ML and AI Research

by Machine learning : nature.com subject feeds on October 20, 2021

by Davide Galliteli (AWS Machine Learning Blog) on October 19, 2021

AWS customers are increasingly building applications that are enhanced with predictions from computer vision models. For example, a fitness application monitors the body posture of users while exercising in front of a camera and provides live feedback to the users as well as periodic insights. Similarly, an inventory inspection tool in a large warehouse captures

by Mark Kitchell (AWS Machine Learning Blog) on October 19, 2021

As more European organizations move from experimentation to production for AI projects, the importance of running these projects on a scalable, secure, and cost-efficient platform becomes clear. Building AI solutions from scratch is often beyond the capabilities of many organizations, especially because it requires in-house AI expertise, which is in short supply. According to analyst

by Lexie Hagen (Microsoft Research) on October 19, 2021

Object recognition systems have made spectacular advances in recent years, but they rely on training datasets with thousands of high-quality, labelled examples per object category. Learning new objects from only a few examples could open the door to many new applications. For example, robotics manufacturing requires a system to quickly learn new parts, while assistive The post Announcing the ORBIT dataset: Advancing real-world few-shot learning using teachable object […]

by Virgil Ennes (AWS Machine Learning Blog) on October 18, 2021

Dealing with content in large scale is often challenging, costly, and a heavy lift operation. The volume of user-generated and third-party content has been increasing substantially in industries like social media, ecommerce, online advertising, and media sharing. Customers may want to review this content to ensure that it follows corporate governance and regulations. But they

by Daniele Angelosante (AWS Machine Learning Blog) on October 18, 2021

Labeling is an indispensable stage of data preprocessing in supervised learning. Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. Ground Truth helps improve the quality of labels through annotation consolidation and audit workflows. Ground Truth is easy to use,

by Dion Harris (The Official NVIDIA Blog) on October 18, 2021

Thorsten Kurth still remembers the night he discovered his team broke the exascale barrier. On the couch at home at 9 p.m., he was pouring over the latest results from one of the first big jobs run on Summit, then the world’s top supercomputer, based at Oak Ridge National Laboratory. The 12-person team had spent Read article > The post An Engineer Recalls How AI Broke the Exascale Barrier appeared first on The Official NVIDIA Blog.

by Lexie Hagen (Microsoft Research) on October 18, 2021

For 30 years, Microsoft Research has brought together great minds from around the world to take on the biggest research challenges facing society. As we enter our fourth decade, the need for collaborative research—and the opportunities it presents—have never been greater. That’s why we’re so thrilled about the inaugural Microsoft Research Summit, October 19–21. It’s The post First ever Microsoft Research Summit explores science and technology aimed at big […]

by Brian Caulfield (The Official NVIDIA Blog) on October 18, 2021

Seventy-five students from Houston Community College’s new two-year artificial intelligence program will attend NVIDIA’s GTC conference next month. Calling it “the greatest field trip of all time,” the program’s coordinator, G. Raymond Brown, sees it as a way to bring students in the program up to speed fast. The program at HCC, launched in August Read article > The post College Students in AI Take ‘World’s Greatest Field Trip’ to GTC appeared first on The […]

by Machine learning : nature.com subject feeds on October 18, 2021

by Machine learning : nature.com subject feeds on October 18, 2021

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 Machine learning : nature.com subject feeds on October 18, 2021

by Machine learning : nature.com subject feeds on October 18, 2021

by Tim Pavlick (AWS Machine Learning Blog) on October 15, 2021

This post is co-written by Ian Avilez and Tim Pavlick from HawkEye 360. HawkEye 360 is a commercial radio frequency (RF) constellation, data, and analytics provider. Their signals of interest include very high frequency (VHF) push-to-talk radios, maritime radar systems, Automatic Identification System (AIS) beacons, emergency beacons, and more. The signals of interest library will

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 Allison Wu (The Official NVIDIA Blog) on October 15, 2021

