Mostrando entradas con la etiqueta Computación Cognitiva. Mostrar todas las entradas
Mostrando entradas con la etiqueta Computación Cognitiva. Mostrar todas las entradas

domingo, 16 de noviembre de 2014

10 IBM Watson-Powered Apps That Are Changing Our World

IBM is investing $1 billion in its IBM Watson Group with the aim of creating an ecosystem of startups and businesses building cognitive computing applications with Watson. Here are 10 examples that are making an impact.

IBM considers Watson to represent a new era of computing — a step forward to cognitive computing, where apps and systems interact with humans via natural language and help us augment our own understanding of the world with big data insights.

Big Blue isn't playing small ball with that claim. It has opened a new IBM Watson Global Headquarters in the heart of New York City's Silicon Alley and is investing $1 billion into the Watson Group, focusing on development and research as well as bringing cloud-delivered cognitive applications and services to the market. That includes $100 million available for venture investments to support IBM's ecosystem of start-ups and businesses building cognitive apps with Watson.

Here are 10 examples of Watson-powered cognitive apps that are already starting to shake things up.

USAA and Watson Help Military Members Transition to Civilian Life
USAA, a financial services firm dedicated to those who serve or have served in the military, has turned to IBM's Watson Engagement Advisor in a pilot program to help military men and women transition to civilian life.

According to the U.S. Bureau of Labor Statistics, about 155,000 active military members transition to civilian life each year. This process can raise many questions, like "Can I be in the reserve and collect veteran's compensation benefits?" or "How do I make the most of the Post-9/11 GI Bill?" Watson has analyzed and understands more than 3,000 documents on topics exclusive to military transitions, allowing members to ask it questions and receive answers specific to their needs.

LifeLearn Sofie is an intelligent treatment support tool for veterinarians of all backgrounds and levels of experience. Sofie is powered by IBM WatsonTM, the world’s leading cognitive computing system. She can understand and process natural language, enabling interactions that are more aligned with how humans think and interact.

Implement Watson

Dive deeper into subjects. Find insights where no one ever thought to look before. From Healthcare to Retail, there's an IBM Watson Solution that's right for your enterprise.


Healthcare
Helping doctors identify treatment options

The challenge

jueves, 21 de agosto de 2014

Siri’s Inventors Are Building a Radical New AI That Does Anything You Ask

Viv was named after the Latin root meaning live. Its San Jose, California, offices are decorated with tsotchkes bearing the numbers six and five (VI and V in roman numerals). Ariel Zambelich

When Apple announced the iPhone 4S on October 4, 2011, the headlines were not about its speedy A5 chip or improved camera. Instead they focused on an unusual new feature: an intelligent assistant, dubbed Siri. At first Siri, endowed with a female voice, seemed almost human in the way she understood what you said to her and responded, an advance in artificial intelligence that seemed to place us on a fast track to the Singularity. She was brilliant at fulfilling certain requests, like “Can you set the alarm for 6:30?” or “Call Diane’s mobile phone.” And she had a personality: If you asked her if there was a God, she would demur with deft wisdom. “My policy is the separation of spirit and silicon,” she’d say.

Over the next few months, however, Siri’s limitations became apparent. Ask her to book a plane trip and she would point to travel websites—but she wouldn’t give flight options, let alone secure you a seat. Ask her to buy a copy of Lee Child’s new book and she would draw a blank, despite the fact that Apple sells it. Though Apple has since extended Siri’s powers—to make an OpenTable restaurant reservation, for example—she still can’t do something as simple as booking a table on the next available night in your schedule. She knows how to check your calendar and she knows how to use Open­Table. But putting those things together is, at the moment, beyond her.

Now a small team of engineers at a stealth startup called Viv Labs claims to be on the verge of realizing an advanced form of AI that removes those limitations. Whereas Siri can only perform tasks that Apple engineers explicitly implement, this new program, they say, will be able to teach itself, giving it almost limitless capabilities. In time, they assert, their creation will be able to use your personal preferences and a near-infinite web of connections to answer almost any query and perform almost any function.

Siri is chapter one of a much longer, bigger story,” says Dag Kittlaus, one of Viv’s cofounders. He should know. Before working on Viv, he helped create Siri. So did his fellow cofounders, Adam Cheyer and Chris Brigham.

For the past two years, the team has been working on Viv Labs’ product—also named Viv, after the Latin root meaning live. Their project has been draped in secrecy, but the few outsiders who have gotten a look speak about it in rapturous terms. “The vision is very significant,” says Oren Etzioni, a renowned AI expert who heads the Allen Institute for Artificial Intelligence. “If this team is successful, we are looking at the future of intelligent agents and a multibillion-dollar industry.

Viv is not the only company competing for a share of those billions. The field of artificial intelligence has become the scene of a frantic corporate arms race, with Internet giants snapping up AI startups and talent. Google recently paid a reported $500 million for the UK deep-learning company DeepMind and has lured AI legends Geoffrey Hinton and Ray Kurzweil to its headquarters in Mountain View, California. Facebook has its own deep-learning group, led by prize hire Yann LeCun from New York University. Their goal is to build a new generation of AI that can process massive troves of data to predict and fulfill our desires.

Viv strives to be the first consumer-friendly assistant that truly achieves that promise. It wants to be not only blindingly smart and infinitely flexible but omnipresent. Viv’s creators hope that some day soon it will be embedded in a plethora of Internet-connected everyday objects. Viv founders say you’ll access its artificial intelligence as a utility, the way you draw on electricity. Simply by speaking, you will connect to what they are calling “a global brain.” And that brain can help power a million different apps and devices.

I’m extremely proud of Siri and the impact it’s had on the world, but in many ways it could have been more,” Cheyer says. “Now I want to do something bigger than mobile, bigger than consumer, bigger than desktop or enterprise. I want to do something that could fundamentally change the way software is built.”

Viv labs is tucked behind an unmarked door on a middle floor of a generic glass office building in downtown San Jose. Visitors enter into a small suite and walk past a pool table to get to the single conference room, glimpsing on the way a handful of engineers staring into monitors on trestle tables. Once in the meeting room, Kittlaus—a product-whisperer whose career includes stints at Motorola and Apple—is usually the one to start things off.

He acknowledges that an abundance of voice-navigated systems already exists. In addition to Siri, there is Google Now, which can anticipate some of your needs, alerting you, for example, that you should leave 15 minutes sooner for the airport because of traffic delays. Microsoft, which has been pursuing machine-learning techniques for decades, recently came out with a Siri-like system called Cortana. Amazon uses voice technology in its Fire TV product.

But Kittlaus points out that all of these services are strictly limited. Cheyer elaborates: “Google Now has a huge knowledge graph—you can ask questions like ‘Where was Abraham Lincoln born?’ And it can name the city. You can also say, ‘What is the population?’ of a city and it’ll bring up a chart and answer. But you cannot say, ‘What is the population of the city where Abraham Lincoln was born?’” The system may have the data for both these components, but it has no ability to put them together, either to answer a query or to make a smart suggestion. Like Siri, it can’t do anything that coders haven’t explicitly programmed it to do.

