ML and AI Research

  • Orchestrate XGBoost ML Pipelines with Amazon Managed Workflows for Apache Airflow
    by Justin Leto (AWS Machine Learning Blog) on July 28, 2021

    The ability to scale machine learning operations (MLOps) at an enterprise is quickly becoming a competitive advantage in the modern economy. When firms started dabbling in ML, only the highest priority use cases were the focus. Businesses are now demanding more from ML practitioners: more intelligent features, delivered faster, and continually maintained over time. An

  • Using AI to map Africa’s buildings
    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 […]

  • Introducing Triton: Open-Source GPU Programming for Neural Networks
    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

  • An AI a Day Keeps Dr.Fill at Play: Matt Ginsberg on Building GPU-Powered Crossword Solver
    by Clarissa Garza (The Official NVIDIA Blog) on July 28, 2021

    9 Down, 14 letters: Someone skilled in creating and solving crossword puzzles. This April, the fastest “cruciverbalist” at the ​​American Crossword Puzzle Tournament was Dr.Fill, a crossword puzzle-solving AI program created by Matt Ginsberg. Dr.Fill perfectly solved the championship puzzle in 49 seconds. The first human champion, Tyler Hinman, filled the 15×15 crossword in exactly Read article > The post An AI a Day Keeps Dr.Fill at Play: Matt Ginsberg on […]

  • Our quantum processor at the Deutsches Museum
    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 […]

  • How Was NVIDIA’s 2021 GTC Keynote Made? Step Inside Our Kitchen Aug. 11 to Find Out
    by Brian Caulfield (The Official NVIDIA Blog) on July 27, 2021

    Ever see a virtual kitchen materialize in real-time? If you caught NVIDIA CEO Jensen Huang’s keynote for our March 2021 GPU Technology Conference you’re no doubt wondering about more than a few of the presentation’s magic tricks. With the premiere of “Connecting in the Metaverse: The Making of the GTC Keynote,” Wednesday, Aug. 11, at Read article > The post How Was NVIDIA’s 2021 GTC Keynote Made? Step Inside Our Kitchen Aug. 11 to Find Out appeared first on […]

  • Announcing specialized support for extracting data from invoices and receipts using Amazon Textract
    by Dhawalkumar Patel (AWS Machine Learning Blog) on July 27, 2021

    Receipts and invoices are documents that are critical to small and medium businesses (SMBs), startups, and enterprises for managing their accounts payable processes. These types of documents are difficult to process at scale because they follow no set design rules, yet any individual customer encounters thousands of distinct types of these documents. In this post,

  • Detect small shapes and objects within your images using Amazon Rekognition Custom Labels
    by Alexa Giftopoulos (AWS Machine Learning Blog) on July 27, 2021

    There are multiple scenarios in which you may want to use computer vision to detect small objects or symbols within a given image. Whether it’s detecting company logos on grocery store shelves to manage inventory, detecting informative symbols on documents, or evaluating survey or quiz documents that contain checkmarks or shaded circles, the size ratio

  • Generally capable agents emerge from open-ended play
    on July 27, 2021

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

  • Bring your own container to project model accuracy drift with Amazon SageMaker Model Monitor
    by Vinay Hanumaiah (AWS Machine Learning Blog) on July 26, 2021

    The world we live in is constantly changing, and so is the data that is collected to build models. One of the problems that is often seen in production environments is that the deployed model doesn’t behave the same way as it did during the training phase. This concept is generally called data drift or

  • Detect defects and augment predictions using Amazon Lookout for Vision and Amazon A2I
    by Dennis Thurmon (AWS Machine Learning Blog) on July 23, 2021

    With machine learning (ML), more powerful technologies have become available that can automate the task of detecting visual anomalies in a product. However, implementing such ML solutions is time-consuming and expensive because it involves managing and setting up complex infrastructure and having the right ML skills. Furthermore, ML applications need human oversight to ensure accuracy

  • Automate annotation of image training data with Amazon Rekognition
    by Samantha Finley (AWS Machine Learning Blog) on July 23, 2021

    Every machine learning (ML) model demands data to train it. If your model isn’t predicting Titanic survival or iris species, then acquiring a dataset might be one of the most time-consuming parts of your model-building process—second only to data cleaning. What data cleaning looks like varies from dataset to dataset. For example, the following is

  • Simplify patient care with a custom voice assistant using Amazon Lex V2
    by David Qiu (AWS Machine Learning Blog) on July 22, 2021

    For the past few decades, physician burnout has been a challenge in the healthcare industry. Although patient interaction and diagnosis are critical aspects of a physician’s job, administrative tasks are equally taxing and time-consuming. Physicians and clinicians must keep a detailed medical record for each patient. That record is stored in the hospital electronic health

  • On infinitely wide neural networks that exhibit feature learning
    by Lexie Hagen (Microsoft Research) on July 22, 2021

    In the pursuit of learning about fundamentals of the natural world, scientists have had success with coming at discoveries from both a bottom-up and top-down approach. Neuroscience is a great example of the former. Spanish anatomist Santiago Ramón y Cajal discovered the neuron in the late 19th century. While scientists’ understanding of these building blocks The post On infinitely wide neural networks that exhibit feature learning appeared first on Microsoft Research.

