# Machine Intelligence Published articles for Machine Intelligence. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## ToolGrad: Efficient tool-use dataset generation with textual "gradients" DevFeed: [ToolGrad: Efficient tool-use dataset generation with textual "gradients"]() Original publisher: [Read original article]() Published: 2026-09-10T22:50:22Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [dataset](), [Large Language Model](), [LLM Techniques](), [Machine learning](), [Fine-tuning](), [LLM evaluation / benchmarking]() Tags: [agent](), [cost](), [datasets](), [evaluation](), [fine-tuning](), [generation](), [llm](), [machine-intelligence](), [machine-learning](), [natural-language-processing](), [prompt-engineering](), [tool]() ### AI overview ToolGrad generates tool-use chains before deriving corresponding user queries, aiming to create complex training data for LLM tool use more efficiently and at lower cost than exploration-based approaches. ### Source excerpt Machine Intelligence ## The AI policy window is open. We need to act. DevFeed: [The AI policy window is open. We need to act.]() Original publisher: [Read original article]() Published: 2026-09-09T13:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Responsibility & Safety]() Tags: [ai](), [ai-safety](), [frontier-ai](), [global](), [global-affairs](), [government](), [industry](), [machine-intelligence](), [models](), [monitoring](), [openai](), [policy](), [safety]() ### AI overview The article calls for stronger AI safety regulation, shared industry and international standards, and technical alignment and monitoring as advanced AI capabilities accelerate. ### Source excerpt Chris Lehane argues that stronger AI capabilities require stronger safety evidence, shared standards, and durable policy action while the policy window remains open. ## Jakub Pachocki Calls for Caution and Stronger Safeguards as AI Capabilities Advance DevFeed: [Jakub Pachocki Calls for Caution and Stronger Safeguards as AI Capabilities Advance]() Original publisher: [Read original article]() Published: 2026-09-06T09:00:00Z Content type: opinion Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Machine Intelligence](), [Language models](), [OpenAI]() Tags: [ai](), [ai-research](), [alignment](), [language-models](), [machine-intelligence](), [openai](), [research](), [safety]() ### AI overview Jakub Pachocki reflects on the rapid growth of reasoning language models and the possibility of continued capability advances through recursive self-improvement. He urges extreme caution, stronger alignment and monitoring efforts, defensive systems, and broader international interventions. ### Source excerpt Jakub Pachocki reflects on increasingly capable AI and the challenge of keeping it aligned. He calls for stronger safeguards and international coordination. ## Transfer learning for genomic prediction in underrepresented populations DevFeed: [Transfer learning for genomic prediction in underrepresented populations]() Original publisher: [Read original article]() Published: 2026-09-03T18:20:31Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Fine-tuning](), [Model Development](), [Google](), [datasets]() Tags: [datasets](), [evaluation](), [general-science](), [google](), [healthcare](), [learning](), [machine-intelligence](), [research](), [training]() ### AI overview Google Research evaluates transfer learning for polygenic risk-score prediction across populations. European-cohort transfer learning improves prediction for small underrepresented target cohorts but can reduce accuracy as target cohorts grow, particularly for population-specific traits. ### Source excerpt General Science ## A connectomics milestone: Mapping the complete male fruit fly brain DevFeed: [A connectomics milestone: Mapping the complete male fruit fly brain]() Original publisher: [Read original article]() Published: 2026-09-03T16:00:03Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [datasets](), [Google]() Tags: [ai](), [datasets](), [general-science](), [google](), [health-bioscience](), [machine-intelligence](), [open-source](), [open-source-models-datasets](), [research](), [science]() ### AI overview Google Research describes a complete wiring map of the male fruit fly's brain and central nervous system, containing over 166,000 neurons and 125 million synaptic connections. The connectome was produced through a decade-long partnership using computing and AI, and is available to explore and download via Neuroglancer. ### Source excerpt General Science ## Mapping global methane emissions