Viv breaks through those constraints by generating its own code on the fly, no programmers required. Take a complicated command like “Give me a flight to Dallas with a seat that Shaq could fit in.” Viv will parse the sentence and then it will perform its best trick: automatically generating a quick, efficient program to link third-party sources of information together—say, Kayak, SeatGuru, and the NBA media guide—so it can identify available flights with lots of legroom. And it can do all of this in a fraction of a second.

Viv is an open system that will let innumerable businesses and applications become part of its boundless brain. The technical barriers are minimal, requiring brief “training” (in some cases, minutes) for Viv to understand the jargon of the specific topic. As Viv’s knowledge grows, so will its understanding; its creators have designed it based on three principles they call its “pillars”:
  • It will be taught by the world, 
  • it will know more than it is taught, and 
  • it will learn something every day. 
As with other AI products, that teaching involves using sophisticated algorithms to interpret the language and behavior of people using the system—the more people use it, the smarter it gets. By knowing who its users are and which services they interact with, Viv can sift through that vast trove of data and find new ways to connect and manipulate the information.

Kittlaus says the end result will be a digital assistant who knows what you want before you ask for it. He envisions someone unsteadily holding a phone to his mouth outside a dive bar at 2 am and saying, “I’m drunk.” Without any elaboration, Viv would contact the user’s preferred car service, dispatch it to the address where he’s half passed out, and direct the driver to take him home. No further consciousness required.


The founders of a stealth startup called Viv Labs—Adam Cheyer, Dag Kittlaus, and Chris Brigham—are building a Siri-like digital assistant that can process massive troves of data, teach itself, and write its own programs on the fly. The goal: to predict and fulfill our desires. Ariel Zambelich

If Kittlaus is in some ways the Steve Jobs of Viv—he is the only non-engineer on the 10-person team and its main voice on strategy and marketing—Cheyer is the company’s Steve Wozniak, the project’s key scientific mind. Unlike the whimsical creator of the Apple II, though, Cheyer is aggressively analytical in every facet of his life, even beyond the workbench. As a kid, he was a Rubik’s Cube champion, averaging 26 seconds a solution. When he encountered programming, he dove in headfirst. “I felt that computers were invented for me,” he says. And while in high school he discovered a regimen to force the world to bend to his will. “I live my life by what I call verbally stated goals,” he says. “I crystallize a feeling, a need, into words. I think about the words, and I tell everyone I meet, ‘This is what I’m doing.’ I say it, and then I believe it. By telling people, you’re committed to it, and they help you. And it works.

He says he used the technique to land his early computing jobs, including the most significant—at SRI International, a Menlo Park think tank that invented the concept of computer windows and the mouse. It was there, in the early 2000s, that Cheyer led the engineering of a Darpa-backed AI effort to build “a humanlike system that could sense the world, understand it, reason about it, plan, communicate, and act.” The SRI-led team built what it called a Cognitive Assistant that Learns and Organizes, or CALO. They set some AI high-water marks, not least being the system’s ability to understand natural language. As the five-year program wound down, it was unclear what would happen next.

That was when Kittlaus, who had quit his job at Motorola, showed up at SRI as an entrepreneur in residence. When he saw a CALO-related prototype, he told Cheyer he could definitely build a business from it, calling it the perfect complement to the just-released iPhone. In 2007, with SRI’s blessing, they licensed the technology for a startup, taking on a third cofounder, an AI expert named Tom Gruber, and eventually renaming the system Siri.

The small team, which grew to include Chris Brigham, an engineer who had impressed Cheyer on CALO, moved to San Jose and worked for two years to get things right. “One of the hardest parts was the natural language understanding,” Cheyer says. Ultimately they had an iPhone app that could perform a host of interesting tasks—call a cab, book a table, get movie tickets—and carry on a conversation with brio. They released it publicly to users in February 2010. Three weeks later, Steve Jobs called. He wanted to buy the company.

“I was shocked at how well he knew our app,” Cheyer says. At first they declined to sell, but Jobs persisted. His winning argument was that Apple could expose Siri to a far wider audience than a startup could reach. He promised to promote it as a key element on every iPhone. Apple bought the company in April 2010 for a reported $200 million.

The core Siri team came to Apple with the project. But as Siri was honed into a product that millions could use in multiple languages, some members of the original team reportedly had difficulties with executives who were less respectful of their vision than Jobs was. Kitt­laus left Apple the day after the launch—the day Steve Jobs died. Cheyer departed several months later. “I do feel if Steve were alive, I would still be at Apple,” Cheyer says. “I’ll leave it at that.” (Gruber, the third Siri cofounder, remains at Apple.)

After several months, Kittlaus got back in touch with Cheyer and Brigham. They asked one another what they thought the world would be like in five years. As they drew ideas on a whiteboard in Kittlaus’ house, Brigham brought up the idea of a program that could put the things it knows together in new ways. As talks continued, they lit on the concept of a cloud-based intelligence, a global brain. “The only way to make this ubiquitous conversational assistant is to open it up to third parties to allow everyone to plug into it,” Brigham says.

In retrospect, they were re-creating Siri as it might have evolved had Apple never bought it. Before the sale, Siri had partnered with around 45 services, from AllMenus.com to Yahoo; Apple had rolled Siri out with less than half a dozen. “Siri in 2014 is less capable than it was in 2010,” says Gary Morgenthaler, one of the funders of the original app.

Cheyer and Brigham tapped experts in various AI and coding niches to fill out their small group. To produce some of the toughest parts—the architecture to allow Viv to understand language and write its own programs—they brought in Mark Gabel from the University of Texas at Dallas. Another key hire was David Gondek, one of the creators of IBM’S Watson.

Funding came from Solina Chau, the partner (in business and otherwise) of the richest man in China, Li Ka-shing. Chau runs the venture firm Horizons Ventures. In addition to investing in Facebook, DeepMind, and Summly (bought by Yahoo), it helped fund the original Siri. When Viv’s founders asked Chau for $10 million, she said, “I’m in. Do you want me to wire it now?

It’s early May, and Kittlaus is addressing the team at its weekly engineering meeting. “You can see the progress,” he tells the group, “see it get closer to the point where it just works.” Each engineer delineates the advances they’ve made and next steps. One explains how he has been refining Viv’s response to “Get me a ticket to the cheapest flight from SFO to Charles de Gaulle on July 2, with a return flight the following Monday.” In the past week, the engineer added an airplane-seating database. Using a laptop-based prototype of Viv that displays a virtual phone screen, he speaks into the microphone. Lufthansa Flight 455 fits the bill. “Seat 61G is available according to your preferences,” Viv replies, then purchases the seat using a credit card.

Viv’s founders don’t see it as just one product tied to a hardware manufacturer. They see it as a service that can be licensed. They imagine that everyone from TV manufacturers and car companies to app developers will want to incorporate Viv’s AI, just as PC manufacturers once clamored to boast of their Intel microprocessors. They envision its icon joining the pantheon of familiar symbols like Power On, Wi-Fi, and Bluetooth.