  • TC Energy builds an intelligent document processing workflow to process over 20 million images with Amazon AI
    by Paul Ngo (AWS Machine Learning Blog) on July 22, 2021

    This is a guest post authored by Paul Ngo, US Gas Technical and Operational Services Data Team Lead at TC Energy. TC Energy operates a network of pipelines, including 57,900 miles of natural gas and 3,000 miles of oil and liquid pipelines, throughout North America. TC Energy enables a stable network of natural gas and

  • GFN Thursday Slays with ‘Orcs Must Die! 3’ Coming to GeForce NOW
    by GeForce NOW Community (The Official NVIDIA Blog) on July 22, 2021

    This GFN Thursday brings in hordes of fun — and a whole lot of orcs. Orcs Must Die! 3, the newest title from the action-packed, orc-slaying series from Robot Entertainment, is joining the GeForce NOW library when it releases tomorrow, Friday, July 23.   In addition, 10 more games are coming to the service this Read article > The post GFN Thursday Slays with ‘Orcs Must Die! 3’ Coming to GeForce NOW appeared first on The Official NVIDIA Blog.

  • Putting the power of AlphaFold into the world’s hands
    on July 22, 2021

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

  • Simplify data annotation and model training tasks with Amazon Rekognition Custom Labels
    by Sherry Ding (AWS Machine Learning Blog) on July 21, 2021

    For a supervised machine learning (ML) problem, labels are values expected to be learned and predicted by a model. To obtain accurate labels, ML practitioners can either record them in real time or conduct offline data annotation, which are activities that assign labels to the dataset based on human intelligence. However, manual dataset annotation can

  • Smart city traffic anomaly detection using Amazon Lookout for Metrics and Amazon Kinesis Data Analytics Studio
    by Ajay Ravindranathan (AWS Machine Learning Blog) on July 21, 2021

    Cities across the world are transforming their public services infrastructure with the mission of enhancing the quality of life of its residents. Roads and traffic management systems are part of the central nervous system of every city. They need intelligent monitoring and automation in order to prevent substantial productivity loss and in extreme cases life-threatening

  • Lecture series aims to help spur dialogue around race and technology
    by Lexie Hagen (Microsoft Research) on July 21, 2021

    In November, NYU media professor Charlton McIlwain joined fellow scholars Safiya Noble, Ruha Benjamin, and André Brock for a virtual discussion on anti-Blackness and technology hosted by the University of California Santa Barbara. The conversation was an engaging one, and McIlwain distinctly recalls the last question of the event: what is your vision for the The post Lecture series aims to help spur dialogue around race and technology appeared first on Microsoft Research.

  • Shopping Smart: AiFi Using AI to Spark a Retail Renaissance
    by Brian Caulfield (The Official NVIDIA Blog) on July 21, 2021

    Walk into a store. Grab your stuff. And walk right out again, without stopping to check out. In just the past three months, California-based AiFi has helped Choice Market increase sales at one of its Denver stores by 20 percent among customers who opted to skip the checkout line. It allowed Żabka, a Polish convenience Read article > The post Shopping Smart: AiFi Using AI to Spark a Retail Renaissance appeared first on The Official NVIDIA Blog.

  • Use Amazon SageMaker Feature Store in a Java environment
    by Ivan Cui (AWS Machine Learning Blog) on July 20, 2021

    Feature engineering is a process of applying transformations on raw data that a machine learning (ML) model can use. As an organization scales, this process is typically repeated by multiple teams that use the same features for different ML solutions. Because of this, organizations are forced to develop their own feature management system. Additionally, you

  • Prepare and clean your data for Amazon Forecast
    by Murat Balkan (AWS Machine Learning Blog) on July 20, 2021

    You might use traditional methods to forecast future business outcomes, but these traditional methods are often not flexible enough to account for varying factors, such as weather or promotions, outside of the traditional time series data considered. With the advancement of machine learning (ML) and the elasticity that the AWS Cloud brings, you can now

  • Use contextual information and third party data to improve your recommendations
    by Angel Goni Oramas (AWS Machine Learning Blog) on July 20, 2021

    Have you noticed that your shopping preferences are influenced by the weather? For example, on hot days would you rather drink a lemonade vs. a hot coffee? Customers from consumer-packaged goods (CPG) and retail industries wanted to better understand how weather conditions like temperature and rain can be used to provide better purchase suggestions to

  • Just What You’re Looking For: Recommender Team Suggests Winning Strategies
    by Rick Merritt (The Official NVIDIA Blog) on July 20, 2021

    The final push for the hat trick came down to the wire. Five minutes before the deadline, the team submitted work in its third and hardest data science competition of the year in recommendation systems. Called RecSys, it’s a relatively new branch of computer science that’s spawned one of the most widely used applications in Read article > The post Just What You’re Looking For: Recommender Team Suggests Winning Strategies appeared first on The Official NVIDIA Blog.