from space with deep learning DevFeed: [Mapping global methane emissions from space with deep learning]() Original publisher: [Read original article]() Published: 2026-09-01T18:40:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Deep neural networks](), [Google]() Tags: [climate-sustainability](), [deep-learning](), [earth-ai](), [framework](), [global](), [google](), [machine-intelligence](), [mapping](), [nasa](), [research](), [space]() ### AI overview The article presents MAPL-EMIT, a deep-learning framework for automating global detection, enhancement prediction, and source estimation of methane plumes from EMIT hyperspectral satellite measurements. ### Source excerpt Climate & Sustainability ## TimesFM-3: A zero-shot foundation model for multivariate forecasting DevFeed: [TimesFM-3: A zero-shot foundation model for multivariate forecasting]() Original publisher: [Read original article]() Published: 2026-08-31T17:19:40Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [Time Series](), [Google](), [datasets](), [Transformer architecture](), [Fine-tuning](), [generalization in machine learning]() Tags: [data-management](), [features](), [fine-tuning](), [foundation-models](), [google](), [machine-intelligence](), [model](), [product](), [time-series](), [transformer-architecture](), [zero-shot]() ### AI overview Google Research introduces TimesFM-3, a 330-million-parameter time-series foundation model designed for accurate multivariate forecasting in a single forward pass. Pre-trained on more than one trillion real-world and synthetic time points, it jointly models coevolving series and external covariates in zero-shot settings without task-specific fine-tuning. ### Source excerpt Data Management ## Planetary prediction engine: Automating global models via Earth AI DevFeed: [Planetary prediction engine: Automating global models via Earth AI]() Original publisher: [Read original article]() Published: 2026-08-27T17:37:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Earth AI](), [Artificial Intelligence](), [data-engineering](), [Google](), [Machine learning](), [AI Development](), [Feature Engineering](), [Model Development]() Tags: [agents](), [ai](), [analytics](), [data-engineering](), [earth-ai](), [evaluation](), [feature-engineering](), [generative-ai](), [google](), [insights](), [machine-intelligence](), [mapping](), [research](), [training](), [validation](), [workflow](), [workflows]() ### AI overview Google Research introduces the Planetary Prediction Engine, an experimental Earth AI capability that autonomously performs geospatial data discovery, cleanup, feature engineering, model training, evaluation, and report generation from natural-language queries. The system targets applications including public health, food security, environmental risk, and socioeconomic analysis, reducing the stated workflow from weeks of manual data engineering to minutes. ### Source excerpt Earth AI ## GlucoFM: Foundation model for continuous glucose monitoring DevFeed: [GlucoFM: Foundation model for continuous glucose monitoring]() Original publisher: [Read original article]() Published: 2026-08-26T18:42:43Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [Google](), [data](), [datasets]() Tags: [data](), [foundation-models](), [google](), [health-bioscience](), [machine-intelligence](), [model](), [research]() ### AI overview GlucoFM is a lightweight, self-supervised foundation model for continuous glucose monitoring. Its dual-stream design separates slower glycemic trends from short-term deviations while preserving time-of-day and missingness information. Evaluated across four cohorts and seven clinical prediction tasks, it achieved higher average PR-AUC than the evaluated GluFormer variant. ### Source excerpt Health & Bioscience ## AgentHands: Generating interactive hand gestures for spatially grounded agent conversations in XR DevFeed: [AgentHands: Generating interactive hand gestures for spatially grounded agent conversations in XR]() Original publisher: [Read original article]() Published: 2026-08-25T19:10:59Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Human-Computer Interaction and Visualization](), [AI Bots]() Tags: [3d](), [ai-agents](), [android-xr](), [human-computer-interaction-and-visualization](), [llm](), [machine-intelligence](), [multimodal](), [research-prototype]() ### AI overview AgentHands is an LLM-powered XR research prototype that adds synchronized hand gestures to conversational agents for spatially grounded guidance in physical tasks. ### Source excerpt Human-Computer Interaction and