From counting cows to detecting weeds to pollinating tomatoes, startups around the world are deploying AI technology to solve problems that farmers have struggled with since the dawn of time. Tomorrow’s World Food Day comes as startups are mobilized to change how our food is grown — with potentially wide implications for the economy, public Read article > The post Food for Thought: Startups Harness AI to Nurture the Future of Agriculture appeared first on The Official […]

by Machine learning : nature.com subject feeds on October 15, 2021

by Machine learning : nature.com subject feeds on October 15, 2021

by Machine learning : nature.com subject feeds on October 15, 2021

by Luis Lopez Soria (AWS Machine Learning Blog) on October 14, 2021

Today, we’re excited to announce a new similar items recommendation recipe (aws-similar-items) in Amazon Personalize that helps you leverage your users’ interaction histories and what you know about the items in your catalog to deliver relevant recommendations. Across Amazon, we provide personalized experiences for each of our users, and based on a user’s interests, we

by Prem Ranga (AWS Machine Learning Blog) on October 14, 2021

The University of Maryland, Baltimore County’s Bina lab is a multidisciplinary research lab for employing advanced computer vision, machine learning (ML), and remote sensing techniques to discover new knowledge of our environment, especially in the Arctic and Antarctic regions. The lab’s work is supported by NSF BIGDATA awards (IIS-1947584, IIS-1838230), the NSF HDR Institute award

by Matan Lion (AWS Machine Learning Blog) on October 14, 2021

This is a guest post by Imperva, a solutions provider for cybersecurity.  Imperva is a cybersecurity leader, headquartered in California, USA, whose mission is to protect data and all paths to it. In the last few years, we’ve been working on integrating machine learning (ML) into our products. This includes detecting malicious activities in databases,

by Katie Burke (The Official NVIDIA Blog) on October 14, 2021

For autonomous vehicles, what’s on the surface matters. While humans are taught to avoid snap judgments, self-driving cars must be able to see, detect and act quickly and accurately to operate safely. This capability requires a robust perception software stack that can comprehensively identify and track the vehicle’s surrounding environment. Startups worldwide are developing these Read article > The post Looks Can Be Perceiving: Startups Build Highly Accurate […]

by GeForce NOW Community (The Official NVIDIA Blog) on October 14, 2021

GeForce NOW members have the power of our NVIDIA RTX Servers to throw at their favorite PC games. They can use that power to stream Crysis Remastered today, and the complete Crysis Remastered Trilogy starting tomorrow — with the best performance and RTX graphics for Founders and Priority members. With this great GFN Thursday also Read article > The post But Can It Run ‘Crysis Remastered?’ With GeForce NOW, Nearly Any Device Can appeared first on The Official NVIDIA […]

by Machine learning : nature.com subject feeds on October 14, 2021

by Machine learning : nature.com subject feeds on October 14, 2021

by Mark Watkins (AWS Machine Learning Blog) on October 13, 2021

When companies deal with data that comes from various sources or the collection of this data has changed over time, the data often becomes difficult to organize. Perhaps you have product category names that are similar but don’t match, and on your website you want to surface these products as a group. Therefore, you need

by Machine learning : nature.com subject feeds on October 13, 2021

by Machine learning : nature.com subject feeds on October 13, 2021

by Machine learning : nature.com subject feeds on October 13, 2021

by Machine learning : nature.com subject feeds on October 13, 2021

by Archis Joglekar (AWS Machine Learning Blog) on October 12, 2021

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. Typically, you can use the pre-built and optimized training and inference containers that have been optimized for AWS hardware. Although those containers cover many deep learning workloads,

by Rosie Connolly (AWS Machine Learning Blog) on October 12, 2021

As you plan your new Amazon Lex application, the following conversation design best practices can help your team succeed in creating a great customer experience. In our previous post, we discussed how to create the foundation for good conversation design. We explored the business value of good conversational design and provided some tips on building a team. We also talked about the importance of identifying use cases to create an informed foundation for your conversational […]

by Scott Martin (The Official NVIDIA Blog) on October 12, 2021

Devastating floods are making headlines worldwide, but advances in deep learning for detection could turn uncertainty over evacuations into yesterday’s news. Applying a data set of 66,000 images, data scientists have created an ensemble of models for predicting flood zones. And the models are generalizable for application to new geographies. The findings, published in a Read article > The post Data Scientists Develop Flood Detection for Early Warning appeared first on […]