Intelligence becomes a utility,” Kittlaus says. “Boy, wouldn’t it be nice if you could talk to everything, and it knew you, and it knew everything about you, and it could do everything?

That would also be nice because it just might provide Viv with a business model. Kittlaus thinks Viv could be instrumental in what he calls “the referral economy.” He cites a factoid about Match.com that he learned from its CEO: The company arranges 50,000 dates a day. “What Match.com isn’t able to do is say, ‘Let me get you tickets for something. Would you like me to book a table? Do you want me to send Uber to pick her up? Do you want me to have flowers sent to the table?’” Viv could provide all those services—in exchange for a cut of the transactions that resulted.

Building that ecosystem will be a difficult task, one that Viv Labs could hasten considerably by selling out to one of the Internet giants. “Let me just cut through all the usual founder bullshit,” Kittlaus says. “What we’re really after is ubiquity. We want this to be everywhere, and we’re going to consider all paths along those lines.” To some associated with Viv Labs, selling the company would seem like a tired rerun. “I’m deeply hoping they build it,” says Bart Swanson, a Horizons adviser on Viv Labs’ board. “They will be able to control it only if they do it themselves.

Whether they will succeed, of course, is not certain. “Viv is potentially very big, but it’s all still potential,” says Morgenthaler, the original Siri funder. A big challenge, he says, will be whether the thousands of third-party components work together—or whether they clash, leading to a confused Viv that makes boneheaded errors. Can Viv get it right? “The jury is out, but I have very high confidence,” he says. “I only have doubt as to when and how.

Most of the carefully chosen outsiders who have seen early demos are similarly confident. One is Vishal Sharma, who until recently was VP of product for Google Now. When Cheyer showed him how Viv located the closest bottle of wine that paired well with a dish, he was blown away. “I don’t know any system in the world that could answer a question like that,” he says. “Many things can go wrong, but I would like to see something like this exist.

Indeed, many things have to go right for Viv to make good on its founders’ promises. It has to prove that its code-making skills can scale to include petabytes of data. It has to continually get smarter through omnivorous learning. It has to win users despite not having a preexisting base like Google and Apple have. It has to lure developers who are already stressed adapting their wares to multiple platforms. And it has to be as seductive as Scarlett Johansson in Her so that people are comfortable sharing their personal information with a robot that might become one of the most important forces in their lives.

The inventors of Siri are confident that their next creation will eclipse the first. But whether and when that will happen is a question that even Viv herself cannot answer. Yet.


 La Tigre

ORIGINAL: Wired


viernes, 8 de agosto de 2014

IBM's Brain-Inspired Computer Chip Comes from the Future

Illustration: IBM

Brain-inspired computers have tickled the public imagination ever since Arnold Schwarzenegger's character in “Terminator 2: Judgment Day” uttered: “My CPU is a neural net processor; a learning computer.” Today, IBM researchers backed by U.S. military funding unveiled a new computer chip that they say could revolutionize everything from smartphones to smart cars—and perhaps pave the way for neural networks to someday approach the computing capabilities of the human brain.

The IBM neurosynaptic computer chip consists of one million programmable neurons and 256 million programmable synapses conveying signals between the digital neurons. Each of chip’s 4,096 neurosynaptic cores includes the entire computing package—memory, computation, and communication. They all operate in parallel based on “event-driven” computing, similar to the signal spikes and cascades of activity when human brain cells work in concert. Such architecture helps to bypass the bottleneck in traditional computing where program instructions and operation data cannot pass through the same route simultaneously.

We have not built a brain,” says Dharmendra Modha, chief scientist and founder of IBM’s Cognitive Computing group at IBM Research-Almaden. “But we have come the closest to creating learning function and capturing it in silicon in a scalable way to provide new computing capability that was not possible before.”

Such capability could enable new mobile device applications that emulate the human brain’s capability to swiftly process information about new events or other changes in real-world environments, whether that involves recognizing familiar sounds or a certain face in a moving crowd. IBM envisions its new chips working together with traditional computing devices as hybrid machines—providing an added dose of brain-like intelligence for smart car sensors, cloud computing applications or mobile devices such as smartphones. The chip's architecture was detailed in a new paper published in the 7 August online issue of the journal Science.

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With a total of 5.4 billion transistors the computer chip, named TrueNorth, is one of the largest CMOS chips ever built. Yet the chip uses just 70 milliwatts while running and has a power density of 20 milliwatts per square centimeter— almost 1/10,000th the power of most modern microprocessors. That brings the new chip's efficiency much closer to the human brain’s astounding power consumption of just 20 watts, or less than the average incandescent light bulb.

This is literally a supercomputer the size of a postage stamp, light like a feather, and low power like a hearing aid,” Modha says.

One reason IBM was able to minimize power usage is that its chip's computation only triggers when needed. Traditional computer chips have a clock that uses power to trigger and coordinate all the computational processes. But the IBM chip's digital neurons can work together asynchronously when triggered by the signal spikes. IBM also designed its chip to have low power consumption by creating an on-chip network to interconnect all the neurosynaptic cores and building the chip with a low-power process technology used for making mobile devices.

It’s also a supercomputer that can easily scale up in size. IBM designed its computer chip architecture so that it could simply add new neurosynatpic cores within the chip. The chips themselves can be arranged in a repeatable 2-D tile pattern to create bigger machines—IBM has already tested that idea with a 16-chip configuration. That’s the “blueprint of a scalable supercomputer,” Modha says.

Past brain-inspired neural networks have used a combination of both analog and digital to represent the individual neurons. IBM chose to represent the neurons in digital form, which provided several advantages. (At least one other project, SpiNNaker also depends on digital.)

First, the choice allowed IBM engineers to avoid the physical problems of dealing with differences in the manufacturing process or temperature fluctuations. Second, it provided a “one to one equivalence with software and hardware” that allowed the IBM software team to build applications on a simulator even before the physical chip had been designed and tested—applications that ran without problems on the finished chip. Third, the lack of analog circuitry allowed the IBM team to dramatically shrink the size of its circuits. (IBM fabricated its chip using Samsung’s 28-nm process technology—typical for manufacturing chips for mobile devices.)


IBM’s new chip represents the culmination of a decade of Modha’s personal research and almost six years of funding from the U.S. Defense Advanced Research Projects Agency (DARPA). Modha currently heads DARPA’s SyNAPSE project, a global effort that has committed US $53 million to making learning computers since 2008.

Now IBM has built an entire ecosystem around its new chip hardware and software, including a new programming language and a curriculum to teach coders everything they need to know. And the company is reaching out to potential customers, universities, government agencies, and IBM employees to fully explore the commercial applications of its chip technology.

Our long-term end goal is to build a ‘brain in a box’ with 100 billion synapses consuming 1 kilowatt of power,” Modha says. “In the near future, we’ll be looking at multiple things for empowering smartphones, mobile devices and cloud services with this technology.



