Next at Microsoft http://blogs.microsoft.com/next Fri, 16 Dec 2016 15:08:58 +0000 en-US hourly 1 https://wordpress.org/?v=4.7.1 Microsoft dataset aims to help researchers create tools to answer questions as well as people http://blogs.microsoft.com/next/2016/12/16/msmarco/ http://blogs.microsoft.com/next/2016/12/16/msmarco/#respond Fri, 16 Dec 2016 15:00:22 +0000 http://blogs.microsoft.com/next/?p=70286 Microsoft has released a set of 100,000 questions and answers that artificial intelligence researchers can use in their quest to create systems that can read and answer questions as well … Read more »

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Microsoft has released a set of 100,000 questions and answers that artificial intelligence researchers can use in their quest to create systems that can read and answer questions as well as a human.

The dataset is called MS MARCO, which stands for Microsoft MAchine Reading COmprehension, and the team behind it says it’s the most useful dataset of its kind because it is based on anonymized real-world data. By making it broadly available for free to researchers, the team is hoping to spur the kind of breakthroughs in machine reading that are already happening in image and speech recognition.

They also hope to facilitate the kind of advances that could lead to the long-term goal of ‘artificial general intelligence,’ or machines that can think like humans.

“In order to move towards artificial general intelligence, we need to take a step towards being able to read a document and understand it as well as a person,” said Rangan Majumder, a partner group program manager with Microsoft’s Bing search engine division who is leading the effort. “This is a step in that direction.”

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Right now, Majumder said, systems to answer sophisticated questions are still in their infancy. Search engines like Bing and virtual assistants like Cortana can answer basic questions, like “What day does Hanukkah start?” or “What’s 2,000 times 43?”

But in many cases, Majumder said search engines and virtual assistants will instead point the user to a set of search engine results. Users can still get the information they need, but it requires culling through the results and finding the answer on the web page.

In order to make automated question-and-answer systems better, researchers need a strong source of what is called training data. These datasets can be used to teach artificial intelligence systems to recognize questions and formulate answers and, eventually, to create systems that can come up with their own answers based on unique questions they haven’t seen before.

Majumder and his team – which includes Microsoft researchers and people working on Microsoft products – say the MS MARCO dataset is particularly useful because the questions are based on real, anonymized queries from Microsoft’s Bing search engine and Cortana virtual assistant. The team chose the anonymized questions based on the queries they thought would be more interesting to researchers. In addition, the answers were written by humans, based on real web pages, and verified for accuracy.

By providing realistic questions and answers, the researchers say they can train systems to better deal with the nuances and complexities of questions regular people actually ask – including those queries that have no clear answer or multiple possible answers.

For example, the dataset contains the question, “What foods did ancient Greeks eat?” To answer the question correctly they culled through snippets of information from multiple documents or pieces of text to come up with foods such as grains, cake, milk, olives, fish, garlic and cabbage.

Li Deng, partner research manager of Microsoft’s Deep Learning Technology Center, said previous datasets were designed with certain limitations, or constraints. That made it easier for researchers to create solutions that could be formulated as what machine learning researchers call “classification problems,” rather than by seeking to understand that actual text of the question.

Li Deng, partner research manager of Microsoft’s Deep Learning Technology Center.

Li Deng, partner research manager of Microsoft’s Deep Learning Technology Center.

He said MS MARCO is designed so that researchers can experiment with more advanced deep learning models designed to push artificial intelligence research further forward.

“Our dataset is designed not only using real-world data but also removing such constraints so that the new-generation deep learning models can understand the data first before they answer questions,” he said.

Majumder said the ability for systems to answer complex questions could augment human abilities by helping people get information more efficiently.

Let’s say a Canadian student wants to know if she qualifies for a certain loan program. A search engine might direct that user to a set of websites, where she would have to read through the data and come up with an answer on her own. With better tools, a virtual assistant could scan that information for her and quickly provide a more nuanced and perhaps even personalized answer.

“Given much of the world’s knowledge is found in a written format, if we can get machines to be able to read and understand documents as well as humans, we can unlock all of these kinds of scenarios,” Majumder said.

Long-term goal: ‘Artificial general intelligence’
For now at least, researchers are still far from creating systems that can truly understand or comprehend what humans are saying, seeing or writing – what many refer to as “artificial general intelligence.”

But in the last few years, machine learning and artificial intelligence researchers at Microsoft and elsewhere have made great strides in creating systems that can recognize the words in a conversation and correctly identify the elements of an image.

“Microsoft has led the way in speech recognition and image recognition, and now we want to lead the way in reading comprehension,” Majumder said.

But, he noted, this isn’t a problem that any one company can solve alone. Majumder said one reason his team released the dataset is because they want to work with others in the field.

MS MARCO is modeled on similar training sets that were created to help spur breakthroughs in other areas of machine learning and artificial intelligence. That includes the ImageNet database, which is considered to be the premier dataset for testing advances in image recognition. A team at Microsoft used ImageNet to test its first deep residual networks, sparking major leaps in the accuracy of image recognition.

The MS MARCO team also plans to follow ImageNet’s example by creating a leaderboard that shows which teams of researchers are getting the best results. Eventually, they may create a more formal competition along the lines of ImageNet’s annual challenges.

The MS MARCO dataset is available for free to any researcher who wants to download it and use it for non-commercial applications.

Related:

Allison Linn is a senior writer at Microsoft. Follow her on Twitter.

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Microsoft Translator erodes language barrier for in-person conversations http://blogs.microsoft.com/next/2016/12/13/microsoft-translator-erodes-language-barrier-person-conversations/ http://blogs.microsoft.com/next/2016/12/13/microsoft-translator-erodes-language-barrier-person-conversations/#respond Tue, 13 Dec 2016 19:35:21 +0000 http://blogs.microsoft.com/next/?p=58850 For James Simmonds-Read, overcoming language barriers is essential. He works at The Children’s Society in London with migrants and refugees, mostly young men who are victims of human trafficking. “They … Read more »

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For James Simmonds-Read, overcoming language barriers is essential. He works at The Children’s Society in London with migrants and refugees, mostly young men who are victims of human trafficking.

“They are all asylum seekers and a large number of them have issues around language,” he said. “Very frequently, we need to use translators.”

That has its own challenges, because it means the young men must disclose sensitive information to third-party interpreters.

Recently, The Children’s Society found a better solution. They started using the Microsoft Translator live feature, a new tool currently in preview that delivers live, in-person speech translation capabilities via Internet-connected smartphones, tablets and personal computers. The feature was introduced Tuesday at a Microsoft AI event in San Francisco.

The technology, Simmonds-Read said, allows him to communicate directly with the young men The Children’s Society serves without requiring third-party interpreters.

The in-person machine translation technology, developed by the machine translation group at Microsoft’s research lab in Redmond, Washington, also can ease communications for travelers in a foreign country who need to speak with hotel receptionists, taxi drivers and museum tour guides. Teachers can use it to communicate with parents of students who speak different languages at home.

During a pilot project in New York City, the technology helped non-English speakers apply for state-issued identification cards.

“At the end of the day, our goal is breaking the language barrier,” said Olivier Fontana, the director of product strategy for Microsoft Translator.

The technology is applicable in one-to-one conversations such as a traveler getting information from a concierge, one-to-many interactions such as a tour guide speaking to a throng of tourists, and many-to-many communications such as multiple immigrants sharing experiences in a support group.

Personal Universal Translator

The feature builds on the same Microsoft Translator speech translation technology that powers Skype Translator, a service that allows people to communicate in real time over the Internet while speaking different languages.

Skype Translator works well for people separated by geographic as well as language barriers, “but it doesn’t meet face-to-face needs,” said Arul Menezes, general manager of the Microsoft AI and Research machine translation team.

Face-to-face communication across language barriers, he and his colleagues determined, requires a technology that more closely resembles the universal translator concept popularized by Star Trek and other works of science fiction.

Instead of requiring a dedicated piece of translation hardware, the team decided to leverage mobile devices already in wide circulation.

Tanvi Surti is a program manager on the Microsoft Translator team. (Photo by Dan DeLong)

Tanvi Surti is a program manager on the Microsoft Translator team. (Photo by Dan DeLong)

“Almost everyone has a smartphone on their person,” said Tanvi Surti, a program manager on the Microsoft Translator team who is leading the development of the Microsoft Translator live feature.

The team fashioned the technology to integrate with the existing Microsoft Translator app as well as a standalone website.

“We spent a lot of time thinking about the set-up experience,” Surti noted. “Let’s say you and I speak two different languages, how do we get connected quickly without spending too much time thinking about how to connect and spending more time in the conversation itself.”

To get started, a user signs on to the service via the app or website, picks their language and initiates a new conversation. The process generates a code and a QR code that other participants enter or scan on their devices, which they set to their language of choice.

From there, conversation happens.

The speaker presses the keyboard space bar or an on-screen button in walkie-talkie-like fashion when talking. Seconds later, translated text of their spoken words appears on the screen of the other participants’ devices – in their native languages. For some languages, audible translation is also available.

Like most cutting-edge technology, it’s not perfect yet.

“ls the quality perfect? No. Is the setup totally seamless? No. But really, once you get set up, you have a universal translator experience amongst multiple people talking in multiple languages,” said Fontana.

Deep neural networks

The machine translation technology itself is powered by algorithms running in the cloud, using deep neural network-based translations, which offer more fluid, human-sounding translations than the predecessor technology known as statistical machine translation.

Arul Menezes is general manager of the Microsoft AI and Research machine translation team. (Photo by Dan DeLong)

Arul Menezes is general manager of the Microsoft AI and Research machine translation team. (Photo by Dan DeLong)

Both methods involve training algorithms on the text of previously professionally translated documents, so the system can learn how words and phrases in one language are represented in another language. The statistical method, however, is limited to translating a word within the context of one or two surrounding words, which can lead to clunky and stilted translations.

Neural networks are inspired by people’s theories about how the pattern-recognition process that occurs in the brains of multilingual humans works, leading to more natural-sounding translations.

In the non-neural world, for example, the words “cat” and “cats” are treated as distinct entities. Human brains – and neural networks – see two closely related words. The networks also can parse the distinction between June the month and the girl’s name.