  • Machine learning, molecular simulation, and the opportunity for societal good with Chris Bishop and Max Welling
    by Alyssa Hughes (Microsoft Research) on July 20, 2021

    Episode 129 | July 20, 2021 - Unlocking the challenge of molecular simulation has the potential to yield significant breakthroughs in how we tackle such societal issues as climate change, drug discovery, and the treatment of disease, and Microsoft is ramping up its efforts in the space. In this episode, Chris Bishop, Lab Director of Microsoft Research Cambridge, welcomes renowned machine learning researcher Max Welling to the Microsoft Research team as head of the new […]

  • Automate continuous model improvement with Amazon Rekognition Custom Labels and Amazon A2I: Part 2
    by Les Chan (AWS Machine Learning Blog) on July 19, 2021

    In Part 1 of this series, we walk through a continuous model improvement machine learning (ML) workflow with Amazon Rekognition Custom Labels and Amazon Augmented AI (Amazon A2I). We explained how we use AWS Step Functions to orchestrate model training and deployment, and custom label detection backed by a human labeling private workforce. We described

  • Automate continuous model improvement with Amazon Rekognition Custom Labels and Amazon A2I: Part 1
    by Les Chan (AWS Machine Learning Blog) on July 19, 2021

    If you need to integrate image analysis into your business process to detect objects or scenes unique to your business domain, you need to build your own custom machine learning (ML) model. Building a custom model requires advanced ML expertise and can be a technical challenge if you have limited ML knowledge. Because model performance

  • Unlock information in unstructured text to personalize product and content recommendations with Amazon Personalize
    by James Jory (AWS Machine Learning Blog) on July 19, 2021

    Amazon Personalize now enables you to tap into the information trapped in product descriptions, product reviews, movie synopses, or other unstructured text and use it when generating personalized recommendations. Product descriptions provide important information about the features and benefits of products. Amazon Personalize can use the investments made to create these narratives to increase the

  • Project Arno: How Microsoft Research created the technology and industry momentum for Azure to empower telecom operators in the cloud
    by Alexis Hagen (Microsoft Research) on July 19, 2021

    Editor’s note: In recent years, telecommunications operators have faced a growing challenge to meet surging global demand for immersive online services and collaboration tools. Upgrading their proprietary networks to prepare for 5G and beyond would require major capital expenditures, even as competition was driving down the prices they could charge. Cloud computing offered an inevitable The post Project Arno: How Microsoft Research created the technology and industry […]

  • From Concept to Credits, Faster: NVIDIA Studio Ecosystem Improves Game Creation With RTX-Acceleration and AI
    by Stanley Tack (The Official NVIDIA Blog) on July 19, 2021

    Top game artists, producers, developers and designers are coming together this week for the annual Game Developers Conference. As they exchange ideas, educate and inspire each other, the NVIDIA Studio ecosystem of RTX-accelerated apps, hardware and drivers is helping advance their craft. GDC 2021 marks a major leap in game development with NVIDIA RTX technology Read article > The post From Concept to Credits, Faster: NVIDIA Studio Ecosystem Improves Game Creation With […]

  • Arm Is RTX ON! World’s Most Widely Used CPU Architecture Meets Real-Time Ray Tracing, DLSS
    by Brian Burke (The Official NVIDIA Blog) on July 19, 2021

    A pair of new demos running GeForce RTX technologies on the Arm platform unveiled by NVIDIA today show how advanced graphics can be extended to a broader, more power-efficient set of devices. The two demos, shown at this week’s Game Developers Conference, included Wolfenstein: Youngblood from Bethesda Softworks and MachineGames, as well as The Bistro Read article > The post Arm Is RTX ON! World’s Most Widely Used CPU Architecture Meets Real-Time Ray Tracing, DLSS […]

  • NVIDIA CEO Awarded Lifetime Achievement Accolade by Asian American Engineer of the Year
    by Isha Salian (The Official NVIDIA Blog) on July 18, 2021

    NVIDIA CEO Jensen Huang was today conferred the Distinguished Lifetime Achievement Award by Asian American Engineer of the Year, an annual event that recognizes outstanding Asian American scientists, engineers and role models. In a virtual ceremony, Huang was awarded for his contributions as “a visionary and innovator in parallel computing technology that accelerates the realization Read article > The post NVIDIA CEO Awarded Lifetime Achievement Accolade by Asian […]

  • Unlock patient data insights using Amazon HealthLake
    by Dr. Taha A. Kass-Hout (AWS Machine Learning Blog) on July 16, 2021

    AWS just announced the General Availability of Amazon HealthLake, a HIPAA-eligible service for healthcare providers, health insurance companies, and pharmaceutical companies to securely store, transform, query, analyze, and share health data in the cloud at petabyte scale. We believe that the combination of the innovation trends in healthcare (such as reimbursement models around data-driven evidence),

  • How MEDHOST is migrating electronic health record data to AWS for compliance and gaining valuable insights
    by Daniel Ness (AWS Machine Learning Blog) on July 16, 2021

    Healthcare technology companies often turn to AWS to help them accelerate their clinical and business objectives. MEDHOST has provided enterprise information technology and electronic health record (EHR) solutions to full-service community hospitals for more than 35 years. Today, more than 1,000 healthcare facilities are partnering with MEDHOST and enhancing their patient care and operational excellence

  • Simplify secure search solutions with Amazon Kendra’s Principal Store
    by Abhinav Jawadekar (AWS Machine Learning Blog) on July 15, 2021

    For many enterprises, critical business information is often stored as unstructured data scattered across multiple content repositories. It is challenging for organizations to make this information available to users when they need it. It is also difficult to do so securely so that relevant information is available to the right users or user groups. Different

  • Announcing the InterSystems HealthShare Message Transformation Service for Amazon HealthLake
    by Todd Sylvester (AWS Machine Learning Blog) on July 15, 2021

    Amazon HealthLake is a new HIPAA-eligible service designed to store, transform, query, and analyze health data at scale. Amazon HealthLake removes the heavy lifting of organizing, indexing, and structuring patient information to provide a complete view of the health of individual patients and entire patient populations in a secure, compliant, and auditable manner. With the

  • A Sparkle in Their AIs: Students Worldwide Rev Robots with Jetson Nano
    by Kalyan Meher Vadrevu (The Official NVIDIA Blog) on July 15, 2021

    Every teacher has a story about the moment a light switched on for one of their students. David Tseng recalls a high school senior in Taipei excited at a summer camp to see a robot respond instantly when she updated her software. After class, she had a lot of questions and later built an AI-powered Read article > The post A Sparkle in Their AIs: Students Worldwide Rev Robots with Jetson Nano appeared first on The Official NVIDIA Blog.

