In the video below, Le Trung demonstrates Aiko’s internal operating system, which gives the robot many abilities, including the ability to speak two languages (English and Japanese), solve high school math problems, communicate the weather forecast, understand more than 13,000 sentences, sing songs, identify objects, focus on objects or people of importance, read newspapers and other materials, and mimic human physical touch.

As Le Trung explains, in some ways the BRAINS software is even more powerful than a human brain because it can link to infinite sources of data. Similar to a human brain, the software is designed to interact with the surrounding environment, process it, and record the information in its internal memory. Once the internal memory is at full capacity, the information can be transferred into a server database. The information can then be shared with current and future robots.

With the BRAINS software, Aiko (whose name means “beloved one”) has the potential for many applications. For example, in the home, Aiko could help elderly people by reminding them when to take their medicine and helping them read the newspaper. It could also help kids with their math homework. In work and public environments, the robot could be used at information desks, where it could give directions and inform people when and where events take place. Le Trung also suggests that, with Aiko’s ability to detect 250 faces per second, it could be useful in airports to quickly scan and filter faces, as well as answer questions regarding flight times and gate locations. In addition, Aiko’s sensitivity sensors and humanlike appearance offer the potential for its use as a companion robot.

“The most recent improvement with Aiko is the BRAINS software,” Le Trung said. “I have just finished re-architecting the BRAINS software to have triple threads, which will make the software run a bit smoother and process about 15% faster for 3D recognition. As a result, Aiko can distinguish the difference between a $20 Canadian bill and $20 American bill. Aiko also has new improved facial expressions with 21 recognition points. Aiko will know when you are angry, happy, etc. Finally, the BRAINS can now process newspaper reading much faster and more accurate.”

Le Trung, whose background is in microbiology and chemistry, was originally inspired to build Aiko after watching “Chobits,” a Japanese manga that explores the relationships between humans and personal computers. While he hopes to continue to improve Aiko’s software, he currently faces a hardware limitation, as the CPU is currently at 99% capacity. Le Trung hopes to raise funds to upgrade the CPU.

In the future, Le Trung hopes to enable Aiko to achieve further skills, such as making tea, coffee, and a breakfast of eggs and bacon; cleaning a human’s ears with a Q-tip; giving a neck massage; writing; and cleaning windows, shelves, and bathrooms. He also hopes that, one day, he will be able to mass produce sister copies of Aiko for an estimated cost of about $17,000 – $20,000.

“Future improvements include making the voice with more emotions and feelings when speaking, improving the silicone material on her face so that she can do facial expressions like humans, and redesigning the body and arm system to move more naturally and carry heavier things,” Le Trung said.

More information:

• www.projectaiko.com
• A Perfect Female Companion: Project Aiko

August 25, 2009 by Lisa Zyga

The notion that a self-aware computing system would emerge spontaneously from the interconnections of billions of computers and computer networks goes back in science fiction at least as far as Arthur C. Clarke’s “Dial F for Frankenstein.” A prescient short story that appeared in 1961, it foretold an ever-more-interconnected telephone network that spontaneously acts like a newborn baby and leads to global chaos as it takes over financial, transportation and military systems.

Today, artificial intelligence, once the preserve of science fiction writers and eccentric computer prodigies, is back in fashion and getting serious attention from NASA and from Silicon Valley companies like Google as well as a new round of start-ups that are designing everything from next-generation search engines to machines that listen or that are capable of walking around in the world. A.I.’s new respectability is turning the spotlight back on the question of where the technology might be heading and, more ominously, perhaps, whether computer intelligence will surpass our own, and how quickly.

The concept of ultrasmart computers — machines with “greater than human intelligence” — was dubbed “The Singularity” in a 1993 paper by the computer scientist and science fiction writer Vernor Vinge. He argued that the acceleration of technological progress had led to “the edge of change comparable to the rise of human life on Earth.” This thesis has long struck a chord here in Silicon Valley.

Artificial intelligence is already used to automate and replace some human functions with computer-driven machines. These machines can see and hear, respond to questions, learn, draw inferences and solve problems. But for the Singulatarians, A.I. refers to machines that will be both self-aware and superhuman in their intelligence, and capable of designing better computers and robots faster than humans can today. Such a shift, they say, would lead to a vast acceleration in technological improvements of all kinds.

