI & Machine Learning: Alibaba’s Omni-Modal AI and Quantum Material Discovery This week in AI and Machine Learning, key advancements include the release of Alibaba’s omni-modal AI, Qwen3-Omni, a new tool from MIT called SCIGEN that uses AI to discover quantum materials, a novel AI framework from Florida Atlantic University for managing complex systems, and a breakthrough in healthcare using AI to predict spinal cord injury outcomes from blood tests. ...

MLCommons Releases MLPerf Inference v5.1, Setting New AI Performance Benchmarks MLCommons has released MLPerf Inference v5.1, the latest version of its benchmark suite, introducing new tests to evaluate AI system performance on modern workloads. This update saw a record 27 organizations submitting systems for evaluation. The new version features three new benchmarks, including one for the DeepSeek-R1 reasoning model, and implements tighter latency requirements for certain large language model tests. Notably, the popular Llama 2 70B benchmark demonstrated significant performance gains, with top systems running up to 50% faster than in the previous release. The v5.1 release also marked the first submission of a heterogeneous system that intelligently balances inference workloads across different accelerator types. ...

How Google AI’s New Method Reduces LLM Training Data by 10,000x Google Research has unveiled a groundbreaking method for fine-tuning large language models (LLMs) that reduces required training data by up to 10,000 times. This innovative approach utilizes active learning to focus expert labeling on the most informative examples, especially “boundary cases” where model uncertainty is highest. In experiments with Gemini Nano models, this technique matched or surpassed the quality of models trained on 100,000 random labels with as few as 250 to 450 targeted examples. This development promises to make AI model development significantly leaner, more agile, and more cost-effective. ...