ORIGINAL: IEEE Spectrum
By Jeremy Hsu
Posted 7 Aug 2014 | 18:00 GMT

jueves, 6 de febrero de 2014

IBM Brings Watson to Africa for Project Lucy

Using cognitive systems to tackle a continent’s grand challenges

Lucy is the name given to the earliest known human descendant, whose remains were discovered in Africa 40 years ago. Today, IBM Watson, the first cognitive computing system, is coming to Africa as part of a 10-year, $100 million initiative to address the fast-growing continent's greatest business and societal challenges. With "Project Lucy", IBM researchers in Africa, together with their business and academic partners, will use Watson and related cognitive technologies to learn and discover insights from Big Data and develop commercially viable solutions to Africa’s grand challenges in healthcare, education, water and sanitation, human mobility and agriculture.

"In the last decade, Africa has been a tremendous growth story, yet the continent's challenges, stemming from population growth, water scarcity, disease, low agricultural yield and other factors are impediments to inclusive economic growth," said Kamal Bhattacharya, director, IBM Research - Africa.

"With the ability to learn from emerging patterns and discover new correlations, Watson's cognitive capabilities hold enormous potential in Africa - helping it to achieve in the next two decades what today's developed markets have achieved over two centuries."

To get the big picture of these challenges and opportunities, IBM asked people from every African country to send in photos of their environments. The response was overwhelming: more than 1,200 photos from 930 participants tell a diverse technology and innovation story. The slideshow below is a selection that-illustrates this diversity and the mission of IBM Research – Africa. Contest winners will be named on February 28, 2014.

Nairobi is a city hard at work 24 hours every day. It is obvious when you look at the city at night from any of the vantage points. I shot this from the KICC helipad one night. Seven out of the world's 10 fastest growing economies will be African nations over the next ten years; Nairobi is rapidly emerging as one of Africa's business and technology hubs. Photo Mutua Matheka, Kenya City Lights.

ORIGINAL: IBM Research

lunes, 13 de enero de 2014

Computer science: The learning machines

Using massive amounts of data to recognize photos and speech, deep-learning computers are taking a big step towards true artificial intelligence.

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Three years ago, researchers at the secretive Google X lab in Mountain View, California, extracted some 10 million still images from YouTube videos and fed them into Google Brain — a network of 1,000 computers programmed to soak up the world much as a human toddler does. After three days looking for recurring patterns, Google Brain decided, all on its own, that there were certain repeating categories it could identify: human faces, human bodies and … cats1.

Google Brain's discovery that the Internet is full of cat videos provoked a flurry of jokes from journalists. But it was also a landmark in the resurgence of deep learning: a three-decade-old technique in which massive amounts of data and processing power help computers to crack messy problems that humans solve almost intuitively, from recognizing faces to understanding language.

Deep learning itself is a revival of an even older idea for computing: neural networks. These systems, loosely inspired by the densely interconnected neurons of the brain, mimic human learning by changing the strength of simulated neural connections on the basis of experience. Google Brain, with about 1 million simulated neurons and 1 billion simulated connections, was ten times larger than any deep neural network before it. Project founder Andrew Ng, now director of the Artificial Intelligence Laboratory at Stanford University in California, has gone on to make deep-learning systems ten times larger again.

Such advances make for exciting times in artificial intelligence (AI) — the often-frustrating attempt to get computers to think like humans. In the past few years, companies such as Google, Apple and IBM have been aggressively snapping up start-up companies and researchers with deep-learning expertise. For everyday consumers, the results include software better able to sort through photos, understand spoken commands and translate text from foreign languages. For scientists and industry, deep-learning computers can search for potential drug candidates, map real neural networks in the brain or predict the functions of proteins.

AI has gone from failure to failure, with bits of progress. This could be another leapfrog,” says Yann LeCun, director of the Center for Data Science at New York University and a deep-learning pioneer.

Over the next few years we'll see a feeding frenzy. Lots of people will jump on the deep-learning bandwagon,” agrees Jitendra Malik, who studies computer image recognition at the University of California, Berkeley. But in the long term, deep learning may not win the day; some researchers are pursuing other techniques that show promise. “I'm agnostic,” says Malik. “Over time people will decide what works best in different domains.

Inspired by the brain

Back in the 1950s, when computers were new, the first generation of AI researchers eagerly predicted that fully fledged AI was right around the corner. But that optimism faded as researchers began to grasp the vast complexity of real-world knowledge — particularly when it came to perceptual problems such as what makes a face a human face, rather than a mask or a monkey face. Hundreds of researchers and graduate students spent decades hand-coding rules about all the different features that computers needed to identify objects. “Coming up with features is difficult, time consuming and requires expert knowledge,” says Ng. “You have to ask if there's a better way.

IMAGES: ANDREW NG

 In the 1980s, one better way seemed to be deep learning in neural networks. These systems promised to learn their own rules from scratch, and offered the pleasing symmetry of using brain-inspired mechanics to achieve brain-like function. The strategy called for simulated neurons to be organized into several layers. Give such a system a picture and
  • the first layer of learning will simply notice all the dark and light pixels. 
  • The next layer might realize that some of these pixels form edges; 
  • the next might distinguish between horizontal and vertical lines. 
  • Eventually, a layer might recognize eyes, and might realize that two eyes are usually present in a human face (see 'Facial recognition').

The first deep-learning programs did not perform any better than simpler systems, says Malik. Plus, they were tricky to work with. “Neural nets were always a delicate art to manage. There is some black magic involved,” he says. The networks needed a rich stream of examples to learn from — like a baby gathering information about the world. In the 1980s and 1990s, there was not much digital information available, and it took too long for computers to crunch through what did exist. Applications were rare. One of the few was a technique — developed by LeCun — that is now used by banks to read handwritten cheques.

By the 2000s, however, advocates such as LeCun and his former supervisor, computer scientist Geoffrey Hinton of the University of Toronto in Canada, were convinced that increases in computing power and an explosion of digital data meant that it was time for a renewed push. “We wanted to show the world that these deep neural networks were really useful and could really help,” says George Dahl, a current student of Hinton's.

As a start, Hinton, Dahl and several others tackled the difficult but commercially important task of speech recognition. In 2009, the researchers reported2 that after training on a classic data set — three hours of taped and transcribed speech — their deep-learning neural network had broken the record for accuracy in turning the spoken word into typed text, a record that had not shifted much in a decade with the standard, rules-based approach. The achievement caught the attention of major players in the smartphone market, says Dahl, who took the technique to Microsoft during an internship. “In a couple of years they all switched to deep learning.” For example, the iPhone's voice-activated digital assistant, Siri, relies on deep learning.

Giant leap
When Google adopted deep-learning-based speech recognition in its Android smartphone operating system, it achieved a 25% reduction in word errors. “That's the kind of drop you expect to take ten years to achieve,” says Hinton — a reflection of just how difficult it has been to make progress in this area. “That's like ten breakthroughs all together.

Meanwhile, Ng had convinced Google to let him use its data and computers on what became Google Brain. The project's ability to spot cats was a compelling (but not, on its own, commercially viable) demonstration of unsupervised learning — the most difficult learning task, because the input comes without any explanatory information such as names, titles or categories. But Ng soon became troubled that few researchers outside Google had the tools to work on deep learning. “After many of my talks,” he says, “depressed graduate students would come up to me and say: 'I don't have 1,000 computers lying around, can I even research this?'”

