Showing posts with label machine learning. Show all posts
Showing posts with label machine learning. Show all posts

Tuesday, July 4, 2017

Amazing Wearable Technology for the Blind

We all use technology to make our lives easier.  From apps that help track your health to GPS that guides you to a new restaurant, it is such a part of our daily lives that we take it for granted. For a sight impaired individual though, much of the common technology doesn’t help them.
So when a device is designed specifically for the blind, it is a life-changing event. Horus, named after the ancient Egyptian god who lost an eye in a fight, is a high tech wearable device that uses cameras, sensors, and machine learning to help vision impaired people “see” the world around them. In this article by The Engineer, it talks about the device in more detail:

Device for the blind uses computer vision and machine learning | The Engineer

A new wearable device called Horus is using a combination of computer vision, machine learning and audio cues to improve the lives of visually impaired
Unlock Technology
From Pixabay
people.
Developed by a Swiss startup called Eyra, Horus consists of a headband with stereo cameras on one end that can recognise text, faces and objects. Information from the cameras is fed via a 1m cable into a smartphone-sized box containing a battery and a NVIDIA Tegra K1 processor. This provides GPU-accelerated computer vision, deep learning and sensors that process, analyse and describe the images from the cameras.
Feedback and instruction are delivered via bone conduction audio technology that allows the wearer to hear descriptions even in noisy environments. Similar technology has been developed by BAE Systems for the military and adapted for the Ben Ainslie Racing (BAR) America’s Cup team.
The user is able to activate different functionalities via intuitively shaped buttons on both the headset and the pocket unit. As well as learning and recognising objects and faces, and reading texts from flat and non-flat surfaces, Horus helps users navigate using audio cues. 3D sounds with different intensity, pitch, and frequency represent the position of obstacles, providing assistance in a similar way to parking sensors on a car.
Horus can also be prompted to give a short audio description of what the cameras are seeing, whether that is a room full of people, a photograph or a landscape.
In this video, the creators of Horus discuss how the concept was developed and more about how the device works. Horus includes a headset and a pocket computer that processes images and other data, extracting information and describing it  to the user through an audible message. It is in the testing phase right now with a sizable waiting list, but should be available to the public in either 2017 or 2018:

The cost of Horus is estimated to be around $2,000. Even though it is a bit expensive, it could prove priceless with achieving independence for the blind. Not only can it help read a book, but it assembles a database of contacts with the ability for facial recognition, and it also identifies inanimate objects in the same way. It helps with navigation, preventing the user from running into objects while walking.
Read more about its features in the article by New Atlas:

Horus wearable helps the blind navigate, remember faces and read books

Horus can also build a kind of face-based contacts list by scanning a new face and prompting the user to assign a name. It will then alert the wearer
Face Recognition
Wikimedia Commons
whenever it spots that person again. The same can be done with inanimate objects to help a blind person distinguish between a bottle of juice and a bottle of milk. The device’s object recognition apparently even works in two dimensions, allowing it to describe photographs, read text on signs or even turn any book into an audio book.
In a navigation mode, Horus uses its stereo-camera setup to perceive the distance to objects in front of the user, and will respond with a system of audio cues like parking sensors in a car: the closer something is the faster the device will beep, communicating direction by focusing the sound more in either the left or right ear.
Like most technology, the price will eventually come down because there will be other companies duplicating the idea. This amazing wearable technology for the blind will help so many people live more independently. It opens up a whole new world of deep learning with technology.

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Tuesday, May 30, 2017

Twitter and Machine Learning: The Purchase of Magic Pony Technology

Another big purchase for a social media platform, as Twitter purchased Magic Pony Technology for a nice chunk of money. With this investment, Twitter is stepping up their game with machine learning.  In this case, though, it has to do with ways to compress and enhance photographs and videos.  This type of technology highlights Twitter’s focus on videos and images, but with a sci-fi twist of using neural networks and artificial intelligence to take “Photoshopping” to a new level.
Since this is the 3rd machine learning startup company Twitter has bought, it is obviously part of their future plans.  How exciting to think of the possibilities!  In this article by TechCrunch.com, the acquisition for Twitter and Machine Learning is explained in further detail.

