Category: Mobile


The future of AI is neuromorphic. Meet the scientists building digital ‘brains’ for your phone

By Hugo Angel,

Neuromorphic chips are being designed to specifically mimic the human brain – and they could soon replace CPUs
BRAIN ACTIVITY MAP
Neuroscape Lab
AI services like Apple’s Siri and others operate by sending your queries to faraway data centers, which send back responses. The reason they rely on cloud-based computing is that today’s electronics don’t come with enough computing power to run the processing-heavy algorithms needed for machine learning. The typical CPUs most smartphones use could never handle a system like Siri on the device. But Dr. Chris Eliasmith, a theoretical neuroscientist and co-CEO of Canadian AI startup Applied Brain Research, is confident that a new type of chip is about to change that.
Many have suggested Moore’s law is ending and that means we won’t get ‘more compute’ cheaper using the same methods,” Eliasmith says. He’s betting on the proliferation of ‘neuromorphics’ — a type of computer chip that is not yet widely known but already being developed by several major chip makers.
Traditional CPUs process instructions based on “clocked time” – information is transmitted at regular intervals, as if managed by a metronome. By packing in digital equivalents of neurons, neuromorphics communicate in parallel (and without the rigidity of clocked time) using “spikes” – bursts of electric current that can be sent whenever needed. Just like our own brains, the chip’s neurons communicate by processing incoming flows of electricity – each neuron able to determine from the incoming spike whether to send current out to the next neuron.
What makes this a big deal is that these chips require far less power to process AI algorithms. For example, one neuromorphic chip made by IBM contains five times as many transistors as a standard Intel processor, yet consumes only 70 milliwatts of power. An Intel processor would use anywhere from 35 to 140 watts, or up to 2000 times more power.
Eliasmith points out that neuromorphics aren’t new and that their designs have been around since the 80s. Back then, however, the designs required specific algorithms be baked directly into the chip. That meant you’d need one chip for detecting motion, and a different one for detecting sound. None of the chips acted as a general processor in the way that our own cortex does.
This was partly because there hasn’t been any way for programmers to design algorithms that can do much with a general purpose chip. So even as these brain-like chips were being developed, building algorithms for them has remained a challenge.
 
Eliasmith and his team are keenly focused on building tools that would allow a community of programmers to deploy AI algorithms on these new cortical chips.
Central to these efforts is Nengo, a compiler that developers can use to build their own algorithms for AI applications that will operate on general purpose neuromorphic hardware. Compilers are a software tool that programmers use to write code, and that translate that code into the complex instructions that get hardware to actually do something. What makes Nengo useful is its use of the familiar Python programming language – known for it’s intuitive syntax – and its ability to put the algorithms on many different hardware platforms, including neuromorphic chips. Pretty soon, anyone with an understanding of Python could be building sophisticated neural nets made for neuromorphic hardware.
Things like vision systems, speech systems, motion control, and adaptive robotic controllers have already been built with Nengo,Peter Suma, a trained computer scientist and the other CEO of Applied Brain Research, tells me.
Perhaps the most impressive system built using the compiler is Spaun, a project that in 2012 earned international praise for being the most complex brain model ever simulated on a computer. Spaun demonstrated that computers could be made to interact fluidly with the environment, and perform human-like cognitive tasks like recognizing images and controlling a robot arm that writes down what it’s sees. The machine wasn’t perfect, but it was a stunning demonstration that computers could one day blur the line between human and machine cognition. Recently, by using neuromorphics, most of Spaun has been run 9000x faster, using less energy than it would on conventional CPUs – and by the end of 2017, all of Spaun will be running on Neuromorphic hardware.
Eliasmith won NSERC’s John C. Polyani award for that project — Canada’s highest recognition for a breakthrough scientific achievement – and once Suma came across the research, the pair joined forces to commercialize these tools.
While Spaun shows us a way towards one day building fluidly intelligent reasoning systems, in the nearer term neuromorphics will enable many types of context aware AIs,” says Suma. Suma points out that while today’s AIs like Siri remain offline until explicitly called into action, we’ll soon have artificial agents that are ‘always on’ and ever-present in our lives.
Imagine a SIRI that listens and sees all of your conversations and interactions. You’ll be able to ask it for things like – “Who did I have that conversation about doing the launch for our new product in Tokyo?” or “What was that idea for my wife’s birthday gift that Melissa suggested?,” he says.
When I raised concerns that some company might then have an uninterrupted window into even the most intimate parts of my life, I’m reminded that because the AI would be processed locally on the device, there’s no need for that information to touch a server owned by a big company. And for Eliasmith, this ‘always on’ component is a necessary step towards true machine cognition. “The most fundamental difference between most available AI systems of today and the biological intelligent systems we are used to, is the fact that the latter always operate in real-time. Bodies and brains are built to work with the physics of the world,” he says.
Already, major efforts across the IT industry are heating up to get their AI services into the hands of users. Companies like Apple, Facebook, Amazon, and even Samsung, are developing conversational assistants they hope will one day become digital helpers.
ORIGINAL: Wired
Monday 6 March 2017