“Instead of the word being a thing by itself, it is represented by a 500-dimensional vector, or basically a 500 set of numbers, and each of those numbers capture some aspect of the word,” Menezes explained.

To create a translation, neural networks model the meaning of each word within the context of the entire sentence in a 1,000-dimensional vector, whether the sentence is five or 20 words long, before translation begins. This 1,000-dimension model – not the words – is translated into the other language.

And, what’s more, the quality of the translations improves with experience, noted Fontana, who hopes to see Microsoft Translator adopted by a wide spectrum of users including travelers, tour guides, teachers and social workers.

Simmonds-Read with The Children’s Society said he can already envision multiple uses for the technology, including traveling with non-English speaking migrants and refugees to appointments with government officials and prospective employers.

“People are most isolated when they can barely communicate,” he said.

Related:

John Roach writes about Microsoft research and innovation. Follow him on Twitter.

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Microsoft researchers earn distinctions from premier computing society http://blogs.microsoft.com/next/2016/12/08/microsoft-researchers-earn-distinctions-premier-computing-society/ http://blogs.microsoft.com/next/2016/12/08/microsoft-researchers-earn-distinctions-premier-computing-society/#respond Thu, 08 Dec 2016 13:00:33 +0000 http://blogs.microsoft.com/next/?p=58769 Eight computer scientists at Microsoft research labs around the world have been honored as Fellows of the Association of Computing Machinery, the world’s largest computing society. The organization also named … Read more »

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Eight computer scientists at Microsoft research labs around the world have been honored as Fellows of the Association of Computing Machinery, the world’s largest computing society. The organization also named five Microsoft researchers to their list of Distinguished Members.

The honors recognize the individuals’ significant contributions and impact to computer science across a range of disciplines and highlight the “tremendous respect, reputation and visibility of Microsoft researchers in the external scientific and engineering community,” said Jeannette Wing, corporate vice president, Microsoft Research.

Read on for snapshots of the eight newly named fellows and five distinguished scientists and engineers.

ACM Fellows

Ricardo Bianchini

Ricardo Bianchini

Ricardo Bianchini: In the early 2000s, Ricardo Bianchini pioneered techniques to increase the operational efficiency of datacenters, the warehouse-size buildings full of servers that make up what is known today as the cloud. His research focused on techniques for managing power and energy consumption as well as regulating temperature in datacenters, which reduce operating costs and impacts to society and the environment.

Bianchini performed much of his pioneering research on datacenter management at Rutgers University, including the development of an experimental solar-powered data center. Now at Microsoft’s research lab in Redmond, Washington, he leads the Cloud Efficiency group, where his research focuses more broadly on how to improve the overall cost efficiency of online services and datacenters. “I am looking at things like improving server utilization, being smarter about resource management,” he said. “Power and energy are nothing more than resources like CPUs and memories and disks.”

Xuedong Huang

Xuedong Huang

Xuedong Huang: When Xuedong Huang entered graduate school in China in the early 1980s, he realized a limitation of personal computers: the Chinese language is difficult to type on the traditional keyboard. His solution was to create a Chinese dictation engine, thus bypassing altogether the need to type. Today, as Microsoft’s chief speech scientist, he continues to work on versions of this problem. Along the way, he has advanced the field of speech recognition to the point that computers can now recognize words in a conversation as well as humans.

Steady improvements in data availability, computing power and a subfield of artificial intelligence called machine learning have fueled recent breakthroughs in speech recognition, noted Huang. In addition to human parity in speech recognition, people who speak different languages in different parts of the world can have a real-time conversation via technologies such as Skype Translator, which is based in part on Huang’s research, bringing the world closer to real-life manifestations of science fiction concepts such as the babel fish.

Ravi Kannan

Ravi Kannan

Ravi Kannan: These days, theoretical computer scientist Ravi Kannan in Microsoft’s research lab in Bangalore, India, is focused on developing general principles for the development of machine-learning algorithms, a branch of artificial intelligence that is transforming the computer industry in applications ranging from image and speech recognition to time and resource management. The research continues a decades-long push to integrate theory with the applied side of computer science, providing a solid foundation and guiding light for the computing industry.

About 20 years ago, for example, Kannan started worked on matrix algorithms, which enable efficient computations on large datasets such as millions of webpages or years of search results. “We developed, in theory, an algorithm that is based on sampling a part of the whole matrix and computing on that,” he explained. More than selecting a random sample, the theoretical algorithm quickly finds samples that yield robust results. “We gave a way of figuring out what to pick rather quickly,” said Kannan, adding that he is happiest working on mathematical computer science theory “but I certainly benefit a lot by listening to real-world problems and trying to see what I can do about them.”

K. Rustan M. Leino

K. Rustan M. Leino

K. Rustan M. Leino: When coders make a semantic error in the verification aware programming language Dafny, the mistake is flagged with a squiggly red line like the one Microsoft Word places under questionable spelling and grammar. And just as the word processing program has helped millions of people improve their writing, Dafny is widely used at universities to help computer science students learn to properly code. The language and automated verification tool is one of several designed and built by K. Rustan M. Leino, a principal researcher in Microsoft’s research lab in Redmond.

Beyond the classroom, Leino said Dafny was used at Microsoft for writing the Ironclad and IronFleet applications that allow the secure exchange of data between a user and a remote machine. Dafny, and other tools designed by Leino, is based on Boogie, a programming language he designed for building program verification tools. “Many other verifiers have been built on top of Boogie and then they reach other communities and other languages,” he said.

Venkat Padmanabhan

Venkat Padmanabhan

Venkat Padmanabhan: In the late 1990s, Venkat Padmanabhan, who was then working in Microsoft’s research lab in Redmond, and colleagues anticipated the proliferation of radio-frequency based wireless local area networks and designed a location service, RADAR, to ride on top of these to help people navigate indoor spaces such as office buildings and shopping malls where GPS signals are unavailable. The system is based on mapping a network’s radio frequency signal strength, which is known as RF fingerprinting. The technique continues to influence academic and industry research on location-based services such as how to serve ads to smartphones carried by shoppers walking around the mall.

In a similar vein, in 2001, Padmanabhan pioneered a suite of techniques to infer geographic location from IP addresses, known as IP2Geo, that were adopted by Microsoft to serve geo-targeted ads. In 2007, working out of Microsoft’s research lab in India, he and colleagues connected an accelerometer to an early smartphone, creating an influential sensor-enabled system, called Nericell, to monitor road and traffic conditions. Padmanabhan also invented ProbeGap, which estimates available bandwidth in wireless networks – a technique, he said, that solved “a pressing problem” and has become a standard feature in the Windows operating system.

Ganesan Ramalingam

Ganesan Ramalingam

Ganesan Ramalingam: About 20 years ago, anxiety was building in the computer science world over the pending calendar rollover to the year 2000. The so-called Y2K bug was a concern because most computer programs used two digits instead of four to represent calendar years. “The coding representation would no longer be adequate and programs would start failing,” recalled Ganesan Ramalingam, who works at Microsoft’s research lab in India. Ramalingam is an expert in static program analysis, a method to identify potential runtime errors in code – computer bugs – and other useful runtime properties of interest without executing the program. One of the many tools he developed in this field searched through programs for lines of code that required modification prior to Y2K, helping the computer industry avoid a feared disaster.

In the ensuing years, Ramalingam has applied his static program analysis expertise to programs running multiple computations simultaneously, as well as on methods to automatically generate correct code to make programming easier, more enjoyable and more reliable. An area of relevance today is the cloud, which spreads out computations across multiple machines. Since any machine in the cloud could fail at any time, programs need to work correctly in the presence of failures. “I have been working on languages that guarantee fault tolerance so the programmer doesn’t have to worry about that,” he said. “They can write the code largely assuming no failure and the language implementation takes care of dealing with failures.”

Abigail Sellen: As technology becomes enmeshed with every aspect of the human experience, Abigail Sellen, deputy director and a principal researcher at Microsoft Research Cambridge in the U.K., continues to think deeply about how we use “old-fashioned” technologies such as paper. The promise of the paperless office, which she and her co-author, Richard Harper, debunked in 2003, remains unfilled. “A lot of what we said back then still holds true. We can still learn a lot about our interactions with paper and how we can build tools that support people doing knowledge work, even as these tools become more intelligent,” she said. “We are not there yet. Our technologies need to be much more flexible, tailorable and seamless.”

Sellen has spent her career researching how people do seemingly ordinary tasks, such as mark up paper documents and speak to each other around conference tables, to better design technologies that extend human capabilities and take account of cultural and social norms. In recent years, her research has focused on how to apply computer vision to medical settings, such as manipulating data during vascular surgery and assessing disease progression in patients diagnosed with multiple sclerosis. The research, she noted, highlights that acceptance of artificial intelligence systems requires trust in them. “And to build trust, they have to be understandable by the people who are using them,” Sellen said.

Sudipta Sengupta

Sudipta Sengupta

Sudipta Sengupta: In the late 2000s, solid state drives (SSDs) built with flash technology began to emerge in the marketplace as a replacement for hard drives in desktops and servers. Sudipta Sengupta, a research scientist at Microsoft’s Redmond lab, recognized the potential for the technology to be more than simply a hard disk replacement. He initiated research that refashioned software for data management to exploit the unique properties of flash. Microsoft’s search engine Bing adopted the technology to more efficiently perform services such as serving advertisements alongside search results. The research also was deployed in Microsoft’s database service Azure DocumentDB. Today, the performance benefits of optimizing software for flash is widely recognized in the computing industry.

Sengupta also has led research efforts in the field of data deduplication, which avoids unnecessary duplication of data. The field was originally focused on backup data. Sengupta developed methods to apply deduplication to primary data, such as shared files on a cloud server. His methods are a standard feature in Windows Server software. Sengupta has also designed optimization techniques and routing algorithms to achieve predictable performance in networks with highly variable and unpredictable traffic patterns, such as the cloud. He has applied his ideas on so-called oblivious routing of network traffic in the design of VL2, a modern datacenter network that has been deployed in the Microsoft cloud via Azure.