Twitter pays up to $150M for Magic Pony Technology, which uses neural networks to improve images | TechCrunch

Twitter today is taking another step to build up its machine learning muscle, and also potentially to improve how it delivers photos and videos across its apps: the company is acquiring Magic Pony Technology (that is really the name), a company based out of London that has developed techniques of using neural networks (systems that essentially are designed to think like human brains) and machine learning to provide expanded data for images — used, for example, to enhance a picture or video taken on a mobile phone; or to help develop graphics for virtual reality or augmented reality applications.
Twitter Mobile
From Pixabay
Terms of the deal are not being disclosed but we have two separate sources who tell us that Twitter is paying $150 million in all for the deal. This takes into account retention bonuses for the staff, which numbers about 11, including co-founders Zehan Wang and CEO Rob Bishop.
“Machine learning is increasingly at the core of everything we build at Twitter,” said Jack Dorsey, Twitter CEO and co-founder, in a statement. “Magic Pony’s machine learning technology will help us build strength into our deep learning teams with world-class talent, so Twitter can continue to be the best place to see what’s happening and why it matters, first. We value deep learning research to help make our world better, and we will keep doing our part to share our work and learnings with the community.”
This is the third machine learning startup Twitter has acquired, after Whetlab last year, and Madbits in 2014.
Magic Pony Technology had raised an undisclosed amount of money from investors like Octopus Ventures, Entrepreneur First and Balderton. One of Balderton’s ex-VC’s n fact, invested in the company.
We first learned about Magic Pony Technology when they caught our eye after they presented last year at a Pitch@Palace, a tech event put on at St James’ Palace in London.
It made a few further waves this year, as it further revealed the way that its technology worked to help enhance visuals with information that may not be in the picture itself, but essentially be recreated from composites of similar pictures, much like how the human eye works. In fact, one anecdote I’ve read about the origin of the name “Magic Pony” is that it’s a reference to the remarkable nature of what they do. (“It’s unbelievable, like a magic pony!”)
The company, however, by and large has remained fairly under the radar, with a website that has never offered more than a simple statement about what it does and the number of patents that it has filed. There are around 20 now, with several of them listed here, which will now belong to Twitter.
Magic Pony Technology and Twitter
From Pixabay
As for what Twitter plans to do with the tech, it’s notable that Dorsey keeps his comments to a general statement about the place for machine learning in Twitter’s bigger business (those comments are further elaborated here, where Dorsey notes that the team will be joining Twitter’s Cortex division).
But more specifically, Magic Pony has been building out technology in the area of image processing and this has an obvious avenue into Twitter’s business. Given that a large part of Twitter’s audience posts on and reads Twitter, as well as its video apps Vine and Periscope, using mobile handsets, and given that mobile handsets sometimes produce less than perfect media, there is a clear opportunity for Twitter to use this directly in its own products.
“Twitter has gone after video in a big way and buying Magic Pony demonstrates how important video is for them. That’s the key thing,” Suranga Chandratillake, a partner at Balderton, told TechCrunch.
Less obvious, but also very interesting is that Twitter is gaining a very strong team working in still-emerging tech areas where the company has yet to lay out any intentions, like virtual and augmented reality.
“Magic Pony was already working on a pretty substantial VR and AR strategy before they were acquired and have some very interesting tech in that area,” noted Chandratillake.
“Our team has researched and developed state-of-the-art machine learning techniques for visual processing that can identify the features of imagery and use that information to process it in new ways,” said Rob Bishop, Magic Pony CEO and co-founder. “Joining forces with Twitter gives us the opportunity to bring the benefits of that research to hundreds of millions of people around the world, and allows Magic Pony to contribute to better quality viewing experiences on Twitter.”
This is the pitch video that Magic Pony Technology presented at an tech event last year that caught Twitter’s eye.

The news of the Magic Pony is all over social media today, but especially on Twitter. It will be interesting to see how Twitter uses this deep learning technology. Sci-fi meets reality.
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