IBM’S ‘Rodent Brain’ Chip Could Make Our Phones Hyper-Smart

At a lab near San Jose, IBM has built the digital equivalent of a rodent brain---roughly speaking. It spans 48 of the company's experimental TrueNorth chips, a new breed of processor that mimics the brain's biological building blocks. IBM

DHARMENDRA MODHA WALKS me to the front of the room so I can see it up close. About the size of a bathroom medicine cabinet, it rests on a table against the wall, and thanks to the translucent plastic on the outside, I can see the computer chips and the circuit boards and the multi-colored lights on the inside. It looks like a prop from a ’70s sci-fi movie, but Modha describes it differently. “You’re looking at a small rodent,” he says.

He means the brain of a small rodent—or, at least, the digital equivalent. The chips on the inside are designed to behave like neurons—the basic building blocks of biological brains. Modha says the system in front of us spans 48 million of these artificial nerve cells, roughly the number of neurons packed into the head of a rodent.

Modha oversees the cognitive computing group at IBM, the company that created these “neuromorphic” chips. For the first time, he and his team are sharing their unusual creations with the outside world, running a three-week “boot camp” for academics and government researchers at an IBM R&D lab on the far side of Silicon Valley. Plugging their laptops into the digital rodent brain at the front of the room, this eclectic group of computer scientists is exploring the particulars of IBM’s architecture and beginning to build software for the chip dubbed TrueNorth.

'We want to get as close to the brain as possible while maintaining flexibility.'DHARMENDRA MODHA, IBM

Some researchers who got their hands on the chip at an engineering workshop in Colorado the previous month have already fashioned software that can identify images, recognize spoken words, and understand natural language. Basically, they’re using the chip to run “deep learning” algorithms, the same algorithms that drive the internet’s latest AI services, including the face recognition on Facebook and the instant language translation on Microsoft’s Skype. But the promise is that IBM’s chip can run these algorithms in smaller spaces with considerably less electrical power, letting us shoehorn more AI onto phones and other tiny devices, including hearing aids and, well, wristwatches.

“What does a neuro-synaptic architecture give us? It lets us do things like image classification at a very, very low power consumption,” says Brian Van Essen, a computer scientist at the Lawrence Livermore National Laboratory who’s exploring how deep learning could be applied to national security. “It lets us tackle new problems in new environments.”

The TrueNorth is part of a widespread movement to refine the hardware that drives deep learning and other AI services. Companies like Google and Facebook and Microsoft are now running their algorithms on machines backed with GPUs (chips originally built to render computer graphics), and they’re moving towards FPGAs (chips you can program for particular tasks). For Peter Diehl, a PhD student in the cortical computation group at ETH Zurich and University Zurich, TrueNorth outperforms GPUs and FPGAs in certain situations because it consumes so little power.

The main difference, says Jason Mars, a professor of a computer science at the University of Michigan, is that the TrueNorth dovetails so well with deep-learning algorithms. These algorithms mimic neural networks in much the same way IBM’s chips do, recreating the neurons and synapses in the brain. One maps well onto the other. “The chip gives you a highly efficient way of executing neural networks,” says Mars, who declined an invitation to this month’s boot camp but has closely followed the progress of the chip.

That said, the TrueNorth suits only part of the deep learning process—at least as the chip exists today—and some question how big an impact it will have. Though IBM is now sharing the chips with outside researchers, it’s years away from the market. For Modha, however, this is as it should be. As he puts it: “We’re trying to lay the foundation for significant change.”

The Brain on a Phone

Peter Diehl recently took a trip to China, where his smartphone didn’t have access to the `net, an experience that cast the limitations of today’s AI in sharp relief. Without the internet, he couldn’t use a service like Google Now, which applies deep learning to speech recognition and natural language processing, because most the computing takes place not on the phone but on Google’s distant servers. “The whole system breaks down,” he says.

Deep learning, you see, requires enormous amounts of processing power—processing power that’s typically provided by the massive data centers that your phone connects to over the `net rather than locally on an individual device. The idea behind TrueNorth is that it can help move at least some of this processing power onto the phone and other personal devices, something that can significantly expand the AI available to everyday people.

To understand this, you have to understand how deep learning works. It operates in two stages. 

• First, companies like Google and Facebook must train a neural network to perform a particular task. If they want to automatically identify cat photos, for instance, they must feed the neural net lots and lots of cat photos. 
• Then, once the model is trained, another neural network must actually execute the task. You provide a photo and the system tells you whether it includes a cat. The TrueNorth, as it exists today, aims to facilitate that second stage.