  • A Grand Slam: GFN Thursday Scores 1,000th PC Game, Streaming Instantly on GeForce NOW
    by GeForce NOW Community (The Official NVIDIA Blog) on July 15, 2021

    This GFN Thursday marks a new millennium for GeForce NOW. By adding 13 games this week, our cloud game-streaming service now offers members instant access to 1,000 PC games. That’s 1,000 games that members can stream instantly to underpowered PCs, Macs, Chromebooks, SHIELD TVs, Android devices, iPhones and iPads. Devices that otherwise wouldn’t dream of Read article > The post A Grand Slam: GFN Thursday Scores 1,000th PC Game, Streaming Instantly on GeForce NOW […]

  • New Future of Work: How developer collaboration and productivity are changing in a hybrid work model
    by Alyssa Hughes (Microsoft Research) on July 14, 2021

    Episode 128 | July 14, 2021 - In this episode of The New Future of Work series, Chief Scientist Jaime Teevan and Principal Productivity Engineer Brian Houck discuss what the massive shift to remote work meant for developers—both employees of Microsoft and customers using Microsoft developer platforms to support their work. They’ll talk about how taking a holistic approach to developer productivity can benefit both efficiency and happiness, with an emphasis on the […]

  • NVIDIA’s Liila Torabi Talks the New Era of Robotics Through Isaac Sim
    by Clarissa Garza (The Official NVIDIA Blog) on July 14, 2021

    Robots are not just limited to the assembly line. At NVIDIA, Liila Torabi works on making the next generation of robotics possible. Torabi is the senior product manager for Isaac Sim, a robotics and AI simulation platform powered by NVIDIA Omniverse. Torabi spoke with NVIDIA AI Podcast host Noah Kravitz about the new era of Read article > The post NVIDIA’s Liila Torabi Talks the New Era of Robotics Through Isaac Sim appeared first on The Official NVIDIA Blog.

  • AutoX Unveils Full Self-Driving System Powered by NVIDIA DRIVE
    by Katie Burke (The Official NVIDIA Blog) on July 12, 2021

    Your robotaxi is arriving soon. Self-driving startup AutoX last week took the wraps off its “Gen5” self-driving system. The autonomous driving platform, which is specifically designed for robotaxis, uses NVIDIA DRIVE automotive-grade GPUs to reach up to 2,200 trillion operations per second (TOPS) of AI compute performance. In January, AutoX launched a commercial robotaxi system Read article > The post AutoX Unveils Full Self-Driving System Powered by NVIDIA DRIVE […]

  • NVIDIA and Palo Alto Networks Boost Cyber Defenses with DPU Acceleration
    by Ash Bhalgat (The Official NVIDIA Blog) on July 12, 2021

    Cybercrime cost the American public more than $4 billion in reported losses over the course of 2020, according to the FBI. To stay ahead of emerging threats, Palo Alto Networks, a global cybersecurity leader, has developed the first virtual next-generation firewall (NGFW) designed to be accelerated by NVIDIA’s BlueField data processing unit (DPU). The DPU Read article > The post NVIDIA and Palo Alto Networks Boost Cyber Defenses with DPU Acceleration appeared first on […]

  • Tongues Untied: Dataset Starts Global Dialogue in Conversational AI
    by Jane Polak Scowcroft (The Official NVIDIA Blog) on July 9, 2021

    A startup in East Africa is harnessing conversational AI to get the word out about a third wave of COVID-19 passing through the region. It hopes its Mbaza AI Chatbot will lead to partnerships that use the technology to tackle other concerns across the continent’s many languages. “COVID is here to stay, unfortunately, and it’s Read article > The post Tongues Untied: Dataset Starts Global Dialogue in Conversational AI appeared first on The Official NVIDIA Blog.

  • Math Teaches Math: Researchers Tap AI to Boost Classroom Discussions
    by Scott Martin (The Official NVIDIA Blog) on July 8, 2021

    U.S. math students still aren’t making the grade on the world’s stage, but a team of researchers backed by the National Science Foundation is putting AI to the test for improving the teaching of the subject in public schools. Teachers in two Colorado school districts in the spring started pilot tests with AI for analyzing Read article > The post Math Teaches Math: Researchers Tap AI to Boost Classroom Discussions appeared first on The Official NVIDIA Blog.

  • Squad Up for GFN Thursday with 400+ Multiplayer PC Games, Including 3 Added This Week
    by GeForce NOW Community (The Official NVIDIA Blog) on July 8, 2021

    GFN Thursday is a weekly gaming party, and a party’s always better with friends. With more than 400 multiplayer PC games streaming on GeForce NOW, there’s nothing stopping gamers from joining friends in the action. Take a look at why gaming with a group has never been easier, thanks to the cloud, and the 3 Read article > The post Squad Up for GFN Thursday with 400+ Multiplayer PC Games, Including 3 Added This Week appeared first on The Official NVIDIA Blog.