by Jaya Prakash Kommu (AWS Machine Learning Blog) on October 12, 2021

New trends are shaping the credit card industry as shopping habits have rapidly evolved over the last 18 months. The pandemic has accelerated the move away from cash towards cards. Card issuers are transforming their products to better serve cardmembers through innovations such as contactless payments and mobile wallet. The rapid change in consumer behavior

by Machine learning : nature.com subject feeds on October 12, 2021

by Machine learning : nature.com subject feeds on October 12, 2021

by Bilal Ali (AWS Machine Learning Blog) on October 11, 2021

Fraud teams need a secure, fast, and flexible transaction fraud detection solution to combat global fraudsters. Unlike many solutions on the market, Amazon Fraud Detector allows you to tailor your fraud detection efforts specifically to your data and business challenge while also bringing the latest in fraud detection machine learning (ML) technology to bear on

by Gayatri Ghanakota (AWS Machine Learning Blog) on October 11, 2021

The ability to predict that a particular customer is at a high risk of churning, while there is still time to do something about it, represents a huge potential revenue source for every online business. Depending on the industry and business objective, the problem statement can be multi-layered. The following are some business objectives based

by Alyssa Hughes (Microsoft Research) on October 11, 2021

Today, we’re excited to announce that Microsoft Translator has added 12 new languages and dialects to the growing repertoire of Microsoft Azure Cognitive Services Translator, bringing us to a total of 103 languages! The new languages, which are natively spoken by 84.6 million people, are Bashkir, Dhivehi, Georgian, Kyrgyz, Macedonian, Mongolian (Cyrillic), Mongolian (Traditional), Tatar, The post Microsoft Translator: Now translating 100 languages and counting! appeared […]

by Jeff Running (Microsoft Research) on October 11, 2021

We are excited to introduce the DeepSpeed- and Megatron-powered Megatron-Turing Natural Language Generation model (MT-NLG), the largest and the most powerful monolithic transformer language model trained to date, with 530 billion parameters. It is the result of a research collaboration between Microsoft and NVIDIA to further parallelize and optimize the training of very large AI The post Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, the World’s Largest […]

by Machine learning : nature.com subject feeds on October 11, 2021

by Latest Post on October 11, 2021

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

by Machine learning : nature.com subject feeds on October 11, 2021

by Machine learning : nature.com subject feeds on October 11, 2021

by Jonah Alben (The Official NVIDIA Blog) on October 8, 2021

We are excited to announce that Oski Technology, a company specializing in formal verification methods, will be joining NVIDIA. Modern processors pack tens of billions of transistors, tiny on/off switches connected by billions of microscopic pathways. A bug in a single transistor can prevent a chip from operating correctly, requiring costly revisions to fix. Today, Read article > The post Oski Technology, an Expert in Formal Verification, Joins NVIDIA appeared first on […]

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 Machine learning : nature.com subject feeds on October 8, 2021

by Machine learning : nature.com subject feeds on October 8, 2021

by Ken Shek (AWS Machine Learning Blog) on October 7, 2021

In Part 1 of this series, we showed how to build a brand detection solution using Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels. The solution was built on a serverless architecture with a custom user interface to identify a company brand or logo from video content and get an in-depth view of screen

by Kate Kallot (The Official NVIDIA Blog) on October 7, 2021

Two artists, if given the same paint set, would create distinct works — each showcasing their own points of view. This is especially likely if the artists were to hail from opposite ends of the world. Similarly, developers across the globe use the same tools to create different AI-based applications, each solving challenges relevant to Read article > The post Wonders of the World: NVIDIA Emerging Chapters Program Spurs AI Innovation Across Developing Countries appeared […]

by GeForce NOW Community (The Official NVIDIA Blog) on October 7, 2021

The leaves are changing, the weather’s getting colder and 23 new games are streaming from the cloud. October kicks off the way every first GFN Thursday of the month does — with news of great PC games coming to GeForce NOW. This fall’s harvest includes Far Cry 6, Marvel’s Guardians of the Galaxy and Riders Read article > The post Falling Into October: GFN Thursday Brings ‘Far Cry 6,’ ‘Marvel’s Guardians of the Galaxy,’ ‘Riders Republic’ to GeForce NOW […]

