# AI Research Research into artificial intelligence technologies, including foundational and applied work. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## New AI technique could make minimally invasive surgeries safer and more precise DevFeed: [New AI technique could make minimally invasive surgeries safer and more precise]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-16T15:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [3D](), [navigation]() Tags: [3d](), [ai](), [artificial-intelligence](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [computer-vision](), [electrical-engineering-and-computer-science-eecs](), [health-care](), [images](), [imaging](), [jameel-clinic](), [machine-learning](), [medical-devices](), [medical-imaging](), [minimally-invasive-surgery](), [mit-ibm-computing-research-lab](), [mit-schwarzman-college-of-computing](), [model](), [national-institutes-of-health-nih](), [navigation](), [paper](), [polina-golland](), [precision](), [real-time](), [research](), [school-of-engineering](), [vision](), [vivek-gopalakrishnan]() ### AI overview MIT researchers and collaborators developed xvr, an AI method that adapts to individual patients and rapidly aligns intraoperative X-rays with preoperative 3D medical scans. The technique is intended to improve surgical navigation for minimally invasive procedures. ### Source excerpt This patient-specific method, called xvr, helps doctors use X-rays for surgical navigation in fields such as orthopedics and neurosurgery. ## Research acceleration: The view inside OpenAI DevFeed: [Research acceleration: The view inside OpenAI]() Original publisher: [Read original article]() Published: 2026-09-06T08:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Research](), [AI-assisted coding](), [coding]() Tags: [agents](), [ai-research](), [coding](), [deep-learning](), [experiments](), [frontier-ai](), [openai](), [research]() ### AI overview OpenAI describes how coding agents are being used throughout its AI research workflow, with reported increases in code contribution, experiment execution, task complexity, and success rates. It frames this progress as a step toward supervised automated AI research while emphasizing human control over research priorities and deployment decisions. ### Source excerpt Inside OpenAI, coding agents are reshaping AI research. Explore early data on agent usage, experiment velocity, task complexity, and research acceleration. ## When AI art has no author: Study finds generated images often can't be traced to training data DevFeed: [When AI art has no author: Study finds generated images often can't be traced to training data]() Original publisher: [Read original article]() Author: Rachel Gordon | MIT CSAIL Published: 2026-08-18T16:35:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [datasets](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)]() Tags: [ablation](), [ai](), [ai-and-copyright-law](), [ai-generated-images](), [ai-governance](), [artificial-intelligence](), [arts](), [arts-technology-and-society](), [attribution-decay](), [causal-inference](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [counterfactual-analysis](), [counterfactual-radius](), [data](), [data-attribution](), [datasets](), [david-gifford](), [diffusion-ensembles](), [electrical-engineering-and-computer-science-eecs](), [ethics](), [generative-ai-images](), [generative-diffusion-models](), [image-similarity-metrics](), [images](), [law](), [machine-learning](), [machine-unlearning](), [mit-csail](), [mit-schwarzman-college-of-computing](), [model](), [model-interpretability](), [paper](), [privacy-preserving-machine-learning](), [research](), [school-of-engineering](), [science](), [technology-and-policy](), [technology-and-society](), [training-data-attribution](), [training-data-influence](), [zheng-dai]() ### AI overview MIT CSAIL researchers describe attribution decay, a phenomenon in which the influence of individual training examples on a generative model's outputs diminishes as datasets grow. Their method removes training examples and retrains models to test whether generated samples change. ### Source excerpt A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves. ## 🗓 This Week In AI Research (1-7 August 26) DevFeed: [🗓 This Week In AI Research (1-7 August 26)]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-08-13T19:29:25Z Content type: article Language: en Sources: [Into AI]() Topics: [AI Research](), [releases](), [Benchmark](), [Large Language Model](), [Mixture of Experts (MoE)](), [model architecture](), [qwen]() Tags: [ai](), [ai-research](), [algorithm](), [benchmark](), [llms](), [mixture-of-experts](), [model-architecture](), [qwen](), [reasoning](), [releases]() ### AI overview A weekly roundup of AI research and model releases. It highlights Pathway, Bielik AI, and NYU's BDH-CQ reasoning model, which uses in-context learning with recurrent memory and latent-state reasoning, reports ARC-AGI-1 cost-efficiency results, and describes Alibaba's Qwen3.8-Max release and the U-OPSD self-distillation algorithm. ### Source excerpt The top 10 AI research papers and releases that you must know about this week. ## Announcing Evals and Releases: Evaluate Fin before, during, and after you go live DevFeed: [Announcing Evals and Releases: Evaluate Fin before, during, and after you go live]() Original publisher: [Read original article]() Author: Brian Donohue Published: 2026-08-13T17:33:52Z Content type: article Language: en Sources: [The Intercom Blog]() Topics: [AI Agent](), [AI Research](), [configuration](), [releases]() Tags: [agent](), [ai-agent](), [ai-research](), [evals](), [evaluation](), [news-updates](), [release](), [releases]() ### AI overview Intercom announces Evals and Releases for Fin, paired with Monitors as an eval-driven delivery system. Teams can test Fin with simulated customer conversations, evaluate changes against defined criteria, safely release updates, and monitor live conversations for regressions. ### Source excerpt Providing a complete evaluation system for Fin, you can now test changes before they go live, roll them out with control, evaluate every live conversation, and have confidence in the experience Fin delivers. ## GeoPT helps AI models simulate how objects respond to physical forces DevFeed: [GeoPT helps AI models simulate how objects respond to physical forces]() Original publisher: [Read original article]() Author: Alex Shipps | MIT CSAIL Published: 2026-08-10T19:25:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI Models](), [Simulation](), [AI Research](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)](), [Robotics]() Tags: [3-d-imaging](), [ai](), [ai-models](), [artificial-intelligence](), [computational-fluid-dynamics-cfd](), [computer-graphics](), [computer-modeling](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [crash-simulation](), [design](), [electrical-engineering-and-computer-science-eecs](), [geometric-pre-training](), [geopt](), [haixu-wu](), [human-computer-interaction](), [kaiming-he](), [manufacturing](), [minghao-guo](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [neural-physics-simulation](), [paper](), [physics](), [physics-aware-ai](), [physics-foundation-models](), [research](), [robotics](), [school-of-engineering](), [self-supervised-learning](), [simulation](), [surrogate-modeling](), [synthetic-dynamics](), [transformer-based-simulators](), [wojciech-matusik]() ### AI overview Researchers at MIT CSAIL and Tsinghua University developed GeoPT, a pre-training approach that uses 3D simulations of mechanical interactions to help AI models learn physics more efficiently. The article reports that models using the approach reached peak performance twice as fast and trained on up to 60 percent less data than leading models. ### Source excerpt "GeoPT" helps AI models understand the basics of physics so they can simulate how objects respond to things like wind and water more efficiently and accurately. ## 34 Amazon Research Awards Build on Trainium recipients announced DevFeed: [34 Amazon Research Awards Build on Trainium recipients announced]() Original publisher: [Read original article]() Author: Amazon Research Awards team Published: 2026-08-05T15:00:00Z Content type: news Language: en Sources: [Amazon Science homepage]() Topics: [Artificial Intelligence](), [responsible-ai](), [AWS AI chips](), [AI Research](), [Optimization](), [Inference](), [moe](), [Large Language Model](), [distributed-systems](), [synthetic-data]() Tags: [academic-ai-funding](), [ai](), [ai-research](), [ai-research-grants](), [ai-safety-and-alignment](), [amazon-research-awards](), [ara](), [aws-ai-chips](), [aws-trainium](), [build-on-trainium](), [data](), [deep-learning](), [distributed-systems](), [inference](), [internal-ara-program-updates](), [llm](), [machine-learning-research](), [mixture-of-experts](), [optimization](), [research](), [responsible-ai](), [synthetic](), [synthetic-data-generation]() ### AI