ML and AI Research

by Dr. Ziv Pollak (AWS Machine Learning Blog) on January 27, 2022

This post was cowritten by Ziv Pollak, Machine Learning Team Lead, and Sarvi Loloei, Machine Learning Engineer at Clearly. The content and opinions in this post are those of the third-party authors and AWS is not responsible for the content or accuracy of this post. A pioneer in online shopping, Clearly launched their first site

by Ryan Lowe (OpenAI) on January 27, 2022

We’ve trained language models that are much better at following user intentions than GPT-3 while also making them more truthful and less toxic, using techniques developed through our alignment research. These InstructGPT models, which are trained with humans in the loop, are now deployed as the default language

by Kevin Levitt (The Official NVIDIA Blog) on January 27, 2022

From the largest firms trading on Wall Street to banks providing customers with fraud protection to fintechs recommending best-fit products to consumers, AI is driving innovation across the financial services industry. New research from NVIDIA found that 78 percent of financial services professionals state that their company uses accelerated computing to deliver AI-enabled applications through Read article > The post Nearly 80 Percent of Financial Firms Use AI to Improve […]

by GeForce NOW Community (The Official NVIDIA Blog) on January 27, 2022

GeForce NOW is taking cloud gaming to new heights. This GFN Thursday delivers an upgraded streaming experience as part of an update that is now available to all members. It includes new resolution upscaling options to make members’ gaming experiences sharper, plus the ability to customize streaming settings in session. The GeForce NOW app is Read article > The post Let Me Upgrade You: GeForce NOW Adds Resolution Upscaling and More This GFN Thursday appeared first on The […]

by Machine learning : nature.com subject feeds on January 27, 2022

by Machine learning : nature.com subject feeds on January 27, 2022

by Machine learning : nature.com subject feeds on January 27, 2022

by Machine learning : nature.com subject feeds on January 27, 2022

by Amit Gross (AWS Machine Learning Blog) on January 26, 2022

Logz.io is an AWS Partner Network (APN) Advanced Technology Partner with AWS Competencies in DevOps, Security, and Data & Analytics. Logz.io offers a software as a service (SaaS) observability platform based on best-in-class open-source software solutions for log, metric, and tracing analytics. Customers are sending an increasing amount of data to Logz.io from various data

by Angie Lee (The Official NVIDIA Blog) on January 26, 2022

Fisheries collect millions upon millions of fish eggs, protecting them from predators to increase fish yield and support the propagation of endangered species — but an issue with gathering so many eggs at once is that those infected with parasites can put healthy ones at risk. Jensorter, an Oregon-based startup, has created AI-powered fish egg Read article > The post Hatch Me If You Can: Startup’s Sorting Machines Use AI to Protect Healthy Fish Eggs appeared first on […]

by Craig Rhodes (The Official NVIDIA Blog) on January 26, 2022

UK Biobank is broadening scientists’ access to high-quality genomic data and analysis by making its massive dataset available in the cloud alongside NVIDIA GPU-accelerated analysis tools. Used by more than 25,000 registered researchers around the world, UK Biobank is a large-scale biomedical database and research resource with deidentified genetic datasets, along with medical imaging and Read article > The post UK Biobank Advances Genomics Research with NVIDIA Clara […]

by Machine learning : nature.com subject feeds on January 26, 2022

by Machine learning : nature.com subject feeds on January 26, 2022

by Machine learning : nature.com subject feeds on January 26, 2022

by Arvind Neelakantan (OpenAI) on January 25, 2022

We are introducing embeddings, a new endpoint in the OpenAI API that makes it easy to perform natural language and code tasks like semantic search, clustering, topic modeling, and classification. Embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships

by Angie Lee (The Official NVIDIA Blog) on January 25, 2022

Benjamin Sokomba Dazhi, aka Benny Dee, has learned the ins and outs of the entertainment industry from many angles — first as a rapper, then as a music video director and now as a full-time animator. The post Animator Lets 3D Characters Get Their Groove on With NVIDIA Omniverse and Reallusion appeared first on The Official NVIDIA Blog.

by Ethan Einhorn (The Official NVIDIA Blog) on January 25, 2022

Many different platforms, same great performance. That’s why Vulkan is a very big deal. With the release Tuesday of Vulkan 1.3, NVIDIA continues its unparalleled record of day one driver support for this cross-platform GPU application programming interface for 3D graphics and computing. Vulkan has been created by experts from across the industry working together Read article > The post Vulkan Fan? Six Reasons to Run It on NVIDIA appeared first on The Official NVIDIA […]

by Machine learning : nature.com subject feeds on January 25, 2022

by Latest Post on January 25, 2022

Were proud to launch a new season of DeepMind: The Podcast, with stories of the latest breakthroughs, innovations, and challenges from inside the lab.

by Charlie Boyle (The Official NVIDIA Blog) on January 24, 2022

Meta Platforms gave a big thumbs up to NVIDIA, choosing our technologies for what it believes will be its most powerful research system to date. The AI Research SuperCluster (RSC), announced today, is already training new models to advance AI. Once fully deployed, Meta’s RSC is expected to be the largest customer installation of NVIDIA Read article > The post Meta Works with NVIDIA to Build Massive AI Research Supercomputer appeared first on The Official NVIDIA Blog.

by Tarik Hammadou (The Official NVIDIA Blog) on January 24, 2022

Let’s face it, the global supply chain may not be the most scintillating subject matter. Yet in homes and businesses around the world, it’s quickly become the topic du jour: empty shelves; record price increases; clogged ports and sick truckers leading to disruptions near and far. The business of organizing resources to supply a product Read article > The post How the Intelligent Supply Chain Broke and AI Is Fixing It appeared first on The Official NVIDIA Blog.

by Machine learning : nature.com subject feeds on January 24, 2022

by Machine learning : nature.com subject feeds on January 21, 2022

by Machine learning : nature.com subject feeds on January 21, 2022

by Pablo Nicolas Nunez Polcher (AWS Machine Learning Blog) on January 20, 2022

Even after more than a hundred years after its introduction, histology remains the gold standard in tumor diagnosis and prognosis. Anatomic pathologists evaluate histology to stratify cancer patients into different groups depending on their tumor genotypes and phenotypes, and their clinical outcome [1,2]. However, human evaluation of histological slides is subjective and not repeatable [3].

by Archis Joglekar (AWS Machine Learning Blog) on January 20, 2022

From training new models to deploying them in production, Amazon SageMaker offers the most complete set of tools for startups and enterprises to harness the power of machine learning (ML) and Deep Learning. With its Transformers open-source library and ML platform, Hugging Face makes transfer learning and the latest ML models accessible to the global

by Isha Salian (The Official NVIDIA Blog) on January 20, 2022

Every living cell contains its own bustling microcosm, with thousands of components responsible for energy production, protein building, gene transcription and more. Scientists at the University of Illinois at Urbana-Champaign have built a 3D simulation that replicates these physical and chemical characteristics at a particle scale — creating a fully dynamic model that mimics the Read article > The post NVIDIA GPUs Enable Simulation of a Living Cell appeared first on […]

by GeForce NOW Community (The Official NVIDIA Blog) on January 20, 2022

Hello, Operator. This GFN Thursday brings the launch of Tom Clancy’s Rainbow Six Extraction to GeForce NOW. Plus, four new games are joining the GeForce NOW library to let you start your weekend off right. Your New Mission, Should You Choose to Accept It Grab your gadgets and get ready to game. Tom Clancy’s Rainbow Read article > The post GFN Thursday: ‘Tom Clancy’s Rainbow Six Extraction’ Charges Into GeForce NOW appeared first on The Official NVIDIA Blog.

by Katie Burke (The Official NVIDIA Blog) on January 20, 2022

Robotaxis are on their way to delivering safer transportation, driving across various landscapes and through starry nights. This week, Silicon Valley-based self-driving startup Pony.ai announced its next-generation autonomous computing platform, built on NVIDIA DRIVE Orin for high-performance and scalable compute. The centralized system will serve as the brain for a robotaxi fleet of Toyota Sienna Read article > The post Van, Go: Pony.ai Unveils Next-Gen Robotaxi Fleet […]

by Machine learning : nature.com subject feeds on January 20, 2022

by Alyssa Hughes (Microsoft Research) on January 19, 2022

In the last three years, the largest trained dense models have increased in size by over 1,000 times, from a few hundred million parameters to over 500 billion parameters in Megatron-Turing NLG 530B (MT-NLG). Improvements in model quality with size suggest that this trend will continue, with larger model sizes bringing better model quality. However, The post DeepSpeed: Advancing MoE inference and training to power next-generation AI scale appeared first on Microsoft […]

by John Fanelli (The Official NVIDIA Blog) on January 19, 2022

With a new year underway, NVIDIA is helping enterprises worldwide add modern workloads to their mainstream servers using the latest release of the NVIDIA AI Enterprise software suite. NVIDIA AI Enterprise 1.1 is now generally available. Optimized, certified and supported by NVIDIA, the latest version of the software suite brings new updates including production support Read article > The post New NVIDIA AI Enterprise Release Lights Up Data Centers appeared first on The […]