So back at Stanford, Ng started developing bigger, cheaper deep-learning networks using graphics processing units (GPUs) — the super-fast chips developed for home-computer gaming3. Others were doing the same. “For about US$100,000 in hardware, we can build an 11-billion-connection network, with 64 GPUs,” says Ng.

Victorious machine

But winning over computer-vision scientists would take more: they wanted to see gains on standardized tests. Malik remembers that Hinton asked him: “You're a sceptic. What would convince you?” Malik replied that a victory in the internationally renowned ImageNet competition might do the trick.

In that competition, teams train computer programs on a data set of about 1 million images that have each been manually labelled with a category. After training, the programs are tested by getting them to suggest labels for similar images that they have never seen before. They are given five guesses for each test image; if the right answer is not one of those five, the test counts as an error. Past winners had typically erred about 25% of the time. In 2012, Hinton's lab entered the first ever competitor to use deep learning. It had an error rate of just 15% (ref. 4).

Deep learning stomped on everything else,” says LeCun, who was not part of that team. The win landed Hinton a part-time job at Google, and the company used the program to update its Google+ photo-search software in May 2013.

Malik was won over. “In science you have to be swayed by empirical evidence, and this was clear evidence,” he says. Since then, he has adapted the technique to beat the record in another visual-recognition competition5. Many others have followed: in 2013, all entrants to the ImageNet competition used deep learning.


“Over the next few years we'll see a feeding frenzy. Lots of people will jump on the deep-learning bandwagon.”

With triumphs in hand for image and speech recognition, there is now increasing interest in applying deep learning to natural-language understanding — comprehending human discourse well enough to rephrase or answer questions, for example — and to translation from one language to another. Again, these are currently done using hand-coded rules and statistical analysis of known text. The state-of-the-art of such techniques can be seen in software such as Google Translate, which can produce results that are comprehensible (if sometimes comical) but nowhere near as good as a smooth human translation. “Deep learning will have a chance to do something much better than the current practice here,” says crowd-sourcing expert Luis von Ahn, whose company Duolingo, based in Pittsburgh, Pennsylvania, relies on humans, not computers, to translate text. “The one thing everyone agrees on is that it's time to try something different.

Deep science

In the meantime, deep learning has been proving useful for a variety of scientific tasks. “Deep nets are really good at finding patterns in data sets,” says Hinton. In 2012, the pharmaceutical company Merck offered a prize to whoever could beat its best programs for helping to predict useful drug candidates. The task was to trawl through database entries on more than 30,000 small molecules, each of which had thousands of numerical chemical-property descriptors, and to try to predict how each one acted on 15 different target molecules. Dahl and his colleagues won $22,000 with a deep-learning system. “We improved on Merck's baseline by about 15%,” he says.

Biologists and computational researchers including Sebastian Seung of the Massachusetts Institute of Technology in Cambridge are using deep learning to help them to analyse three-dimensional images of brain slices. Such images contain a tangle of lines that represent the connections between neurons; these need to be identified so they can be mapped and counted. In the past, undergraduates have been enlisted to trace out the lines, but automating the process is the only way to deal with the billions of connections that are expected to turn up as such projects continue. Deep learning seems to be the best way to automate. Seung is currently using a deep-learning program to map neurons in a large chunk of the retina, then forwarding the results to be proofread by volunteers in a crowd-sourced online game called EyeWire.

“Deep learning has the property that if you feed it more data, it gets better and better.”

William Stafford Noble, a computer scientist at the University of Washington in Seattle, has used deep learning to teach a program to look at a string of amino acids and predict the structure of the resulting protein — whether various portions will form a helix or a loop, for example, or how easy it will be for a solvent to sneak into gaps in the structure. Noble has so far trained his program on one small data set, and over the coming months he will move on to the Protein Data Bank: a global repository that currently contains nearly 100,000 structures.

For computer scientists, deep learning could earn big profits: Dahl is thinking about start-up opportunities, and LeCun was hired last month to head a new AI department at Facebook. The technique holds the promise of practical success for AI. “Deep learning happens to have the property that if you feed it more data it gets better and better,” notes Ng. “Deep-learning algorithms aren't the only ones like that, but they're arguably the best — certainly the easiest. That's why it has huge promise for the future.

Not all researchers are so committed to the idea. Oren Etzioni, director of the Allen Institute for Artificial Intelligence in Seattle, which launched last September with the aim of developing AI, says he will not be using the brain for inspiration. It's like when we invented flight,” he says; the most successful designs for aeroplanes were not modelled on bird biology. Etzioni's specific goal is to invent a computer that, when given a stack of scanned textbooks, can pass standardized elementary-school science tests (ramping up eventually to pre-university exams). To pass the tests, a computer must be able to read and understand diagrams and text. How the Allen Institute will make that happen is undecided as yet — but for Etzioni, neural networks and deep learning are not at the top of the list.

One competing idea is to rely on a computer that can reason on the basis of inputted facts, rather than trying to learn its own facts from scratch. So it might be programmed with assertions such as 'all girls are people'. Then, when it is presented with a text that mentions a girl, the computer could deduce that the girl in question is a person. Thousands, if not millions, of such facts are required to cover even ordinary, common-sense knowledge about the world. But it is roughly what went into IBM's Watson computer, which famously won a match of the television game show Jeopardy against top human competitors in 2011. Even so, IBM's Watson Solutions has an experimental interest in deep learning for improving pattern recognition, says Rob High, chief technology officer for the company, which is based in Austin, Texas.

Google, too, is hedging its bets. Although its latest advances in picture tagging are based on Hinton's deep-learning networks, it has other departments with a wider remit. In December 2012, it hired futurist Ray Kurzweil to pursue various ways for computers to learn from experience — using techniques including but not limited to deep learning. Last May, Google acquired a quantum computer made by D-Wave in Burnaby, Canada (see Nature 498, 286–288; 2013). This computer holds promise for non-AI tasks such as difficult mathematical computations — although it could, theoretically, be applied to deep learning.

Despite its successes, deep learning is still in its infancy. “It's part of the future,” says Dahl. “In a way it's amazing we've done so much with so little.” And, he adds, “we've barely begun”. Nature505,146–148(09 January 2014)doi:10.1038/505146a
References Le, Q. V. et al. Preprint at http://arxiv.org/abs/1112.6209 (2011). Show context
Mohamed, A. et al. 2011 IEEE Int. Conf. Acoustics Speech Signal Process. http://dx.doi.org/10.1109/ICASSP.2011.5947494 (2011). Show context
Coates, A. et al. J. Machine Learn. Res. Workshop Conf. Proc. 28, 1337–1345 (2013). Show context
Krizhevsky, A., Sutskever, I. & Hinton, G. E. In Advances in Neural Information Processing Systems 25; available at http://go.nature.com/ibace6 Show context
Girshick, R., Donahue, J., Darrell, T. & Malik, J. Preprint at http://arxiv.org/abs/1311.2524 (2013).

lunes, 18 de noviembre de 2013

How IBM's Watson Will Transform Business And Society

After Watson won on the TV quiz show Jeopardy!, a lot of people didn’t really understand what “Watson” was. They thought it was a particular piece of hardware: a glowing blue supercomputer that IBM built in one of its labs.