overview Amazon announces 34 recipients of its Build on Trainium program, a $110 million credit initiative supporting AI research and university education. The awards fund work in areas including Responsible AI, language models, synthetic data, distributed systems, model architectures, libraries, and optimization on AWS Trainium. ### Source excerpt Amazon announces 34 recipients of the Build on Trainium program, a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI. ## Orchard: An open framework for scalable agentic AI DevFeed: [Orchard: An open framework for scalable agentic AI]() Original publisher: [Read original article]() Author: Baolin Peng, Wenlin Yao, Qianhui Wu, Hao Cheng, Jianfeng Gao Published: 2026-08-03T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [Artificial Intelligence](), [AI Research](), [Open Source](), [Frameworks](), [Kubernetes](), [Reinforcement learning](), [computer-use](), [codex](), [OpenClaw]() Tags: [agentic](), [agentic-ai](), [agents](), [ai](), [artificial-intelligence](), [autonomous](), [autonomous-agents](), [building](), [codex](), [evaluation](), [framework](), [infrastructure](), [kubernetes](), [learning](), [models](), [open](), [open-source](), [research](), [research-blog](), [train](), [workflows]() ### AI overview Orchard is an open-source framework for training and evaluating agentic AI systems across software engineering, web navigation, and personal-assistant tasks. Its reusable Orchard Env provides Kubernetes-based infrastructure for data collection, reinforcement-learning rollouts, and evaluation, while Orchard-SWE, Orchard-GUI, and Orchard-Claw demonstrate strong results from relatively small open-weight models. ### Source excerpt Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure. The post Orchard: An open framework for scalable agentic AI appeared first on Microsoft Research. ## 🗓 This Week In AI Research (17-24 July 26) DevFeed: [🗓 This Week In AI Research (17-24 July 26)]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-07-30T10:44:06Z Content type: article Language: en Sources: [Into AI]() Topics: [AI Research](), [Artificial Intelligence](), [releases](), [Large Language Model](), [Deep neural networks](), [parquet]() Tags: [ai](), [ai-research](), [anthropic](), [claude](), [compression](), [llms](), [neural-networks](), [releases]() ### AI overview A weekly roundup of AI research papers and releases, including Claude Opus 5, the HOPE framework for analyzing knowledge in deep neural networks through compression, and research on how large language models track evolving user intent across conversations. ### Source excerpt The top 10 AI research papers and releases this week (Claude Opus 5, Laguna S 2.1, Loopie, Nanbeige 4.2, Fugu-Cyber, and more) ## Professor Emeritus Dimitri Bertsekas, influential computer scientist and prolific author, dies at 83 DevFeed: [Professor Emeritus Dimitri Bertsekas, influential computer scientist and prolific author, dies at 83]() Original publisher: [Read original article]() Author: Jane Halpern | Department of Electrical Engineering and Computer Science Published: 2026-07-22T17:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Optimization](), [Computer science](), [Electrical engineering and computer science (EECS)](), [AI Research](), [Artificial Intelligence](), [Reinforcement learning]() Tags: [alumni-ae](), [artificial-intelligence](), [asu-ozdaglar](), [athena-scientific](), [bayforest-technologies](), [books-and-authors](), [computer-science](), [computer-science-and-technology](), [convex-analysis](), [dimitri-bertsekas](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [laboratory-for-information-and-decision-systems-lids](), [large-scale-computation](), [mit-books-and-authors](), [mit-eecs-faculty](), [mit-faculty-obituary](), [mit-lids](), [mit-photographers](), [mit-sandbox-innovation-fund-program](), [mit-schwarzman-college-of-computing](), [munther-dahleh](), [network-optimization](), [networks](), [neurodynamic-programming](), [obituaries](), [optimization](), [photography](), [programming](), [reinforcement-learning](), [research](), [robert-gallager](), [school-of-engineering]() ### AI overview An obituary remembers Dimitri