by Angie Lee (The Official NVIDIA Blog) on January 19, 2022

Browse through MORF Gallery — virtually or at an in-person exhibition — and you’ll find robots that paint, digital dreamscape experiences, and fine art brought to life by visual effects. The gallery showcases cutting-edge, one-of-a-kind artwork from award-winning artists who fuse their creative skills with AI, machine learning, robotics and neuroscience. Scott Birnbaum, CEO and Read article > The post Fusing Art and Tech: MORF Gallery CEO Scott Birnbaum on Digital […]

by Machine learning : nature.com subject feeds on January 19, 2022

by Vikram Elango (AWS Machine Learning Blog) on January 18, 2022

Natural language understanding is applied in a wide range of use cases, from chatbots and virtual assistants, to machine translation and text summarization. To ensure that these applications are running at an expected level of performance, it’s important that data in the training and production environments is from the same distribution. When the data that

by Audrey Timmerman (AWS Machine Learning Blog) on January 18, 2022

This is the second post in the two-part series on how Tyson Foods Inc., is using computer vision applications at the edge to automate industrial processes inside their meat processing plants. In Part 1, we discussed an inventory counting application at packaging lines built with Amazon SageMaker and AWS Panorama . In this post, we

by Scott Martin (The Official NVIDIA Blog) on January 18, 2022

Online commerce has rocketed to trillions of dollars worldwide in the past decade, serving billions of consumers. Behind the scenes of this explosive growth in online sales is personalization driven by recommender engines. Recommenders make shopping deeply personalized. While searching for products on e-commerce sites, they find you. Or suggestions can just appear. This wildly Read article > The post Billions Served: NVIDIA Merlin Helps Fuel Clicks for Online Giants […]

by Machine learning : nature.com subject feeds on January 18, 2022

by Machine learning : nature.com subject feeds on January 18, 2022

by Machine learning : nature.com subject feeds on January 18, 2022

by Machine learning : nature.com subject feeds on January 18, 2022

by Machine learning : nature.com subject feeds on January 17, 2022

by Machine learning : nature.com subject feeds on January 17, 2022

by Machine learning : nature.com subject feeds on January 17, 2022

by Dan Noble (AWS Machine Learning Blog) on January 14, 2022

The Amazon Machine Learning Solutions Lab (MLSL) recently created a tool for annotating text with named-entity recognition (NER) and relationship labels using Amazon SageMaker Ground Truth. Annotators use this tool to label text with named entities and link their relationships, thereby building a dataset for training state-of-the-art natural language processing (NLP) machine learning (ML) models. Most

by Angie Lee (The Official NVIDIA Blog) on January 14, 2022

Growing up in the Philippines, award-winning filmmaker Jae Solina says he turned to movies for a reminder that the world was much larger than himself and his homeland. The post From Imagination to Animation, How an Omniverse Creator Makes Films Virtually appeared first on The Official NVIDIA Blog.

by Machine learning : nature.com subject feeds on January 14, 2022

by (AI) on January 13, 2022

Genome sequencing can help us better understand, diagnose and treat disease. For example, healthcare providers are increasingly using genome sequencing to diagnose rare genetic diseases, such as elevated risk for breast cancer or pulmonary arterial hypertension, which are estimated to affect roughly 8% of the population.At Google Health, we’re applying our technology and expertise to the field of genomics. Here are recent research and industry developments we’ve made to […]

by Azita Martin (The Official NVIDIA Blog) on January 13, 2022

At the National Retail Federation’s annual trade show, conversations tend to touch on recurring themes: “Will we be able to stock must-have products for next Christmas?,” “What incentives can I offer to loyal workers?” and “What happens to my margins if Susie Consumer purchases three of the same dresses online and returns two?” The $26 Read article > The post How Retailers Meet Tough Challenges Using NVIDIA AI  appeared first on The Official NVIDIA Blog.

by Brian Caulfield (The Official NVIDIA Blog) on January 13, 2022

Addressing a growing labor crisis among quick-service restaurants, startup Vistry is harnessing AI to automate the process of taking orders. The company will share its story at the NRF Big Show, the annual industry gathering of the National Retail Federation in New York, starting Jan. 16. “They’re closing restaurants because there is not enough labor,” Read article > The post AI Startup to Take a Bite Out of Fast-Food Labor Crunch appeared first on The Official […]

by GeForce NOW Community (The Official NVIDIA Blog) on January 13, 2022

Starting next week, Fortnite on GeForce NOW will launch in a limited-time closed beta for mobile, all streamed through the Safari web browser on iOS and the GeForce NOW Android app. The beta is open for registration for all GeForce NOW members, and will help test our server capacity, graphics delivery and new touch controls Read article > The post GFN Thursday: ‘Fortnite’ Comes to iOS Safari and Android Through NVIDIA GeForce NOW via Closed Beta appeared first on The […]

by Machine learning : nature.com subject feeds on January 13, 2022

by Machine learning : nature.com subject feeds on January 13, 2022

by Machine learning : nature.com subject feeds on January 13, 2022

by Phi Nguyen (AWS Machine Learning Blog) on January 12, 2022

In deep learning, batch processing refers to feeding multiple inputs into a model. Although it’s essential during training, it can be very helpful to manage the cost and optimize throughput during inference time as well. Hardware accelerators are optimized for parallelism, and batching helps saturate the compute capacity and often leads to higher throughput. Batching

by Yanwei Cui (AWS Machine Learning Blog) on January 12, 2022

Recommendation systems are one of the most widely adopted machine learning (ML) technologies in real-world applications, ranging from social networks to ecommerce platforms. Users of many online systems rely on recommendation systems to make new friendships, discover new music according to suggested music lists, or even make ecommerce purchase decisions based on the recommended products.

by Jerome Bachelet (AWS Machine Learning Blog) on January 12, 2022

Cloud security at AWS is the highest priority. Amazon SageMaker Studio offers various mechanisms to protect your data and code using integration with AWS security services like AWS Identity and Access Management (IAM), AWS Key Management Service (AWS KMS), or network isolation with Amazon Virtual Private Cloud (Amazon VPC). Customers in highly regulated industries, like

by Alyssa Hughes (Microsoft Research) on January 12, 2022

From manufacturing and logistics to agriculture and transportation, the expansion of artificial intelligence (AI) in the last decade has revolutionized a multitude of industries—examples include enhancing predictive analytics on the manufacturing floor and making microclimate predictions so that farmers can respond and save their crops in time. The adoption of AI is expected to accelerate The post EzPC: Increased data security in the AI model validation process appeared […]

by Meta Research on January 12, 2022

by Divya Bhargavi (AWS Machine Learning Blog) on January 11, 2022

This is the first in a two-part blog series on how Tyson Foods, Inc., is utilizing machine learning to automate industrial processes at their meat packing plants by bringing the benefits of artificial intelligence applications at the edge. In part one, we discuss an inventory counting application for packaging lines built using Amazon SageMaker and

by Meta Research on January 11, 2022

Today, FORT shares Meta platform data with researchers around the world in a privacy-protective environment. Our focus has been on supporting computational social scientists who apply computational approaches to large-scale human behavioral data in order to study and explain social phenomena.

by Machine learning : nature.com subject feeds on January 11, 2022

by Machine learning : nature.com subject feeds on January 11, 2022

by Machine learning : nature.com subject feeds on January 11, 2022

by Machine learning : nature.com subject feeds on January 11, 2022

by Machine learning : nature.com subject feeds on January 11, 2022

by Machine learning : nature.com subject feeds on January 11, 2022

by Jihye Park (AWS Machine Learning Blog) on January 10, 2022

This is a guest post by Jihye Park, a Data Scientist at MUSINSA.  MUSINSA is one of the largest online fashion platforms in South Korea, serving 8.4M customers and selling 6,000 fashion brands. Our monthly user traffic reaches 4M, and over 90% of our demographics consist of teens and young adults who are sensitive to

by Daniel Burke (AWS Machine Learning Blog) on January 10, 2022

Cybersecurity continues to be a top concern for enterprises. Yet the constantly evolving threat landscape that they face makes it harder than ever to be confident in their cybersecurity protections. To address this, ReliaQuest built GreyMatter, an Open XDR-as-a-Service platform that brings together telemetry from any security and business solution, whether on-premises or in one or multiple clouds, to unify detection, investigation, response, and resilience. In 2021, […]

by Anastasia Pachni Tsitiridou (AWS Machine Learning Blog) on January 7, 2022

With the advent of artificial intelligence (AI) and machine learning (ML), customers and the general public have become increasingly aware of their privacy, as well as the value that it holds in today’s data-driven world. Enterprises are actively seeking out and marketing privacy-first solutions, especially in the Computer Vision (CV) domain. They need to reassure

by (AI) on January 6, 2022

As a university student, Abigail Annkah fell in love with mathematics, which soon led to her interest in artificial intelligence. After graduating from the African Institute for Mathematical Sciences, Abigail made it through the competitive process to become an AI resident at Google Research, Accra. After her residency, Google offered her a job and she’s now a research software engineer working on several high-profile projects.As Google grows its presence in Accra, we […]