ACM Distinguished Scientists

Gang Hua

Gang Hua

Gang Hua: Every face in the crowd belongs to someone. Facial recognition research led by Gang Hua at Microsoft Research Asia in Beijing, China, employs artificial intelligence and computer vision tools such as machine learning, pattern recognition and deep neural aggregation networks to discover each face’s owner. In 2008, he initiated the first facial recognition software development kit at Microsoft, which has been adopted by several Microsoft product groups and set industry standards. In recent years, Hua’s research has focused on solving problems in computer vision with limited or unsupervised data, including techniques to enable pattern discovery from a collection of images and videos jointly instead of handling them individually.

Suman Nath

Suman Nath

Suman Nath: Sensors are everywhere – traffic cameras, radar speed signs, thermometers, rain gauges, security cameras and microphones, to name a few. In the early 2000s, Suman Nath, a principal researcher in Microsoft’s research lab in Redmond, began designing systems and algorithms to aggregate data streams from sensors deployed throughout the world and incorporate them on searchable, online real-time mapping platforms. Deployments of the SenseWeb research help users make informed decisions about what route to take to work, for example, or whether to leave the rain boots at home. His research on data management for sensing and mobile systems has led to location inference techniques that reduce battery drain on smartphones from continuous context-aware applications such as jogging trackers, as well as apps that offer unique greetings when a user is driving.

Tao Mei

Tao Mei

Tao Mei: When users type queries into search engines, many engines will automatically suggest a list of terms that may better match the users’ intent, thus leading to improved results. Tao Mei, a senior researcher at Microsoft Research Asia, pioneered similar technology for video search, called visual query suggestion, that returns a joint result of suggested text and images, allowing users to more effectively find videos of interest. Mei’s research in in multimedia content analysis, computer vision and machine learning has also led to model-free techniques for automatic annotation of video, an innovative approach to predicting video popularity in social networks and online video advertising models that automatically discover the least obtrusive and most effective spot in an online video to insert an ad. A dozen of Mei’s inventions and technologies have shipped to a wide range of Microsoft products. The recently popular chatbot Xiaolce in China uses Mei’s technology for automatic video commenting.

Yu Zheng

Yu Zheng

Yu Zheng: Can computers improve city living? Research led by Yu Zheng, research manager in the Urban Computing Group at Microsoft Research Asia, connects sensing technologies, data management and analytics models, and visualization methods to create win-win-win solutions that improve the urban environment, quality of urban life and city operating systems. For example, he has pioneered cross-domain data fusion methods to tackle challenges such as predicting air quality by fusing data from traffic and meteorology. His method outperforms traditional air-quality forecasting models by 20 percent on accuracy. Other research projects have mined data from vehicles and people to create location-based social networks, algorithms that compute similarity between individuals based on location histories, and to improve urban driving through technologies such as ridesharing applications.

ACM Distinguished Engineer

Tie-Yan Liu

Tie-Yan Liu

Tie-Yan Liu: In a world where humans expect the most relevant information on everything from the best local bookstores and dive bars to the eating habits of Tyrannosaurus rex, search engines are trusted to make the ranked lists. How do they do it? Many, including Bing, employ machine-learning algorithms and theory for learning to rank developed by Tie-Yan Liu, principal research manager at Microsoft Research Asia. Liu also has led research on large-scale machine learning tools including, in 2005, development of the largest text classifier in the world with 250,000 categories and, in 2015, the fastest and largest topic model in the world with 1 million topics. Such models have improved targeted online advertising. In addition to the distinction from ACM, Liu was named a 2017 IEEE Fellow for contributions to machine learning for web search and online advertising.

Related:

Visit the Association for Computing Machinery

John Roach writes about Microsoft research and innovation. Follow him on Twitter.

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17 for ’17: Microsoft researchers on what to expect in 2017 and 2027 http://blogs.microsoft.com/next/2016/12/05/17-17-microsoft-researchers-expect-2017-2027/ http://blogs.microsoft.com/next/2016/12/05/17-17-microsoft-researchers-expect-2017-2027/#respond Mon, 05 Dec 2016 14:00:25 +0000 http://blogs.microsoft.com/next/?p=58607 This week we are celebrating Computer Science Education Week around the globe. In this “age of acceleration,” in which advances in technology and the globalization of business are transforming entire … Read more »

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This week we are celebrating Computer Science Education Week around the globe.

In this “age of acceleration,” in which advances in technology and the globalization of business are transforming entire industries and society itself, it’s more critical than ever for everyone to be digitally literate, especially our kids.

This is particularly true for women and girls who, while representing roughly 50 percent of the world’s population, account for less than 20 percent of computer science graduates in 34 OECD countries, according to this report.

This has far-reaching societal and economic consequences.  Here’s why:

One issue sometimes cited for the dearth of women in computing fields is the lack of professional role models who could inspire girls to pursue their STEM dreams.  We’ve attempted to counteract this by asking 17 women within Microsoft’s global research organization their views on what’s likely to occur in their fields in 2017.  Since it’s prediction season, we also asked them to tell us what’s likely happen 10 years from now.

We hope you’ll enjoy their breadth of knowledge and also take a moment to share this feature on your social networks.  If that simple act of sharing inspires just one more girl or boy to pursue their STEM passions, you will have contributed to the spirit of the week.  Finally, if you believe every student in every school should have the opportunity to learn computer science, you can make your opinion known here.


kalika-bali-01-predictionsKalika Bali, researcher, India research lab
Follow her on Twitter

What will be the key advance in speech and natural language processing in 2017?
Our speech and language technology applications will be increasingly multilingual in 2017. That doesn’t just mean that we will add more languages to our offering. We will do that, but we also will have systems that understand, process and generate the language that an English-Spanish or a French-Arabic or a Hindi-English speaker uses when she effortlessly switches from one language to another, within the same conversation, chat and sometimes even within the same sentence.

What will be the key advance or topic of discussion in speech and natural language processing in 2027?
Language models will be deeply entrenched in cognitive models that enable artificial intelligence (AI) systems to reason and communicate with humans relatively effortlessly, moving between and adapting to different social situations, negotiating, debating and persuading, just as humans do all the time. Computational sociolinguistic and pragmatic models will play a big role in creating socio-culturally aware AI.


MicrosoftJennifer Chayes, distinguished scientist, managing director, New England and New York City research labs
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What will be the key advance in algorithms for machine learning in 2017?
Deep learning is transforming many aspects of our technology, however deep learning algorithms today are still largely heuristic, based on the experience and intuition of leaders in the field. In 2017, we will develop a more principled understanding of deep learning and hence more robust algorithms. The insights here will come from many fields, including the intersection of statistical physics and computer science.

What will be the key advance or topic of discussion in algorithms for machine learning in 2027?
Our lives are being enhanced tremendously by artificial intelligence and machine learning algorithms.  However, current algorithms often reproduce the discrimination and unfairness in our data and, moreover, are subject to manipulation by the input of misleading data. One of the great algorithmic advances of the next decade will be the development of algorithms which are fair, accountable and much more robust to manipulation.


Microsoft researcher Susan Dumais photographed at Building 99 in Redmond, Wash. on November 17, 2016. Photo by Dan DeLong

Susan Dumais, distinguished scientist and deputy managing director Redmond, Washington, research lab
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What will be the key advance in search and information retrieval in 2017?
Deep learning in search and information retrieval will come of age. Over the last few years, we have seen breakthroughs in speech recognition, image understanding and natural language processing, which were driven by new deep learning architectures in combination with much more data and computational power.  Next year, deep learning models will continue to improve the quality of web search results and will lead to more general improvements in document understanding and query articulation.

What will be the key advance or topic of discussion in search and information retrieval in 2027?
The search box will disappear. It will be replaced by search functionality that is more ubiquitous, embedded and contextually sensitive. We are seeing the beginnings of this transformation with spoken queries, especially in mobile and smart home settings.  This trend will accelerate with the ability to issue queries consisting of sound, images or video, and with the use of context to proactively retrieve information related to the current location, content, entities or activities without explicit queries.


sara-jane-dunn-06-predictionsSara-Jane Dunn, scientist, Cambridge, U.K., research lab
Follow her on Twitter

What will be the key advance in biological computation in 2017?
Despite the widespread use of computational and engineering analogies to ‘explain’ biology, the computation performed by cells need not, and probably does not, bear any resemblance to silicon-based computation. The key advance to come in the short term will be a theoretical foundation for understanding biological information processing, which will be fundamental as we seek to design, modify or reprogram cellular behavior.

What will be the key advance or topic of discussion in biological computation in 2027?
If we can imagine the realization of programming biology, in 10 years’ time we will be developing entirely new industries and applications in areas ranging from agriculture and medicine to energy, materials and computing. While the last 50 years were utterly transformed by the ability to program on silicon, we will be entering the next programming revolution: The era of living software.


Microsoft researcher Mar Gonzalez Franco photographed at Building 99 in Redmond, Wash. on November 14, 2016. Photo by Dan DeLong

Mar Gonzalez Franco, researcher, Redmond, MSR NExT
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What will be the key advance in virtual reality in 2017?
In 2017 we will see the emergence of virtual reality devices that feature better body tracking. A positive outcome of this will be the ability to experience embodiment of virtual avatars from a first-person perspective.

What will be the key advance or topic of virtual reality in 2027?
By 2027 we will have ubiquitous virtual reality systems that will provide such rich multisensorial experiences that will be capable of producing hallucinations which blend or alter perceived reality. Using this technology, humans will retrain, recalibrate and improve their perceptual systems. In contrast to current virtual reality systems that only stimulate visual and auditory senses, in the future the experience will expand to other sensory modalities including tactile with haptic devices.


mary-gray-08-predictionsMary L. Gray, senior researcher, New England research lab
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What will be the key advance or inflection point in social sciences in 2017?
Social scientists and computer scientists will join together to develop new methods that map and measure cultural, economic and political “filter bubbles” — online echo chambers of our friends’ news and information — and unpack how they impact people’s everyday “offline” lives.