Bertsekas, an MIT professor emeritus and influential computer scientist who died at 83. His work shaped optimization, control, large-scale computation, reinforcement learning, and artificial intelligence, while his books and mentorship influenced generations of students and researchers. ### Source excerpt Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence. ## This Week In AI Research (🗓 9-16 July 26) DevFeed: [This Week In AI Research (🗓 9-16 July 26)]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-07-22T09:14:26Z Content type: article Language: en Sources: [Into AI]() Topics: [Artificial Intelligence](), [AI Research](), [releases](), [Large Language Model](), [Mixture of Experts (MoE)](), [long-context](), [Open Source Models & Datasets](), [Transformers]() Tags: [ai](), [architecture](), [benchmarks](), [context-window](), [llms](), [models](), [moe](), [open](), [releases](), [research](), [transformers]() ### AI overview A weekly roundup of AI research papers and releases, covering Kimi K3, Expanded Hyper-Connections, VideoChat3, and other developments. It describes Kimi K3's architecture, context window, benchmark performance, and limitations, and summarizes xHC's reported efficiency improvements. ### Source excerpt The top 10 AI research papers and releases this week (Kimi K3, Inkling, WanSong v1.0, Bonsai 27B, and many more) ## Laguna S 2.1 is now available on AI Gateway DevFeed: [Laguna S 2.1 is now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-21T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Models](), [agentic-coding](), [Mixture of Experts (MoE)](), [context window](), [API](), [MLOps](), [AI Research](), [API keys](), [Routing (disambiguation)]() Tags: [agentic-coding](), [ai-models](), [ai-research](), [api-keys](), [context-window](), [cost](), [mixture-of-experts](), [mlops](), [routing](), [sdk](), [support]() ### AI overview Poolside's Laguna S 2.1 is now available through Vercel AI Gateway in free and paid versions, with context windows of 256K and 1M tokens. The open-weight Mixture-of-Experts model supports thinking and no-thinking modes and is designed for agentic coding, long-running tasks, browser tooling, MLOps pipelines, and AI research. ### Source excerpt Laguna S 2.1 from Poolside is now available on AI Gateway. There are 2 versions of the model available: Free version (256K context window): poolside/laguna-s-2.1-free Paid version (1M context window): poolside/laguna-s-2.1 Laguna S 2.1 is an open-weight Mixture-of-Experts model that supports a context window of up to 1M tokens and runs in thinking and no-thinking modes. The model specializes in agentic coding and long-running tasks, including writing and debugging code, running tests, building browser-based tooling, and working on MLOps pipelines and AI research. In thinking mode, Laguna S 2.1 reports 70.2% on Terminal-Bench 2.1, 78.5% on SWE-bench Multilingual, and 59.4% on SWE-Bench Pro. To use Laguna S 2.1, set model to poolside/laguna-s-2.1-free or poolside/laguna-s-2.1 in the AI SDK: AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Try Laguna S 2.1 in the model playground. Read more ## New method aims to keep kids safe from illegal AI-generated content DevFeed: [New method aims to keep kids safe from illegal AI-generated content]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-07-13T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [ai safety](), [AI Research](), [Open Source]() Tags: [ai](), [ai-deepfakes](), [ai-safety](), [algorithms](), [artificial-intelligence](), [ashia-wilson](), [computer-science-and-technology](), [csam](), [ethics](), [generative](), [generative-ai](), [human-computer-interaction](), [institute-for-medical-engineering-and-science-imes](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [marzyeh-ghassemi](), [mit-schwarzman-college-of-computing](), [models](), [online-child-safety](), [open-source](), [privacy](), [public-health](), [research](), [safety](), [school-of-engineering](), [vinith-suriyakumar]() ### AI overview MIT researchers and Thorn developed an auditing technique that assesses whether a generative AI model has been specialized to produce child sexual abuse material without generating illegal outputs. In testing, the procedure identified specialized model variants with 100 percent accuracy. ### Source excerpt Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs. ## 🗓 This Week