  • Launching the AI Academy for small newsrooms
    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 […]

  • Kick like a pro with Footy Skills Lab
    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 […]

  • Elastic Distributed Training with XGBoost on Ray
    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.

  • Ask a Techspert: What’s a neural network?
    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. […]

  • New Future of Work: Staying productive and happy when our office is our home with Jaime Teevan and Sonia Jaffe
    by Alyssa Hughes (Microsoft Research) on July 7, 2021

    Episode 127 | July 7, 2021 - In this episode of The New Future of Work series of the podcast, Chief Scientist Jaime Teevan and Senior Research Economist Sonia Jaffe delve into the “Personal Productivity and Well-Being” chapter of the report, beginning with why measuring productivity isn’t as easy as just observing output or counting hours worked. They also explore how people already working from home helped them better understand how people adjusted to remote work, the […]

  • NVIDIA CEO Unveils ‘First Big Bet’ on Digital Biology Revolution with UK-Based Cambridge-1
    by Craig Rhodes (The Official NVIDIA Blog) on July 7, 2021

    Introducing NVIDIA’s “first big bet” on the digital biology revolution, NVIDIA CEO Jensen Huang Wednesday unveiled Cambridge-1, a $100 million investment that promises to harness partnerships across the U.K. for breakthroughs with a “global impact.” The U.K.’s most powerful supercomputer, Cambridge-1 will advance research at AstraZeneca, GSK, King’s College London, Oxford Nanopore, and Guy’s and Read article > The post NVIDIA CEO Unveils ‘First Big […]

  • GFN Thursday Goes Full Steam Ahead: Over 700 Steam Summer Sale Games Streaming on GeForce NOW
    by GeForce NOW Community (The Official NVIDIA Blog) on July 1, 2021

    Wake up, wake up, wake up, it’s the first of the month. This first of the month is a GFN Thursday celebration. The Steam Summer Sale now has over 700 PC games on sale that are playable on GeForce NOW. And since it’s also the first GFN Thursday of the month, it’s time to check Read article > The post GFN Thursday Goes Full Steam Ahead: Over 700 Steam Summer Sale Games Streaming on GeForce NOW appeared first on The Official NVIDIA Blog.

  • An update on our progress in responsible AI innovation
    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, […]

  • New Future of Work: Meeting and collaborating in a remote and hybrid world with Jaime Teevan and Abigail Sellen
    by Alyssa Hughes (Microsoft Research) on June 30, 2021

    Episode 126 | June 29, 2021 - In this episode of The New Future of Work series of the podcast, Chief Scientist Jaime Teevan and Abigail Sellen, Deputy Lab Director at Microsoft Research Cambridge in the United Kingdom, explore the dynamics of meetings and collaborations in the context of remote work. They specifically address the difference between weak and strong ties in our professional networks and why both matter to employee and company success. They also break down the […]

  • CausalCity: Introducing a high-fidelity simulation with agency for advancing causal reasoning in machine learning
    by Alexis Hagen (Microsoft Research) on June 29, 2021

    The ability to reason about causality, and ask “what would happen if…?’’ is one property that sets human intelligence apart from artificial intelligence. Modern AI algorithms perform well on clearly defined pattern recognition tasks but fall short generalizing in the ways that human intelligence can. This often leads to unsatisfactory results on tasks that require The post CausalCity: Introducing a high-fidelity simulation with agency for advancing causal reasoning […]

  • Douglas Coupland fuses AI and art to inspire students
    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 […]

  • How we’re supporting 30 new AI for Social Good projects
    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 […]

  • Microsoft LReasoner leads the ReClor challenge on logical reasoning
    by Alexis Hagen (Microsoft Research) on June 24, 2021

    For many years AI researchers have sought to build upon traditional machine learning, which trains technology to process facts and learn from them, and develop machine reasoning, in which programs apply logic to data and solve problems – comparable to the way humans think. For a system to analyze multiple sets of logical arguments, it The post Microsoft LReasoner leads the ReClor challenge on logical reasoning appeared first on Microsoft Research.

  • New Future of Work: Driving innovation via cross-company research with Jaime Teevan and Brent Hecht
    by Alyssa Hughes (Microsoft Research) on June 24, 2021

    Episode 125 | June 24, 2021 - In this episode of The New Future of Work series of the podcast, Chief Scientist Jaime Teevan and Director of Applied Science Brent Hecht of the Experiences and Devices group in Microsoft share how an internal SharePoint document led to what they believe is the largest collection of research on the pandemic’s impact on work. They’ll discuss the role of research during times of disruption, the widening scope of productivity tools, why going […]

  • Improving Language Model Behavior by Training on a Curated Dataset
    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.

  • An update on our racial justice efforts
    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.

  • OpenAI Startup Fund
    by OpenAI (OpenAI) on May 26, 2021

    Investing in startups with big ideas about AI.

