[Nfbk] {Spam?} Re: [Nfbktad] {Disarmed} De cades of computer vision research, one ‘Swiss Army knife’

[David Andrews]

It is not a list, it means the message was 
scanned for viruses etc., along the way, and found ok.

Dave

At 08:50 AM 4/6/2016, you wrote:
>
>Good morning,
>
>I keep seeing things with {disarmed}.  What is 
>this?  Is it a list?  If so, how does one aubscribe?  Thanks.
>----- Original Message -----
>From: <mailto:nfbk at nfbnet.org>Cathy Jackson via Nfbk
>To: <mailto:nfbktad at nfbnet.org>NFB of Kentucky, 
>Technology Assistance Division
>Cc: <mailto:cathyj1949 at gmail.com>Cathy Jackson ; <mailto:nfbk at nfbnet.org>NFBK
>Sent: Saturday, April 02, 2016 9:58 AM
>Subject: Re: [Nfbk] [Nfbktad] {Disarmed} Decades 
>of computer vision research, one ‘Swiss Army knife’
>
>Ann worked for our national office not NFB of Maryland.
>Cathy
>
>Sent from my iPhone
>
>On Apr 2, 2016, at 9:03 AM, Kevin Pearl via 
>Nfbktad <<mailto:nfbktad at nfbnet.org>nfbktad at nfbnet.org> wrote:
>
>>Anne Taylor is a senior project manager at 
>>Microsoft and a former NFBK member. She also worked for the NFB in Baltimore.
>>
>>
>>
>>Decades of computer vision research, one 
>>‘Swiss Army knife’ - Next at Microsoft
>>
>>
>>
>>When Anne Taylor walks into a room, she wants 
>>to know the same things that any person would.
>>
>>Where is there an empty seat? Who is walking up 
>>to me, and is that person smiling or frowning? What does that sign say?
>>
>>For Taylor, who is blind, there aren’t always 
>>easy ways to get this information. Perhaps 
>>another person can direct her to her seat, 
>>describe her surroundings or make an introduction.
>>
>>There are apps and tools available to help 
>>visually impaired people, she said, but they 
>>often only serve one limited function and they 
>>aren’t always easy to use. It’s also 
>>possible to ask other people for help, but most 
>>people prefer to navigate the world as independently as possible.
>>
>>That’s why, when Taylor arrived at Microsoft 
>>about a year ago, she immediately got 
>>interested in working with a group of 
>>researchers and engineers on a project that she 
>>affectionately calls a potential “Swiss Army 
>>knife” of tools for visually impaired people.
>>
>>“I said, ‘Let’s do something that really 
>>matters to the blind community,’” said 
>>Taylor, a senior project manager who works on 
>>ways to make Microsoft products more 
>>accessible. “Let’s find a solution for a scenario that really matters.”
>>
>>That project is 
>><https://youtu.be/3WP7Id8SxYQ>Seeing AI, a 
>>research project that uses computer vision and 
>>natural language processing to describe a 
>>person’s surroundings, read text, answer 
>>questions and even identify emotions on 
>>people’s faces. Seeing AI, which can be used 
>>as a cell phone app or via smart glasses from 
>><http://www.pivothead.com/>Pivothead, made its 
>>public debut at the company’s 
>><http://build.microsoft.com/>Build conference 
>>this week. It does not currently have a release date.
>>
>>Taylor said Seeing AI provides another layer of 
>>information for people who also are using 
>>mobility aids such as white canes and guide dogs.
>>
>>“This app will help level the playing field,” Taylor said.
>>
>>At the same conference, Microsoft also unveiled 
>><https://www.captionbot.ai/>CaptionBot, a 
>>demonstration site that can take any image and 
>>provide a detailed description of it.
>>
>>Very deep neural networks, natural language processing and more
>>Seeing AI and CaptionBot represent the latest 
>>advances in this type of technology, but they 
>>are built on decades of cutting-edge research 
>>in fields including computer vision, image 
>>recognition, natural language processing and machine learning.
>>
>>In recent years, a spate of breakthroughs has 
>>allowed computer vision researchers to do 
>>things they might not have thought possible even a few years before.
>>
>>“Some people would describe it as a 
>>miracle,” said 
>><http://research.microsoft.com/en-us/people/xiaohe/>Xiaodong 
>>He, a senior Microsoft researcher who is 
>>leading the image captioning effort that is 
