Explore the latest insights and updates from top sources in technology, artificial intelligence, and innovation. Our curated collection of RSS feeds brings you real-time content from renowned platforms, including OpenAI, Google, and more. Stay informed about the cutting-edge developments, research breakthroughs, and industry trends, all in one central hub.
A faster, better way to train general-purpose robots
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
Making it easier to verify an AI model’s responses
By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.
Combining next-token prediction and video diffusion in computer vision and robotics
A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.
Equipping doctors with AI co-pilots
Alumni-founded Ambience Healthcare automates routine tasks for clinicians before, during, and after patient visits.
Artificial intelligence meets “blisk” in new DARPA-funded collaboration
Collaborative multi-university team will pursue new AI-enhanced design tools and high-throughput testing methods for next-generation turbomachinery.
Bubble findings could unlock better electrode and electrolyzer designs
A new study of bubbles on electrode surfaces could help improve the efficiency of electrochemical processes that produce fuels, chemicals, and materials.
Modeling relationships to solve complex problems efficiently
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
How AI is improving simulations with smarter sampling techniques
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
AI simulation gives people a glimpse of their potential future self
By enabling users to chat with an older version of themselves, Future You is aimed at reducing anxiety and guiding young people to make better choices.
AI pareidolia: Can machines spot faces in inanimate objects?
New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.
Helping robots zero in on the objects that matter
A new method called Clio enables robots to quickly map a scene and identify the items they need to complete a given set of tasks.
MIT launches new Music Technology and Computation Graduate Program
The program will invite students to investigate new vistas at the intersection of music, computing, and technology.
New security protocol shields data from attackers during cloud-based computation
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
3 Questions: Should we label AI systems like we do prescription drugs?
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.
Accelerating particle size distribution estimation
MIT researchers speed up a novel AI-based estimator for medication manufacturing by 60 times.
Google AI Blog - The latest research
Generative AI to quantify uncertainty in weather forecasting
Posted by Lizao (Larry) Li, Software Engineer, and Rob Carver, Research Scientist, Google Research Accurate weather forecasts can have a direct impact on people’s lives, from helping make routine decisions, like what to pack for a day’s activities, to informing urgent actions, for example, protecting people in the face of…
AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks
Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data's patterns (prior probability), collecting evidence (e.g., new time series data), and continuously updating that assumption to form a…
Computer-aided diagnosis for lung cancer screening
Posted by Atilla Kiraly, Software Engineer, and Rory Pilgrim, Product Manager, Google Research Lung cancer is the leading cause of cancer-related deaths globally with 1.8 million deaths reported in 2020. Late diagnosis dramatically reduces the chances of survival. Lung cancer screening via computed tomography (CT), which provides a detailed 3D…
Using AI to expand global access to reliable flood forecasts
Posted by Yossi Matias, VP Engineering & Research, and Grey Nearing, Research Scientist, Google Research Floods are the most common natural disaster, and are responsible for roughly $50 billion in annual financial damages worldwide. The rate of flood-related disasters has more than doubled since the year 2000 partly due to…
ScreenAI: A visual language model for UI and visually-situated language understanding
Posted by Srinivas Sunkara and Gilles Baechler, Software Engineers, Google Research Screen user interfaces (UIs) and infographics, such as charts, diagrams and tables, play important roles in human communication and human-machine interaction as they facilitate rich and interactive user experiences. UIs and infographics share similar design principles and visual language…
SCIN: A new resource for representative dermatology images
Posted by Pooja Rao, Research Scientist, Google Research Health datasets play a crucial role in research and medical education, but it can be challenging to create a dataset that represents the real world. For example, dermatology conditions are diverse in their appearance and severity and manifest differently across skin tones.…
MELON: Reconstructing 3D objects from images with unknown poses
Posted by Mark Matthews, Senior Software Engineer, and Dmitry Lagun, Research Scientist, Google Research A person's prior experience and understanding of the world generally enables them to easily infer what an object looks like in whole, even if only looking at a few 2D pictures of it. Yet the capacity…
HEAL: A framework for health equity assessment of machine learning performance
Posted by Mike Schaekermann, Research Scientist, Google Research, and Ivor Horn, Chief Health Equity Officer & Director, Google Core Health equity is a major societal concern worldwide with disparities having many causes. These sources include limitations in access to healthcare, differences in clinical treatment, and even fundamental differences in the…
Cappy: Outperforming and boosting large multi-task language models with a small scorer
Posted by Yun Zhu and Lijuan Liu, Software Engineers, Google Research Large language model (LLM) advancements have led to a new paradigm that unifies various natural language processing (NLP) tasks within an instruction-following framework. This paradigm is exemplified by recent multi-task LLMs, such as T0, FLAN, and OPT-IML. First, multi-task…
Talk like a graph: Encoding graphs for large language models