  • Finding any Cartier watch in under 3 seconds
    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 […]

  • 11 ways we're innovating with AI
    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 […]

  • Maysam Moussalem teaches Googlers human-centered AI
    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 […]

  • Google I/O 2021: Being helpful in moments that matter
    by (AI) on May 18, 2021

    It’s great to be back hosting our I/O Developers Conference this year. Pulling up to our Mountain View campus this morning, I felt a sense of normalcy for the first time in a long while. Of course, it’s not the same without our developer community here in person. COVID-19 has deeply affected our entire global community over the past year and continues to take a toll. Places such as Brazil, and my home country of India, are now going through their most difficult moments […]

  • Tackling tuberculosis screening with AI
    by (AI) on May 18, 2021

    Today we’re sharing new AI research that aims to improve screening for one of the top causes of death worldwide: tuberculosis (TB). TB infects 10 million people per year and disproportionately affects people in low-to-middle-income countries. Diagnosing TB early is difficult because its symptoms can mimic those of common respiratory diseases.Cost-effective screening, specifically chest X-rays, has been identified as one way to improve the screening process. However, […]

  • Using AI to help find answers to common skin conditions
    by (AI) on May 18, 2021

    Artificial intelligence (AI) has the potential to help clinicians care for patients and treat disease — from improving the screening process for breast cancer to helping detect tuberculosis more efficiently. When we combine these advances in AI with other technologies, like smartphone cameras, we can unlock new ways for people to stay better informed about their health, too.  Today at  I/O, we shared a preview of an AI-powered dermatology assist tool that helps you […]

  • A smoother ride and a more detailed Map thanks to AI
    by (AI) on May 18, 2021

    AI is a critical part of what makes Google Maps so helpful. With it, we’re able to map roads over 10 times faster than we could five years ago, and we can bring maps filled with useful information to virtually every corner of the world. Today, we’re giving you a behind-the-scenes look at how AI makes two of the features we announced at I/O possible.Teaching Maps to identify and forecast when people are hitting the brakesLet’s start with our routing update that helps […]

  • Unveiling our new Quantum AI campus
    by (AI) on May 18, 2021

    Within the decade, Google aims to build a useful, error-corrected quantum computer. This will accelerate solutions for some of the world’s most pressing problems, like sustainable energy and reduced emissions to feed the world’s growing population, and unlocking new scientific discoveries, like more helpful AI.To begin our journey, today we’re unveiling our new Quantum AI campus in Santa Barbara, California. This campus includes our first quantum data center, our […]

  • LaMDA: our breakthrough conversation technology
    by (AI) on May 18, 2021

    We've always had a soft spot for language at Google. Early on, we set out to translate the web. More recently, we’ve invented machine learning techniques that help us better grasp the intent of Search queries. Over time, our advances in these and other areas have made it easier and easier to organize and access the heaps of information conveyed by the written and spoken word.But there’s always room for improvement. Language is remarkably nuanced and adaptable. It can be […]

  • OpenAI Scholars 2021: Final Projects
    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

  • Advancing sports analytics through AI research
    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.

  • Game theory as an engine for large-scale data analysis
    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.

  • Woolaroo: a new tool for exploring indigenous languages
    by (AI) on May 5, 2021

    “Our dictionary doesn’t have a word for shoe” my Uncle Allan Lena said, so when kids ask him what to call it in Yugambeh, he’ll say “jinung gulli” - a foot thing.Uncle Allan Lena is a frontline worker in the battle to reteach the Yugambeh Aboriginal language to the children of southeast Queensland, Australia, where it hasn’t been spoken fluently for decades and thus is – like many other languages around the world – in danger of disappearing.  For the […]

  • Will Hurd Joins OpenAI’s Board of Directors
    by OpenAI (OpenAI) on May 4, 2021

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

  • When artists and machine intelligence come together
    by (AI) on April 29, 2021

    Throughout history, from photography to video to hypertext, artists have pushed the expressive limits of new technologies, and artificial intelligence is no exception. At I/O 2019, Google Research and Google Arts & Culture launched the Artists + Machine Intelligence Grants, providing a range of support and technical mentorship to six artists from around the globe following an open call for proposals. The inaugural grant program sought to expand the field of artists […]

  • A whale of a tale about responsibility and AI
    by (AI) on April 22, 2021

    A couple of years ago, Google AI for Social Good’s Bioacoustics team created a ML model that helps the scientific community detect the presence of humpback whale sounds using acoustic recordings. This tool, developed in partnership with the National Oceanic and Atmospheric Association, helps biologists study whale behaviors, patterns, population and potential human interactions. We realized other researchers could use this model for their work, too — it could help them […]

  • GPT-3 Powers the Next Generation of Apps
    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.

  • Multimodal Neurons in Artificial Neural Networks
    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.

  • Scaling Kubernetes to 7,500 Nodes
    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

  • DALL·E: Creating Images from Text
    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.

  • CLIP: Connecting Text and Images
    by OpenAI (OpenAI) on January 5, 2021

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

  • Organizational Update from OpenAI
    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

  • MuZero: Mastering Go, chess, shogi and Atari without rules
    on December 23, 2020

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

  • OpenAI at NeurIPS 2020
    by OpenAI (OpenAI) on December 5, 2020

    Live demos and discussions at our virtual booth.

  • Using JAX to accelerate our research
    on December 4, 2020

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

  • AlphaFold: a solution to a 50-year-old grand challenge in biology
    on November 30, 2020

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

  • Breaking down global barriers to access
    on November 5, 2020

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

  • FermiNet: Quantum Physics and Chemistry from First Principles
    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.

  • Fast reinforcement learning through the composition of behaviours
    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.