Google Unveils Neural Network with “Superhuman” Ability to Determine the Location of Almost Any Image

By Hugo Angel,

Guessing the location of a randomly chosen Street View image is hard, even for well-traveled humans. But Google’s latest artificial-intelligence machine manages it with relative ease.
Here’s a tricky task. Pick a photograph from the Web at random. Now try to work out where it was taken using only the image itself. If the image shows a famous building or landmark, such as the Eiffel Tower or Niagara Falls, the task is straightforward. But the job becomes significantly harder when the image lacks specific location cues or is taken indoors or shows a pet or food or some other detail.Nevertheless, humans are surprisingly good at this task. To help, they bring to bear all kinds of knowledge about the world such as the type and language of signs on display, the types of vegetation, architectural styles, the direction of traffic, and so on. Humans spend a lifetime picking up these kinds of geolocation cues.So it’s easy to think that machines would struggle with this task. And indeed, they have.

Today, that changes thanks to the work of Tobias Weyand, a computer vision specialist at Google, and a couple of pals. These guys have trained a deep-learning machine to work out the location of almost any photo using only the pixels it contains.

Their new machine significantly outperforms humans and can even use a clever trick to determine the location of indoor images and pictures of specific things such as pets, food, and so on that have no location cues.

Their approach is straightforward, at least in the world of machine learning.

  • Weyand and co begin by dividing the world into a grid consisting of over 26,000 squares of varying size that depend on the number of images taken in that location.
    So big cities, which are the subjects of many images, have a more fine-grained grid structure than more remote regions where photographs are less common. Indeed, the Google team ignored areas like oceans and the polar regions, where few photographs have been taken.

 

  • Next, the team created a database of geolocated images from the Web and used the location data to determine the grid square in which each image was taken. This data set is huge, consisting of 126 million images along with their accompanying Exif location data.
  • Weyand and co used 91 million of these images to teach a powerful neural network to work out the grid location using only the image itself. Their idea is to input an image into this neural net and get as the output a particular grid location or a set of likely candidates. 
  • They then validated the neural network using the remaining 34 million images in the data set.
  • Finally they tested the network—which they call PlaNet—in a number of different ways to see how well it works.

The results make for interesting reading. To measure the accuracy of their machine, they fed it 2.3 million geotagged images from Flickr to see whether it could correctly determine their location. “PlaNet is able to localize 3.6 percent of the images at street-level accuracy and 10.1 percent at city-level accuracy,” say Weyand and co. What’s more, the machine determines the country of origin in a further 28.4 percent of the photos and the continent in 48.0 percent of them.

That’s pretty good. But to show just how good, Weyand and co put PlaNet through its paces in a test against 10 well-traveled humans. For the test, they used an online game that presents a player with a random view taken from Google Street View and asks him or her to pinpoint its location on a map of the world.

Anyone can play at www.geoguessr.com. Give it a try—it’s a lot of fun and more tricky than it sounds.

GeoGuesser Screen Capture Example

Needless to say, PlaNet trounced the humans. “In total, PlaNet won 28 of the 50 rounds with a median localization error of 1131.7 km, while the median human localization error was 2320.75 km,” say Weyand and co. “[This] small-scale experiment shows that PlaNet reaches superhuman performance at the task of geolocating Street View scenes.

An interesting question is how PlaNet performs so well without being able to use the cues that humans rely on, such as vegetation, architectural style, and so on. But Weyand and co say they know why: “We think PlaNet has an advantage over humans because it has seen many more places than any human can ever visit and has learned subtle cues of different scenes that are even hard for a well-traveled human to distinguish.

They go further and use the machine to locate images that do not have location cues, such as those taken indoors or of specific items. This is possible when images are part of albums that have all been taken at the same place. The machine simply looks through other images in the album to work out where they were taken and assumes the more specific image was taken in the same place.