The idea is not just the province of science fiction authors; a generation of computer hackers, engineers and programmers have come to believe deeply in the idea of exponential technological change as explained by Gordon Moore, a co-founder of the chip maker Intel.

In 1965, Dr. Moore first described the repeated doubling of the number transistors on silicon chips with each new technology generation, which led to an acceleration in the power of computing. Since then “Moore’s Law” — which is not a law of physics, but rather a description of the rate of industrial change — has come to personify an industry that lives on Internet time, where the Next Big Thing is always just around the corner.

Several years ago the artificial-intelligence pioneer Raymond Kurzweil took the idea one step further in his 2005 book, “The Singularity Is Near: When Humans Transcend Biology.” He sought to expand Moore’s Law to encompass more than just processing power and to simultaneously predict with great precision the arrival of post-human evolution, which he said would occur in 2045.

In Dr. Kurzweil’s telling, rapidly increasing computing power in concert with cyborg humans would then reach a point when machine intelligence not only surpassed human intelligence but took over the process of technological invention, with unpredictable consequences.

Profiled in the documentary “Transcendent Man,” which had its premier last month at the TriBeCa Film Festival, and with his own Singularity movie due later this year, Dr. Kurzweil has become a one-man marketing machine for the concept of post-humanism. He is the co-founder of Singularity University, a school supported by Google that will open in June with a grand goal — to “assemble, educate and inspire a cadre of leaders who strive to understand and facilitate the development of exponentially advancing technologies and apply, focus and guide these tools to address humanity’s grand challenges.”

Not content with the development of superhuman machines, Dr. Kurzweil envisions “uploading,” or the idea that the contents of our brain and thought processes can somehow be translated into a computing environment, making a form of immortality possible — within his lifetime.

That has led to no shortage of raised eyebrows among hard-nosed technologists in the engineering culture here, some of whom describe the Kurzweilian romance with supermachines as a new form of religion.

The science fiction author Ken MacLeod described the idea of the singularity as “the Rapture of the nerds.” Kevin Kelly, an editor at Wired magazine, notes, “People who predict a very utopian future always predict that it is going to happen before they die.”

However, Mr. Kelly himself has not refrained from speculating on where communications and computing technology is heading. He is at work on his own book, “The Technium,” forecasting the emergence of a global brain — the idea that the planet’s interconnected computers might someday act in a coordinated fashion and perhaps exhibit intelligence. He just isn’t certain about how soon an intelligent global brain will arrive.

Others who have observed the increasing power of computing technology are even less sanguine about the future outcome. The computer designer and venture capitalist William Joy, for example, wrote a pessimistic essay in Wired in 2000 that argued that humans are more likely to destroy themselves with their technology than create a utopia assisted by superintelligent machines.

Mr. Joy, a co-founder of Sun Microsystems, still believes that. “I wasn’t saying we would be supplanted by something,” he said. “I think a catastrophe is more likely.”

Moreover, there is a hot debate here over whether such machines might be the “machines of loving grace,” of the Richard Brautigan poem, or something far darker, of the “Terminator” ilk.

“I see the debate over whether we should build these artificial intellects as becoming the dominant political question of the century,” said Hugo de Garis, an Australian artificial-intelligence researcher, who has written a book, “The Artilect War,” that argues that the debate is likely to end in global war.

Concerned about the same potential outcome, the A.I. researcher Eliezer S. Yudkowsky, an employee of the Singularity Institute, has proposed the idea of “friendly artificial intelligence,” an engineering discipline that would seek to ensure that future machines would remain our servants or equals rather than our masters.

Nevertheless, this generation of humans, at least, is perhaps unlikely to need to rush to the barricades. The artificial-intelligence industry has advanced in fits and starts over the past half-century, since the term “artificial intelligence” was coined by the Stanford University computer scientist John McCarthy in 1956. In 1964, when Mr. McCarthy established the Stanford Artificial Intelligence Laboratory, the researchers informed their Pentagon backers that the construction of an artificially intelligent machine would take about a decade. Two decades later, in 1984, that original optimism hit a rough patch, leading to the collapse of a crop of A.I. start-up companies in Silicon Valley, a time known as “the A.I. winter.”

Such reversals have led the veteran Silicon Valley technology forecaster Paul Saffo to proclaim: “never mistake a clear view for a short distance.”