by Machine learning : nature.com subject feeds on October 7, 2021

by Machine learning : nature.com subject feeds on October 7, 2021

by Machine learning : nature.com subject feeds on October 7, 2021

by Machine learning : nature.com subject feeds on October 7, 2021

by Alex Thewsey (AWS Machine Learning Blog) on October 6, 2021

From application forms, to identity documents, recent utility bills, and bank statements, many business processes today still rely on exchanging and analyzing human-readable documents—particularly in industries like financial services and law. In this post, we show how you can use Amazon SageMaker, an end-to-end platform for machine learning (ML), to automate especially challenging document analysis

by Hasan Poonawala (AWS Machine Learning Blog) on October 6, 2021

AWS customers are increasingly using computer vision (CV) models on large input payloads that can take a few minutes of processing time. For example, space technology companies work with a stream of high-resolution satellite imagery to detect particular objects of interest. Similarly, healthcare companies process high-resolution biomedical images or videos like echocardiograms to detect anomalies.

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 Angie Lee (The Official NVIDIA Blog) on October 6, 2021

Lennon and McCartney. Ashford and Simpson. Many of our all-time favorite tunes have come from songwriting duos. Now, anyone can find a canny compositional partner in AI. Maya Ackerman is the CEO of WaveAI, a Silicon Valley startup using AI and machine learning to, as the company motto puts it, “unlock new heights of human Read article > The post Sing It, Sister! Maya Ackerman on LyricStudio, an AI-Based Writing Assistant appeared first on The Official NVIDIA Blog.

by Machine learning : nature.com subject feeds on October 6, 2021

by Machine learning : nature.com subject feeds on October 6, 2021

by Machine learning : nature.com subject feeds on October 6, 2021

by Machine learning : nature.com subject feeds on October 6, 2021

by Matthew Rhodes (AWS Machine Learning Blog) on October 5, 2021

According to a recent study, defective products cost industries over $2 billion from 2012–2017. Defect detection within manufacturing is an important business use case, especially in high-value product industries like the automotive industry. This allows for early diagnosis of anomalies to improve production line efficacy and product quality, and saves capital costs. Although advanced anomaly

by Prithiviraj Jothikumar (AWS Machine Learning Blog) on October 5, 2021

Social media platforms provide a channel of communication for consumers to talk about various products, including the medications they take. For pharmaceutical companies, monitoring and effectively tracking product performance provides customer feedback about the product, which is vital to maintaining and improving patient safety. However, when an unexpected medical occurrence resulting from a pharmaceutical product

by Peter Chung (AWS Machine Learning Blog) on October 5, 2021

A continuous integration and continuous delivery (CI/CD) pipeline helps you automate steps in your machine learning (ML) applications such as data ingestion, data preparation, feature engineering, modeling training, and model deployment. A pipeline across multiple AWS accounts improves security, agility, and resilience because an AWS account provides a natural security and access boundary for your

by John Fanelli (The Official NVIDIA Blog) on October 5, 2021

NVIDIA and VMware are marking another milestone in their collaboration to develop an AI-ready enterprise platform that brings the world’s leading AI stack and optimized software to the infrastructure used by hundreds of thousands of enterprises worldwide. Today at VMworld 2021, VMware announced an upcoming update to VMware vSphere with Tanzu, the industry’s leading virtualization Read article > The post Out of the Box, Into the Container: NVIDIA and VMware Deliver AI […]

by Machine learning : nature.com subject feeds on October 5, 2021

by Katie Burke (The Official NVIDIA Blog) on October 4, 2021

Everything that moves will one day be autonomous, transforming industries and creating new business models. The NVIDIA GPU Technology Conference brings together the leaders, researchers and developers who are building this self-driving future. The virtual conference also features experts from industries transformed by AI, such as healthcare, robotics and finance. And it’s all free to Read article > The post Grab a Front Row Seat to the Autonomous Future at GTC appeared […]

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 Scott Martin (The Official NVIDIA Blog) on October 1, 2021