But now, as Watson comes of age and makes the transition from science experiment to a force to be reckoned with in business and society, I think it’s time to give people a new way of thinking about it. So here goes:

Watson is a cognitive capability that resides in the computing cloud — just like Google and Facebook and Twitter.

This new capability is designed to help people penetrate complexity so they can make better decisions and live and work more successfully. Eventually, a host of cognitive services will be delivered to people at any time and anywhere through a wide variety of handy devices. Laptops. Tablets. Smart phones. You name it.

In other words, you won’t need to be a TV producer or a giant corporation to take advantage of Watson’s capabilities. Everybody will have Watson — or a relative of the Watson technologies — at his or her fingertips.

Indeed, Watson represents the first wave in a new era of technology: the era of cognitive computing. This new generation of technology has the potential to transform business and society just as radically as today’s programmable computers did so over the past 60+ years. Cognitive systems will be capable of making sense of vast quantities of unstructured information, by learning, reasoning and interacting with people in ways that are more natural for us.

You may be familiar with the first steps for Watson after the Jeopardy! victory. Our scientists and engineers have been working with Memorial Sloan Kettering Cancer Center, Cleveland Clinic, WellPoint and other healthcare institutions. The goal is to help professionals and organizations deal with the deluge of medical information and transform how medicine is taught, practiced and paid for. For patients, the quality and speed of care will be improved through individualized, evidence based medicine.

But healthcare is just the start. IBM is working with companies in a wide range of industries to bring new cognitive capabilities to the way they do business. In a next step, we recently announced a new service called IBM Watson Engagement Advisor, which is being used by companies in retail, banking, insurance and telecommunications, to crunch big data in real time and transform the way they engage clients via customer service, marketing and sales.

Many more applications will come:
  • In a big city, cognitive systems will help city leaders react, prioritize and respond to citizens more effectively by using data to gain insights into complex systems.
  • In the home, intelligent assistant apps on smart phones will help elderly citizens and their health care providers better manage chronic diseases and promote wellness.
  • In companies, cognitive systems will help engineers and designers create new products and services that respond better to the demands of consumers or even anticipate their needs.
IBM will create some of these services and continue to play a major role as the cognitive era unfolds. Our clients will embed Watson-like technologies in many aspects of how they run their businesses: from supply chain management and manufacturing, to accounting and market research.

We also anticipate that many other companies will develop new capabilities enabled by cognitive technologies. In addition, independent software and services companies will build new cognitive services on top of IBM’s technology platform. You can think of these as cognitive apps, just like Apple offers apps made by others to run on its iPhones and iPads.


So, don’t think of Watson as something that’s locked up in a box. Rather, think of it as a cloud service, available anywhere. And think of it as the foundation for an ecosystem of innovative companies — all of them focused on bringing new capabilities to individuals, businesses and society.

If you’re like to learn more about cognitive technologies and their impact on the world, you can download a free chapter of the upcoming book, Smart Machines: IBM’s Watson and the Era of Cognitive Computing, by IBM Research Director John E. Kelly III. As IBM General Manager of Watson Solutions, Manoj Saxena is responsible for the commercialization efforts of IBM’s Watson technology globally. Prior to this role, Saxena held several other leadership positions at IBM. Before joining IBM in 2006, Saxena was an active member of the IT venture capital community and led two successful venture-backed software companies.

ORIGINAL: Forbes
By Manoj Saxena, General Manager, IBM Watson Solutions
IBM Smarter Planet Contributor, IBM Smarter Planet

lunes, 23 de septiembre de 2013

Lovelace lecture 2013 Grady Booch

ORIGINAL: BCS

Lovelace lecture
This is an annual public lecture delivered by the winner of Lovelace medal.

2013 lecture - Grady Booch
The 2013 Lovelace lecture was delivered by Grady Booch, Chief Scientist of Software Engineering at IBM Research.
Grady Booch 2011. Wikipedia

Main lecture 



Q&A session

In producing this video for educational and research purposes, BCS acknowledges the following copyright holders:
  • Star Trek: The Next Generation -- Paramount Television. 
  • I, Robot -- Twentieth Century Fox Film Corp. 
  • Asimo -- Honda. 
  • Aiko -- Aiko Innovation Inc. 
  • Dr Ed Feigenbaum's Search for A.I -- Computer History Museum. 
  • Shakey -- SRI International. 
  • Watson -- IBM. 
  • Closer to Truth -- The Kuhn Foundation. 
  • Starlings on Otmoor -- Dylan Winter (You Tube). 
  • Kismet -- MIT AI Lab. 
  • Sir Roger Penrose -- BBC. 
  • President Obama -- www.whitehouse.gov.
  • Live Free or Die Hard - Twentieth Century Fox Film Corp. 
  • Harry Potter & the Order of the Phoenix -- Warner Bros. 
  • King Kong -- Universal Pictures. 
  • The Chronicles of Narnia: 
  • The Lion, the Witch and the Wardrobe -- Walt Disney Pictures. 
  • Night at the Museum - Twentieth Century Fox Film Corp. 
  • 10,000 BC -- Warner Bros. 
  • Flags of our Fathers -- Dreamworks SKG. 
  • Charlotte's Web -- Paramount Pictures. 
  • Film montage: El Ranchito. 
  • Mr X Inc. BlackGinger. DNA Productions. 
  • MPC. Weta Digital. 
  • The Mill. Digital Domain. 
  • The Filter FX. 
  • Method. Post Modern. 
  • Framestore CFC. 
  • Baxter Robot -- Rethink Robotics. 
  • Google car -- Google.
Synopsis

'I think, therefore I am: Is the mind computable?'

The human race may be singular, unique across all of time and space. It may be just one of multitudes. Most likely, however, it is an extremely rare thing, an exquisitely precious consequence of the unfolding of the laws of the universe. Still, one truth that we can assert with confidence is that we are. We are self-aware; we know that we know we exist.

And yet, we don't want to be alone in our existence; there seems to be within humanity a drive to create machines in our own image. From the Golem of Jewish mythology, to Leonardo’s robot, to the contemporary Kenshiro robot, we project our hopes and our fears into cunning mechanism that mirror us. While these anthropomorphic robots are interesting, there is a less visible revolution taking place in cognitive computing, whose advances are not only helping us better understand the operation of the human brain, they are leading us to create the illusion of sentience.

Grady explores the development of intelligent computers as projections of what we both dream and what we fear. We examine what it means to be intelligent, and take a journey through past and future approaches to building sentient software-intensive systems. Some such as Minsky believe the mind to be computable; others such as Penrose do not. In the end, we are compelled to consider the question of what it means to be human.