Indeed, despite this high-technology heartland’s deeply held consensus about exponential progress, the worst fate of all for the Valley’s digerati would be to be the generation before the generation that lives to see the singularity.

“Kurzweil will probably die, along with the rest of us not too long before the ‘great dawn,’ ” said Gary Bradski, a Silicon Valley roboticist. “Life’s not fair.”

Source: New York times

http://www.nytimes.com/2009/05/24/weekinreview/24markoff.html?_r=2

Although the system is still new, it has already produced massive interest and excitement among technology pundits and internet watchers. Computer experts believe the new search engine will be an evolutionary leap in the development of the internet. Nova Spivack, an internet and computer expert, said that Wolfram Alpha could prove just as important as Google. “It is really impressive and significant,” he wrote. “In fact it may be as important for the web (and the world) as Google, but for a different purpose.

Tom Simpson, of the blog Convergenceofeverything.com, said: “What are the wider implications exactly? A new paradigm for using computers and the web? Probably. Emerging artificial intelligence and a step towards a self-organising internet? Possibly… I think this could be big.”

Wolfram Alpha will not only give a straight answer to questions such as “how high is Mount Everest?”, but it will also produce a neat page of related information – all properly sourced – such as geographical location and nearby towns, and other mountains, complete with graphs and charts.

The real innovation, however, is in its ability to work things out “on the fly”, according to its British inventor, Dr Stephen Wolfram. If you ask it to compare the height of Mount Everest to the length of the Golden Gate Bridge, it will tell you. Or ask what the weather was like in London on the day John F Kennedy was assassinated, it will cross-check and provide the answer. Ask it about D sharp major, it will play the scale. Type in “10 flips for four heads” and it will guess that you need to know the probability of coin-tossing. If you want to know when the next solar eclipse over Chicago is, or the exact current location of the International Space Station, it can work it out.

Dr Wolfram, an award-winning physicist who is based in America, added that the information is “curated”, meaning it is assessed first by experts. This means that the weaknesses of sites such as Wikipedia, where doubts are cast on the information because anyone can contribute, are taken out. It is based on his best-selling Mathematica software, a standard tool for scientists, engineers and academics for crunching complex maths.

“I’ve wanted to make the knowledge we’ve accumulated in our civilisation computable,” he said last week. “I was not sure it was possible. I’m a little surprised it worked out so well.”

Dr Wolfram, 49, who was educated at Eton and had completed his PhD in particle physics by the time he was 20, added that the launch of Wolfram Alpha later this month would be just the beginning of the project.

“It will understand what you are talking about,” he said. “We are just at the beginning. I think we’ve got a reasonable start on 90 per cent of the shelves in a typical reference library.”

The engine, which will be free to use, works by drawing on the knowledge on the internet, as well as private databases. Dr Wolfram said he expected that about 1,000 people would be needed to keep its databases updated with the latest discoveries and information.

He also added that he would not go down the road of storing information on ordinary people, although he was aware that others might use the technology to do so.

Wolfram Alpha has been designed with professionals and academics in mind, so its grasp of popular culture is, at the moment, comparatively poor. The term “50 Cent” caused “absolute horror” in tests, for example, because it confused a discussion on currency with the American rap artist. For this reason alone it is unlikely to provide an immediate threat to Google, which is working on a similar type of search engine, a version of which it launched last week.

“We have a certain amount of popular culture information,” Dr Wolfram said. “In some senses popular culture information is much more shallowly computable, so we can find out who’s related to who and how tall people are. I fully expect we will have lots of popular culture information. There are linguistic horrors because if you put in books and music a lot of the names clash with other concepts.”

He added that to help with that Wolfram Alpha would be using Wikipedia’s popularity index to decide what users were likely to be interested in.

With Google now one of the world’s top brands, worth $100bn, Wolfram Alpha has the potential to become one of the biggest names on the planet.

Dr Wolfram, however, did not rule out working with Google in the future, as well as Wikipedia. “We’re working to partner with all possible organisations that make sense,” he said. “Search, narrative, news are complementary to what we have. Hopefully there will be some great synergies.”

What the experts say

“For those of us tired of hundreds of pages of results that do not really have a lot to do with what we are trying to find out, Wolfram Alpha may be what we have been waiting for.”

Michael W Jones, Tech.blorge.com

“If it is not gobbled up by one of the industry superpowers, his company may well grow to become one of them in a small number of years, with most of us setting our default browser to be Wolfram Alpha.”