What will be the key advance, inflection point, or topic of discussion in social sciences in 2027?
By 2027, nearly 30 percent of adults in the United States will do some form of gig work, delivering AI-driven goods and services like tax advice or healthcare support. Consumers may or may not know when they are reliant on a human worker-in-the-loop. The social sciences will play a key role in developing technologies and public policy for a new social safety net of portable benefits to meet the needs of a 21st century AI-powered labor force.


katja-hofmann-02-predictionsKatja Hofmann, researcher, Cambridge, U.K., research lab
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What will be the key advance in artificial intelligence and machine learning in 2017?
In 2017 computer games will take center stage in the development of AI. Experimentation platforms based on games, such as Project Malmo — which my team and I have developed to enable AI experimentation in Minecraft – will allow for rapid testing of new ideas. I am especially excited about the potential for collaborative AI. We are now at the point where we can start to understand how AI can learn from us and collaborate with us to help us achieve our goals.

What will be the key advance or topic of discussion in artificial intelligence and machine learning  in 2027?
AI is progressing very rapidly. It has great potential to empower people and help us tackle key global challenges. To me, the most important topic of discussion is how to ensure that by 2027 these advances and great potential translate into AI technology that results in the greatest possible benefit to society.


nicole-immorlica-04-predictionsNicole Immorlica, senior researcher, New England research lab
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What will be the key advance in economics and/or game theory in 2017?
As people face increasingly complex decisions in this era of big data, it will become difficult to make optimal choices. Economists will develop new theories about potentially sub-optimal behavior in the face of complexity, and computer scientists will develop automated machine learning tools to help people navigate these scenarios.

What will be the key advance or topic of discussion in economics and/or game theory in 2027?
By 2027, automation will give rise to a new economy in which most people’s societal contribution comes from the data they generate as they go about their lives rather than the work they do. Economists will be talking about ways to fairly compensate people for these contributions. This will most probably involve heavier redistribution of wealth through mechanisms such as taxes or social programs.


kristin-lauter-02-predictionsKristin Lauter, principal researcher, Redmond research lab
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What will be the key advance in mathematics and cryptography in 2017?
New mathematical solutions allowing for computation on encrypted data will be deployed to protect the privacy of medical and genomic data for patients and hospitals.  The new homomorphic encryption schemes will secure the data while allowing the cloud to compute on it to make useful risk predictions and provide analysis and alerts. Homomorphic encryption will be deployed soon in the financial sector to protect sensitive banking data.

What will be the key advance or topic of discussion in mathematics and cryptography in 2027?
Deep advances in mathematics will continue to be the foundation for next-generation cryptosystems.  By 2027 we will have a quantum computer that can break at least low-strength traditional cryptographic systems.  For long-term data protection, a first wave of post-quantum cryptosystems is already under development based on recently proposed hard problems in mathematics.  With advances in mathematical techniques and algorithms, in 10 years we will see a second wave of post-quantum cryptographic solutions with new proposals and attacks on existing proposals.


Microsoft researcher Kathryn McKinley photographed at Building 99 in Redmond, Wash. on November 15, 2016. Photo by Dan DeLong

Kathryn S. McKinley, principal researcher, Redmond research lab
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What will be the key advance in programming languages and software engineering in 2017?
In programming languages research, the most revolutionary change on the horizon is probabilistic programming, in which developers produce models that estimate the real world and explicitly reason about uncertainty in data and computations. The programming language community is continuing to build the foundations of this new software world and some exciting new applications will emerge by the end of 2017.

What will be the key advance or topic of discussion in programming languages and software engineering in 2027?
By 2027, the majority of software engineers will be facile in programming systems that reason about estimates and produce models with statistical methods.  This sea change will deliver applications that seamlessly integrate sensors, machine learning and approximation to interact with human beings in entirely new, meaningful and correct ways.


cecily-morrison-04-predictionsCecily Morrison, researcher, Cambridge, U.K., research lab

What will be the key advance in human-centered computing and accessibility in 2017?
People with visual disabilities will be the power users of personal agents, helping technologists move from hype to transformational technology.

What will be the key advance or topic of discussion in human-centered computing and accessibility in 2027?
All children, including those with disabilities, will have appropriate tools to learn to code. Add 20 years and those children with disabilities will bring their unique experience of the world to change the landscape of technology.


olya-ohrimenko-04-predictionsOlya Ohrimenko, researcher, Cambridge, U.K., research lab

What will be the key advance in security and privacy in 2017?
Trusted hardware will inspire new kinds of applications and tools with strong security as their distinctive feature, appealing to users and programmers.

What will be the key advance or topic of discussion in security and privacy in 2027?
Advances in hardware and cryptography will lift data privacy guarantees to a new level: Only an encrypted form of our personal data will be used in medical and administrative analyses, machine learning algorithms and our daily online activities.


Microsoft researcher Oriana Riva photographed at Building 99 in Redmond, Wash. on November 14, 2016. Photo by Dan DeLong

Oriana Riva, researcher, Redmond, MSR NExT

What will be the key advance in mobile computing in 2017?
In 2017, systems will increasingly re-architect themselves to support interactions without a graphical user interface.  We’ll see fewer users installing apps on their devices and more apps turning into behind-the-scenes services for chatbots and personal digital assistants.

What will be the key advance or topic of discussion in mobile computing in 2027?
The key advance in mobile computing by 2027 will be a significantly expanded relationship with the digital world, encompassing almost any “thing” that exists in our surroundings. We’ll engage with intelligent and personal systems that truly understand us, that we can trust and that know us well enough to anticipate and serve our needs.


Microsoft researcher Asta Roseway photographed at Building 99 in Redmond, Wash. on November 17, 2016. Photo by Dan DeLong

Asta Roseway, principal research designer, Redmond research lab

What will be the key advance in ecology, environment and design in 2017?
In 2017 we will see early efforts commence around the Internet of Things for agriculture, including a fusion of ubiquitous sensing, computer vision capabilities and cloud storage to maximize machine learning and analytics. These services, combined with design, will enable farmers to monitor, analyze, comprehend and diagnose the health of their farms from micro to macro levels.

What will be the key advance or topic of discussion in ecology, environment and design in 2027?
Farmers will leverage artificial intelligence streaming capabilities to maintain healthy yields regardless of climate change, drought and disaster. The future of food depends on our ability to preserve and improve the use of our planet’s key resources and reduce over-farmed soil by shifting from traditional farming practices to alternative low energy ones such as vertical farming and aquaponics. Environmental and ecological focus will be on saving our forests with help from advanced sensors and technology while leveraging more urban spaces to shoulder our agricultural needs.


Microsoft researcher Karin Strauss photographed at Building 99 in Redmond, Wash. on November 15, 2016. Photo by Dan DeLong

Karin Strauss, senior researcher, Redmond research lab
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What will be the key advance in hardware and devices in 2017?
Moore’s Law has been slowing down. It is getting too expensive to scale general purpose, silicon-based processors and capacitive memories at the same pace as before. As a result, in 2017 we will see a number of new custom hardware accelerators, mostly on FPGA fabrics, proliferate in the cloud to improve performance and lower costs, instead of simply relying on Moore’s Law. Of course, general purpose processors will continue to improve, just at a slower rate. The result will be more interesting, responsive and secure services backed by the cloud. We also will see more virtual reality and augmented reality devices and accessories, both cheap and expensive, come to market. This will result in a number of new applications experimenting with these platforms, as well as interesting developments in content creation for virtual and augmented reality, including 360 degree video recording and similar devices.

What will be the key advance or topic of discussion in hardware and devices in 2027?
From now to 2027, we will witness a number of new technologies that depart from regular silicon scaling come to fruition. Carbon nanotubes and other molecular-scale manufacturing techniques, new architectures such as those that perform computation closer to the data (near data processing), and new computing and storage paradigms such as quantum computers and DNA storage drives may become commercial realities by then. Low power artificial intelligence and near eye displays will see significant improvements as well, resulting in more intelligent devices and higher quality augmented and virtual reality experiences.


xiaoyan12Xiaoyan Sun, lead researcher, Asia research lab
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What will be the key advance in computer vision in 2017?
The key advance in 2017 will be the continued rapid progress in computer vision based on deep learning methods. This will be evident in highly accurate object recognition technologies that approach human ability, lightweight portable vision systems and wide adoption of vision platforms.

What will be the key advance or topic of discussion in computer vision in 2027?
By 2027 the ability for computers to “see” will be ubiquitous as we will have highly developed imaging devices, powerful computing resources and combined deep and wide learning techniques. Advances in these techniques will lead to ubiquitous vision “eyes” that can “see” and empower humans in daily life and all kinds of professions, from manufacturing and health care to finance and security.


Microsoft researcher Dongmei Zhang photographed at Building 99 in Redmond, Wash. on November 14, 2016. Photo by Dan DeLong

Dongmei Zhang, principal research manager, Asia research lab

What will be the key advance in data analytics and vision in 2017?
In 2017, the key technology advance of data analytics and visualization will be in smart data discovery, with interactive, intuitive and instant insights at its core. Such insights will be generated automatically and recommended to users based on their analysis context. The insight generation and recommendation will take into account user response, thus creating an effective and fast feedback loop. Users will be able to complete their analytic tasks more quickly and with less effort.

What will be the key advance or topic of discussion in data analytics and vision in 2027?
In 2027, advances in data analytics and visualization will enable cross-data source and cross-domain analytics at different semantic levels. Individuals will be able to use natural interaction mechanisms, such as natural language, to obtain broad and in-depth information on various aspects of their lives in an easy and seamless way.

Related: To see how our researchers fared in their predictions last year, check out From AI and data science to cryptography, Microsoft researchers offer 16 predictions for 2016.

Photography by Dan DeLong in Redmond, Washington, John Brecher in New York, Jonathan Banks in Cambridge, U.K., Dana J. Quigley in Cambridge, Massachusetts, Mahesh Bhat in Bangalore, India, Michael Svoboda in San Diego, California, and Tong Wang in Beijing.