by Assaf Elovic (AWS Machine Learning Blog) on January 5, 2022

With over 200 million users worldwide, Wix is a leading cloud-based development platform for building fully personalized, high-quality websites. Wix makes it easy for anyone to create a beautiful and professional web presence. When Wix started, it was easy to understand user sentiment and identify product improvement opportunities because the user base was small. Such

by Nancy Clarke (AWS Machine Learning Blog) on January 5, 2022

In parts one and two of our guide to conversation design with Amazon Lex, we discussed how to gather requirements for your conversational AI application and draft conversational flows. In this post, we help you bring all the pieces together. You’ll learn how draft an interaction model to deliver natural conversational experiences, and how to

by Ram Vegiraju (AWS Machine Learning Blog) on January 5, 2022

Amazon SageMaker Serverless Inference (Preview) was recently announced at re:Invent 2021 as a new model hosting feature that lets customers serve model predictions without having to explicitly provision compute instances or configure scaling policies to handle traffic variations. Serverless Inference is a new deployment capability that complements SageMaker’s existing options for deployment that include: SageMaker

by Ahmed Saef Zamzam (AWS Machine Learning Blog) on January 5, 2022

We’re living in a world of everything-as-an-online-service. Service providers from almost every industry are in the race to feature the best user experience for their online channels like web portals and mobile applications. This raises a new challenge. How do we stop illegal and fraudulent behaviors without impacting typical legitimate interactions? This challenge is even

by Raghu Ramesha (AWS Machine Learning Blog) on January 4, 2022

Deployment guardrails in Amazon SageMaker provide a new set of deployment capabilities allowing you to implement advanced deployment strategies that minimize risk when deploying new model versions on SageMaker hosting. Depending on your use case, you can use a variety of deployment strategies to release new model versions. Each of these strategies relies on a

by Zichen Wang (AWS Machine Learning Blog) on January 4, 2022

There are over 180,000 unique proteins with 3D structures determined, with tens of thousands new structures resolved every year. This is only a small fraction of the 200 million known proteins with distinctive sequences. Recent deep learning algorithms such as AlphaFold can accurately predict 3D structures of proteins using their sequences, which help scale the

by (AI) on December 30, 2021

Google’s Quantum AI team has had a productive 2021. Despite ongoing global challenges, we’ve made significant progress in our effort to build a fully error-corrected quantum computer, working towards our next hardware milestone of building an error-corrected quantum bit (qubit) prototype. At the same time, we have continued our commitment to realizing the potential of quantum computers in various applications. That's why we published results in top journals, collaborated […]

by Meta Research on December 22, 2021

In this Q&A, Vamivakas shares more about his research background, recent projects, the start of his collaboration with Facebook, his research interests, and his collaboration with Meta.

by Meta Research on December 22, 2021

Meta Data for Good announces a new re-weighted version of displacement maps that aims to better represent displacement trends on the ground.

by Meta Research on December 21, 2021

Meta announces the winners of the Mathematical modeling and optimization for large-scale distributed systems request for proposals

by Brenda Potts (Microsoft Research) on December 20, 2021

KEAR (Knowledgeable External Attention for commonsense Reasoning)—along with recent milestones in computer vision and neural text-to-speech—is part of a larger Azure AI mission to provide relevant, meaningful AI solutions and services that work better for people because they better capture how people learn and work—with improved vision, knowledge understanding, and speech capabilities. At the center of these efforts is The post Azure AI milestone: Microsoft KEAR […]

by Meta Research on December 20, 2021

Meta's Core Data Science team discusses opportunities for academics at Meta such as full-time employment, temporary employment, and dual affiliation.

by Meta Research on December 18, 2021

by Alyssa Hughes (Microsoft Research) on December 17, 2021

Neural Text-to-Speech—along with recent milestones in computer vision and question answering—is part of a larger Azure AI mission to provide relevant, meaningful AI solutions and services that work better for people because they better capture how people learn and work—with improved vision, knowledge understanding, and speech capabilities. At the center of these efforts is XYZ-code, The post Azure AI milestone: New Neural Text-to-Speech models more closely mirror […]

by (AI) on December 17, 2021

In the summer, Dr. Gino Caspari’s day starts at 5:30 a.m. in Siberia, where he studies the ancient Scythians with the Swiss National Science Foundation. There, he looks for burial places of these nomadic warriors who rode through Asia 2,500 years ago. The work isn’t easy, from dealing with extreme temperatures, to swamps covered with mosquitos. But the biggest challenge is staying one step ahead of tomb raiders.It’s believed that more than 90% of the tombs — called […]

by Meta Research on December 17, 2021

by Jacob Hilton (OpenAI) on December 16, 2021

We've fine-tuned GPT-3 to more accurately answer open-ended questions using a text-based web browser. Our prototype copies how humans research answers to questions online—it submits search queries, follows links, and scrolls up and down web pages. It is trained to cite its sources, which makes it

by Lexie Hagen (Microsoft Research) on December 15, 2021

Over the past 30 years, Microsoft Research has undergone a shift in how it approaches innovation, broadening its mission to include not only advancing the state of computing but also using technology to tackle some of the world’s most pressing challenges. That evolution has never been more prominent than it was during this past year. The post Research at Microsoft 2021: Collaborating for real-world change appeared first on Microsoft Research.

by Meta Research on December 15, 2021

Today, we’re excited to announce an early beta release of Bean Machine, a PyTorch-based probabilistic programming system that makes it easy to represent and to learn about uncertainties in the machine learning models that we work with every day.

by Lexie Hagen (Microsoft Research) on December 14, 2021

The Project Florence Team Florence v1.0—along with recent milestones in Neural Text-to-Speech and question answering—is part of a larger Azure AI mission to provide relevant, meaningful AI solutions and services that work better for people because they better capture how people learn and work—with improved vision, knowledge understanding, and speech capabilities. At the center of The post Azure AI milestone: New foundation model Florence v1.0 advances state of the […]

by Rachel Lim (OpenAI) on December 14, 2021

Fine-tune with a single command

by Lexie Hagen (Microsoft Research) on December 10, 2021

The drug development process is an iterative one that consists of discovery, design, and testing. Historically, drugs were derived from plants and discovered through trial-and-error experiments. Fortunately, this drug discovery process now occurs in a lab, with each iteration of custom-designed compounds producing a more promising candidate. While much safer and more effective, this process The post FS-Mol: Bringing Deep Learning to Early-Stage Drug Discovery appeared first […]

by Meta Research on December 10, 2021

In August, Meta, formerly known as Facebook, launched the Building Tools to Enhance Transparency in Fairness and Privacy request for proposals (RFP). Today, we’re announcing the winners of this award.

by Latest Post on December 9, 2021

Using neural networks to model electron interactions in molecules and materials, advancing the state-of-the-art towards the long-standing challenge of the density functional in quantum chemistry

by Lexie Hagen (Microsoft Research) on December 8, 2021

Finding and fixing bugs in code is a time-consuming, and often frustrating, part of everyday work for software developers. Can deep learning address this problem and help developers deliver better software, faster? In a new paper, Self-Supervised Bug Detection and Repair, presented at the 2021 Conference on Neural Information Processing Systems (NeurIPS 2021), we show The post Finding and fixing bugs with deep learning appeared first on Microsoft Research.

by (AI) on December 8, 2021

Human rights defenders need information from many sources to do their work effectively. But as issues evolve and new precedents are set, finding the right information to defend a particular case can be like looking for a needle in a haystack.For example, a human rights advocate campaigning for LGBTQ rights may want to know which countries have made the most progress and what resolutions they’ve passed. To do so, they have to manually sift through thousands of pages of […]

by Latest Post on December 8, 2021

We are releasing three papers on language models, Gopher, ethical considerations, and retrieval.

by Lexie Hagen (Microsoft Research) on December 6, 2021

Recent progress in natural language understanding (NLU) has been driven in part by the availability of large-scale benchmarks that provide an environment for researchers to test and measure the performance of AI models. Most of these benchmarks are designed for academic settings–typically datasets that feature independent and identically distributed (IID) training, validation, and testing sections The post You get what you measure: New NLU benchmarks for few-shot learning […]

by Lexie Hagen (Microsoft Research) on December 2, 2021

As part of Microsoft AI at Scale, the Turing family of NLP models are being used at scale across Microsoft to enable the next generation of AI experiences. Today, we are happy to announce that the latest Microsoft Turing model (T-NLRv5) is the state of the art at the top of SuperGLUE and GLUE leaderboards, The post Efficiently and effectively scaling up language model pretraining for best language representation model on GLUE and SuperGLUE appeared first on Microsoft […]

by (AI) on December 1, 2021

The text in the video above reads as follows: Welcome to SignTown! An interactive experience where you can learn sign language with a little help from AI. Like how to order at a restaurant ('milk tea?'). Or checking into a hotel and requesting shampoo or soap. How does it work? All it takes is a webcam and machine learning to detect your body poses, facial expressions and hand movements. Give it a try now at http://www.sign.townGoogle has spent over twenty years helping to make […]

by Latest Post on December 1, 2021

Discovering new patterns in the fields of topology and representation theory with machine learning