>>part of 
>><https://www.microsoft.com/cognitive-services>Microsoft 
>>Cognitive Services. “The intelligence we can 
>>say we have developed today is so much better than six years ago.”
>>
>>The field is moving so fast that it’s 
>><http://research.microsoft.com/pubs/264408/ImageCaptionInWild.pdf>substantially 
>>better than even six months ago, he said. For 
>>example, 
>><http://research.microsoft.com/people/ktran/>Kenneth 
>>Tran, a senior research engineer on his team 
>>who is leading the development effort, recently 
>>figured out a way to make the image captioning 
>>system more than 20 times faster, allowing 
>>people who use tools like Seeing AI to get the 
>>information they need much more quickly.
>>
>>A major a-ha moment came a few years ago, when 
>>researchers hit on the idea of using deep 
>>neural networks, which roughly mimic the 
>>biological processes of the human brain, for machine learning.
>>
>>Machine learning is the general term for a 
>>process in which systems get better at doing 
>>something as they are given more training data 
>>about that task. For example, if a computer 
>>scientist wants to build an app that helps 
>>bicyclists recognize when cars are coming up 
>>behind them, it would feed the computer tons of 
>>pictures of cars, so the app learned to 
>>recognize the difference between a car and, say, a sign or a tree.
>>
>>Computer scientists had used neural networks 
>>before, but not in this way, and the new 
>>approach resulted in big leaps in computer vision accuracy.
>>
>>Several months ago, Microsoft researchers 
>><http://research.microsoft.com/en-us/people/jiansun/>Jian 
>>Sun and 
>><http://research.microsoft.com/en-us/um/people/kahe/>Kaiming 
>>Hemade another big leap when they unveiled a 
>>new system that uses very deep neural networks 
>>– called 
>><http://arxiv.org/abs/1512.03385>residual 
>>neural networks – to correctly identify photos. 
>>The 
>><http://blogs.microsoft.com/next/2015/12/10/microsoft-researchers-win-imagenet-computer-vision-challenge/>new 
>>approach to recognizing images resulted in huge 
>>improvements in accuracy. The researchers 
>>shocked the academic community and won two 
>>major contests, the 
>><http://www.image-net.org/>ImageNet and 
>><http://mscoco.org/home/>Microsoft Common Objects in Contextchallenges.
>>
>>Tools to recognize and accurately describe images
>>That approach is now being used by Microsoft 
>>researchers who are working on ways to not just 
>>recognize images but also write captions about 
>>them. This research, which combines image 
>>recognition with natural language processing, 
>>can help people who are visually impaired get 
>>an accurate description of an image. It also 
>>has applications for people who need 
>>information about an image but can’t look at 
>>it, such as when they are driving.
>>
>>The image captioning work also has received 
>><https://blogs.technet.microsoft.com/inside_microsoft_research/2015/06/11/microsoft-researchers-tie-for-best-image-captioning-technology/>accolades 
>>for its accuracy as compared to other research 
>>projects, and it is the basis for the 
>>capabilities in Seeing AI and Caption Bot. Now, 
>>the researchers are working on expanding the 
>>training set so it can give users a deeper sense of the world around them.
>><https://mscorpmedia.azureedge.net/mscorpmedia/2016/03/FSPB4720.jpg>
>>Margaret Mitchell
>>
>>
>>Margaret Mitchell
>>
>><http://m-mitchell.com/>Margaret Mitchell, a 
>>Microsoft researcher who specializes in natural 
>>language processing and has been one of the 
>>industry’s leading researchers on image 
>>captioning, said she and her colleagues also 
>>are looking at ways a computer can describe an image in a more human way.
>>
>>For example, while a computer might accurately 
>>describe a scene as “a group of people that 
>>are sitting next to each other,” a person may 
>>say that it’s “a group of people having a 
>>good time.” The challenge is to help the 
>>technology understand what a person would think 
>>was <http://arxiv.org/abs/1512.06974>most 
>>important, and worth saying, about the picture.