About the speaker

Grady is recognised internationally for his innovative work in improving the art and the science of software development. He is currently developing a major transmedia project for broadcast, titled Computing: The Human Experience.



Now in the role of Chief Scientist of Software Engineering at IBM Research, Grady has served as architect and architectural mentor for numerous complex software-intensive systems around the world in just about every domain imaginable.? The author of six best-selling books, Grady has published several hundred articles on computing and has lectured around the world on topics as diverse as software methodology and the morality of computing. He is an IBM Fellow, an ACM Fellow, an IEEE Fellow, a World Technology Network Fellow, and a Software Development Forum Visionary. Grady serves on the board of the Computer History Museum. Grady received his bachelor of science from the United States Air Force Academy in 1977 and his masters of science in electrical engineering from the University of California at Santa Barbara in 1979.

Past lectures

2012 Dr Hermann Hauser 'Computer Architectures'

2011 Professor John Reynolds 'Making Program Logics Intelligible'

2010 Professor Yorick Wilks 'What will a companionable computational agent be like?'

2009 Maurice Perks (presented on behalf of Dr Tony Storey) 'The Sins of IT Projects and why they can fail'

2008 Dr Ann Copestake (dedicated to the memory of Karen Spärck Jones) 'What do we mean? Computational approaches to natural semantics'

2007 Sir Tim Berners-Lee 'Looking Back, Looking Forward'

2006 Professor Nick McKeown 'Internet Routers: Past, Present and Future'

2005 Professor Christopher M Bishop 'Machines that learn'

2004 Dr John E Warnock 'The Invention of PostScript and Acrobat'

Previous winners of the Lovelace medal have also included:

2002 Dr Ian Foster and Dr Carl Kesselman for their pioneering work in Grid technology

2001 Dr Douglas C Engelbart

2000 Linus Torvalds for his creation of LINUX

1998 Professor Michael Jackson and Mr Chris Burton

jueves, 8 de agosto de 2013

IBM Research Creates New Foundation to Program SyNAPSE Chips

ORIGINAL: ZeitNews
August 8, 2013

credit: IBM Research
Scientists from IBM (NYSE: IBM) today unveiled a breakthrough software ecosystem designed for programming silicon chips that have an architecture inspired by the function, low power, and compact volume of the brain. The technology could enable a new generation of intelligent sensor networks that mimic the brain’s abilities for perception, action, and cognition.

Dramatically different from traditional software, IBM’s new programming model breaks the mold of sequential operation underlying today's von Neumann architectures and computers. It is instead tailored for a new class of distributed, highly interconnected, asynchronous, parallel, large-scale cognitive computing architectures.

Architectures and programs are closely intertwined and a new architecture necessitates a new programming paradigm,” said Dr. Dharmendra S. Modha, Principal Investigator and Senior Manager, IBM Research. “We are working to create a FORTRAN for synaptic computing chips. While complementing today’s computers, this will bring forth a fundamentally new technological capability in terms of programming and applying emerging learning systems.

To advance and enable this new ecosystem, IBM researchers developed the following breakthroughs that support all aspects of the programming cycle from design through development, debugging, and deployment:
  • Simulator: A multi-threaded, massively parallel and highly scalable functional software simulator of a cognitive computing architecture comprising a network of neurosynaptic cores.
  • Neuron Model: A simple, digital, highly parameterized spiking neuron model that forms a fundamental information processing unit of brain-like computation and supports a wide range of deterministic and stochastic neural computations, codes, and behaviors. A network of such neurons can sense, remember, and act upon a variety of spatio-temporal, multi-modal environmental stimuli.
  • Programming Model: A high-level description of a “program” that is based on composable, reusable building blocks called “corelets.” Each corelet represents a complete blueprint of a network of neurosynaptic cores that specifies a based-level function. Inner workings of a corelet are hidden so that only its external inputs and outputs are exposed to other programmers, who can concentrate on what the corelet does rather than how it does it. Corelets can be combined to produce new corelets that are larger, more complex, or have added functionality.
  • Library: A cognitive system store containing designs and implementations of consistent, parameterized, large-scale algorithms and applications that link massively parallel, multi-modal, spatio-temporal sensors and actuators together in real-time. In less than a year, the IBM researchers have designed and stored over 150 corelets in the program library.
  • Laboratory: A novel teaching curriculum that spans the architecture, neuron specification, chip simulator, programming language, application library and prototype design models. It also includes an end-to-end software environment that can be used to create corelets, access the library, experiment with a variety of programs on the simulator, connect the simulator inputs/outputs to sensors/actuators, build systems, and visualize/debug the results.
These innovations are being presented at The International Joint Conference on Neural Networks in Dallas, TX.

Paving the Path to SyNAPSE

Modern computing systems were designed decades ago for sequential processing according to a pre-defined program. Although they are fast and precise “number crunchers,” computers of traditional design become constrained by power and size while operating at reduced effectiveness when applied to real-time processing of the noisy, analog, voluminous, Big Data produced by the world around us (ie. DNA). In contrast, the brain—which operates comparatively slowly and at low precision—excels at tasks such as recognizing, interpreting, and acting upon patterns, while consuming the same amount of power as a 20 watt light bulb and occupying the volume of a two-liter bottle.

In August 2011, IBM successfully demonstrated a building block of a novel brain-inspired chip architecture based on a scalable, interconnected, configurable network of “neurosynaptic cores.” Each core brings 
  • memory (“synapses”), 
  • processors (“neurons”), and 
  • communication (“axons”) 
in close proximity, executing activity in an event-driven fashion. These chips serve as a platform for emulating and extending the brain’s ability to respond to biological sensors and analyzing vast amounts of data from many sources at once.

Having completed Phase 0, Phase 1, and Phase 2, IBM and its collaborators (Cornell University and iniLabs, Ltd) have recently been awarded approximately $12 million in new funding from the Defense Advanced Research Projects Agency (DARPA) for Phase 3 of the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project, thus bringing the cumulative funding to approximately $53 million.

Smarter Sensors
IBM’s long-term goal is to build a chip system with ten billion neurons and hundred trillion synapses, while consuming merely one kilowatt of power and occupying less than two liters of volume.

Systems built from these chips could bring the real-time capture and analysis of various types of data closer to the point of collection. They would not only gather symbolic data, which is fixed text or digital information, but also gather sub-symbolic data, which is sensory based and whose values change continuously. This raw data reflects activity in the world of every kind ranging from commerce, social, logistics, location, movement, and environmental conditions.

Take the human eyes, for example. They sift through over a Terabyte of data per day. Emulating the visual cortex, low-power, light-weight eye glasses designed to help the visually impaired could be outfitted with multiple video and auditory sensors that capture and analyze this optical flow of data.

These sensors would gather and interpret large-scale volumes of data to signal how many individuals are ahead of the user, distance to an upcoming curb, number of vehicles in a given intersection, height of a ceiling or length of a crosswalk. Like a guide dog, sub-symbolic data perceived by the glasses would allow them to plot the safest pathway through a room or outdoor setting and help the user navigate the environment via embedded speakers or ear buds. This same technology -- at increasing levels of scale -- can form sensory-based data input capabilities and on-board analytics for automobiles, medical imagers, healthcare devices, smartphones, cameras, and robots.