by OpenAI (OpenAI) on November 30, 2021

As part of our effort to support and develop AI talent, we're excited to announce the OpenAI Residency. This new program offers a pathway to a full-time role at OpenAI for researchers and engineers who don't currently focus on artificial intelligence. We are excited to get

by (AI) on November 18, 2021

Twenty years ago, Google started using machine learning, and 10 years ago, it helped spur rapid progress in AI using deep learning. Jeff Dean and Marian Croak of Google Research take a look at how we’ve innovated on these techniques and applied them in helpful ways, and look ahead to a responsible and inclusive path forward.Jeff DeanFrom research demos to AI that really worksI was first introduced to neural networks — computer systems that roughly imitate how biological […]

by OpenAI (OpenAI) on November 18, 2021

Wider availability made possible by safety progress

by (AI) on November 9, 2021

For millions of people, being able to speak and be understood can be difficult as a result of conditions that can impact speech, including stroke, ALS, Cerebral Palsy, traumatic brain injury or Parkinson's disease. Today, we’re inviting an initial group of people to test Project Relate, a new Android app that aims to help people with speech impairments communicate more easily with others and interact with the Google Assistant.Project Relate is a continuation of years of […]

by (AI) on November 9, 2021

In 2018 we began our flood forecasting initiative to help combat the catastrophic damage from floods each year by equipping those in harm’s way with accurate and detailed alerts. This work is a part of Google’s broader Crisis Response program which provides people access to trusted information and resources in critical moments. For over a decade, our Crisis Response team has been partnering with front line and emergency workers to develop technology and programs that […]

by (AI) on November 2, 2021

City transportation is crucial to connecting residents to education, employment and essential services. At the same time, the transportation sector is where global greenhouse gas (GHG) emissions are rising the quickest.In 2018, we launched the Environmental Insights Explorer (EIE) in collaboration with the Global Covenant of Mayors for Climate & Energy (GCoM). As part of Google's most ambitious decade of climate action, we’ve committed to helping more than 500 cities […]

by Latest Post on November 2, 2021

Koray Kavukcuoglu, VP of Research, discusses why addressing real-world challenges now helps advance the development of true AGI.

by Karl Cobbe (OpenAI) on October 29, 2021

We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset, while our

by (AI) on October 28, 2021

When I reflect on the past two decades of computer science research, few things inspire me more than the remarkable progress we’ve seen in the field of artificial intelligence.In 2001, some colleagues sitting just a few feet away from me at Google realized they could use an obscure technique called machine learning to help correct misspelled Search queries. (I remember I was amazed to see it work on everything from “ayambic pitnamiter” to “unnblevaiabel”). Today, […]

by Latest Post on October 18, 2021

As part of DeepMind's mission of advancing science, we have acquired the MuJoCo physics simulator and are making it freely available for everyone, to support research everywhere.

by (AI) on October 15, 2021

This week we sat down for an interview with Qubit the dog, whose human Julian Kelly is one of our lead Research Scientists with Google Quantum AI. Qubit was born in 2012, right when Julian and team were first designing the qubits that now underlie Google’s quantum computers. He nearly received the honor of pressing the submit button for the team’s beyond-classical result published in Nature, but he was narrowly edged out by a human.Qubit has never been interviewed before […]

by Latest Post on October 11, 2021

Introducing RGB-Stacking as a new benchmark for vision-based robotic manipulation.

by (AI) on October 8, 2021

My life has always involved centering the voices of those historically marginalized in order to foster equitable communities. Growing up, I lived in a small suburb just outside of Cleveland, Ohio and I was fortunate enough to attend Laurel School, an all-girls school focused on encouraging young women to think critically and solve difficult world problems. But my lived experience at school was so different from kids who lived even on my same street. I was grappling with […]

by (AI) on October 6, 2021

Over guacamole and corn chips at a party, a friend mentions that her favorite phone game uses augmented reality. Another friend points her phone at the host and shouts, "Watch out—a t-rex is sneaking up behind you." Eager to join the conversation, you blurt, "My blender has an augmented reality setting."If only you had looked up augmented reality in Google's Machine Learning Glossary, which defines over 460 terms related to artificial intelligence, you’d know what the […]

by Latest Post on October 4, 2021

Our new Enformer architecture advances genetic research by improving the ability to predict how DNA sequence influences gene expression.

by (AI) on September 29, 2021

Today, there’s more information accessible at people’s fingertips than at any point in human history. And advances in artificial intelligence will radically transform the way we use that information, with the ability to uncover new insights that can help us both in our daily lives and in the ways we are able to tackle complex global challenges.At our Search On livestream event today, we shared how we’re bringing the latest in AI to Google’s products, giving people […]

by Latest Post on September 29, 2021

Our latest research and state-of-the-art model advances the science of Precipitation Nowcasting.

by OpenAI (OpenAI) on September 23, 2021

Scaling human oversight of AI systems for tasks that are difficult to evaluate.

by (AI) on September 21, 2021

Look around you right now and consider everything that was created by an inventor. The computer you’re reading this article on, the internet necessary to load this article, the electricity that powers the screen, even the coffee maker you used this morning. To recognize the incredible contributions of those inventors and the benefits they bring to our everyday life, the National Inventors Hall of Fame has inducted a new group of honorees every year since 1973. In this […]

by OpenAI (OpenAI) on September 8, 2021

Today, we’re excited to announce the appointment of Helen Toner to our Board of Directors. As the Director of Strategy at Georgetown’s Center for Security and Emerging Technology (CSET), Helen has deep expertise in AI policy and global AI strategy research. This appointment advances our dedication

by (AI) on September 1, 2021

There’s a lot to love about online shopping: It’s fast, it’s easy and there are a ton of options to choose from. But there’s one obvious challenge — you can’t try anything on. This is something Google product manager Debbie Biswas noticed, as a tech industry veteran and startup founder herself. “Historically, the fashion industry only celebrates people of a certain size and skin color,” she says. “This was something I wanted to change.”Debbie grew up in […]

by (AI) on August 23, 2021

“Farmers feed the entire world — so how might we support them to be resilient and build sustainable systems that also support global food security?” It’s a question that Diana Akrong found herself asking last year. Diana is a UX researcher based in Accra, Ghana, and the founding member of Google’s Accra UX team.Across the world, her manager Dr. Courtney Heldreth, was equally interested in answering this question. Courtney is a social psychologist and a staff UX […]

by (AI) on August 18, 2021

Before the pandemic, Alicia Chang was working on a new project. “I was experimenting with non-traditional ways to help teach Googlers the AI Principles,” she says. Alicia is a technical writer on the Engineering Education team focused on designing learning experiences to help Googlers learn about our AI Principles and how to apply them in their own work.The challenge for Alicia would be how many people she needed to educate. “There are so many people spread over […]

by Wojciech Zaremba (OpenAI) on August 10, 2021

We’ve created an improved version of OpenAI Codex, our AI system that translates natural language to code, and we are releasing it through our API in private beta starting today. Codex is the model that powers GitHub Copilot, which we built and launched in partnership with GitHub a

by Latest Post on August 3, 2021

Perceiver IO, a more general version of the Perceiver architecture, can produce a wide variety of outputs from many different inputs.

by (AI) on July 29, 2021

From the early days of the internet to the development of the Human Genome Project, U.S. government-funded R&D has yielded remarkable progress for society, and today it is an important engine for AI research. That’s why, last year, we were proud to announce our partnership with the U.S. National Science Foundation (NSF) to provide $5M to support the establishment of national research institutes working in the area of Human-AI Interaction and Collaboration (HAIC). This […]

by Philippe Tillet (OpenAI) on July 28, 2021

We're releasing Triton 1.0, an open-source Python-like programming language which enables researchers with no CUDA experience to write highly efficient GPU code—most of the time on par with what an expert would be able to produce. Triton makes it possible to reach peak hardware performance

by Latest Post on July 27, 2021

In new work, algorithmic advances and new training environments lead to agents which exhibit general heuristic behaviours.

by Latest Post on July 22, 2021

In partnership with EMBL-EBI, were incredibly proud to be launching the AlphaFold Protein Structure Database.

by Michael Mui (Machine Learning Archives - Uber Engineering Blog) on July 7, 2021

Introduction Since we productionized distributed XGBoost on Apache Spark™ at Uber in 2017, XGBoost has powered a wide spectrum of machine learning (ML) use cases at Uber, spanning from optimizing marketplace dynamic pricing policies for Freight, improving times of … The post Elastic Distributed Training with XGBoost on Ray appeared first on Uber Engineering Blog.

by Irene Solaiman (OpenAI) on June 10, 2021

Our latest research finds we can improve language model behavior with respect to specific behavioral values by fine-tuning on a small, curated dataset.

by Latest Post on June 4, 2021

To help combat racism and advance racial equity, we've made donations to organisations that support Black communities in the AI/ML space.

by OpenAI (OpenAI) on May 26, 2021

Investing in startups with big ideas about AI.

by OpenAI (OpenAI) on May 10, 2021

We’re proud to announce that the 2021 class of OpenAI Scholars has completed our six-month mentorship program and have produced an open-source research project with stipends and support from OpenAI. Working alongside leading OpenAI researchers that created GPT-3 and DALL·E, our Scholars explored topics like AI

by Latest Post on May 7, 2021

Sports analytics is in the midst of a remarkably important era, offering interesting opportunities for AI researchers and sports leaders alike.