Visualization ## How mobility gives language models a deeper understanding of place DevFeed: [How mobility gives language models a deeper understanding of place]() Original publisher: [Read original article]() Published: 2026-08-21T10:54:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Language models](), [AI Models](), [data](), [datasets](), [Google]() Tags: [ai](), [ai-models](), [algorithms-theory](), [artificial-intelligence](), [benchmark](), [benchmarks](), [data](), [datasets](), [earth-ai](), [embedding](), [google](), [language-models](), [machine-intelligence](), [mobility](), [model](), [models](), [points](), [research](), [vector]() ### AI overview Google Research introduces Mobility-Embedded POIs (ME-POIs), a framework that combines language-model-based text representations of places with aggregated, anonymized mobility patterns. The resulting embeddings capture both a place's identity and its changing functional activity, improving predictions such as visit intent, price level, opening hours, and busyness. ### Source excerpt Algorithms & Theory ## Introducing Intelligence Age DevFeed: [Introducing Intelligence Age]() Original publisher: [Read original article]() Published: 2026-08-20T07:00:00Z Content type: opinion Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [Machine Intelligence](), [Responsibility & Safety]() Tags: [ai](), [ai-safety](), [blog](), [intelligence-age](), [machine-intelligence](), [openai](), [opinion](), [policy]() ### AI overview OpenAI introduces Intelligence Age, a Strategic Futures team and blog focused on how transformative AI may reshape power, governance, the economy, and individual freedom. The article examines concentration-of-power risks and the possibility that autonomous systems and machine intelligence could reduce states' dependence on human cooperation and labor. ### Source excerpt Introducing Intelligence Age, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and individual freedom. ## Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery DevFeed: [Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery]() Original publisher: [Read original article]() Published: 2026-08-17T10:34:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Deep neural networks](), [Deep learning](), [Machine learning](), [Google]() Tags: [deep-learning](), [general-science](), [google](), [health](), [health-bioscience](), [machine-intelligence](), [machine-learning](), [monitoring](), [multimodal](), [science]() ### AI overview Google Research presents PhotoScan, a deep learning approach that estimates body composition from smartphone photos and predicts insulin resistance with accuracy comparable to DXA scans in a clinical research setting. The article explains how body-composition measures such as fat distribution and visceral fat may complement wearable data for earlier cardiometabolic risk assessment. ### Source excerpt General Science ## Advancing AMIE towards expert-level audio-visual clinical consultations DevFeed: [Advancing AMIE towards expert-level audio-visual clinical consultations]() Original publisher: [Read original article]() Published: 2026-08-11T17:04:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Google](), [real-time]() Tags: [ai](), [audio](), [communication](), [health-bioscience](), [machine-intelligence](), [multimodal](), [partnership](), [reasoning](), [research](), [video]() ### AI overview Google Research describes an advance to AMIE, a medical AI system designed for real-time video consultations. In simulated consultations, AMIE demonstrated expert-level performance in a randomized controlled study while integrating spoken history with visual and auditory clinical cues. ### Source excerpt Health & Bioscience ## Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence DevFeed: [Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence]() Original publisher: [Read original article]() Published: 2026-07-30T20:36:36Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Google](), [AI-generated research reports](), [Large language models (LLMs)](), [Hallucination detection](), [LLM evaluation / benchmarking]() Tags: [agents](), [ai](), [autonomous-agents](), [benchmarks](), [evaluation](), [general-science](), [google](), [hallucinations](), [language-models](), [machine-intelligence](), [natural-language-processing](), [research](), [research-prototype]() ### AI overview Google Research introduces the Science One Framework, an experimental autonomous research prototype built around Chain-of-Evidence. It is designed to make AI-generated research verifiable