 

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As machine learning breakthroughs abound, researchers look to democratize benefits http://blogs.microsoft.com/next/2016/12/01/machine-learning-breakthroughs-abound-researchers-look-democratize-benefits/ http://blogs.microsoft.com/next/2016/12/01/machine-learning-breakthroughs-abound-researchers-look-democratize-benefits/#respond Fri, 02 Dec 2016 06:00:14 +0000 http://blogs.microsoft.com/next/?p=58544 When Robert Schapire started studying theoretical machine learning in graduate school three decades ago, the field was so obscure that what is today a major international conference was just a … Read more »

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When Robert Schapire started studying theoretical machine learning in graduate school three decades ago, the field was so obscure that what is today a major international conference was just a tiny workshop, so small that even graduate students were routinely excluded.

Machine learning still isn’t exactly a topic of discussion at most family dinner tables. But it has become one of the hottest fields in computer science, turning once-obscure academic gatherings like the upcoming Annual Conference on Neural Information Processing Systems in Barcelona, Spain, into a sold-out affair attended by thousands of computer scientists from top corporations and academic institutions.

“It’s been really something to see this field develop, and to see things that seemed impossible become possible in my lifetime,” said Schapire, a principal researcher in Microsoft’s New York City research lab whose machine learning research is widely used in the field.

The NIPS conference, which starts Monday, is so popular because machine learning has quickly become an indispensable tool for developing technology that consumers and businesses want, need and love. Machine learning is the basis for technology that can translate speech in real time, help doctors  read radiology scans and even recognize emotions on people’s faces. Machine learning also helps you sort the spam out of your inbox and remember your day’s tasks.

Robert Schapire. Photo by John Brecher.

Robert Schapire is a prominent AI researcher. Photo by John Brecher.

It’s a far cry from Schapire’s early days in the field, when he said some of the hard problems were things like getting a computer to accurately read handwritten digits.

“Bit by bit, we’ve really been building this field from the bottom up, starting with basic problems,” Schapire said. “Machine learning has become applicable to such a huge array of problems. It’s really amazing.”

Related: Here’s a look at how Microsoft is participating in NIPS

Along the way, researchers say the field has benefited from people who dreamed about big breakthroughs with real-world benefits, such as the ability to create technology that can recognize words in a conversation as well as a person.

“Somehow the field of machine learning has been very fortunate in that we’ve had brilliant theorists who had a very practical outlook on things,” said Alekh Agarwal, a researcher in Microsoft’s New York lab.

Democratizing machine learning
Schapire, Agarwal and their colleagues at Microsoft and elsewhere say this is just the beginning.  With the work they are presenting at NIPS and beyond, they are investigating ways to make machine learning even more useful for – and accessible to – a broader array of people.

The Microsoft researchers say they are at the forefront of efforts to democratize machine learning by making it easier for developers and engineers without a machine learning background to take advantage of these breakthroughs. That puts them on the cutting edge of finding ways to share the benefits of these systems widely with the rest of us.

“Machine learning has traditionally been a field where if you didn’t have a Ph.D. you’d be at a loss – and if you did have a Ph.D. you might still be at a loss,” said John Langford, a principal researcher in Microsoft’s New York lab. “We’re trying to make these things useful to someone who’s a programmer without a lot of machine learning expertise.”

Researchers at Microsoft office in New York City. John Langford

John Langford is working on ways to democratize AI. Photo by John Brecher.

Machine learning is useful in part because it can help people make predictions about anything from how many servers they’ll need to deploy for a certain task to what news article a person might want to read. One of Langford’s recent projects is looking at ways to make multiple predictions less burdensome, by creating systems that systematically eliminate common data errors with applications that use reinforcement learning and structured learning.

With reinforcement learning, researchers aim to get systems to use trial and error to figure out how to achieve a task. For example, a program could learn how to win at backgammon by playing against itself over and over again, picking up on what worked and what didn’t over the course of those many games. The system is given very little outside guidance to make those decisions. Instead, decisions it makes early in the process can then affect how it succeeds later on.

Reinforcement learning is a counterpart to supervised learning, in which systems get better at doing things as they are fed more relevant data. For example, a supervised machine learning tool may learn to recognize faces in pictures after being shown a training set containing a huge array of faces.

Helping with decision making
In the more recent reinforcement approach Langford has been working on, the system also gets partial credit for choosing actions that are partially correct, making it easier to winnow down to the right answer.

Microsoft researchers say the decision service is such an exciting breakthrough because it can help systems make decisions using context.

“When you make a decision, you usually have some idea of how good it was,” said Siddhartha Sen, a researcher in the New York lab. “Here’s an opportunity to use machine learning to optimize those decisions.”

The researchers say the cloud-based system, which is available in preview, is groundbreaking in part because it can be applied to so many different situations.

For example, it could be used by a news service that wanted to personalize content recommendations, a mobile health app that could personalize fitness activities or a cloud provider looking to optimize server resources.

Sarah Bird

Sarah Bird

 

Sen said one key goal for the testing service is to make it easy and accessible for people who may not be able to build these kinds of machine learning techniques on their own.

“The way it’s democratizing machine learning is by making it very easy to interface with the system,” said Sen, who will help run a workshop on the intersection of machine learning and systems design at NIPS. “We tried to hide all the difficult steps.”

Microsoft has been developing the building blocks for a system like the decision service for years. But the system’s current abilities would not have been possible even a few years ago, said Sarah Bird, who began working on it as a postdoctoral researcher in Microsoft’s New York lab.

Bird, who is now a technical advisor in Microsoft’s Azure division, said systems like these are improving rapidly because all the elements needed for machine learning – the computing power of the cloud, the algorithms and the data – are improving quickly, and at the same time.

“It’s really amazing to watch all the pieces we need mature in parallel,” she said. “It’s a fun time for consumers and developers and researchers.”

Fast pace of change
Many researchers say reinforcement learning holds a lot of promise because it could be used to create artificial intelligence systems that would be able to make the type of independent and complex decisions that could truly augment and complement human abilities.

Researchers caution that they are still in the early stages of finding success with reinforcement learning, but they say what they are seeing so far is promising.

“The sense of what’s achievable is constantly changing, and that’s what makes it so exciting to me,” said Katja Hofmann, a researcher in Microsoft’s Cambridge, UK, research lab. Hofmann has led development of Project Malmo, which uses Minecraft as the testing ground for reinforcement learning, and which will be demonstrated at NIPS.

Together with her colleagues, Hofmann has most recently been looking at ways that artificial intelligence agents can learn to do several tasks, rather than just one, and can apply the experience of how they completed one task to another. For example, an artificial intelligence navigating one Minecraft space may learn to recognize lava, and then use that knowledge to avoid lava in another place. Some of this research is being presented at the European Workshop on Reinforcement Learning, which is co-located with NIPS.

Related:

Learn more about Microsoft’s presence at NIPS

Chris Bishop: Substance, not hype, powers AI excitement at premier machine learning conference

Jennifer Wortman Vaughan: Making better use of the crowd

Follow Sarah Bird  and Katja Hofmann on Twitter

Allison Linn is a senior writer at Microsoft. Follow her on Twitter.

 

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Microsoft doubles down on quantum computing bet http://blogs.microsoft.com/next/2016/11/20/microsoft-doubles-quantum-computing-bet/ http://blogs.microsoft.com/next/2016/11/20/microsoft-doubles-quantum-computing-bet/#respond Mon, 21 Nov 2016 00:48:06 +0000 http://blogs.microsoft.com/next/?p=58475 Microsoft is doubling down on its commitment to the tantalizing field of quantum computing, making a strong bet that it is possible to create a scalable quantum computer using what … Read more »

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Microsoft is doubling down on its commitment to the tantalizing field of quantum computing, making a strong bet that it is possible to create a scalable quantum computer using what is called a topological qubit.

Longtime Microsoft executive Todd Holmdahl – who has a history of successfully bringing seemingly magical research projects to life as products – will lead the scientific and engineering effort to create scalable quantum hardware and software.

“I think we’re at an inflection point in which we are ready to go from research to engineering,” said Holmdahl, who is corporate vice president of Microsoft’s quantum program.

Holmdahl, who previously played a key role in the development of the Xbox, Kinect and HoloLens, noted that success is never guaranteed. But, he said, he thinks the company’s long investment in quantum research has been fruitful enough that there’s a clear roadmap to a scalable quantum computer.

“None of these things are a given,” Holmdahl said. “But you have to take some amount of risk in order to make a big impact in the world, and I think we’re at the point now that we have the opportunity to do that.”

Microsoft has hired two leaders in the field of quantum computing, Leo Kouwenhoven and Charles Marcus. The company also will soon bring on two other leaders in the field, Matthias Troyer and David Reilly.

Marcus is the Villum Kann Rasmussen Professor at the Niels Bohr Institute at the University of Copenhagen and director of the Danish National Research Foundation-sponsored Center for Quantum Devices.

Kouwenhoven is a distinguished professor at Delft University of Technology in the Netherlands and was founding director of QuTech, the Advanced Research Center on Quantum Technologies.

ms-stories-station-q-marcus-kouwenhoven-10-lo243w

From left, Leo Kouwenhoven and Charles Marcus attend the 2014 Microsoft’s Station Q conference in Santa Barbara, California. (Photo by Brian Smale)

Marcus and Kouwenhoven have been collaborating with Microsoft’s quantum team for years, with Microsoft funding an increasing share of the topological qubit research in their labs.  After they join Microsoft, they will retain their academic titles and affiliation to their host universities, continue to run their university research groups and contribute to building dedicated Microsoft quantum labs at their respective universities.

Both researchers say that joining Microsoft is the best path to ensuring that their breakthroughs can help create a scalable quantum computer.

“It’s very exciting,” Kouwenhoven said. “I started working on this as a student way back, and at that time we had not a clue that this could ever be used for anything practical.”

Kouwenhoven’s collaboration with Microsoft began casually enough, after a visit to the company’s Santa Barbara, California, lab and a “nice walk along the beach” with Michael Freedman, the lab’s director and a specialist in topological mathematics.

After years of scientific collaboration, Kouwenhoven said, they’ve reached a point where they can benefit from an engineer’s perspective on how to bring the work to reality.

“The engineering will also help move the science forward,” Kouwenhoven said.

That’s important because Microsoft isn’t just interested in creating one qubit that can work in one perfect lab environment – what Marcus calls “a demonstration of quantum information.”