The views expressed are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. Approved for Public Release, Distribution Unlimited.

domingo, 4 de agosto de 2013

Watson and the future of cognitive computing

ORIGINAL: ComputerWorld
Rohan Pearce (Computerworld)
30 July, 2013 14:30

IBM's Watson made a memorable TV debut in 2011, and some of the concepts behind it may a more deep-going impact on the world
"I expected Watson's bag of cognitive tricks to be fairly shallow, but I felt an uneasy sense of familiarity as its programmers briefed us before the big match: The computer's techniques for unraveling Jeopardy! clues sounded just like mine," game-show contestant Ken Jennings wrote after his game show loss to IBM's Watson supercomputer.

"...Just as factory jobs were eliminated in the 20th century by new assembly-line robots, Brad [Rutter] and I were the first knowledge-industry workers put out of work by the new generation of 'thinking' machines."

"'Quiz show contestant' may be the first job made redundant by Watson, but I'm sure it won't be the last," Jennings concluded.
[ Get served with the latest developments on data centres and servers in Computerworld's Storage newsletter ]

Watson's 2011 victory was a publicity triumph for its creators at IBM. But according to the company, it also symbolised the birth of a new era of intelligent systems – or 'cognitive computing'. Cognitive computing, according to IBM, involves systems that interact naturally with human, learn from their experiences and generate and evaluate evidence-based hypotheses, says

Computerworld Australia caught up with Glenn Wightwick, Director of IBM Research - Australia, for a brief chat about how the technologies behind Watson have already began finding a home in businesses, as well at what distinguishes, in IBM's view, cognitive computing from past approaches to machine learning and natural language processing.

How would you distinguish the concept of 'cognitive computing' from the more general idea of artificial intelligence? Is it just a subset (or superset) of AI-related concepts?

AI is a very broad field that and one where there isn't a universally accepted definition, since people continue to discuss and debate exactly what intelligence is! Certainly there is a high degree of overlap between cognitive computing and AI in areas such as machine learning algorithms, knowledge representation, natural language processing and so on.

IBM recently received the 2013 Feigenbaum Prize (awarded to outstanding AI research which uses computer science methods) for our work on the Watson system, and we have seen a strong resurgence in the field of AI since Watson beat Ken Jennings and Brad Rutter in Jeopardy! in February, 2011.

Why are we seeing the emergence of it now? Is it just the product of accumulated software and hardware advances or a product of emerging needs, such as analysing unstructured and semi-structured data?

Many of the building blocks of cognitive computing have been around for some time. Certainly the types of problems we are starting to explore with cognitive computing rely on being able to perform an enormous amount of processing over very large volumes of data in a very short period of time, so that a cognitive system can engage with a human in a natural way, whether that is to win at Jeopardy! or to help an oncologist develop a cancer treatment plan. So the underlying technology is really important.

But the breakthrough here is the approach we have taken and the class of problems that we are tackling. Work has been done in many of these applications areas for years of course, using more traditional rules-based systems or more recently machine learning.

What we have discovered is that our approach to building cognitive systems, based on our Watson technology is yielding wonderful results in many areas. This isn't just about analysing vast quantities of structured and unstructured data.

Is it a real transformation in computing or just an novel implementation of language analysis and data processing?

What is dramatically different about how we are approaching cognitive computing based on the Watson technology is we approached natural language as a stochastic problem.

We haven't built this using classic deterministic algorithms. We do not bias on the standard rules of grammar, and we do not rely on hand-crafted (and otherwise brittle) ontologies.

Everything Watson knows about the language it knows probabilistically. We believe the technique can be applied to other aspects of human cognition (perception, foresight, investigation, etc.), but with special emphasis on the space of unstructured data (i.e. written text) that also tends to be the nesting ground of human cognition as well.

Watson learns through a combination of training via machine learning, adapting for features of the language that are new to a particular domain, and ingesting all the information it can find on the domain.

I know IBM has started offering a Watson-based customer service system and done some work with healthcare providers. Which industries do you think these kinds of technologies could have the most impact on?

Cognitive computing has applications for almost every industry where humans engage in dialogue, ask questions, test ideas and make decisions. These include healthcare, finance, education, law, government services and commerce.

We are seeing an enormous interest from organisations and have been working in healthcare and finance, as well as creating capabilities to support call centres and so on.

How about at the level of the consumer – what's the potential impact there?

A lot of our interaction with computers is still somewhat transactional. Think of the way you buy products via the Internet. Certainly, the explosion of mobile devices such as smartphones and tablets have created user interfaces that are far more powerful and intuitive. In the future, we will see cognitive computing deliver far more natural experiences where you will engage in a dialog.

In the e-commerce example, if I want to book an airline flight, I still end up doing a lot of searching for schedules and fares using my favourite travel tool or airline website.

In the future, I could actually have a conversation with a cognitive system and start out saying "I want to travel with my family to Bali in the next school holidays and I'm looking for the best value fares but I really hate red-eye flights" and the system would understand what school holidays are.

It’d come back with something like, "There are some great fares on Jetstar but you would need to take your children out of school a couple of days earlier. Do you want to do this?"

The use of Watson-derived technologies so far seems to have been quite domain-specific. But looking forward, can you imagine more generalised forms of cognitive computing?

The approach we adopted in developing Watson to play the Jeopardy! game was really quite clever. By tackling this problem, we were forced to deal with a completely open domain. There was no way you could build a rule-based system to deal with the incredible variety and complexity of the Jeopardy! questions. Hence, our approach to cognitive computing has been domain-independent from the start.

As we have applied Watson to new fields (like healthcare, for example), we are doing two things. Firstly, we have to train the system in that field by building a corpus of knowledge specific to that field. So in the case of healthcare, we would need to source medical text books, references, journals, medical databases etc.

The second thing we need to do is to integrate Watson into the workflow of the particular domain. So again, using the healthcare example, we need to understand how an oncologist works and make sure Watson has access to the necessary medical records, patient care systems, and other data elements.

What's the next frontier for cognitive computing?

Our current work in cognitive computing is yielding capabilities such as  
  • recall, 
  • learning, 
  • judgement, 
  • reasoning and 
  • inference
Our focus is on expanding these capabilities to recognise emotions, be more expressive in generating speech, add perception and creativity, as well as expanding beyond English text to multiple languages, images and other senses.
Watson seems to still relies on quantitative advances in hardware and software development. Is this fair to say? Can you envisage the development of forms of cognitive computing that represent a rupture from current computer and software architectures?

Watson is built on state-of-the-art hardware and software technology but the underlying architecture is still von Neuman, which is the basis of almost all computers in the world today.

We are already working on new and novel technologies such as SyNAPSE which combines digital “neurons” and on-chip “synapses” in working silicon. We’re also working on memory technologies that are far more dense, including phase-change memory, atomic-scale memory and race-track memory.

Glenn Wightwick is speaking at the 31 July-1 August Wired for Wonder conference in Sydney