Robots require huge training leaps in AI to become versatile general helpers, says robotics pioneer Pieter Abbeel, who laid out a vision for such neural network brains of the future. Home robots are electronically and mechanically possible today, Abbeel said, but they lack the AI know-how to navigate a wide variety of situations. “It’s just Read article > The post AI Offers Versatile General Robots on Horizon, Says Robotics Visionary appeared first on The Official […]

by Brian Caulfield (The Official NVIDIA Blog) on October 1, 2021

It would be an exaggeration to say you’ll never again read a news article overhyping a medical breakthrough. But, thanks to researchers at the University of Copenhagen, spotting hyperbole may one day get more manageable. In a new paper, Dustin Wright and Isabelle Augenstein explain how they used NVIDIA GPUs to train an “exaggeration detection Read article > The post Exaggeration Detector Could Lead to More Accurate Health Science Journalism appeared first on The […]

by Daniel Saaristo (The Official NVIDIA Blog) on October 1, 2021

Wildfires are responsible for about 6 percent of the planet’s CO2 emissions, according to Earth System Science Data. Australia’s bushfire season of 2019-2020 alone saw over 46 million acres of land devastated by wildfire, including the loss of almost half a billion mammals, birds and reptiles. NVIDIA Inception member OroraTech is addressing these issues head-on Read article > The post Shoot From the Stars: Startup Provides Early Detection of Wildfires From Space […]

by GeForce NOW Community (The Official NVIDIA Blog) on September 30, 2021

Each week GeForce NOW deals out a ready-to-play stream of games. While this week is no exception, it is exceptional as the GeForce NOW library increases with a b-e-a-utiful collection of games from one of the leaders in gaming, Electronic Arts. Four games from the popular Battlefield, Mirror’s Edge, Unravel and Dragon Age franchises released Read article > The post GFN Thursday Really Heats Up as Electronic Arts Headlines Streaming Releases This Week appeared first on […]

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 Scott Martin (The Official NVIDIA Blog) on September 29, 2021

By day, Akira Fukabori and Kevin Kajitani worked at Japan’s largest airlines holdings company, but by evening the friends liked to develop new concepts. Last year they convinced the company’s board of directors to take a big leap on one of their ideas: robots as a service. The aerospace engineers enlisted a partner, Charith Fernando, Read article > The post On-Demand Robots: Startup Rolls Out Bot Reservation Service for Museums appeared first on The Official NVIDIA […]

by Latest Post on September 29, 2021

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

by Clarissa Garza (The Official NVIDIA Blog) on September 28, 2021

Outscanding: That’s one way to describe the groundbreaking work in AI and medical imaging from researchers at top medical centers and universities. The arrival of AI in healthcare is arguably nowhere more apparent than in radiology, where machine learning is supporting workflows side by side with caregivers every step of the way. At St. Jude Read article > The post Keeping an AI on MRIs: Industry Experts Share Latest on AI and Machine Learning in Medical Imaging […]

by Jeff Running (Microsoft Research) on September 28, 2021

Today, we are excited to announce that with our latest Turing universal language representation model (T-ULRv5), a Microsoft-created model is once again the state of the art and at the top of the Google XTREME public leaderboard. Resulting from a collaboration between the Microsoft Turing team and Microsoft Research, the 2.2 billion-parameter T-ULRv5 XL outperforms The post Microsoft Turing Universal Language Representation model, T-ULRv5, tops XTREME leaderboard and trains […]

by OpenAI (OpenAI) on September 23, 2021

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

by Alyssa Hughes (Microsoft Research) on September 23, 2021

From the intense shock of the COVID-19 pandemic to the effects of climate change, our global society has never faced greater risk. The Societal Resilience team at Microsoft Research was established in recognition of this risk and tasked with developing open technologies that enable a scalable response in times of crisis. And just as we think about scalability in a holistic way—scaling across different forms of common problems, for different partners, in […]

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 Lexie Hagen (Microsoft Research) on September 14, 2021

It is springtime in Eastern Washington, USA, and the temperature is slightly above freezing. A farmer is preparing to fertilize his fields of wheat and lentils as winter runoff and frost are nearly finished. The plants are susceptible to fertilizer at freezing temperatures, so the farmer checks forecasts from the local weather station, which is The post Micro-climate predictions: Enabling hyper-local decisions for agriculture and renewables appeared first on Microsoft […]

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 Lexie Hagen (Microsoft Research) on August 18, 2021

Today, we are proud to announce DeepSpeed MoE, a high-performance system that supports massive scale mixture of experts (MoE) models as part of the DeepSpeed optimization library. MoE models are an emerging class of sparsely activated models that have sublinear compute costs with respect to their parameters. For example, the Switch Transformer consists of 1.6 The post DeepSpeed powers 8x larger MoE model training with high performance appeared first on Microsoft Research.