That’s impressive work that shows deep neural nets flexing their muscles once again. Perhaps more impressive still is that the model uses a relatively small amount of memory unlike other approaches that use gigabytes of the stuff. “Our model uses only 377 MB, which even fits into the memory of a smartphone,” say Weyand and co.

That’s a tantalizing idea—the power of a superhuman neural network on a smartphone. It surely won’t be long now!

Ref: arxiv.org/abs/1602.05314 : PlaNet—Photo Geolocation with Convolutional Neural Networks

ORIGINAL: TechnoplogyReview
by Emerging Technology from the arXiv
February 24, 2016

Silicon Chips That See Are Going to Make Your Smartphone Brilliant

By admin,

Many gadgets will be able to understand images and video thanks to chips designed to run powerful artificial-intelligence algorithms.
WHY IT MATTERS
Many applications for mobile computers could be more powerful with advanced image recognition.
Many of the devices around us may soon acquire powerful new abilities to understand images and video, thanks to hardware designed for the machine-learning technique called deep learning.
Companies like Google have made breakthroughs in image and face recognition through deep learning, using giant data sets and powerful computers (see “10 Breakthrough Technologies 2013: Deep Learning”). Now two leading chip companies and the Chinese search giant Baidu say hardware is coming that will bring the technique to phones, cars, and more.
Chip manufacturers don’t typically disclose their new features in advance. But at a conference on computer vision Tuesday, Synopsys, a company that licenses software and intellectual property to the biggest names in chip making, showed off a new image-processor core tailored for deep learning. It is expected to be added to chips that power smartphones, cameras, and cars. The core would occupy about one square millimeter of space on a chip made with one of the most commonly used manufacturing technologies.
Pierre Paulin, a director of R&D at Synopsys, told MIT Technology Review that the new processor design will be made available to his company’s customers this summer. Many have expressed strong interest in getting hold of hardware to help deploy deep learning, he said.
Synopsys showed a demo in which the new design recognized speed-limit signs in footage from a car. Paulin also presented results from using the chip to run a deep-learning network trained to recognize faces. It didn’t hit the accuracy levels of the best research results, which have been achieved on powerful computers, but it came pretty close, he said. “For applications like video surveillance it performs very well,” he said. The specialized core uses significantly less power than a conventional chip would need to do the same task.
The new core could add a degree of visual intelligence to many kinds of devices, from phones to cheap security cameras. It wouldn’t allow devices to recognize tens of thousands of objects on their own, but Paulin said they might be able to recognize dozens.
That might lead to novel kinds of camera or photo apps. Paulin said the technology could also enhance car, traffic, and surveillance cameras. For example, a home security camera could start sending data over the Internet only when a human entered the frame. “You can do fancier things like detecting if someone has fallen on the subway,” he said.

Jeff Gehlhaar, vice president of technology at Qualcomm Research, spoke at the event about his company’s work on getting deep learning running on apps for existing phone hardware. He declined to discuss whether the company is planning to build support for deep learning into its chips. But speaking about the industry in general, he said that such chips are surely coming. Being able to use deep learning on mobile chips will be vital to helping robots navigate and interact with the world, he said, and to efforts to develop autonomous cars.
I think you will see custom hardware emerge to solve these problems,” he said. “Our traditional approaches to silicon are going to run out of gas, and we’ll have to roll up our sleeves and do things differently.” Gehlhaar didn’t indicate how soon that might be. Qualcomm has said that its coming generation of mobile chips will include software designed to bring deep learning to camera and other apps (see “Smartphones Will Soon Learn to Recognize Faces and More”).
Ren Wu, a researcher at Chinese search company Baidu, also said chips that support deep learning are needed for powerful research computers in daily use. “You need to deploy that intelligence everywhere, at any place or any time,” he said.
Being able to do things like analyze images on a device without connecting to the Internet can make apps faster and more energy-efficient because it isn’t necessary to send data to and fro, said Wu. He and Qualcomm’s Gehlhaar both said that making mobile devices more intelligent could temper the privacy implications of some apps by reducing the volume of personal data such as photos transmitted off a device.
You want the intelligence to filter out the raw data and only send the important information, the metadata, to the cloud,” said Wu.
ORIGINAL: Tech Review
May 14, 2015

How It Works: IBM’s Concept Insights

By admin,

Concept Insights is a new Web service available on the IBM Watson Developer Cloud, where developers can tap into the capability via our Bluemix development platform for Web services and mobile apps.

