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This article introduces a new method proposed by Google Research, Cornell, and USC that takes snapshots of RNN memory and caches them, enabling RNNs to efficiently handle long contexts. It combines Transformer-like strong memory with RNN-like low cost, offering a new direction for long-context AI.
Google researchers propose SensorFM, a foundation model trained on over 1 trillion minutes of unlabeled wearable data from 5 million people, which learns general physiological patterns and outperforms engineered features on 34 of 35 health prediction tasks.
Google announces Empirical Research Assistance (ERA), an AI tool using Gemini to write and optimize scientific code, now published in Nature and being rolled out as part of Gemini for Science to help scientists worldwide accelerate computational discovery.
This paper introduces a psychometric framework and the AIQ Benchmark to evaluate the cognitive profiles of generative AI models, revealing uneven evolution with strong verbal skills but stagnant perceptual reasoning.
Google Cloud AI Research introduces SkillOS, a reinforcement learning framework enabling LLM-based agents to self-evolve by curating reusable skills from past experiences.
Google Research releases TimesFM 2.5, an open-source time-series foundation model for forecasting, with 200M parameters, 16k context length, and support for quantile forecasts up to 1k horizon. The model is available on PyPI and Hugging Face, with fine-tuning via LoRA and integration into Google products like BigQuery ML, Google Sheets, and Vertex Model Garden.