by linking claims to supporting evidence and by auditing papers against their code and evidence. The article reports that the framework eliminates phantom references and produces fully verifiable scores in the described evaluations. ### Source excerpt General Science ## Towards a quantum computer that learns from its errors DevFeed: [Towards a quantum computer that learns from its errors]() Original publisher: [Read original article]() Published: 2026-07-22T18:40:21Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Reinforcement learning](), [Neural Network](), [Artificial Intelligence](), [Google]() Tags: [ai](), [autonomous](), [errors](), [learning](), [machine-intelligence](), [quantum](), [reinforcement-learning](), [research](), [rl]() ### AI overview Google Research describes a reinforcement-learning framework that uses quantum error detections to continuously adjust control parameters during computation, helping stabilize a quantum computer against drift. The article also discusses quantum error correction and the AlphaQubit neural-network decoder. ### Source excerpt Machine Intelligence ## Towards demystifying the creativity of diffusion models DevFeed: [Towards demystifying the creativity of diffusion models]() Original publisher: [Read original article]() Published: 2026-07-15T18:06:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Generative AI](), [Mathematics](), [Neural Network](), [Algorithms & Theory](), [Training AI Models](), [datasets](), [Google]() Tags: [algorithms-theory](), [data](), [datasets](), [diffusion](), [generation](), [generative-ai](), [google](), [iclr](), [iclr-2026](), [images](), [machine-intelligence](), [model](), [models](), [research](), [retrieval](), [training]() ### AI overview Google Research explains that diffusion models can generate novel data rather than merely memorize training examples. It attributes this creativity to neural networks learning a smoothed score function, which causes denoising to interpolate between training data points along a hidden data manifold. ### Source excerpt Algorithms & Theory ## SensorFM: Towards a general intelligence and interface for wearable health data DevFeed: [SensorFM: Towards a general intelligence and interface for wearable health data]() Original publisher: [Read original article]() Published: 2026-07-09T09:56:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [datasets](), [multimodal](), [data](), [Google]() Tags: [ai](), [data](), [generative-ai](), [google](), [health](), [machine-intelligence](), [model](), [models](), [multimodal](), [research](), [train](), [training]() ### AI overview Google Research introduces SensorFM, a large sensor foundation model trained on more than one trillion minutes of de-identified, multimodal wearable data from five million consented participants. The model learns reusable representations of human physiology that transfer across health prediction tasks and support label-efficient adaptation and data infilling. ### Source excerpt Generative AI ## Introducing TabFM: A zero-shot foundation model for tabular data DevFeed: [Introducing TabFM: A zero-shot foundation model for tabular data]() Original publisher: [Read original article]() Published: 2026-06-30T10:26:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [datasets](), [Machine learning](), [Google](), [Feature Engineering](), [Hyperparameter optimization](), [Language models](), [BigQuery](), [GitHub](), [hugging face](), [AI Inference]() Tags: [bigquery](), [classification](), [data](), [data-management](), [feature-engineering](), [github](), [google](), [hugging-face](), [hyperparameter-optimization](), [machine-intelligence](), [model](), [product](), [zero-shot]() ### AI overview Google Research introduces TabFM, a zero-shot foundation model for tabular-data classification and regression. It frames prediction as in-context learning, reducing the need for dataset-specific training, hyperparameter optimization, and feature engineering, with availability through Hugging Face, GitHub, and BigQuery. ### Source excerpt Data Management ## Accelerating Gemini Nano models on Pixel with frozen Multi-Token Prediction DevFeed: [Accelerating Gemini Nano models on Pixel with frozen Multi-Token Prediction]() Original publisher: [Read original article]() Published: 2026-06-26T18:30:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [AI Inference](), [On-device AI](), [Google](), [LLMs](), [gemma]() Tags: [ai](), [efficiency](), [energy](), [gemini](), [inference](), [machine-intelligence](), [mobile-systems](), [natural-language-processing](), [on-device](), [on-device-ai](), [phones]() ### AI