Instead, the company hopes to create dependable tools that scientists without a quantum background can use to solve some of the world’s most difficult problems. By doing that, they believe they will help usher in a “quantum economy” that could revolutionize industries such as medicine and materials science.

Marcus – whose collaboration with Microsoft began almost by happenstance when he happened to be seated next to Microsoft’s Freedman at a dinner some years ago – said he came to realize that a quantum economy would never be realized unless the scientists and the engineers began partnering more closely.

“I knew that to get over the hump and get to the point where you started to be able to create machines that have never existed before, it was necessary to change the way we did business,” Marcus said. “We need scientists, engineers of all sorts, technicians, programmers, all working on the same team.”

That effort includes bringing other longtime collaborators on board.

Troyer is currently a professor of computational physics at ETH Zurich in Switzerland, one of the leading universities in the world.  Among his areas of expertise are simulations of quantum materials, the testing of quantum devices, optimization of quantum algorithms and the development of software for quantum computers.

Reilly, an experimental physicist, is a professor and director of the Centre for Quantum Machines at the University of Sydney in Australia. He leads a team of physicists and engineers working on the challenges of scaling up quantum systems.

Making the building blocks of a quantum computer
Microsoft’s approach to building a quantum computer is based on a type of qubit – or unit of quantum information – called a topological qubit.

Qubits are the key building block to a quantum computer. Using qubits, researchers believe that quantum computers could very quickly process multiple solutions to a problem at the same time, rather than sequentially.

One of the biggest challenges to building a working quantum computer is how picky qubits can be. A quantum system can only remain in a quantum state when it’s not being disturbed, so quantum computers are built to be in incredibly cold, unique environments.

The Microsoft team believes that topological qubits are better able to withstand challenges such as heat or electrical noise, allowing them to remain in a quantum state longer. That, in turn, makes them much more practical and effective.

“A topological design is less impacted by changes in its environment,” Holmdahl said.

At the same time as Microsoft is working to build a quantum computer, it’s also creating the software that could run on it. The goal is to have a system that can begin to efficiently solve complex problems from day one.

“Similar to classical high-performance computing, we need not just hardware but also optimized software,” Troyer said.

To the team, that makes sense: The two systems can work together to solve certain problems, and the research from each can help the other side.

“A quantum computer is much more than the qubits,” Reilly said. “It includes all of the classical hardware systems, interfaces and connections to the outside world.”

An even smarter cloud, and the ability to solve seemingly intractable problems
With effective quantum hardware and software, quantum experts say they could create vast computing power that could address some of the world’s most pressing problems, from climate change and hunger to a multitude of medical challenges.

That’s partly because the computers could emulate physical systems, speeding up things like drug development or our understanding of plant life. Researchers say the intelligent cloud could be exponentially more powerful, similar to how cell phones evolved into smart phones.

“There is a real opportunity to apply these computers to things that I’ll call material sciences of physical systems,” Holmdahl said. “A lot of these problems are intractable on a classical computer, but on a quantum computer we believe that they are tractable in a reasonable period of time.”

Kouwenhoven said that applies to the field of quantum physics itself, such as research into dark matter and other fundamental questions about our understanding of the universe itself.

“I would find it interesting to go back to my science background and use the quantum computer to solve quantum problems,” he said.

The transistor and the ash sucker
Then there’s the vast unknown. Computer scientists will often point out that when scientists invented the very first transistor, they had no way of conceiving of an application like a smart phone.

“My guess is that back in the 40s and 50s, when they were thinking about the first transistor, they didn’t necessarily know how this thing was going to be used. And I think we’re a little bit like that,” Holmdahl said.

One of those inventors was Walter Brattain. He grew up in the same small town of Tonasket, Washington, as Holmdahl. Being a technology history buff, Holmdahl has long been fascinated by Brattain’s life.

With quantum computing, Holmdahl said he sees an opportunity to be among the people who are following in Brattain’s footsteps.

“The opportunity to be at the beginning of the next transistor is not lost on me,” Holmdahl said.

When he took this role, Holmdahl also was thinking about another man who’s had a great influence on his life: His 20-year-old son, who told Holmdahl that if you think you’re one of the smartest guys at the table, you need to find a new table.

“This is definitely a new table for me,” said Holmdahl, a Stanford-educated engineer who now spends his free time reading about things like quantum physics and entanglement.

When Marcus thinks about what a quantum computer could do, he often thinks about an old car his family once had. It was a top-of-the-line car of its day, with all the latest technologies — including a dashboard gadget designed to suck ash directly from a cigarette.

At the time, Marcus has often thought, someone must have believed that was as good as it was ever going to get in car technology.

“Nobody, when they were designing ash suckers, was thinking about self-driving cars,” he said.

The same thing could easily apply to computational power.

“People who think of computation as being completed are in the ash sucker phase,” he said.

Related:

Allison Linn is a senior writer at Microsoft. Follow her on Twitter.

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Microsoft researchers detect lung-cancer risks in web search logs http://blogs.microsoft.com/next/2016/11/10/microsoft-researchers-detect-lung-cancer-risks-web-search-logs/ http://blogs.microsoft.com/next/2016/11/10/microsoft-researchers-detect-lung-cancer-risks-web-search-logs/#respond Thu, 10 Nov 2016 16:00:12 +0000 http://blogs.microsoft.com/next/?p=58433 Smoking cigarettes is the leading cause of lung cancer, the most common cause of cancer death in the world. But nearly 20 percent of lung-cancer diagnoses are made in people … Read more »

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Smoking cigarettes is the leading cause of lung cancer, the most common cause of cancer death in the world. But nearly 20 percent of lung-cancer diagnoses are made in people who are non-smokers. That means in addition to smoking, geographic, demographic and genetic factors play a role in the devastating disease.

A project from Microsoft’s research labs is exploring the feasibility of using anonymized web search data to learn more about lung-cancer risk factors and provide early warning to people who are candidates for disease screening.

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“People tend to whisper their health concerns into search engines on a regular basis.” – Eric Horvitz

The findings, published Thursday in JAMA Oncology, extend research that team members published last June on the feasibility of using the text of questions people ask search engines to predict diagnoses of pancreatic cancer. The machine-learning method builds on patterns found in the search queries.

“Here, we are not just looking at the text of the queries; we also consider the locations that people are in when they issue these queries and we tie that back to contextual risk factors linked to those locations,” says study co-author Ryen White, chief technology officer for health intelligence at Microsoft Health in Redmond, Washington.

For example, the model developed by the researchers determines the ZIP code where the search was issued and correlates the location data with maps from the U.S. Geological Survey to determine environmental levels of radon gas, a known lung-cancer risk. Census data reveal the average age of homes in each region, which is relevant as older homes are poorly ventilated and thus can trap radon.

Knowing ZIP codes also helps the researchers infer users’ socioeconomic status and race, providing additional clues on cancer risk. According to the Centers for Disease Control and Prevention, people living below the poverty level have higher rates of smoking than the general population, and death rates for people with cancer are highest among black Americans.

In addition, the model uses algorithms to determine searchers’ likely gender and age from patterns of queries. Searches from the same mobile device within hours of each other from ZIP codes separated by hundreds, or thousands, of miles could indicate air travel.

Taken together, these data “allow us to discover new risk factors, things that might not have been thought of in the past that might actually be important,” White says. “We looked at air travel, for example, as one of the factors that might be tied to a higher likelihood.”

Ryen White

Ryen White, chief technology officer for Microsoft Health and an information retrieval expert. (Photography by Scott Eklund/Red Box Pictures)

The findings are associations, not evidence of a cause, emphasizes study co-author Eric Horvitz, technical fellow and managing director of Microsoft’s research lab in Redmond. But, he adds, they can suggest directions for future clinical studies on lung cancer.

Take plane travel, for example. Horvitz says that although it was useful in their predictive models, the researchers have yet to confirm a causal connection between plane travel and lung cancer. “However, the result frames a hypothesis that can be pursued and studied. Same with how radon gas and older homes link up,” he says.

To develop the model, Horvitz and White identified so-called experiential queries such as “I have just been diagnosed with lung cancer,” which are then followed up with behaviors that provide evidence of a recent diagnosis, such as multiple queries on treatment options and side effects.

The model then looks back in time at the anonymized logs for searches that might signal a pending diagnosis. These include searches about symptoms such as hoarseness and others that provide evidence of known and potential risk factors such as cigarette use, locations linked to elevated radon levels and frequent long-distance travel.

The researchers ran the model on the anonymized logs of nearly 5 million searchers and found that it can identify 1.5 percent to nearly 40 percent of searchers a year in advance of when they will input queries consistent with a lung cancer diagnosis. The percentages vary as the sensitivity of the model is shifted to limit false positive rates from 1 in 100,000 to 1 in 1,000. The approach performs more effectively for searchers identified as high risk, such as living in a ZIP code with elevated radon levels.

The research, explains Horvitz, who holds both a Ph.D. and MD from Stanford University, is part of a broader and ongoing effort to use the vast aggregations of data compiled from human interactions with the web to help advance clinical medicine.

“People tend to whisper their health concerns into search engines on a regular basis,” he says. “This kind of data can serve as a complement to more formal clinical information.”

The research, he adds, “shows promise for identifying new clinically relevant findings in multiple areas of healthcare.”

The researchers are still discussing how the research might eventually be used. For example, White says, at some point in the future people might consent to having relevant information and inferences from web search logs and other data streams shared with their doctor.

At this point, Horvitz notes, this is purely research. But with publication of the findings in the medical literature, the work could stimulate interest by clinical researchers and inform the development of future screening systems that can catch cancers earlier in their progression.

“The first step,” he says, “is to see if these kinds of things are feasible.”

Related:

Follow Eric Horvitz on Twitter

John Roach writes about Microsoft research and innovation. Follow him on Twitter.

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Microsoft researchers release graph that helps machines conceptualize http://blogs.microsoft.com/next/2016/11/01/microsoft-researchers-release-graph-that-helps-machines-conceptualize/ http://blogs.microsoft.com/next/2016/11/01/microsoft-researchers-release-graph-that-helps-machines-conceptualize/#respond Tue, 01 Nov 2016 14:00:28 +0000 http://blogs.microsoft.com/next/?p=58334 “Jaguar.” To most computers, that word printed on an otherwise blank screen is simply a string of characters. It’s different for people. You see a word associated with a big … Read more »

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“Jaguar.”