In AI Research (1-8 July 26) DevFeed: [🗓 This Week In AI Research (1-8 July 26)]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-07-12T11:25:32Z Content type: article Language: en Sources: [Into AI]() Topics: [Artificial Intelligence](), [AI Research](), [Large Language Model](), [Transformer](), [qwen](), [grpo](), [Algorithms](), [releases]() Tags: [agentic](), [ai](), [algorithm](), [algorithms](), [grpo](), [llm](), [models](), [performance](), [qwen](), [ranking](), [releases](), [research](), [rl](), [training](), [update]() ### AI overview A weekly roundup of AI research papers and releases highlights findings that reinforcement-learning gains can be concentrated in a single transformer layer and presents LLM-as-a-Verifier, a framework for continuous scoring and ranking of agentic-task solutions. ### Source excerpt The top 10 research papers and AI releases this week (SpaceXAI's Grok 4.5, OpenAI's GPT-Live voice models, Cognition's SWE-1.7, Meta's Muse Spark 1.1, and many more) ## Jesse Thaler named director of the Laboratory for Nuclear Science DevFeed: [Jesse Thaler named director of the Laboratory for Nuclear Science]() Original publisher: [Read original article]() Author: Julia C. Keller | School of Science Published: 2026-07-07T14:45:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [AI, ML & Data Engineering]() Tags: [ai](), [ai-in-physics](), [artificial-intelligence](), [bolek-wyslouch](), [data](), [department-of-energy-doe](), [faculty](), [idss](), [jesse-thaler](), [laboratory-for-nuclear-science](), [leadership](), [mit-center-for-theoretical-physics-ctp](), [mit-ctp-li](), [mit-faculty-appointments](), [mit-iaifi](), [mit-idss](), [mit-leadership](), [mit-lns](), [national-science-foundation-nsf](), [nergis-mavalvala](), [particle-physics](), [physics](), [quantum](), [research](), [school-of-science](), [science](), [tracey-slatyer]() ### AI overview Professor Jesse Thaler has been named director of MIT's Laboratory for Nuclear Science, effective Aug. 1. He succeeds Bolek Wyslouch and will lead the laboratory while continuing research combining particle physics and machine learning. ### Source excerpt The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics. ## This Week In AI Research (21-30 June 26) 🗓 DevFeed: [This Week In AI Research (21-30 June 26) 🗓]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-07-03T02:04:12Z Content type: article Language: en Sources: [Into AI]() Topics: [Artificial Intelligence](), [AI Research](), [Large Language Model](), [LLMs](), [OpenAI](), [Meta](), [real-time](), [coding]() Tags: [agentic](), [ai](), [ai-research](), [benchmarks](), [coding](), [gpt](), [llms](), [meta](), [model](), [openai](), [real-time](), [release](), [research]() ### AI overview A weekly roundup of AI research and releases, including Brain2Qwerty v2 for real-time decoding of typed sentences from MEG recordings, OpenAI's GPT-5.6 Sol, and Sakana Fugu orchestrator LLMs. The supplied excerpt also discusses agent architectures and performance on several benchmarks. ### Source excerpt The top 10 research papers of this week: GPT-5.6, Sonnet 5, Meta's real-time brain-to-text decoder, a 35B model that beats trillion-parameter LLMs & more! ## Research into how AI can help users understand skin conditions DevFeed: [Research into how AI can help users understand skin conditions]() Original publisher: [Read original article]() Published: 2026-06-12T17:52:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Human-AI evaluation](), [AI Research](), [Google](), [AI Models](), [datasets]() Tags: [ai](), [ai-models](), [datasets](), [google](), [health](), [health-bioscience](), [human-computer-interaction-and-visualization](), [model](), [research](), [validation]() ### AI overview Google Research presents recent and past studies on how AI-powered informational tools may help people understand skin concerns and make better decisions about next steps. The work examines consumer understanding, human factors, model validation, and supporting datasets in dermatology-related health information. ### Source excerpt Health & Bioscience ## Physics AI research that's shaping the industry. DevFeed: [Physics AI research that's shaping the industry.]