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 Alyssa Hughes (Microsoft Research) on August 12, 2021

In this episode of The New Future of Work series, Chief Scientist Jaime Teevan and Senior Principal Researcher Siddharth Suri explore the many ways people were impacted by work shifts during the COVID-19 pandemic. They talk about how race, gender, income, and other factors are indicative of how people have fared and what this means for the future of work. The researchers discuss the importance of examining potential hidden consequences—and […]

by Lexie Hagen (Microsoft Research) on August 11, 2021

Since the computing world began embracing an open-source approach to programming, building software has become increasingly collaborative. Members of development teams with as few as two developers and as many as thousands are simultaneously editing different components in creating software systems and keeping them functioning optimally, and a three-way merge is the mechanism for integrating The post Safe program merges at scale: A grand challenge for program repair […]

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 (AI) on July 28, 2021

Between 2020 and 2050, Africa’s population is expected to double, adding 950 million more people to its urban areas alone. However, according to 2018 figures, a scarcity of affordable housing in many African cities has forced over half of the city dwellers in Sub-Saharan Africa to live in informal settlements. And in rural areas, many also occupy makeshift structures due to widespread poverty. These shelters have remained largely undetectable using traditional monitoring […]

by (AI) on July 28, 2021

In 2019, our Quantum AI team achieved a beyond-classical computation by outperforming the world’s fastest classical computer. Today, a quantum processor from the Sycamore generation that accomplished this important computing milestone will be donated to the Deutsches Museum of Masterpieces of Science and Technology in Munich, Germany. The Deutsches Museum has one of the largest collections of science and technology artifacts in the world. This means that the Sycamore will […]

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 (AI) on July 8, 2021

As people searched for the latest information on COVID-19 last year, including school reopenings and travel restrictions, the BBC recognized they needed to find new ways of bringing their journalism to their audiences. They released a new online tool, the BBC Corona Bot, which uses artificial intelligence to draw on BBC News’ explanatory journalism. It responds with an answer to a reader’s specific question where possible, or points to health authorities’ websites when […]

by (AI) on July 8, 2021

When I was growing up in Brisbane, Aussie Rules football wasn’t offered as a school sport – and there weren’t any professional female role models to look up to and learn from. Despite these limitations, we got resourceful. We organized football games in our lunch breaks with friends, using soccer or rugby goal posts and adding sticks or cones to serve as point posts. We practised accuracy using rubbish bins as targets.A decade later, women have truly made their mark in […]

by Michael Mui (Machine Learning – 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 (AI) on July 7, 2021

Back in the day, there was a surefire way to tell humans and computers apart: You’d present a picture of a four-legged friend and ask if it was a cat or dog. A computer couldn’t identify felines from canines, but we humans could answer with doggone confidence. That all changed about a decade ago thanks to leaps in computer vision and machine learning – specifically,  major advancements in neural networks, which can train computers to learn in a way similar to humans. […]

by (AI) on June 30, 2021

Over the past year, responsibly developed AI has transformed health screenings, supportedfact-checking to battle misinformation and save lives, predicted Covid-19 cases to support public health, and protected wildlife after bushfires. Developing AI in a way that gets it right for everyone requires openness, transparency, and a clear focus on understanding the societal implications. That is why we were among the first companies to develop and publish AI Principles and why, […]

by (AI) on June 29, 2021

Have you ever noticed that the word art is embedded in the phrase artificial intelligence? Neither did we, but when the opportunity presented itself to explore how artificial intelligence (AI) inspires artistic expression — with the help of internationally renowned Canadian artist Douglas Coupland — the Google Research team jumped on it. This collaboration, with the support of Google Arts & Culture, culminated in a project called Slogans for the Class of 2030, which […]

by (AI) on June 29, 2021

Over recent years, we have seen remarkable progress in AI’s ability to confront new problems and help solve old ones. Advancing these efforts was one reason we set up the Google Research India lab in 2019, with a particular emphasis on AI research that could make a positive social impact. It’s also why we've supported nonprofit organizations through the Google AI Impact Challenge.Working in partnership with Google.org and Google’s University Relations program, our goal […]