To most computers, that word printed on an otherwise blank screen is simply a string of characters.

It’s different for people. You see a word associated with a big cat, a large mammal. Given the context of valet parking, it might also bring to mind a luxury brand that is similar to Mercedes and BMW.

Put another way, you have a collection of ideas, or concepts, of what “Jaguar” means and the mental agility to use context to infer which concept the writer of the word intended to convey.

On Tuesday, a team of scientists from Microsoft Research Asia, Microsoft’s research lab in Beijing, China, announced the public release of technology designed to help computers conceptualize in a humanlike fashion.

dsc01441-v2

From left, Lei Ji, Jun Yan and Dawei Zhang of Microsoft Research Asia were key players in the development of Microsoft Concept Graph. (Photo credit: Microsoft.)

The Microsoft Concept Graph, as it is known, is a massive graph of concepts – more than 5.4 million and growing – that machine-learning algorithms are culling from billions of web pages and years’ worth of anonymized search queries.

“We want to provide machines some commonsense, high-level concepts” so that they can better understand, and process, human communication, says Jun Yan, a senior research manager at Microsoft Research Asia, who is working on the project.

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“We want to provide machines some commonsense, high-level concepts.” – Jan Yun
Microsoft researchers release graph that helps machines conceptualize.

Knowledge graphs such as this one are a major component of ongoing efforts in industry and academia to computationally simulate human thinking, which computer scientists argue is a hallmark of true artificial intelligence.

“The limitation of computers is that they do not have commonsense knowledge or semantics. They can only understand the characters of words,” Yan explains. “But with humans it is different. Humans have a lot of background knowledge to understand things.”

Conceptual computing
The research behind the Microsoft Concept Graph has been ongoing for six years. The technology has potential applications that range from keyword advertising and search enhancement to the development of human-like chatbots.

For example, in traditional search advertising, a luxury car company buys a list of keywords related to products it wants to sell, such as various models of sport utility vehicles, or SUVs, Yan explains. When those models are queried, the engine surfaces an ad for the car company.

Using data from the Microsoft Concept Graph, the keyword sales team can also suggest that the car company buy related keywords, such as “upmarket SUV,” “top crossover” and potentially hundreds more.

illustration3-v2

“This is an opportunity to earn more revenue from the advertiser, and for the advertiser to reach a larger audience,” Yan says.

Daxin Jiang, a China-based principal development manager with Microsoft’s search engine Bing, has collaborated with the Concept Graph team for three years to incorporate conceptualization techniques to improve the ranking and relevance of search results.

For example, the graph recognizes certain phrases as single entities. When “Microsoft Research Asia” is queried, Bing ranks documents with the phrase “Microsoft Research Asia” higher than documents where “Microsoft,” “Research” and “Asia” are separated by additional words or punctuation.

His group is also leveraging the Concept Graph for question answering. For example, the graph can answer the question “What are the Asian developing countries?”

“The Concept Graph scans through web pages and extracts instances that belong to concepts,” Jiang explains. “’Asian developing countries’ is a concept and China, India, etc., are all instances for this concept.”

Learning conceptualization
To create the Microsoft Concept Graph, Yan and colleagues trained a machine-learning algorithm to search through the database of indexed web pages and search queries for word associations linked together by basic, common speech patterns including the phrases “such as” and “is a.”

For example, if a web page contains the text “an animal, such as a dog,” the algorithm selects “animal” as a candidate concept for the instance “dog,” Yan explains. The text “Microsoft is a technology company” results in the instance “Microsoft” paired with the concept “technology company.”

The algorithm also performs a statistical analysis to weed out rare or incorrect instance-concept pairs that arise from semantic ambiguity.

For example, on the first pass, the sentence “domestic animals other than dogs such as cats” produces two results: “cat is a dog” and “cat is a domestic animal,” which are both derived from the pattern “such as.”

As the algorithm processes more and more pages of text, it learns that “cat is a domestic animal” is more frequent than “cat is a dog.” When the frequency difference between the two ambiguous meanings crosses a defined threshold, the algorithm weeds out “cat is a dog.”

illustration1-v2

“We only keep the frequently mentioned things by different people on different webpages,” Yan says. “That way we have confidence in the instance and concept pair.”

Humans, too, are recruited to look over segments of the data for erroneous pairs, which helps improve the quality of the graph.

The result is millions of concepts, ranging from the common “cities” and “musicians” to the rare “wedding dress designers” and “acid blocking heartburn drugs.”

Each concept is linked to a set of instances and described by attributes such as person, thing and object as well as relationships such as located in, friend of and president of.

Tagging model
Along with the Microsoft Concept Graph, the researchers released a related technology called the Microsoft Concept Tagging Model, which automatically maps instances to concepts with a probability score, enabling machines humanlike conceptualization.

The model is based on a machine-learning algorithm that weights, or scores, matches for a given instance-concept pair. In this way, the most computationally useful concept, a so-called basic-level concept, is ranked highest.

For example, the instance “Microsoft” automatically maps to the concepts “company,” “software company” and “largest OS vendor.” Both “company” and “largest OS vendor” are highly related to Microsoft, but “software company” is the most useful, and thus highest ranked, concept.

Why?

Microsoft is certainly a “company,” but so too are ExxonMobil and McDonalds, which have little else in common with Microsoft. Whereas “largest OS vendor” applies only to Microsoft. “Software company” is a concept that relates to Microsoft, as well as similar companies such as IBM, Adobe and Oracle.

In other words, “software company” is specific without being too specific; it is general enough to be related to several other instances, which makes it useful for semantic computation such as performing searches or answering questions.

The accuracy of the model increases as it incorporates the context of surrounding words.

For example, for the sentence, “I want to eat an apple,” the tagging model gives the “fruit” concept more weight, as a person is unlikely to eat the well-known technology company. The weighting is reversed for “I want to visit Apple” since “visit” is more likely associated with “technology company.”

“Based on the context of previous terms, we can distinguish the detail of the concept to further filter out irrelevant concepts,” Yan explains. “When you see ‘eat apple’ we know the high probability thing is the fruit.”

Model release
The public release of the Microsoft Concept Graph and Microsoft Concept Tagging Model are intended to support research on natural language understanding for technologies such as search engines, chatbots and other artificial intelligence systems, according to Yan.

“We want to encourage more people to utilize our fundamental service,” he says.

Yanghua Xiao, an associate professor of computer science at Fudan University in Shanghai, China, for example, is using the graph in his research on enabling machines to understand human language, including natural language questions.

Take, for example, the question: “How many people are there in New York?” which is about the population of a city.

“Whatever the city is, say Shanghai or London, they share the same semantic template,” he notes. “The Concept Graph, which contains facts like ‘New York is a city’ can help us build the template so that the machine can understand the question with the template and answer the question with exact answers.”

The Microsoft Concept Graph and Microsoft Concept Tagging Model are available to download for research purposes. The current release includes the core version of concept data in English mined from billions of web pages and search queries.

Future releases will include conceptualization with context for understanding short and long texts as well as support for Chinese.

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John Roach writes about Microsoft research and innovation. Follow him on Twitter.

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Microsoft releases beta of Microsoft Cognitive Toolkit for deep learning advances http://blogs.microsoft.com/next/2016/10/25/microsoft-releases-beta-microsoft-cognitive-toolkit-deep-learning-advances/ http://blogs.microsoft.com/next/2016/10/25/microsoft-releases-beta-microsoft-cognitive-toolkit-deep-learning-advances/#respond Tue, 25 Oct 2016 14:59:51 +0000 http://blogs.microsoft.com/next/?p=58244 Microsoft has released an updated version of Microsoft Cognitive Toolkit, a system for deep learning that is used to speed advances in areas such as speech and image recognition and search … Read more »

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Microsoft has released an updated version of Microsoft Cognitive Toolkit, a system for deep learning that is used to speed advances in areas such as speech and image recognition and search relevance on CPUs and NVIDIA® GPUs.

The toolkit, previously known as CNTK, was initially developed by computer scientists at Microsoft who wanted a tool to do their own research more quickly and effectively. It quickly moved beyond speech and morphed into an offering that customers including a leading international appliance maker and Microsoft’s flagship product groups depend on for a wide variety of deep learning tasks.

“We’ve taken it from a research tool to something that works in a production setting,” said Frank Seide, a principal researcher at Microsoft Artificial Intelligence and Research and a key architect of Microsoft Cognitive Toolkit.

Frank Seide. (Photography by Scott Eklund/Red Box Pictures)

Frank Seide (Photography by Scott Eklund/Red Box Pictures)

The latest version of the toolkit, which is available on GitHub via an open source license, includes new functionality that lets developers use Python or C++ programming languages in working with the toolkit.  With the new version, researchers also can do a type of artificial intelligence work called reinforcement learning.

Finally, the toolkit is able to deliver better performance than previous versions. It’s also faster than other toolkits, especially when working on big datasets across multiple machines. That kind of large-scale deployment is necessary to do the type of deep learning across multiple GPUs that is needed to develop consumer products and professional offerings.

It’s also key to speeding up research breakthroughs. Last week, Microsoft Artificial Intelligence and Research announced that they had, for the first time, created a technology that recognizes words in a conversation as well as a person does. The team credited Microsoft Cognitive Toolkit for vastly improving the speed at which they could reach this milestone.

The team that developed the Microsoft toolkit says the ability to work across multiple servers is a key advantage over other deep learning toolkits, which can see suboptimal performance and accuracy when they start tackling bigger datasets. Microsoft Cognitive Toolkit has built-in algorithms to minimize such degradation of computation.

“One key reason to use Microsoft Cognitive Toolkit is its ability to scale efficiently across multiple GPUs and multiple machines on massive data sets,” said Chris Basoglu, a partner engineering manager at Microsoft who has played a key role in developing the toolkit.