Once a model is trained in a massive computer data center, the chip helps you execute the model. And because it’s small and uses so little power, it can fit onto a handheld device. This lets you do more at a faster speed, since you don’t have to send data over a network. If it becomes widely used, it could take much of the burden off data centers. “This is the future,” Mars says. “We’re going to see more of the processing on the devices.”

Neurons, Axons, Synapses, Spikes

Google recently discussed its efforts to run neural networks on phones, but for Diehl, the TrueNorth could take this concept several steps further. The difference, he explains, is that the chip dovetails so well with deep learning algorithms. Each chip mimics about a million neurons, and these can communicate with each other via something similar to a synapse, the connections between neurons in the brain.

'Silicon operates in a very different way than the stuff our brains are made of.'

The setup is quite different than what you find in chips on the market today, including GPUs and FPGAs. Whereas these chips are wired to execute particular “instructions,” the TrueNorth juggles “spikes,” much simpler pieces of information analogous to the pulses of electricity in the brain. Spikes, for instance, can show the changes in someone’s voice as they speak—or changes in color from pixel to pixel in a photo. “You can think of it as a one-bit message sent from one neuron to another.” says Rodrigo Alvarez-Icaza, one of the chip’s chief designers.

The upshot is a much simpler architecture that consumes less power. Though the chip contains 5.4 billion transistors, it draws about 70 milliwatts of power. A standard Intel computer processor, by comparison, includes 1.4 billion transistors and consumes about 35 to 140 watts. Even the ARM chips that drive smartphones consume several times more power than the TrueNorth.

Of course, using such a chip also requires a new breed of software. That’s what researchers like Diehl are exploring at the TrueNorth boot camp, which began in early August and runs for another week at IBM’s research lab in San Jose, California. In some cases, researchers are translating existing code into the “spikes” that the chip can read (and back again). But they’re also working to build native code for the chip.

Parting Gift

Like these researchers, Modha discusses the TrueNorth mainly in biological terms. Neurons. Axons. Synapses. Spikes. And certainly, the chip mirrors such wetware in some ways. But the analogy has its limits. “That kind of talk always puts up warning flags,” says Chris Nicholson, the co-founder of deep learning startup Skymind. “Silicon operates in a very different way than the stuff our brains are made of.”

Modha admits as much. When he started the project in 2008, backed by $53.5M in funding from Darpa, the research arm for the Department of Defense, the aim was to mimic the brain in a more complete way using an entirely different breed of chip material. But at one point, he realized this wasn’t going to happen anytime soon. “Ambitions must be balanced with reality,” he says.

In 2010, while laid up in bed with the swine flu, he realized that the best way forward was a chip architecture that loosely mimicked the brain—an architecture that could eventually recreate the brain in more complete ways as new hardware materials were developed. “You don’t need to model the fundamental physics and chemistry and biology of the neurons to elicit useful computation,” he says. “We want to get as close to the brain as possible while maintaining flexibility.”

This is TrueNorth. It’s not a digital brain. But it is a step toward a digital brain. And with IBM’s boot camp, the project is accelerating. The machine at the front of the room is really 48 separate machines, each built around its own TrueNorth processors. Next week, as the boot camp comes to a close, Modha and his team will separate them and let all those academics and researchers carry them back to their own labs, which span over 30 institutions on five continents. “Humans use technology to transform society,” Modha says, pointing to the room of researchers. “These are the humans.”.

ORIGINAL: Wired

By CADE METZ

08.17.15

App Captures the Boston Bombing’s Psychological Effects

ORIGINAL: IEEE Spectrum
By Eliza Strickland
27 Aug 2013


Could 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.”

New Darpa program may accelerate synthetic biology path to advanced nanotechnology

ORIGINAL: Foresight

Darpa's Living Foundries program is looking to transform biology into an engineering practice.  Photo: VA

Synthetic biology promises near-term breakthroughs in medicine, materials, and energy, and is also one promising development pathway leading to advanced nanotechnology and a general capability for programmable, atomically-precise manufacturing. Darpa (US Defense Advanced Research Projects Agency) has launched a new program that could greatly accelerate progress in synthetic biology by creating a library of standardized, modular biological units that could be used to build new devices and circuits. A hat tip to KurzweilAI.net for pointing to a recent article in Wired Danger Room “Darpa, Venter launch assembly line for genetic engineering“:

… The program, called “Living Foundries,” was first announced by the agency last year. Now, Darpa’s handed out seven research awards worth $15.5 million to six different companies and institutions. Among them are several Darpa favorites, including the University of Texas at Austin and the California Institute of Technology. Two contracts were also issued to the J. Craig Venter Institute. Dr. Venter is something of a biology superstar: He was among the first scientists to sequence a human genome, and his institute was, in 2010, the first to create a cell with entirely synthetic genome.