by Latest Post on May 6, 2021

Our research explored a new approach to an old problem: we reformulated principal component analysis (PCA), a type of eigenvalue problem, as a competitive multi-agent game we call EigenGame.

by OpenAI (OpenAI) on May 4, 2021

We’re delighted to announce that Congressman Will Hurd has joined our board of directors.

by Latest Post on December 23, 2020

Planning winning strategies in unknown environments is a step forward in the pursuit of general-purpose algorithms.

by Latest Post on December 4, 2020

An introduction to our JAX ecosystem and why we find it useful for our AI research.

by Latest Post on November 30, 2020

In a major scientific advance, AlphaFoldis recognised as a solution to the protein folding problem.

by Latest Post on November 5, 2020

We're expanding our scholars programme to support more countries currently underrepresented in AI.

by Latest Post on October 19, 2020

Weve developed a new neural network architecture, the Fermionic Neural Network or FermiNet, which is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.

by Latest Post on October 12, 2020

The combination of reinforcement learning and deep learning has led to impressive results, such as agents that can learn how to play boardgames, the full spectrum of Atari games, as well as more modern, difficult video games like Dota and StarCraft II.

by Latest Post on September 3, 2020

Working with our partners at Google Maps, we used advanced machine learning techniques including Graph Neural Networks, to improve the accuracy of real time ETAs by up to 50%.

by Jiale Zhi (Machine Learning Archives - Uber Engineering Blog) on June 30, 2020

Project Homepage: GitHub Over the past several years, increasing processing power of computing machines has led to an increase in machine learning advances. More and more, algorithms exploit parallelism and rely on distributed training to process an enormous amount of … The post Fiber: Distributed Computing for AI Made Simple appeared first on Uber Engineering Blog.

by Latest Post on June 23, 2020

We answer the Women in Machine Learning community's questions about applying for a job in industry.

by Bea Schuster (Machine Learning Archives - Uber Engineering Blog) on June 16, 2020

Self-driving cars have long been considered the future of transportation, but they’re becoming more present everyday. Uber ATG (Advanced Technologies Group) is at the forefront of this technology, helping bring safe, reliable self-driving vehicles to the streets. Of course, … The post Profiles in Coding: Diana Yanakiev, Uber ATG, Pittsburgh appeared first on Uber Engineering Blog.

by Vivek Panyam (Machine Learning Archives - Uber Engineering Blog) on June 8, 2020

At Uber Advanced Technologies Group (ATG), we leverage deep learning to provide safe and reliable self-driving technology. Using deep learning, we can build and train models to handle tasks such as processing sensor input, identifying objects, and predicting where … The post Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine appeared first on Uber Engineering Blog.

by Steffon Davis (Machine Learning Archives - Uber Engineering Blog) on June 2, 2020

How did the pedestrian cross the road? Contrary to popular belief, sometimes the answer isn’t as simple as “to get to the other side.” To bring safe, reliable self-driving vehicles (SDVs) to the streets at Uber Advanced Technologies Group (ATG)… The post Inside Uber ATG’s Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine Learning appeared first on Uber Engineering Blog.

by Ankit Jain (Machine Learning Archives - Uber Engineering Blog) on May 29, 2020

This article is based on the paper “Meta-Graph: Few Shot Link Prediction via Meta Learning” by Joey Bose, Ankit Jain, Piero Molino, and William L. Hamilton Many real-world data sets are structured as graphs, and as such, machine … The post Meta-Graph: Few-Shot Link Prediction Using Meta-Learning appeared first on Uber Engineering Blog.

by Latest Post on May 18, 2020

Vision loss among the elderly is a major healthcare issue: about one in three people have some vision-reducing disease by the age of 65. Age-related macular degeneration (AMD) is the most common cause of blindness in the developed world. In Europe, approximately 25% of those 60 and older have AMD. The dry form is relatively common among people over 65, and usually causes only mild sight loss. However, about 15% of patients with dry AMD go on to develop a more serious form of […]

by Latest Post on April 21, 2020

Specification gaming is a behaviour that satisfies the literal specification of an objective without achieving the intended outcome. We have all had experiences with specification gaming, even if not by this name. Readers may have heard the myth of King Midas and the golden touch, in which the king asks that anything he touches be turned to gold - but soon finds that even food and drink turn to metal in his hands. In the real world, when rewarded for doing well on a homework […]

by Latest Post on April 6, 2020

Under a microscope, a pane of window glass doesnt look like a collection of orderly molecules, as a crystal would, but rather a jumble with no discernable structure. Glass is made by starting with a glowing mixture of high-temperature melted sand and minerals. Once cooled, its viscosity (a measure of the friction in the fluid) increases a trillion-fold, and it becomes a solid, resisting tension from stretching or pulling. Yet the molecules in the glass remain in a seemingly […]

by Latest Post on March 31, 2020

The Atari57 suite of games is a long-standing benchmark to gauge agent performance across a wide range of tasks. Weve developed Agent57, the first deep reinforcement learning agent to obtain a score that is above the human baseline on all 57 Atari 2600 games. Agent57 combines an algorithm for efficient exploration with a meta-controller that adapts the exploration and long vs. short-term behaviour of the agent.

by Yu Guo (Machine Learning Archives - Uber Engineering Blog) on March 4, 2020

As Uber experienced exponential growth over the last few years, now supporting 14 million trips each day, our engineers proved they could build for scale. That value extends to other areas, including Uber ATG (Advanced Technologies Group) and its quest … The post Under the Hood of Uber ATG’s Machine Learning Infrastructure and Versioning Control Platform for Self-Driving Vehicles appeared first on Uber Engineering Blog.

by Sam Xiao (Machine Learning Archives - Uber Engineering Blog) on February 13, 2020

With operations in over 700 cities worldwide and gross bookings of over $16 billion in Q3 2019 alone, Uber leverages forecast models to ensure accurate financial planning and budget management. These models, derived from data science practices and platformed for … The post Building a Backtesting Service to Measure Model Performance at Uber-scale appeared first on Uber Engineering Blog.

by Latest Post on February 10, 2020

This blog introduces a new long-range memory model, the Compressive Transformer, alongside a new benchmark for book-level language modelling, PG19. We provide the conceptual tools needed to understand this new research in the context of recent developments in memory models and language modelling.

by Emily Bailey (Machine Learning Archives - Uber Engineering Blog) on January 28, 2020

Uber is a company built on data science. We leverage map data to get users from point A to point B; speech and text data to communicate between riders and drivers; restaurant and dish data to recommend food … The post Women in Data Science at Uber: Moving the World With Data in 2020—and Beyond appeared first on Uber Engineering Blog.

by Latest Post on January 15, 2020

A recent development in computer science may provide a deep, parsimonious explanation for several previously unexplained features of reward learning in the brain.

by Latest Post on January 15, 2020

Our Nature paper describes AlphaFold, a system that generates3D models of proteins that are far more accurate than any that have come before.

by Lezhi Li (Machine Learning Archives - Uber Engineering Blog) on January 7, 2020

In January 2019, Uber introduced Manifold, a model-agnostic visual debugging tool for machine learning that we use to identify issues in our ML models. To give other ML practitioners the benefits of this tool, today we are excited to … The post Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning appeared first on Uber Engineering Blog.

by Bryant Luong (Machine Learning Archives - Uber Engineering Blog) on January 2, 2020

Uber’s Data Visualization team builds software that enables us to better understand how cities move through dynamic visualizations. The Uber Engineering Blog periodically highlights visualizations that showcase how these technologies can turn aggregated data into actionable insights.   For SpeedsUp, … The post Uber Visualization Highlights: Displaying City Street Speed Clusters with SpeedsUp appeared first on Uber Engineering Blog.

by Zoubin Ghahramani (Machine Learning Archives - Uber Engineering Blog) on December 18, 2019

Artificial intelligence powers many of the technologies and services underpinning Uber’s platform, allowing engineering and data science teams to make informed decisions that help improve user experiences for products across our lines of business.  At the forefront of this effort … The post Uber AI in 2019: Advancing Mobility with Artificial Intelligence appeared first on Uber Engineering Blog.

by Latest Post on December 18, 2019

We demonstrate an early proof of concept of how text-to-speech technologies can synthesise a high-quality, natural sounding voice using minimal recorded speech data.

by Latest Post on December 13, 2019

We present a new method for training reinforcement learning agents from human feedback in the presence of unknown unsafe states.

by Joseph Wang (Machine Learning Archives - Uber Engineering Blog) on December 10, 2019

Michelangelo, Uber’s machine learning (ML) platform, powers machine learning model training across various use cases at Uber, such as forecasting rider demand, fraud detection, food discovery and recommendation for Uber Eats, and improving the accuracy of … The post Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber appeared first on Uber Engineering Blog.

by Latest Post on December 5, 2019

Weve come a long way in building the organisation we need to achieve our long-term mission.

by Ersin Yumer (Machine Learning Archives - Uber Engineering Blog) on November 26, 2019