Posted by Bahare Fatemi and Bryan Perozzi, Research Scientists, Google Research Imagine all the things around you — your friends, tools in your kitchen, or even the parts of your bike. They are all connected in different ways. In computer science, the term graph is used to describe connections between…
Chain-of-table: Evolving tables in the reasoning chain for table understanding
Posted by Zilong Wang, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team People use tables every day to organize and interpret complex information in a structured, easily accessible format. Due to the ubiquity of such tables, reasoning over tabular data has long been a central topic in natural…
Health-specific embedding tools for dermatology and pathology
Posted by Dave Steiner, Clinical Research Scientist, Google Health, and Rory Pilgrim, Product Manager, Google Research There’s a worldwide shortage of access to medical imaging expert interpretation across specialties including radiology, dermatology and pathology. Machine learning (ML) technology can help ease this burden by powering tools that enable doctors to…
Social learning: Collaborative learning with large language models
Posted by Amirkeivan Mohtashami, Research Intern, and Florian Hartmann, Software Engineer, Google Research Large language models (LLMs) have significantly improved the state of the art for solving tasks specified using natural language, often reaching performance close to that of people. As these models increasingly enable assistive agents, it could be…
Croissant: a metadata format for ML-ready datasets
Posted by Omar Benjelloun, Software Engineer, Google Research, and Peter Mattson, Software Engineer, Google Core ML and President, MLCommons Association Machine learning (ML) practitioners looking to reuse existing datasets to train an ML model often spend a lot of time understanding the data, making sense of its organization, or figuring…
Google at APS 2024
Posted by Kate Weber and Shannon Leon, Google Research, Quantum AI Team Today the 2024 March Meeting of the American Physical Society (APS) kicks off in Minneapolis, MN. A premier conference on topics ranging across physics and related fields, APS 2024 brings together researchers, students, and industry professionals to share…
Microsoft Research Blog - The latest
AI-powered microgrids facilitate energy resilience and equity in regional communities
When augmented with AI, small power grids can create opportunities for decentralized, equitable, and resilient power. Microsoft’s collaboration shows AI’s potential to optimize energy distribution and empower communities. The post AI-powered microgrids facilitate energy resilience and equity in regional communities appeared first on Microsoft Research.
Research Focus: Week of October 28, 2024
New Research | FLASH: Workflow automation agent for diagnosing recurring incidents; METAREFLECTION: Learning instructions for language agents using past reflections; Boosting LLM training efficiency through faster communication between GPUs; and more. The post Research Focus: Week of October 28, 2024 appeared first on Microsoft Research.
Introducing DRIFT Search: Combining global and local search methods to improve quality and efficiency
GraphRAG leverages semantic structuring of data to generate responses to complex user queries. A collaboration with Uncharted expands the frontiers of this technology, developing a new approach to processing local queries: DRIFT search. The post Introducing DRIFT Search: Combining global and local search methods to improve quality and efficiency appeared…
Intern Insights: Vaishnavi Ranganathan with Angela Busheska
Undergrad Angela Busheska has a passion for sustainability. She talks with researcher Vaishnavi Ranganathan about the why behind her drive, the work she did at Microsoft on a platform for tracking land use, and her advice for making the internship experience count. The post Intern Insights: Vaishnavi Ranganathan with Angela…
Research Focus: Week of October 7, 2024
Simplifying secure decision tree training; Improving accuracy of audio content detection; A novel neurosymbolic system for converting text to tables; New video series: AI for Business Transformation; TEE security protections for container workloads. The post Research Focus: Week of October 7, 2024 appeared first on Microsoft Research.
Data Formulator: Exploring how AI can help analysts create rich data visualizations
Data Formulator investigates combining UI interactions with natural language input. Powered by AI, it can help users create or adapt visualizations and supports continuous refinement through an iterative process. Now available on GitHub. The post Data Formulator: Exploring how AI can help analysts create rich data visualizations appeared first on…
Stress-testing biomedical vision models with RadEdit: A synthetic data approach for robust model deployment
RadEdit stress-tests biomedical vision models by simulating dataset shifts through precise image editing. It uses diffusion models to create realistic, synthetic datasets, helping to identify model weaknesses and evaluate robustness. The post Stress-testing biomedical vision models with RadEdit: A synthetic data approach for robust model deployment appeared first on Microsoft…
Abstracts: September 30, 2024
The personalizable object recognizer Find My Things was recently recognized for accessible design. Researcher Daniela Massiceti and software development engineer Martin Grayson talk about the research project’s origins and the tech advances making it possible. The post Abstracts: September 30, 2024 appeared first on Microsoft Research.
Microsoft Research Forum Episode 4: The future of multimodal models, a new “small” language model, and other AI updates
Explore multimodal & small language models, plus advanced benchmarks for AI evaluation. Microsoft researchers are working on breakthroughs in weather prediction, materials design, even a new kind of computer for AI inference and hard optimization problems. The post Microsoft Research Forum Episode 4: The future of multimodal models, a new…
Research Focus: Week of September 23, 2024
Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Time-series forecasting is a technique used to predict future values based on previously observed data points over time. It has extensive applications for…