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 (AI) on May 21, 2021

Cartier is legendary in the world of luxury — a name that is synonymous with iconic jewelry and watches, timeless design,  savoir-faire and exceptional customer service. Maison Cartier’s collection dates back to the opening of Louis-François Cartier’s very first Paris workshop in 1847. And with over 174 years of history, the Maison’s catalog is extensive, with over a thousand wristwatches, some with only slight variations between them. Finding specific models, or […]

by (AI) on May 20, 2021

AI is integral to so much of the work we do at Google. Fundamental advances in computing are helping us confront some of the greatest challenges of this century, like climate change. Meanwhile, AI is also powering updates across our products, including Search, Maps and Photos — demonstrating how machine learning can improve your life in both big and small ways. In case you missed it, here are some of the AI-powered updates we announced at Google I/O.LaMDA is a […]

by (AI) on May 19, 2021

Originally, Maysam Moussalem dreamed of being an architect. “When I was 10, I looked up to see the Art Nouveau dome over the Galeries Lafayette in Paris, and I knew I wanted to make things like that,” she says. “Growing up between Austin, Paris, Beirut and Istanbul just fed my love of architecture.” But she found herself often talking to her father, a computer science (CS) professor, about what she wanted in a career. "I always loved art and science and I wanted to […]

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 OpenAI (OpenAI) on March 25, 2021

Over 300 applications are delivering GPT-3–powered search, conversation, text completion, and other advanced AI features through our API.

by OpenAI (OpenAI) on March 4, 2021

We’ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually.

by OpenAI (OpenAI) on January 25, 2021

We've scaled Kubernetes clusters to 7,500 nodes, producing a scalable infrastructure for large models like GPT-3, CLIP, and DALL·E, but also for rapid small-scale iterative research such as Scaling Laws for Neural Language Models. Scaling a single Kubernetes cluster to this size is rarely done

by OpenAI (OpenAI) on January 5, 2021

We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language.

by OpenAI (OpenAI) on January 5, 2021

We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision.

by OpenAI (OpenAI) on December 29, 2020

It’s been a year of dramatic change and growth at OpenAI. In May, we introduced GPT-3—the most powerful language model to date—and soon afterward launched our first commercial product, an API to safely access artificial intelligence models using simple, natural-language prompts. We’re

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 OpenAI (OpenAI) on December 5, 2020

Live demos and discussions at our virtual booth.

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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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

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

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 Lezhi Li (Machine Learning – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 – 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 […]

by Latest Post on January 26, 2018

Consider the simple task of going shopping for your groceries. If you fail to pick-up an item that is on your list, what does it tell us about the functioning of your brain? It might indicate that you have difficulty shifting your attention from object to object while searching for the item on your list. It might indicate a difficulty with remembering the grocery list. Or it could it be something to do with executing both skills simultaneously.

by Latest Post on January 17, 2018

As AI systems start to play an increasing role in the real world it is important to understand how different systems will interact with one another.In our latest paper, published in the journal Scientific Reports, we use a branch of game theory to shed light on this problem. In particular, we examine how two intelligent systems behave and respond in a particular type of situation known as an asymmetric game, which include Leduc poker and various board games such as Scotland […]

by Latest Post on December 21, 2017

In July, the world number one Go player Ke Jie spoke after a streak of 20 wins. It was two months after he had played AlphaGo at the Future of Go Summit in Wuzhen, China.After my match against AlphaGo, I fundamentally reconsidered the game, and now I can see that this reflection has helped me greatly, he said. I hope all Go players can contemplate AlphaGos understanding of the game and style of thinking, all of which is deeply meaningful. Although I lost, I discovered that […]

by Latest Post on December 19, 2017

Working as a doctor in the NHS for over 10 years, I felt that I had developed good understanding of how patients and their families felt when faced with an upsetting diagnosis or important health decision. I had been lucky with my own health, having only spent one night in hospital for what ended up being a false alarm. But when my son was born prematurely two years ago, I had a glimpse into what being on the other side feels like - an experience that has profoundly shaped […]

by Latest Post on December 1, 2017

Between 04-09 December, thousands of researchers and experts will gather for the Thirty-first Annual Conference on Neural Information Processing Systems (NIPS) in Long Beach, California.Here you will find an overview of the papers DeepMind researchers will present.