Chris Basoglu (Photography by Scott Eklund/Red Box Pictures)

Chris Basoglu (Photography by Scott Eklund/Red Box Pictures)

Microsoft Cognitive Toolkit can easily handle anything from relatively small datasets to very, very large ones, using just one laptop or a series of computers in a data center. It can run on computers that use traditional CPUs or GPUs, which were once mainly associated with graphics-heavy gaming but have proven to be very effective for running the algorithms needed for deep learning.

“Microsoft Cognitive Toolkit represents tight collaboration between Microsoft and NVIDIA to bring advances to the deep learning community,” said Ian Buck, general manager of the Accelerated Computing Group at NVIDIA.  “Compared to the previous version, it delivers almost two times performance boost in scaling to eight Pascal GPUs in an NVIDIA DGX-1™.”

Microsoft Cognitive Toolkit is designed to run on multiple GPUs, including Azure’s GPU offering, which is currently in preview. The toolkit has been optimized to best take advantage of the NVIDIA hardware and Azure networking capabilities that are part of the Azure offering.

Democratizing AI, and its tools
The toolkit is being released at a time when everyone from small startups to major technology companies are seeing the possibilities for using deep learning for things like speech understanding  and image recognition.

Broadly speaking, deep learning is an artificial intelligence technique in which developers and researchers use large amounts of data – called training sets – to teach computer systems to recognize patterns from inputs such as images or sounds.

For example, a deep learning system can be given a training set showing all sorts of pictures of fruits and vegetables, after which it learns to recognize images of fruits and vegetables on its own. It gets better as it gets more data, so each time it encounters a new, weird-looking eggplant or odd-shaped apple, it can refine the algorithm to become even more accurate.

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In this example of using Microsoft Cognitive Toolkit for training a Speech Acoustic Model, as more data is applied to the model it converges with better accuracy.

These types of achievements aren’t just research milestones. Thanks to advances in deep learning, fueled in part by big jumps in computing horsepower, we now have consumer products like Skype Translator, which recognizes speech and provides real-time voice translation, and the Cortana digital assistant, which can understand your voice and help you do everything from search for plane tickets to remember appointments.

“This is an example of democratizing AI using Microsoft Cognitive Toolkit,” said Xuedong Huang, Microsoft distinguished engineer.

More flexibility for more sophisticated work
When they first developed the toolkit, Basoglu said they figured many developers couldn’t, or wouldn’t, want to write a lot of code. So, they created a custom system that made it easy for developers to configure their systems for deep learning without any extra coding.

As the system grew more popular, however, they heard from developers who wanted to combine their own Python or C++ code with the toolkit’s deep learning capabilities.

They also heard from researchers who wanted to use the toolkit to enable reinforcement learning research. That’s a research area in which an agent learns the right way to do something – like find their way around a room or form a sentence – through lots of trial and error. That’s the kind of research that could eventually lead to true artificial intelligence, in which systems can make complex decisions on their own. The new version gives developers that ability as well.

Using Microsoft Cognitive Toolkit to avoid wasting food and live a healthier life
Although Microsoft Cognitive Toolkit was originally developed by speech researchers, it can now be used for a much wider variety of purposes.

Liebherr, the specialist in cooling, is using it to simplify daily life.

The company has installed cameras in its refrigerators that do more than just display images — they will actually recognize individual food items in the refrigerator and automatically incorporate this information into an inventory shopping list.

In the future, this technology will help in shopping and meal planning. The stored groceries can be recorded and monitored by using cameras with object recognition.

“People know at any time, and from anywhere, what is still in the fridge and what should be on the shopping list,” said Andreas Giesa, the ebusiness manager for Liebherr.

This will help customers avoid having food spoil and make daily life more comfortable.

The Bing relevance team uses it as part of its effort to find better ways to discover latent, or hidden, connections in search terms in order to give users better results.

For example, with deep learning a system can be trained to automatically figure out that when a user types in, “How do you make an apple pie?” they are looking for a recipe, even though the word “recipe” doesn’t appear in the search query. Without such a system, that type of rule would have to be engineered manually.

Clemens Marschner, a principal software development engineer who works on Bing relevance, said the team worked very closely with the toolkit’s creators to make it work well for developers doing other types of deep learning beyond speech. For them, the payoff was a system that lets them use massive computing power to quickly get results.

“No other solution allows us to scale learning to large data sets in GPU clusters as easily,” he said.

Microsoft also is continuing to use the Microsoft Cognitive Toolkit to improve speech recognition. Yifan Gong, a principal applied science manager in speech services, said they have been using the toolkit to develop more accurate acoustic models for speech recognition in Microsoft products including Windows and Skype Translator.

Gong said his team relied on the toolkit to develop new deep learning architectures, including using a technique called long short term memory, to deliver customers more accurate results.

Those improvements will make it easier for Microsoft systems to better understand what users are trying to say even when they are giving voice commands or interacting with Cortana in noisy environments such as at a party, driving on the highway or in an open floor plan office.

For the user, the benefits of this type of improvements are obvious.

“If you have higher recognition accuracy, you don’t have to repeat yourself as often,” Gong said.

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Allison Linn is a senior writer at Microsoft. Follow her on Twitter.

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Microsoft computing method makes key aspect of genomic sequencing seven times faster http://blogs.microsoft.com/next/2016/10/18/microsoft-computing-method-makes-key-aspect-genomic-sequencing-seven-times-faster/ http://blogs.microsoft.com/next/2016/10/18/microsoft-computing-method-makes-key-aspect-genomic-sequencing-seven-times-faster/#respond Tue, 18 Oct 2016 13:00:46 +0000 http://blogs.microsoft.com/next/?p=58010 Microsoft has come up with a way to significantly reduce the time it takes to do the major computational aspects of sequencing a genome. Microsoft’s method of running the Burrows-Wheeler … Read more »

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Microsoft has come up with a way to significantly reduce the time it takes to do the major computational aspects of sequencing a genome.

Microsoft’s method of running the Burrows-Wheeler Aligner (BWA) and the Broad Institute’s Genome Analysis Toolkit (GATK) on its Azure cloud computing system is seven times faster than the previous version, allowing researchers and medical professionals to get results in just four hours instead of 28.  BWA and GATK are two of the most common computational tools used in combination for genome sequencing.

The time savings is critical for a number of reasons. For example, it could allow doctors to diagnose rare and dangerous genetic conditions 24 hours earlier, getting the patient lifesaving treatment faster.

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Faster genome sequencing with Broad Institute on Azure
Microsoft Research accelerates by 7x Broad Institute genome sequencing tools

“There’s a lot of actionable information in which speed is really important,” said Ravi Pandya, a principal software architect in Microsoft’s genomics group who has been key to this acceleration work.

Ravi Pandiya

Ravi Pandiya

Over time, experts say the ability to sequence genomic data of plants and animals also could hasten important breakthroughs in other research fields, such as renewable energy and efficient food production.

A ‘genomics revolution’
The quicker Azure-based offering comes as the ability to analyze genomic data is becoming much more affordable, making it available to more people who need it and fueling a genomics revolution.

David Heckerman, who directs Microsoft’s genomics group, said the requests from hospitals, clinics and research institutions to process genomics data is growing at an extremely high rate.

“It’s getting to the point where tens of thousands of genomes are being sequenced, so efficiency really matters,” Pandya said.

That wasn’t always the focus.

Geraldine Van der Auwera, who works for the Broad Institute on the GATK platform and directs its 36,000-user  online support forum, said that for a long time, genomic analysis was mainly used for research purposes instead of medical care. That meant there wasn’t as much of an urgency to shave hours or minutes off the time it took to do the computations.

In addition, she said, researchers were primarily focused on making sure their methods were right.

“For a long time we focused on accuracy at the expense of speed,” she said.

As the tools have matured and researchers have become more confident in the accuracy, that’s changed, she said.

Geraldine Van der Auwera

Geraldine Van der Auwera

“As this type of information is used more often in the clinical setting, the emphasis on speed becomes much stronger,” Van der Auwera said.

That’s where computer scientists can help.

Many of the tools used for genomic analysis were written by biologists who developed an interest in computer science because computation was becoming so valuable to their work.

Meanwhile, Pandya said, computer scientists such as himself started developing an interest in biological sciences because they saw so many possibilities. Now, those computer scientists are augmenting the work of biologists.

With BWA and GATK, the Microsoft team scoured the code for places where they could make the algorithms run more smoothly, efficiently and reliably, without compromising the attention to accuracy.

“We took Microsoft’s keen expertise in software development and applied it to the algorithms, making them faster,” Heckerman said.

Microsoft holds a nonexclusive license from the Broad Institute to provide GATK on Azure. It plans to work with the Broad Institute to incorporate these performance improvements into future versions of GATK. Broad Institute would then make these improvements available to researchers.

Heng Li, a research scientist at Broad who initially developed the BWA tool and worked with Microsoft to make it faster, said the collaborative nature of the work made for better results.

“They have knowledge that I don’t possess, but on the other hand I know what’s important and what’s not on the biological analysis side,” Li said.

The cloud for storage and computation
As genomic analysis becomes more critical for health and other applications, Broad has started working with Microsoft and other technology companies to move tools like GATK and BWA to cloud computing platforms.

Cloud computing is ideal for this type of computational work, because it takes a lot computing power, requires a lot of data storage and requests can come in fits and bursts. For most hospitals, research labs and other biological sciences facilities it would be too expensive to invest in the necessary computing capability, and impractical to take on the job of hosting all that data on their own, if only because the sheer volume of data is growing exponentially.

As these tools become more useful, most researchers and clinicians also want to focus on getting the results they need, rather than worrying about the technical side of things.

“When you get to this next level you just want answers,” Pandya said. “You want it to be really simple.”

Eventually, the Microsoft team hopes to use another company strength – developing an ecosystem around a technology – to help hospitals and other institutions implement these systems. Microsoft’s genomics team is talking to independent software vendors about ways to make that happen.

The tool is part of Microsoft’s broader health-related efforts. On Monday, as part of an update to its Cancer Moonshot initiative, the White House announced that Microsoft had joined an effort to maintain cancer genomic data in the cloud. The effort is a partnership between the public and private sector.

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Allison Linn is a senior writer at Microsoft. Follow her on Twitter.

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