overview Google Research describes a method for retrofitting Multi-Token Prediction onto frozen Gemini Nano v3 production models to accelerate on-device inference on Pixel phones. The approach targets mobile energy and memory constraints, improving the speed and energy efficiency of features such as notification summaries and proofreading without requiring separate drafting models. ### Source excerpt Machine Intelligence ## Thinking to recall: How reasoning unlocks parametric knowledge in LLMs DevFeed: [Thinking to recall: How reasoning unlocks parametric knowledge in LLMs]() Original publisher: [Read original article]() Published: 2026-06-24T16:51:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [Large language models (LLMs)](), [Chain-of-thought](), [Language models](), [LLM evaluation / benchmarking]() Tags: [chain-of-thought](), [generative-ai](), [google](), [large-language-models-llms](), [machine-intelligence](), [natural-language-processing](), [reasoning](), [research]() ### AI overview Google Research examines why generating chain-of-thought reasoning can help large language models recall simple facts that are already encoded in their parametric memory. Controlled experiments identify two mechanisms: latent computation through generated reasoning tokens and factual priming through related facts. ### Source excerpt Generative AI ## From pixels to planning: Earth AI for nature restoration DevFeed: [From pixels to planning: Earth AI for nature restoration]() Original publisher: [Read original article]() Published: 2026-06-16T17:30:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Earth AI](), [Artificial Intelligence](), [dataset](), [Deep learning](), [Google](), [data]() Tags: [ai](), [climate-sustainability](), [data](), [deep-learning](), [earth-ai](), [google](), [machine-intelligence](), [open-source-models-datasets](), [research](), [resource](), [sustainability](), [uk](), [vector]() ### AI overview Google Research describes a high-resolution deep learning approach that converts pixel-based maps of fine-scale ecological features into a vectorized dataset. The resource is intended to support nature restoration, carbon accounting, and biodiversity efforts across working landscapes in the UK while considering food security. ### Source excerpt Climate & Sustainability ## Unlocking dependable responses with Gemini Enterprise Agent Platform's Agentic RAG DevFeed: [Unlocking dependable responses with Gemini Enterprise Agent Platform's Agentic RAG]() Original publisher: [Read original article]() Published: 2026-06-05T11:26:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Retrieval Augmented Generation (RAG)](), [Artificial Intelligence](), [Google](), [Cloud](), [Data Management](), [Large Language Model]() Tags: [agentic](), [ai](), [data-management](), [gemini](), [google](), [google-cloud](), [machine-intelligence](), [natural-language-processing](), [product](), [rag](), [reasoning](), [retrieval]() ### AI overview Google Research and Google Cloud introduce an agentic RAG framework for complex enterprise queries. Its multi-agent workflow plans and reasons across multiple data sources, iteratively searches for sufficient context, and aims to produce more dependable and accurate responses than single-step RAG. ### Source excerpt Data Management ## Towards passive heart health monitoring via smartphone camera DevFeed: [Towards passive heart health monitoring via smartphone camera]() Original publisher: [Read original article]() Published: 2026-06-04T19:47:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Monitoring](), [webcam](), [dataset](), [Deep learning](), [Google](), [Ground truth / benchmark quality]() Tags: [deep-learning](), [devices](), [google](), [health](), [health-bioscience](), [heart-rate-monitoring](), [human-computer-interaction-and-visualization](), [machine-intelligence](), [model](), [monitoring](), [publication](), [research](), [resource](), [smartphones]() ### AI overview Google Research presents PHRM, a research system that passively estimates heart rate and resting heart rate from facial video captured by a smartphone's front-facing camera during everyday use. The system applies deep learning to video recorded after face unlock events and reports accuracy comparable to electrocardiogram-derived ground truth and wearable trackers. The publication also releases a large, diverse smartphone-video dataset and the pre-trained PHRM-mini model for qualified researchers. ### Source excerpt Health & Bioscience [Next page]()