App Captures the Boston Bombing’s Psychological Effects

By admin,

ORIGINAL: IEEE Spectrum
By Eliza Strickland
27 Aug 2013Could psychological-monitoring apps become as common as fitness and activity gadgets?

Image: Cogito A Mind Minder: Cogito’s mood-monitoring app can detect signals of psychological distress

In April, the software company Cogito was halfway through a clinical trial to see if it could detect symptoms of depression and post-traumatic stress disorder (PTSD) through a smartphone app. All of the 100 participants in the study lived around Boston. Then, on 15 April, two bombs went off near the finish line of the Boston Marathon, killing three people and injuring hundreds. Suddenly, Cogito’s clinical trial was a lot more relevant.

The trial was funded by the Defense Advanced Research Projects Agency (DARPA) under its Detection and Computational Analysis of Psychological Signals program. To address the troubling number of psychological problems and suicides among active-duty military personnel and veterans, the U.S. Department of Defense is seeking technologies that can identify at-risk individuals so professionals can help them.

Cogito, a Boston-based MIT spin-off, developed an app that keeps track of a person’s social behavior and vocal characteristics. The app monitors the phone’s location and time of use and also logs phone calls and text messages. (It doesn’t look at the content of those calls and texts.) Finally, there’s an active component: Participants can choose to fill out questionnaires about their mood and can record audio diaries. Cogito’s expertise is in automated speech analysis, which it applies to those audio diaries; future iterations could mine phone conversations for information as well.

Put all the data together and you’re able to tell a lot about a person, says Cogito CEO Joshua Feast. Sometimes you even find signs of distress that people don’t want to admit to or haven’t recognized themselves. “We’re able to look at sleep, mood, social isolation, and physical isolation,” says Feast, all of which can serve as “honest signals” of psychological trouble. In the Boston trial, Cogito was only testing the sophisticated algorithms it developed to aggregate the data. If the trial works out, future versions of the software could provide these summaries to clinicians to allow them to intervene and could also give the information to the subjects themselves.

All of this can seem rather creepy—apps that get inside your head and reveal your emotional secrets. But Feast says that’s why his company places so much emphasis on privacy and trust. If Cogito’s system becomes a commercial product, there will be legal guarantees that a user will always own and control his or her own data. For example, a user could choose whether or not to share the data with a clinician. Feast says he doesn’t think users would have it any other way. “Morally it’s the right thing to do, and also for adoption it’s the right thing to do,” he says.

The participants in the Boston trial included veterans, civilians with histories of trauma or depression, and some healthy civilians. While the bulk of the data from the study is still being analyzed, Feast says the impact of the April bombing is already clear. The algorithms picked up more markers of stress in the participants, including decreased use of the app’s interactive components. “Fewer survey questions were being answered, and fewer audio diaries were being recorded,” he says.

Further study of the data will answer other important questions about the nature of depression and PTSD, says Feast: “What is resilience? What kind of people fared better after the bombing? What happens to people with vulnerability when things like this happen?” The company is still formulating its research questions, he says.

The Durkheim Project, another initiative funded by this DARPA program, focuses more narrowly on identifying veterans at risk of suicide. Chris Poulin, director of the project, explains that his system predicts suicide risk by analyzing veterans’ text messages and their posting on social-networking sites like Facebook and Twitter. Poulin says he’s impressed with the scope of Cogito’s data collection and its incorporation of voice monitoring. “There are other people out there collecting mobile data and looking at activity metrics, but very few people have integrated voice data,” he says.

Cogito’s voice-analysis software, Cogito Dialog, monitors vocal characteristics such as level of excitement and fluidity of speech. Feast explains that it’s tricky to get clear data in this area because there’s so much natural variation in people’s speech habits. However, the system can detect changes to an individual’s speech patterns over time and can also be useful in telemedicine. For example, if a clinician calls veterans and asks them all the same series of questions, a monitoring system can flag people with unusual responses. “Speech analysis is well suited for looking at population norms and deviation from the norms,” says Feast.

Feast believes that the company’s experience with the Boston bombing provides a preview of a possible future where psychological monitoring apps are as common as the fitness and activity gadgets that proliferate today. “When there’s an earthquake or terrorist attack or traumatic event that hits a population center, this technology could support a rapid response team for psychological distress,” he says. “It would be like the CDC [Centers for Disease Control] responding to a flu outbreak.