Connecting the digital and physical worlds safely and reliably on the Uber platform presents exciting technological challenges and opportunities. For Uber, artificial intelligence (AI) is essential to developing systems that are capable of optimized, automated decision making at scale. AI … The post Announcing the 2020 Uber AI Residency appeared first on Uber Engineering Blog.

by Latest Post on November 21, 2019

AI requires people with different experiences, knowledge and backgrounds, which is why we started the DeepMind Scholarship programme and supportuniversitiesand the wider ecosystem.

by Latest Post on November 18, 2019

In collaboration with Google Play,our team that leads on collaborations with Googlehas driven significant improvements in the Play Store's discovery systems, helping to deliver a more personalised and intuitive Play Store experience for users.

by Latest Post on October 30, 2019

AlphaStar is the first AI to reach the top league of a widely popular esport without any game restrictions.

by Anne Holler (Machine Learning Archives - Uber Engineering Blog) on October 16, 2019

Michelangelo, Uber’s machine learning (ML) platform, supports the training and serving of thousands of models in production across the company. Designed to cover the end-to-end ML workflow, the system currently supports classical machine learning, time series forecasting, and deep … The post Evolving Michelangelo Model Representation for Flexibility at Scale appeared first on Uber Engineering Blog.

by Latest Post on October 3, 2019

Decisions based on machine learning (ML) are potentially advantageous over human decisions, but the data used to train them often contains human and societal biases that can lead to harmful decisions.

by Latest Post on September 18, 2019

Heres what the future looks like for the team.

by Wayne Cunningham (Machine Learning Archives - Uber Engineering Blog) on September 17, 2019

At Uber, we take advanced research work and use it to solve real world problems. In our  Science at Uber video series, Uber employees talk about how we apply data science, artificial intelligence, machine learning, and other innovative technologies … The post Science at Uber: Improving Transportation with Artificial Intelligence appeared first on Uber Engineering Blog.

by Latest Post on September 17, 2019

In this special extended episode, Hannah meets Demis Hassabis, the CEO and co-founder of DeepMind.

by Bea Schuster (Machine Learning Archives - Uber Engineering Blog) on September 11, 2019

Uber’s services require real-world coordination between a wide range of customers, including driver-partners, riders, restaurants, and eaters. Accurately forecasting things like rider demand and ETAs enables this coordination, which makes our services work as seamlessly as possible.  In an effort … The post Three Approaches to Scaling Machine Learning with Uber Seattle Engineering appeared first on Uber Engineering Blog.

by Wayne Cunningham (Machine Learning Archives - Uber Engineering Blog) on September 10, 2019

At Uber, we take advanced research work and use it to solve real world problems. In our  Science at Uber video series, Uber employees talk about how we apply data science, artificial intelligence, machine learning, and other innovative technologies … The post Science at Uber: Powering Machine Learning at Uber appeared first on Uber Engineering Blog.

by Latest Post on September 10, 2019

AI researchers around the world are trying to create a general purpose learning system that can learn to solve a broad range of problems without being taught how. Hannah explores the journey to get there.

by Latest Post on September 6, 2019

Our waking and sleeping lives are punctuated by fragments of recalled memories: a sudden connection in the shower between seemingly disparate thoughts, or an ill-fated choice decades ago that haunts us as we struggle to fall asleep.

by Latest Post on September 3, 2019

Hannah investigates the more human side of the technology, some ethical issues around how it is developed and used, and the efforts to create a future of AI that works for everyone.

by Latest Post on August 27, 2019

Hannah Fry meets the scientists building systems that could be used to save the sight of thousands; help us solve one of the most fundamental problems in biology, and reduce energy consumption in an effort to combat climate change.

by Molly Vorwerck (Machine Learning Archives - Uber Engineering Blog) on August 21, 2019

The past few months have been a whirlwind for Jeff Clune, Senior Research Manager at Uber and a founding member of Uber AI Labs. In June 2019, research by him and his collaborators on POET, an algorithm … The post Advancing AI: A Conversation with Jeff Clune, Senior Research Manager at Uber appeared first on Uber Engineering Blog.

by Latest Post on August 20, 2019

Forget what sci-fi has told you about superintelligent robots that are uncannily human-like; the reality is more prosaic. Inside DeepMinds robotics laboratory, Hannah explores what researchers call embodied AI.

by Latest Post on August 19, 2019

Video games have become a favourite tool for AI researchers to test the abilities of their systems. Why?

by Latest Post on August 18, 2019

The story of AlphaGo, first computer program to defeat a professional human player at the game of Go, a milestone considered a decade ahead of its time.

by Latest Post on August 17, 2019

What can the human brain teach us about AI? And what can AI teach us about our own intelligence?

by Latest Post on August 16, 2019

This eight part series hosted by mathematician and broadcaster Hannah Fry aims to give listeners an inside look at the fascinating world of AI research and explores some of the questions and challenges the whole field is wrestling with today.

by Latest Post on August 8, 2019

The Serengeti is one of the last remaining sites in the world that hosts an intact community of large mammals. These animals roam over vast swaths of land, some migrating thousands of miles across multiple countries following seasonal rainfall. As human encroachment around the park becomes more intense, these species are forced to alter their behaviours in order to survive. Increasing agriculture, poaching, and climate abnormalities contribute to changes in animal behaviours […]

by Latest Post on July 31, 2019

Artificial intelligence can now predict one of the leading causes of avoidable patient harm up to two days before it happens, as demonstrated byour latest research published in Nature.

by Latest Post on July 25, 2019

by Alex Gajewski (Machine Learning Archives - Uber Engineering Blog) on July 22, 2019

Tools that enable fast and flexible experimentation democratize and accelerate machine learning research. Take for example the development of libraries for automatic differentiation, such as Theano, Caffe, TensorFlow, and PyTorch: these libraries have been instrumental in … The post Introducing EvoGrad: A Lightweight Library for Gradient-Based Evolution appeared first on Uber Engineering Blog.

by Latest Post on June 25, 2019

One in a series of posts explaining the theories underpinning our research. Over the last decade, machine learning has made unprecedented progress in areas as diverse as image recognition, self-driving cars and playing complex games like Go. These successes have been largely realised by training deep neural networks with one of two learning paradigmssupervised learning and reinforcement learning. Both paradigms require training signals to be designed by a human and passed to […]

by Haoyang Chen (Machine Learning Archives - Uber Engineering Blog) on June 24, 2019

At Uber, we use marketplace algorithms to connect drivers and riders. Before the algorithms roll out globally, Uber fully tests and evaluates them to create an optimal user experience that maps to our core marketplace principles. To make product … The post Gaining Insights in a Simulated Marketplace with Machine Learning at Uber appeared first on Uber Engineering Blog.

by Molly Vorwerck (Machine Learning Archives - Uber Engineering Blog) on June 14, 2019

Machine learning models perform a diversity of tasks at Uber, from improving our maps to streamlining chat communications and even preventing fraud. In addition to serving a variety of use cases, it is important that we make machine learning … The post No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox appeared first on Uber Engineering Blog.

by Latest Post on May 30, 2019

Mastering the strategy, tactical understanding, and team play involved in multiplayer video games represents a critical challenge for AI research. Now, through new developments in reinforcement learning, our agents have achieved human-level performance in Quake III Arena Capture the Flag, a complex multi-agent environment and one of the canonical 3D first-person multiplayer games. These agents demonstrate the ability to team up with both artificial agents and human players.

by Yuehai Xu (Machine Learning Archives - Uber Engineering Blog) on April 25, 2019

Reliable transportation requires a robust map stack that provides services like routing,  navigation instructions, and ETA calculation. Errors in map data can significantly impact services, leading to a suboptimal user experience. Uber engineers use various sources of feedback to identify … The post Improving Uber’s Mapping Accuracy with CatchME appeared first on Uber Engineering Blog.

by Latest Post on March 28, 2019

One in a series of posts explaining the theories underpinning our research. Bugs and software have gone hand in hand since the beginning of computer programming. Over time, software developers have established a set of best practices for testing and debugging before deployment, but these practices are not suited for modern deep learning systems. Today, the prevailing practice in machine learning is to train a system on a training data set, and then test it on another set. […]

by Jianyong Zhang (Machine Learning Archives - Uber Engineering Blog) on March 18, 2019

Machine learning (ML) pervades many aspect of Uber’s business. From responding to customer support tickets, optimizing queries, and forecasting demand, ML provides critical insights for many of our teams. Our teams encountered many different challenges while incorporating … The post Accessible Machine Learning through Data Workflow Management appeared first on Uber Engineering Blog.

by Molly Vorwerck (Machine Learning Archives - Uber Engineering Blog) on March 13, 2019

Fran Bell has always been a scientist; theorizing, modeling and testing how the world works. An ever-curious child, she was fascinated by the natural world, poring over biology and chemistry books, but was never satisfied with just knowing; she … The post Data Science at Scale: A Conversation with Uber’s Fran Bell appeared first on Uber Engineering Blog.