  • OpenAI Licenses GPT-3 Technology to Microsoft
    by OpenAI (OpenAI) on September 22, 2020

    OpenAI released its first commercial product back in June: an API for developers to access advanced technologies for building new applications and services. The API features a powerful general purpose language model, GPT-3, and has received tens of thousands of applications to date. In addition to offering GPT-3 and future

  • Learning to Summarize with Human Feedback
    by OpenAI (OpenAI) on September 4, 2020

    We've applied reinforcement learning from human feedback to train language models that are better at summarization. Our models generate summaries that are better than summaries from 10x larger models trained only with supervised learning. Even though we train our models on the Reddit TL;DR dataset, the same

  • Traffic prediction with advanced Graph Neural Networks
    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%.

  • OpenAI Scholars 2020: Final Projects
    by OpenAI (OpenAI) on July 9, 2020

    Our third class of OpenAI Scholars presented their final projects at virtual Demo Day, showcasing their research results from over the past five months.

  • Fiber: Distributed Computing for AI Made Simple
    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.

  • Applying for technical roles
    on June 23, 2020

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

  • Profiles in Coding: Diana Yanakiev, Uber ATG, Pittsburgh
    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.

  • Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine
    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.

  • Inside Uber ATG’s Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine Learning
    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.

  • Meta-Graph: Few-Shot Link Prediction Using Meta-Learning
    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.

  • Using AI to predict retinal disease progression
    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 […]

  • Specification gaming: the flip side of AI ingenuity
    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 […]

  • Towards understanding glasses with graph neural networks
    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 […]

  • Agent57: Outperforming the human Atari benchmark
    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.

  • Under the Hood of Uber ATG’s Machine Learning Infrastructure and Versioning Control Platform for Self-Driving Vehicles
    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.

  • Building a Backtesting Service to Measure Model Performance at Uber-scale
    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.

  • A new model and dataset for long-range memory
    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.

  • Women in Data Science at Uber: Moving the World With Data in 2020—and Beyond
    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.

  • Dopamine and temporal difference learning: A fruitful relationship between neuroscience and AI
    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.

  • AlphaFold: Using AI for scientific discovery
    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.

  • Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning
    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.

  • Uber Visualization Highlights: Displaying City Street Speed Clusters with SpeedsUp
    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.

  • Uber AI in 2019: Advancing Mobility with Artificial Intelligence
    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.

  • Using WaveNet technology to reunite speech-impaired users with their original voices
    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.

  • Learning human objectives by evaluating hypothetical behaviours
    on December 13, 2019

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

  • Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber
    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.

  • From unlikely start-up to major scientific organisation: Entering our tenth year at DeepMind
    on December 5, 2019

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

  • Announcing the 2020 Uber AI Residency
    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.

  • Strengthening the AI community
    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.

  • Advanced machine learning helps Play Store users discover personalised apps
    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.

  • AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
    on October 30, 2019

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

  • Evolving Michelangelo Model Representation for Flexibility at Scale
    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.

  • Causal Bayesian Networks: A flexible tool to enable fairer machine learning
    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.

  • DeepMind’s health team joins Google Health
    on September 18, 2019

    Heres what the future looks like for the team.

  • Science at Uber: Improving Transportation with Artificial Intelligence
    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.

  • Episode 8: Demis Hassabis - The interview
    on September 17, 2019

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

  • Three Approaches to Scaling Machine Learning with Uber Seattle Engineering
    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.

  • Science at Uber: Powering Machine Learning at Uber
    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.

  • Episode 7: Towards the future
    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.

  • Replay in biological and artificial neural networks
    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.

  • Episode 6: AI for everyone
    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.

  • Episode 5: Out of the lab
    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.

  • Advancing AI: A Conversation with Jeff Clune, Senior Research Manager at Uber
    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.

  • Episode 4: AI, Robot
    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.

  • Episode 3: Life is like a game
    on August 19, 2019

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

  • Episode 2: Go to Zero
    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.

  • Episode 1: AI and neuroscience - The virtuous circle
    on August 17, 2019

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

  • Welcome to the DeepMind podcast
    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.

  • Using machine learning to accelerate ecological research
    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 […]

  • Using AI to give doctors a 48-hour head start on life-threatening illness
    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.

  • Introducing EvoGrad: A Lightweight Library for Gradient-Based Evolution
    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.

  • Unsupervised learning: The curious pupil
    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 […]

  • Gaining Insights in a Simulated Marketplace with Machine Learning at Uber
    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.

  • No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox
    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.

  • Capture the Flag: the emergence of complex cooperative agents
    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.

  • Improving Uber’s Mapping Accuracy with CatchME
    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.

  • Identifying and eliminating bugs in learned predictive models
    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. […]

  • Accessible Machine Learning through Data Workflow Management
    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.

  • Data Science at Scale: A Conversation with Uber’s Fran Bell
    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.

  • TF-Replicator: Distributed Machine Learning for Researchers
    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 […]

  • Machine learning can boost the value of wind energy
    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 […]

  • Uber Open Source: Catching Up with Fritz Obermeyer and Noah Goodman from the Pyro Team
    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.

  • Introducing Ludwig, a Code-Free Deep Learning Toolbox
    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.

  • AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
    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 […]

  • Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber
    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.

  • POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer
    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.

  • Open Source at Uber: Meet Alex Sergeev, Horovod Project Lead
    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.