() Original publisher: [Read original article]() Published: 2026-05-27T12:00:05Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [AI Research](), [Physics-guided deep learning](), [Artificial Intelligence](), [data](), [datasets](), [AI Foundation Models](), [GPU](), [Machine learning](), [Transformer]() Tags: [ai](), [ai-research](), [data](), [datasets](), [deep-learning](), [design](), [energy](), [engineering](), [gpu](), [industry](), [machine-learning](), [models](), [performance](), [physics](), [real-time](), [research]() ### AI overview Mistral describes its acquisition of Emmi AI and its focus on Physics AI for industrial engineering. The article surveys published work on CFD, neural surrogates, foundation models, datasets, plasma turbulence, and real-time industrial simulation across aerospace, automotive, semiconductors, and energy. ### Source excerpt Published breakthroughs pushing the state of the art. ## ConvApparel: Measuring and bridging the realism gap in user simulators DevFeed: [ConvApparel: Measuring and bridging the realism gap in user simulators]() Original publisher: [Read original article]() Published: 2026-04-09T11:22:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Conversational AI](), [Generative AI](), [LLMs](), [Artificial Intelligence](), [dataset](), [AI Research](), [Statistics](), [data](), [Google]() Tags: [agents](), [ai](), [ai-agents](), [ai-research](), [conversational-ai](), [data](), [evaluation](), [generative](), [generative-ai](), [llm](), [machine-intelligence](), [natural-language-processing](), [research](), [testing](), [training](), [validation]() ### AI overview Google Research introduces ConvApparel, a human-AI conversation dataset and evaluation framework for measuring the realism gap in LLM-based user simulators. It uses Good and Bad agents and validates results through population-level statistics, human-likeness scoring, and counterfactual validation. ### Source excerpt Generative AI ## Google Research at The Check Up: from healthcare innovation to real-world care settings DevFeed: [Google Research at The Check Up: from healthcare innovation to real-world care settings]() Original publisher: [Read original article]() Published: 2026-03-17T19:47:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [Artificial Intelligence](), [AI Research](), [datasets](), [data](), [Ground truth / benchmark quality]() Tags: [ai](), [ai-research](), [data](), [datasets](), [google](), [health](), [health-bioscience](), [healthcare](), [innovation](), [machine-intelligence](), [research](), [science](), [uk](), [us]() ### AI overview Google Research highlights AI applications in healthcare, including a Personal Health Agent for preventative care, multimodal analysis of wearable data, and diagnostic research for improving breast cancer detection. The article emphasizes collaboration with healthcare professionals and the use of diverse datasets and expert-validated ground truth data. ### Source excerpt Health & Bioscience ## Testing LLMs on superconductivity research questions DevFeed: [Testing LLMs on superconductivity research questions]() Original publisher: [Read original article]() Published: 2026-03-16T17:31:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Large Language Model](), [World models](), [Human-AI evaluation](), [Benchmark](), [AI Research](), [Artificial Intelligence](), [Google]() Tags: [ai](), [analysis](), [benchmark](), [education-innovation](), [evaluation](), [general-science](), [google](), [large-language-model](), [llms](), [machine-intelligence](), [natural-language-processing](), [research](), [testing]() ### AI overview Google Research reports an expert evaluation of six large language models on challenging high-temperature superconductivity questions. Experts graded the responses, finding that NotebookLM and a custom system performed best when drawing on certified, quality-controlled sources, while all systems showed areas for improvement. The study aims to inform the development of trustworthy AI tools for scientific discovery. ### Source excerpt Education Innovation ## WAXAL: A large-scale open resource for African language speech technology DevFeed: [WAXAL: A large-scale open resource for African language speech technology]() Original publisher: [Read original article]() Published: 2026-03-06T20:06:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [asr](), [datasets](), [voice ai](), [data](), [Google](), [AI Research](), [systems]() Tags: [africa](), [ai](), [ai-research](), [asr](), [audio](), [datasets](), [google](), [natural-language-processing](), [open-source-models-datasets](), [research](), [resources](), [speech](), [text-to-speech](), [transcription](), [voice]() ### AI overview Google Research introduces WAXAL, an open-access speech dataset covering 27 Sub-Saharan African languages. The release includes approximately 1,846 hours of transcribed ASR data and more than 565 hours of high-fidelity TTS recordings under a CC-BY-4.0 license. ### Source excerpt Natural Language Processing ## How Balyasny Asset Management built an AI research engine DevFeed: [How Balyasny Asset Management built an AI research engine]() Original publisher: [Read original article]() Published: 2026-03-06T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [AI Research](), [LLM evaluation / benchmarking](), [Orchestration](), [data]() Tags: [agent](), [ai](), [ai-research](), [benchmarks](), [data](), [evaluation](), [openai](), [orchestration](), [workflows]() ### AI overview Balyasny Asset Management built an AI investment research system that combines OpenAI models, internal models, agent workflows, and rigorous evaluation. The system helps investment teams complete deep research in hours instead of days while operating across financial data and institutional compliance requirements. ### Source excerpt By combining rigorous model evaluation, full-platform use of OpenAI, and agent workflows, Balyasny is reinventing investment research. ## Booking.com 2026 GenAI and ML PhD Research Internship in Amsterdam DevFeed: [Booking.com 2026 GenAI and ML PhD Research Internship in Amsterdam]() Original publisher: [Read original article]() Author: Yang Yang Published: 2026-02-05T10:39:26Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [Machine Learning & Artificial Intelligence](), [genai](), [AI Research](), [Python](), [Large Language Model](), [Apache Spark](), [SQL](), [Hadoop](), [Reinforcement learning](), [Synthetic Data Generation](), [Transformer]() Tags: [2026](), [ai](), [big-data](), [blog-posts](), [featured](), [genai](), [internship](), [machine-learning](), [ml](), [python](), [research]() ### AI overview Booking.com is recruiting current PhD students in quantitative fields for a three-month GenAI and machine learning research internship in Amsterdam in 2026. Projects include LLM alignment, transformer explainability, embeddings, context engineering, and synthetic data generation. ### Source excerpt At Booking.com, we don't just use Machine Learning -- we use it to solve some of the most complex travel challenges in the world. We're looking for the next generation of researchers to join our Machine Learning community in Amsterdam for a 3-month deep dive into cutting-edge AI. The Program As a Research Intern, you'll be embedded in our teams, working alongside world-class mentors. Your mission? To tackle real-world problems and push the boundaries of the state-of-the-art. Are You the One? We're looking for current PhD students in quantitative fields (CS, Math, AI, Physics) who can conduct independent research and have a solid grip on Python and Big Data tech (SQL, Spark, Hadoop). What's in it for you? You won't just be "an intern". You'll be a contributor to our Machine Learning community. You'll have the opportunity to contribute to the existing efforts of the Machine Learning teams, participate in internal knowledge-sharing sessions, and enjoy the collaborative, high-energy environment of our Amsterdam HQ. Projects Regularized Target Encoding for large real-world datasets Multi-Agent Collaboration Aligning LLMs with user feedback via reinforcement learning Multi-level treatments Interpretable Foundations: Explainability Methods for Transformer Models on Sequential Event Data Scalable and generalisable ID embedding learning Improving property embeddings with better handling of rich and long-context data Utility-aware retrieval for context engineering in travel planning Synthetic Data Generation in Images Requirements We are looking for independent researchers with strong understanding of Machine Learning topics (see requirements for each project in the Linkedin ad), have a track record of peer-reviewed publications and a passion for solving complex problems. Why Booking.com? You'll join a vibrant, diverse community of data scientists and researchers who love to experiment. Beyond the code, you'll experience the unique culture of our Amsterdam headquarters -- a hub [Next page]()