by Latest Post on March 7, 2019

At DeepMind, the Research Platform Team builds infrastructure to empower and accelerate our AI research. Today, we are excited to share how we developed TF-Replicator, a software library that helps researchers deploy their TensorFlow models on GPUs and Cloud TPUs with minimal effort and no previous experience with distributed systems. TF-Replicators programming model has now been open sourced as part of TensorFlows tf.distribute.Strategy. This blog post gives an overview of […]

by Latest Post on February 26, 2019

Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged. However, the variable nature of wind itself makes it an unpredictable energy sourceless useful than one that can reliably deliver power at a set time.In search of a […]

by Molly Vorwerck (Machine Learning Archives - Uber Engineering Blog) on February 21, 2019

Over the past several years, artificial intelligence (AI) has become an integral component of many enterprise tech stacks, facilitating faster, more efficient solutions for everything from self-driving vehicles to automated messaging platforms. On the AI spectrum, deep probabilistic programming, a … The post Uber Open Source: Catching Up with Fritz Obermeyer and Noah Goodman from the Pyro Team appeared first on Uber Engineering Blog.

by Piero Molino (Machine Learning Archives - Uber Engineering Blog) on February 11, 2019

Over the last decade, deep learning models have proven highly effective at performing a wide variety of machine learning tasks in vision, speech, and language. At Uber we are using these models for a variety of tasks, including customer support… The post Introducing Ludwig, a Code-Free Deep Learning Toolbox appeared first on Uber Engineering Blog.

by Latest Post on January 24, 2019

Games have been used for decades as an important way to test and evaluate the performance of artificial intelligence systems. As capabilities have increased, the research community has sought games with increasing complexity that capture different elements of intelligence required to solve scientific and real-world problems. In recent years, StarCraft, considered to be one of the most challenging Real-Time Strategy (RTS) games and one of the longest-played esports of all […]

by Lezhi Li (Machine Learning Archives - Uber Engineering Blog) on January 14, 2019

Machine learning (ML) is widely used across the Uber platform to support intelligent decision making and forecasting for features such as ETA prediction and fraud detection. For optimal results, we invest a lot of resources in developing accurate predictive … The post Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber appeared first on Uber Engineering Blog.

by Rui Wang (Machine Learning Archives - Uber Engineering Blog) on January 8, 2019

Jeff Clune and Kenneth O. Stanley were co-senior authors. We are interested in open-endedness at Uber AI Labs because it offers the potential for generating a diverse and ever-expanding curriculum for machine learning entirely on its own. Having vast amounts … The post POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer appeared first on Uber Engineering Blog.

by Molly Vorwerck (Machine Learning Archives - Uber Engineering Blog) on December 13, 2018

For Alex Sergeev, the decision to open source his team’s new distributed deep learning framework, Horovod, was an easy one. Tasked with training the machine learning models that power the sensing and perception systems used by our Advanced … The post Open Source at Uber: Meet Alex Sergeev, Horovod Project Lead appeared first on Uber Engineering Blog.

by Latest Post on December 6, 2018

In late 2017 we introduced AlphaZero, a single system that taught itself from scratch how to master the games of chess, shogi (Japanese chess), and Go, beating a world-champion program in each case. We were excited by the preliminary results and thrilled to see the response from members of the chess community, who saw in AlphaZeros games a ground-breaking, highly dynamic and unconventional style of play that differed from any chess playing engine that came before it.Today, […]

by Latest Post on December 2, 2018

We're excited to share DeepMinds first significant milestone in demonstrating how artificial intelligence research can drive and accelerate new scientific discoveries. With a strongly interdisciplinary approach to our work, DeepMind has brought together experts from the fields of structural biology, physics, and machine learning to apply cutting-edge techniques to predict the 3D structure of a protein based solely on its genetic sequence.Our system, AlphaFold, which we have […]

by Ryan Turner (Machine Learning Archives - Uber Engineering Blog) on November 29, 2018

Generative Adversarial Networks (GANs) have achieved impressive feats in realistic image generation and image repair. Art produced by a GAN has even been sold at auction for over $400,000! At Uber, GANs have myriad potential applications, including strengthening our … The post How to Get a Better GAN (Almost) for Free: Introducing the Metropolis-Hastings GAN appeared first on Uber Engineering Blog.

by Wayne Cunningham (Machine Learning Archives - Uber Engineering Blog) on November 20, 2018

Uber held its first open source summit on November 15, 2018, inviting members of the open source community for presentations given by experts on some of the projects we have contributed in the fields of big data, visualization, machine learning, … The post Collaboration at Scale: Highlights from Uber Open Summit 2018 appeared first on Uber Engineering Blog.

by Wayne Cunningham (Machine Learning Archives - Uber Engineering Blog) on November 15, 2018

Whenever a rider gets dropped off at their location, one of our driver-partners finishes a session laden with trips, or an eater gets food delivered to their door, data underlies these interactions on the Uber platform. And our teams could … The post Experience in AI: Uber Hires Jan Pedersen appeared first on Uber Engineering Blog.

by Molly Vorwerck (Machine Learning Archives - Uber Engineering Blog) on November 14, 2018

NVIDIA, inventor of the GPU, creates solutions for building and training AI-enabled systems. In addition to providing hardware and software for much of the industry’s AI research, NVIDIA is building an AI computing platform for developers of self-driving vehicles. With … The post NVIDIA: Accelerating Deep Learning with Uber’s Horovod appeared first on Uber Engineering Blog.

by Latest Post on November 13, 2018

Were excited to announce that the team behind Streams our mobile app that supports doctors and nurses to deliver faster, better care to patientswill be joining Google.Its been a phenomenal journey to see Streams go from initial idea to live deployment, and to hear how its helped change the lives of patients and the nurses and doctors who treat them. The arrival of world-leading health expert Dr. David Feinberg at Google will accelerate these efforts, helping to make a […]

by Samuel Zemedkun (Machine Learning Archives - Uber Engineering Blog) on November 12, 2018

I was born in Addis Ababa, Ethiopia and was raised there with my five younger sisters. My father made traditional fabrics, weaving one thread at a time. Weaving in Ethiopia is a family business and every member of the family … The post My Journey from Working as a Fabric Weaver in Ethiopia to Becoming a Software Engineer at Uber in San Francisco appeared first on Uber Engineering Blog.

by Latest Post on November 5, 2018

In August, we announced the first stage of our joint research partnership with Moorfields Eye Hospital, which showed how AI could match world-leading doctors at recommending the correct course of treatment for over 50 eye diseases, and also explain how it arrives at its recommendations.Now were excited to start working on the next research challenge whether we can help clinicians predict eye diseases before symptoms set in.There are two types of age-related macular […]

by Kevin Stumpf (Machine Learning Archives - Uber Engineering Blog) on October 23, 2018

As a company heavily invested in AI, Uber aims to leverage machine learning (ML) in product development and the day-to-day management of our business. In pursuit of this goal, our data scientists spend considerable amounts of time prototyping and validating … The post Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development appeared first on Uber Engineering Blog.

by Chun-Chen Kuo (Machine Learning Archives - Uber Engineering Blog) on October 22, 2018

High quality map data powers many aspects of the Uber trip experience. Services such as Search, Routing, and Estimated Time of Arrival (ETA) prediction rely on accurate map data to provide a safe, convenient, and efficient experience for riders, drivers, … The post Applying Customer Feedback: How NLP & Deep Learning Improve Uber’s Maps appeared first on Uber Engineering Blog.

by Latest Post on October 17, 2018

Today we are open sourcing a new library of useful building blocks for writing reinforcement learning (RL) agents in TensorFlow. Named TRFL (pronounced truffle), it represents a collection of key algorithmic components that we have used internally for a large number of our most successful agents such as DQN, DDPG and the Importance Weighted Actor Learner Architecture.A typical deep reinforcement learning agent consists of a large number of interacting components: at the very […]

by Latest Post on October 4, 2018

Japanese version followsSix months ago, we joined a groundbreaking new research partnership led by the Cancer Research UK Imperial Centre at Imperial College London to explore whether AI technology could help clinicians diagnose breast cancers on mammograms quicker and more effectively.Breast cancer is a huge global health problem. Around the world, over 1.6 million people are diagnosed with the disease every single year, and 500,000 lose their life to it partly because […]

by Yue Weng (Machine Learning Archives - Uber Engineering Blog) on September 28, 2018

Imagine standing curbside, waiting for your Uber ride to arrive. On your app, you see that the car is barely moving. You send them a message to find out what’s going on. Unbeknownst to you, your driver-partner is stuck in … The post Improving Driver Communication through One-Click Chat, Uber’s Smart Reply System appeared first on Uber Engineering Blog.

by Robbie Gruener (Machine Learning Archives - Uber Engineering Blog) on September 21, 2018

In recent years, deep learning has taken a central role in solving a wide range of problems in pattern recognition. At Uber Advanced Technologies Group (ATG), we use deep learning to solve various problems in the autonomous driving space, since … The post Introducing Petastorm: Uber ATG’s Data Access Library for Deep Learning appeared first on Uber Engineering Blog.