  • AlphaZero: Shedding new light on chess, shogi, and Go
    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, […]

  • AlphaFold: Using AI for scientific discovery
    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 […]

  • How to Get a Better GAN (Almost) for Free: Introducing the Metropolis-Hastings GAN
    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.

  • Collaboration at Scale: Highlights from Uber Open Summit 2018
    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.

  • Experience in AI: Uber Hires Jan Pedersen
    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.

  • NVIDIA: Accelerating Deep Learning with Uber’s Horovod
    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.

  • Scaling Streams with Google
    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 […]

  • My Journey from Working as a Fabric Weaver in Ethiopia to Becoming a Software Engineer at Uber in San Francisco
    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.

  • Predicting eye disease with Moorfields Eye Hospital
    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 […]

  • Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development
    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.

  • Applying Customer Feedback: How NLP & Deep Learning Improve Uber’s Maps
    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.

  • Open sourcing TRFL: a library of reinforcement learning building blocks
    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 […]

  • Expanding our research on breast cancer screening to Japan
    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 […]

  • Improving Driver Communication through One-Click Chat, Uber’s Smart Reply System
    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.

  • Introducing Petastorm: Uber ATG’s Data Access Library for Deep Learning
    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.

  • Preserving Outputs Precisely while Adaptively Rescaling Targets
    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, […]

  • Using AI to plan head and neck cancer treatments
    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 […]

  • Food Discovery with Uber Eats: Recommending for the Marketplace
    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.

  • Safety-first AI for autonomous data centre cooling and industrial control
    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 […]

  • A major milestone for the treatment of eye disease
    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 […]

  • Objects that Sound
    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 […]

  • Measuring abstract reasoning in neural networks
    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.

  • DeepMind papers at ICML 2018
    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 […]

  • DeepMind Health Response to Independent Reviewers' Report 2018
    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 […]

  • Neural scene representation and rendering
    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. […]

  • Royal Free London publishes findings of legal audit in use of Streams
    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 […]

  • Prefrontal cortex as a meta-reinforcement learning system
    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 […]

  • Navigating with grid-like representations in artificial agents
    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 […]

  • DeepMind, meet Android
    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:

  • DeepMind papers at ICLR 2018
    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 […]

  • Our first COO Lila Ibrahim takes DeepMind to the next level
    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 […]

  • Learning to navigate in cities without a map
    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 […]

  • Retour à Paris / A return to Paris
    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 […]

  • Learning to write programs that generate images
    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, […]

  • Understanding deep learning through neuron deletion
    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 […]

  • Stop, look and listen to the people you want to help
    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 […]

  • Learning by playing
    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 […]

  • Researching patient deterioration with the US Department of Veterans Affairs
    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. […]

  • Scalable agent architecture for distributed training
    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 […]

  • Learning explanatory rules from noisy data
    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 […]

  • Open-sourcing Psychlab
    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.

  • Game-theory insights into asymmetric multi-agent games
    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 […]

  • 2017: DeepMind's year in review
    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 […]

  • Collaborating with patients for better outcomes
    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 […]

  • DeepMind papers at NIPS 2017
    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.

  • Why doesn't Streams use AI?
    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 […]

  • Specifying AI safety problems in simple environments
    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 […]

  • Population based training of neural networks
    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 […]

  • Applying machine learning to mammography screening for breast cancer
    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 […]

  • High-fidelity speech synthesis with WaveNet
    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 […]

  • Sharing our insights from designing with clinicians
    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 […]

  • Bringing Streams to Yeovil District Hospital NHS Foundation Trust
    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 […]

  • AlphaGo Zero: Starting from scratch
    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 […]

  • Strengthening our commitment to Canadian research
    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 […]

  • WaveNet launches in the Google Assistant
    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 […]

  • Why we launched DeepMind Ethics & Society
    on October 3, 2017

    At DeepMind, were proud of the role weve played in pushing forward the science of AI, and our track record of exciting breakthroughs and major publications. We believe AI can be of extraordinary benefit to the world, but only if held to the highest ethical standards. Technology is not value neutral, and technologists must take responsibility for the ethical and social impact of their work.As history attests, technological innovation in itself is no guarantee of broader […]

  • The hippocampus as a predictive map
    on October 2, 2017

    Think about how you choose a route to work, where to move house, or even which move to make in a game like Go. All of these scenarios require you to estimate the likely future reward of your decision. This is tricky because the number of possible scenarios explodes as one peers farther and farther into the future. Understanding how we do this is a major research question in neuroscience, while building systems that can effectively predict rewards is a major focus in AI […]

  • DeepMind and Blizzard open StarCraft II as an AI research environment
    on August 9, 2017

    DeepMind's scientific mission is to push the boundaries of AI by developing systems that can learn to solve complex problems. To do this, we design agents and test their ability in a wide range of environments from the purpose-built DeepMind Lab to established games, such as Atari and Go.Testing our agents in games that are not specifically designed for AI research, and where humans play well, is crucial to benchmark agent performance. That is why we, along with our partner […]

  • DeepMind papers at ICML 2017 (part three)
    on August 4, 2017

    The final part of our three-part series that gives an overview of the papers we are presenting at the ICML 2017 Conference in Sydney, Australia.

  • DeepMind papers at ICML 2017 (part one)
    on August 4, 2017

    The first of our three-part series, which gives brief descriptions of the papers we are presenting at the ICML 2017 Conference in Sydney, Australia.