by Latest Post on November 29, 2017

One of the questions Im most often asked about Streams, our secure mobile healthcare app, is why is DeepMind making something that doesnt use artificial intelligence?Its a fair question to ask of an artificial intelligence (AI) company. When we first started thinking about working in healthcare, our natural focus was on AI and how it could be used to help the NHS and its patients. We see huge potential for AI to revolutionise our understanding of diseases - how they develop […]

by Latest Post on November 28, 2017

As AI systems become more general and more useful in the real world, ensuring they behave safely will become even more important. To date, the majority of technical AI safety research has focused on developing a theoretical understanding about the nature and causes of unsafe behaviour. Our new paper builds on a recent shift towards empirical testing (see Concrete Problems in AI Safety) and introduces a selection of simple reinforcement learning environments designed […]

by Latest Post on November 27, 2017

Neural networks have shown great success in everything from playing Go and Atari games to image recognition and language translation. But often overlooked is that the success of a neural network at a particular application is often determined by a series of choices made at the start of the research, including what type of network to use and the data and method used to train it. Currently, these choices - known as hyperparameters - are chosen through experience, random search […]

by Latest Post on November 24, 2017

We founded DeepMind Health to develop technologies that could help address some of societys toughest challenges. So were very excited to announce that our latest research partnership will focus on breast cancer.Well be working with a group of leading research institutions, led by the Cancer Research UK Centre at Imperial College London, and alongside the AI health research team at Google, to determine if cutting-edge machine learning technology could help improve the […]

by Latest Post on November 22, 2017

In October we announced that our state-of-the-art speech synthesis model WaveNet was being used to generate realistic-sounding voices for the Google Assistant globally in Japanese and the US English. This production model - known as parallel WaveNet - is more than 1000 times faster than the original and also capable of creating higher quality audio.Our latest paper introduces details of the new model and the probability density distillation technique we developed to allow […]

by Latest Post on November 10, 2017

[Editors note: this is the first in a series of blog posts about what weve learned about working in healthcare. Its both exceptionally hard and exceptionally important to get right, and we hope that by sharing our experiences well help other health innovators along the way]In our design studio, we have Indi Youngs mantra on the wall as a reminder to fall in love with the problem, not the solution. Nowhere is this more true than in health, where there are so many real […]

by Latest Post on November 5, 2017

Were excited to announce that weve agreed a five year partnership with Yeovil District Hospital NHS Foundation Trust. Well be providing them with Streams, our secure mobile app that helps nurses and doctors access important clinical information and get the right care to the right patient as quickly as possible.This will be our fourth Streams partnership, following on from our work with Taunton and Somerset NHS Foundation Trust, Imperial College Healthcare NHS Trust and the […]

by Latest Post on October 18, 2017

Artificial intelligence research has made rapid progress in a wide variety of domains from speech recognition and image classification to genomics and drug discovery. In many cases, these are specialist systems that leverage enormous amounts of human expertise and data.However, for some problems this human knowledge may be too expensive, too unreliable or simply unavailable. As a result, a long-standing ambition of AI research is to bypass this step, creating algorithms that […]

by Latest Post on October 6, 2017

(French translation below)Three months ago we announced the opening of DeepMinds first ever international AI research laboratory in Edmonton, Canada. Today, we are thrilled to announce that we are strengthening our commitment to the Canadian AI community with the opening of a DeepMind office in Montreal, in close collaboration with McGill University.Opening a second office is a natural next step for us in Canada, a country that is globally recognised as a leader in […]

by Latest Post on October 4, 2017

Just over a year ago we presented WaveNet, a new deep neural network for generating raw audio waveforms that is capable of producing better and more realistic-sounding speech than existing techniques. At that time, the model was a research prototype and was too computationally intensive to work in consumer products. But over the last 12 months we have worked hard to significantly improve both the speed and quality of our model and today we are proud to announce that an […]