Multitask learning trains a model to perform more than one task, often by sharing an encoder or intermediate layers while using separate output heads. Learning related objectives together can improve generalization, reduce the amount of task-specific data needed, and produce reusable representations. For example, a language system might learn classification, entity detection, and question answering within one training program. Benefits are not automatic: tasks can compete for capacity, produce gradients that interfere, or appear at very different frequencies. Effective designs balance losses, sampling, architecture, and evaluation so strong performance on a dominant task does not hide deterioration elsewhere. Unrelated or poorly labeled tasks may create negative transfer.
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.’
OpenAI has announced a strategic partnership with Broadcom to design and deploy 10 gigawatts of custom AI accelerators, expanding its hardware ecosystem beyond Nvidia and AMD.
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.