“Living Foundries” aspires to turn the slow, messy process of genetic engineering into a streamlined and standardized one. Of course, the field is already a burgeoning one: Scientists have tweaked cells in order to develop renewable petroleum and spider silk that’s tough as steel. And a host of companies are investigating the pharmaceutical and agricultural promise lurking — with some tinkering, of course — inside living cells.

But those breakthroughs, while exciting, have also been time-consuming and expensive. As Darpa notes, even the most cutting-edge synthetic biology projects “often take 7+ years and tens to hundreds of millions of dollars” to complete. Venter’s synthetic cell project, for example, cost an estimated $40 million.

Synthetic biology, as Darpa notes, has the potential to yield “new materials, novel capabilities, fuel and medicines” — everything from fuels to solar cells to vaccines could be produced by engineering different living cells. But the agency isn’t content to wait seven years for each new innovation. In fact, they want the capability for “on-demand production” of whatever bio-product suits the military’s immediate needs.

To do it, Darpa will need to revamp the process of bio-engineering — from the initial design of a new material, to its construction, to its subsequent efficacy evaluation. The starting point, and one that agency-funded researchers will have to create, is a library of “modular genetic parts”: Standardized biological units that can be assembled in different ways — like LEGO — to create different materials.

Once that library is created, the agency wants researchers to come up with a set of “parts, regulators, devices and circuits” that can reliably yield various genetic systems. After that, they’ll also need “test platforms” to quickly evaluate new bio-materials. Think of it as a biological assembly line: Products are designed, pieced together using standardized tools and techniques, and then tested for efficacy. …

The Darpa Living Foundries solicitation will remind long-term Nanodot readers of discussions of the need for an engineering perspective in the development of advanced nanotechnology centered on molecular manufacturing:

The Microsystems Technology Office (MTO) of the Defense Advanced Research Projects Agency (DARPA) is sponsoring an Industry Day for “Living Foundries,” a new DARPA program. The goal of the Living Foundries program is to apply an engineering framework to biology to harness its use as a technology and drive its advance as a manufacturing platform. In turning biological production into an engineering space where the only limit is the creativity of the designer, Living Foundries aims to enable on-demand production of new and high-value materials, devices and capabilities for the Department of Defense and establish a new manufacturing capability for the United States.

Because of the multidisciplinary nature of Living Foundries, DARPA is looking to engage the wider research community from fields both outside and inside the biological sciences to develop new ideas, approaches and tools to overcome current limitations and to create revolutionary capabilities.

Current, primitive examples of engineering biology rely on an ad hoc, laborious, trial-and-error process, wherein one successful project does not inform subsequent, new designs. This approach combined with the complexity of biological systems restricts current, one-off efforts to modifying only a small set of genes and constructing simple, isolated genetic circuits and metabolic pathways. Consequently, we are limited to producing only a small fraction of the vast number of possible chemicals, materials, and living systems that would be enabled by the ability to truly engineer biology. Through an engineering-driven approach to biology, Living Foundries aims to create a rapid, reliable manufacturing capability where multiple cellular functions can be fabricated, mixed and matched on demand and the whole system controlled by integrated circuitry, opening up the full space of biologically produced materials and systems. Key to success will be the democratization of the biological design and manufacturing process, breaking open the field to those outside the biological sciences.

In order to achieve the vision of Living Foundries, new tools, technologies and methodologies must be developed to transform biology into an engineering practice, decoupling design from fabrication and speeding the biological design, build, test cycle. These include:

• design tools that span from high-level description to fabrication in cells; 
• modular genetic parts that allow a combination of systems to be designed and reproducibly assembled; 
• methods for developing and fine-tuning new genetic parts and systems; 
• well-understood test platforms, “cell-like” systems and chassis that readily integrate new genetic designs in a predictable fashion; 
• next generation DNA synthesis and assembly techniques; and 
• tools that allow for routine system characterization and debugging, among others. 

Further, these technological advances and innovations must be integrated to prove-out and push the boundaries of biological design towards the ultimate vision of point-of-use, on-demand, mass-customization biological manufacturing. …

If Darpa’s Living Foundries program achieves its ambitious goals, it should create a methodology, toolbox, and a large group of practitioners ready to pursue a synthetic biology pathway to building complex molecular machine systems, and eventually, atomically precise manufacturing systems.

—James Lewis, PhD