>>
>>“There’s a separation between what’s in 
>>an image and what we say about the image,” 
>>said Mitchell, who also is one of the leads on the Seeing AI project.
>>
>>Other Microsoft researchers are developing ways 
>>that the latest image recognition tools can 
>>provide more thorough explanations of pictures. 
>>For example, instead of just describing an 
>>image as “a man and a woman sitting next to 
>>each other,” it would be more helpful for the 
>>technology to say, “Barack Obama and Hillary 
>>Clinton are posing for a picture.”
>>
>>That’s where 
>><http://research.microsoft.com/en-us/people/leizhang/>Lei Zhang comes in.
>>
>>When you search the Internet for an image 
>>today, chances are high that the search engine 
>>is relying on text associated with that image 
>>to return a picture of Kim Kardashian or Taylor Swift.
>>
>>Zhang, a senior researcher at Microsoft, is 
>>working with researchers including Yandong Guo 
>>on a system that uses machine learning to 
>>identify celebrities, politicians and public 
>>figures based on the elements of the image 
>>rather than the text associated with it.
>>
>>Zhang’s research will be included in the 
>>latest vision tools that are part of 
>><https://www.microsoft.com/cognitive-services>Microsoft 
>>Cognitive Services. That’s a set of tools 
>>that is  based on Microsoft’s cutting-edge 
>>machine learning research, and which developers 
>>can use to build apps and services that do 
>>things like recognize faces, identify emotions 
>>and distinguish various voices. Those tools 
>>also have provided the technical basis for 
>>Microsoft showcase apps and demonstration 
>>websites such as 
>><http://how-old.net/>how-old.net, which guesses 
>>a person’s age, and 
>><http://news.microsoft.com/features/fetch-new-microsoft-garage-app-uses-artificial-intelligence-to-name-that-breed/>Fetch, 
>>which can  identify a dog’s breed.
>>
>>Microsoft Cognitive Services is an example of 
>>what is becoming a more common phenomenon – the 
>>lightning-fast transfer of the latest research 
>>advances into products that people can actually 
>>use. The engineers who work on Microsoft 
>>Cognitive Services say their job is a bit like 
>>solving a puzzle, and the pieces are the latest research.
>>
>>“All these pieces come together and we need 
>>to figure out, how do we present those to an 
>>end user?” said Chris Buehler, a software 
>>engineering manager who works on Microsoft Cognitive Services.
>>
>> From research project to helpful product
>>Seeing AI, the research project that could 
>>eventually help visually impaired people, is 
>>another example of how fast research can become 
>>a really helpful tool. It was conceived at last 
>>year’s 
>><http://blogs.microsoft.com/firehose/2015/07/27/oneweek-hackathon-2015-heard-around-the-world/>//oneweek 
>>Hackathon, an event in which Microsoft 
>>employees from across the company work together 
>>to try to make a crazy idea become a reality.
>>
>>The group that built Seeing AI included 
>>researchers and engineers from all over the 
>>world who were attracted to the project because 
>>of the technological challenges and, in many 
>>cases, also because they had a personal reason 
>>for wanting to help visually impaired people operate more independently.
>>
>>“We basically had this super team of 
>>different people from different backgrounds, 
>>working to come up with what was needed,” 
>>said Anirudh Koul, who has been a lead on the 
>>Seeing AI project since its inception and 
>>became interested in it because his grandfather is losing his ability to see.
>>
>>For Taylor, who joined Microsoft to represent 
>>the needs of blind people, it was a great 
>>experience that also resulted in a potential 
>>product that could make a real difference in people’s lives.
>>
>>“We were able to come up with this one Swiss 
>>Army knife that is so valuable,” she said.
>>
>>This article is online at:

         David Andrews and long white cane Harry.
E-Mail:  dandrews at visi.com or david.andrews at nfbnet.org
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