by Latest Post on September 13, 2018

Multi-task learning - allowing a single agent to learn how to solve many different tasks - is a longstanding objective for artificial intelligence research. Recently, there has been a lot of excellent progress, with agents likeDQN able to use the same algorithm to learn to play multiple games including Breakout and Pong. These algorithms were used to train individual expert agents for each task. As artificial intelligence research advances to more complex real world domains, […]

by Latest Post on September 13, 2018

Early results from our partnership with the Radiotherapy Department at University College London Hospitals NHS Foundation Trust suggest that we are well on our way to developing an artificial intelligence (AI) system that can analyse and segment medical scans of head and neck cancer to a similar standard as expert clinicians. This segmentation process is an essential but time-consuming step when planning radiotherapy treatment. The findingsalso show that our system can […]

by Yuyan Wang (Machine Learning Archives - Uber Engineering Blog) on September 10, 2018

Even as we improve Uber Eats to better understand eaters’ intentions when they use search, there are times when eaters just don’t know what they want to eat. In those situations, the Uber Eats app provides a personalized experience for … The post Food Discovery with Uber Eats: Recommending for the Marketplace appeared first on Uber Engineering Blog.

by Latest Post on August 17, 2018

Many of societys most pressing problems have grown increasingly complex, so the search for solutions can feel overwhelming. At DeepMind and Google, we believe that if we can use AI as a tool to discover new knowledge, solutions will be easier to reach.In 2016, we jointly developed an AI-powered recommendation system to improve the energy efficiency of Googles already highly-optimised data centres. Our thinking was simple: even minor improvements would provide significant […]

by Latest Post on August 13, 2018

We are delighted to announce the results of the first phase of our joint research partnership with Moorfields Eye Hospital, which could potentially transform the management of sight-threatening eye disease.The results, published online inNature Medicine(open access full text, see end of blog), show that our AI system can quickly interpret eye scans from routine clinical practice with unprecedented accuracy. It can correctly recommend how patients should be referred for […]

by Latest Post on August 6, 2018

Visual and audio events tend to occur together: a musician plucking guitar strings and the resulting melody; a wine glass shattering and the accompanying crash; the roar of a motorcycle as it accelerates. These visual and audio stimuli are concurrent because they share a common cause. Understanding the relationship between visual events and their associated sounds is a fundamental way that we make sense of the world around us.In Look, Listen, and Learn and Objects that Sound […]

by Latest Post on July 11, 2018

Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalisation itself.

by Latest Post on July 9, 2018

The 2018 International Conference on Machine Learning will take place in Stockholm, Sweden from 10-15 July.For those attending and planning the week ahead, we are sharing a schedule of DeepMind presentations at ICML (you can download a pdf version here). We look forward to the many engaging discussions, ideas, and collaborations that are sure to arise from the conference!Efficient Neural Audio SynthesisAuthors: Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Nouri, Norman […]

by Latest Post on June 15, 2018

When we set up DeepMind Health we believed that pioneering technology should be matched with pioneering oversight. Thats why when we launched in February 2016, we did so with an unusual and additional mechanism: a panel of Independent Reviewers, who meet regularly throughout the year to scrutinise our work. This is an innovative approach within tech companies - one that forces us to question not only what we are doing, but how and why we are doing it - and we believe that […]

by Latest Post on June 14, 2018

There is more than meets the eye when it comes to how we understand a visual scene: our brains draw on prior knowledge to reason and to make inferences that go far beyond the patterns of light that hit our retinas. For example, when entering a room for the first time, you instantly recognise the items it contains and where they are positioned. If you see three legs of a table, you will infer that there is probably a fourth leg with the same shape and colour hidden from view. […]

by Latest Post on June 13, 2018

Last July, the Information Commissioner concluded an investigation into the use of the Streams app at the Royal Free London NHS Foundation Trust. As part of the investigation the Royal Free signed up to a set of undertakings one of which was to commission a third party to audit the Royal Frees current data processing arrangements with DeepMind, to ensure that they fully complied with data protection law and respected the privacy and confidentiality rights of its […]

by Latest Post on May 14, 2018

Recently, AI systems have mastered a range of video-games such as Atari classics Breakout and Pong. But as impressive as this performance is, AI still relies on the equivalent of thousands of hours of gameplay to reach and surpass the performance of human video game players. In contrast, we can usually grasp the basics of a video game we have never played before in a matter of minutes.The question of why the brain is able to do so much more with so much less has given rise […]

by Latest Post on May 9, 2018

Most animals, including humans, are able to flexibly navigate the world they live in exploring new areas, returning quickly to remembered places, and taking shortcuts. Indeed, these abilities feel so easy and natural that it is not immediately obvious how complex the underlying processes really are. In contrast, spatial navigation remains a substantial challenge for artificial agents whose abilities are far outstripped by those of mammals.In 2005, a potentially crucial part […]

by Latest Post on May 8, 2018

Were delighted to announce a new collaboration between DeepMind for Google and Android, the worlds most popular mobile operating system. Together, weve created two new features that will be available to people with devices running Android P later this year:

by Latest Post on April 26, 2018

Between 30 April and 03 May, hundreds of researchers and engineers will gather in Vancouver, Canada, for the Sixth International Conference on Learning RepresentationsHere you can read details of all DeepMinds accepted papers and find out where you can see the accompanying poster sessions and talks. Maximum a posteriori policy optimisationAuthors: Abbas Abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, Yuval Tassa, Remi MunosWe introduce a new algorithm for reinforcement […]

by Latest Post on April 11, 2018

One of the greatest pleasures of coming to work every day at DeepMind is the chance to collaborate with brilliant researchers and engineers from so many different fields and perspectives - with machine learning experts alongside neuroscientists, physicists, mathematicians, roboticists, ethicists and more.This level of interdisciplinary collaboration is both challenging and unusual, and it requires a unique type of organisation. We built DeepMind to combine the rigour and […]

by Latest Post on March 29, 2018

How did you learn to navigate the neighborhood of your childhood, to go to a friends house, to your school or to the grocery store? Probably without a map and simply by remembering the visual appearance of streets and turns along the way. As you gradually explored your neighborhood, you grew more confident, mastered your whereabouts and learned new and increasingly complex paths. You may have gotten briefly lost, but found your way again thanks to landmarks, or perhaps even […]

by Latest Post on March 29, 2018

English version followsLorsque nous avons tabli notre sige Londres en 2010, nous voulions faire de DeepMind le nec plus ultra de la recherche de pointe dans le domaine de lintelligence artificielle. Nous voulions galement aider la communaut de lintelligence artificielle se dvelopper. Nous avons ainsi publi des articles dans les confrences et journaux les plus slectifs (plus de 180 ce jour!) et partag nos connaissances dans ce domaine; nous avons incit nos experts […]

by Latest Post on March 27, 2018

Through a humans eyes, the world is much more than just the images reflected in our corneas. For example, when we look at a building and admire the intricacies of its design, we can appreciate the craftsmanship it requires. This ability to interpret objects through the tools that created them gives us a richer understanding of the world and is an important aspect of our intelligence.We would like our systems to create similarly rich representations of the world. For example, […]

by Latest Post on March 21, 2018

Deep neural networks are composed of many individual neurons, which combine in complex and counterintuitive ways to solve a wide range of challenging tasks. This complexity grants neural networks their power but also earns them their reputation as confusing and opaque black boxes.Understanding how deep neural networks function is critical for explaining their decisions and enabling us to build more powerful systems. For instance, imagine the difficulty of trying to build a […]

by Latest Post on March 6, 2018

I like to take things slow. Take it slowly and get it right first time, one participant said, but was quickly countered by someone else around the table: But Im impatient, I want to see the benefits now. This exchange neatly captures many of the conversations I heard at DeepMind Healths recent Collaborative Listening Summit. It also represents, in laymans terms, the debate that tech thinkers and policy-makers are having right now about the future of artificial […]

by Latest Post on February 28, 2018

Getting children (and adults) to tidy up after themselves can be a challenge, but we face an even greater challenge trying to get our AI agents to do the same. Success depends on the mastery of several core visuo-motor skills: approaching an object, grasping and lifting it, opening a box and putting things inside of it. To make matters more complicated, these skills must be applied in the right sequence.Control tasks, like tidying up a table or stacking objects, require an […]

by Latest Post on February 22, 2018

Were excited to announce a medical research partnership with the US Department of Veterans Affairs (VA), one of the worlds leading healthcare organisations responsible for providing high-quality care to veterans and their families across the United States.This project will see us analyse patterns from historical, depersonalised medical records to predict patient deterioration.Patient deterioration is a significant global health problem that often has fatal consequences. […]

by Latest Post on February 5, 2018

Deep Reinforcement Learning (DeepRL) has achieved remarkable success in a range of tasks, from continuous control problems in robotics to playing games like Go and Atari. The improvements seen in these domains have so far been limited to individual tasks where a separate agent has been tuned and trained for each task.In our most recent work, we explore the challenge of training a single agent on many tasks.Today we are releasing DMLab-30, a set of new tasks that span a large […]

by Latest Post on January 29, 2018

Suppose you are playing football. The ball arrives at your feet, and you decide to pass it to the unmarked striker. What seems like one simple action requires two different kinds of thought.First, you recognise that there is a football at your feet. This recognition requires intuitive perceptual thinking -you cannot easily articulate how you come to know that there is a ball at your feet, you just see that it is there. Second, you decide to pass the ball to a particular […]