Doug Lenat, Semanticuniverse.com

“It’s like plugging into an electric brain.”

Matt Marshall, Venturebeat.com

“This is like a Holy Grail… the ability to look inside data sources that can’t easily be crawled and provide answers from them.”

Danny Sullivan, editor-in-chief of searchengineland.com

Source: http://www.independent.co.uk/life-style/gadgets-and-tech/news/an-invention-that-could-change-the-internet-for-ever-1678109.html

With more than 40 organizations in India hiring its services, CRL has made a mark in a field where only a few organizations have dared to venture. In a period of recession, Eka has opened up new possibilities for industries that require the massive crunching power of supercomputers, but only during certain times of their product development lifecycle.

For example, Tata Elxsi used Eka’s processing power to crunch the time required for rendering the animation movie, ‘Roadside Romeo.’ The activity, which would have taken the firm approximately 36-40 months to digitally render the movie in a studio, took only six months, due to the computing power of Eka. It is also significant to note that the firm achieved this feat using only one-third of the processing power of Eka.

Similarly, leading aerospace company, Boeing, is using Eka’s capability to bring its ideas faster to the market, by offering design and simulation support. Group company, Tata Motors, is using Eka for vehicle simulation and testing digital prototypes. CRL is also looking at potential opportunities in sectors such as Life Science, weather forecasting, animation and in the automotive and aviation industries. This is just a glimpse of the processing power.

Speaking with Seetha Rama Krishna, Head, HPC Engineering and Operations, CRL, one can gauge the ambitious goals of this small company. “‘Supercomputing made easy’ is our goal,” declares Seetha Rama Krishna, while elaborating on his firm’s vision to take supercomputers down to the retail level.

CRL’s initiative is a pioneering effort, as it is the first time that a corporate institution is taking the lead in extending the domain of High Performance Computing (HPC) from the academic field, to the enterprise. This is partly because a large-scale supercomputing infrastructure has been typically owned by government institutions, and is not largely used to its full potential. Being research focused, these institutions have little inclination or capability to deliver it as a utility service.

On the other hand, while private institutions in the oil and gas sector, or the automotive industry, would love to use a supercomputer, they cannot justify the cost of investing in a supercomputer that will be used only during specific periods. CRL is attempting to walk the tight rope between these two worlds, by offering services that are cost-effective even for small companies. “As an Indian company, it is in our ‘genes’ to be cost-effective, and deliver services that are at par or better than our global counterparts,” says Krishna, elaborating on the huge interest global firms have shown in his company’s services. Krishna certainly has the experience to walk the talk, as he has been the founder of the HPC solutions division at C-DAC, the state-owned firm that gave India its first supercomputer, way back in the 80’s.

CRL has succeeded because few organizations today have the financial bandwidth to afford a supercomputer; or because they are unwilling to invest and pay for the administration costs of maintaining a supercomputer. In this scenario, the concept of offering computing power as a service, has struck the right chord, as clients are happy to do this on a need basis at costs they can afford.

Eka is also attracting huge attention from Indian scientific and research institutions, as research shows that a majority of these institutions have supercomputers that are idle for a significant amount of time. Additionally, a significant number of institutions do not have resources with the requisite skills to effectively use or utilize a supercomputer. Eka is looking to occupy this space, with supercomputing solutions that can be effectively used by a large as well as small enterprise.

PAY-PER-USE
To encourage more enterprises to start using the supercomputers on rent, CRL is offering the services through three options: a pay-per-use model, a fixed capacity model, and through turnkey based customized solutions. Krishna envisages percolating the concept of supercomputers on rent, to small-scale enterprises or even professionals who might want to use the processing power of a supercomputer, for a specific period.

While few have succeeded in this field, the present economic condition is perhaps the perfect time to make this concept successful. “Eka’s tremendous number-crunching ability is ideal for research labs that require computing power for smaller durations, but do not have the money or technical skills to build or maintain these kind of solutions,” says Sandeep Lodha, Vice President, Netweb Technologies, a firm with huge domain experience in the HPC field. The firm has implemented over 60 HPC installations for some major Indian scientific institutions, and a number of firms in the bioinformatics domain, which are using supercomputers to cut down the time required for drug discovery. Netweb also has a small setup in its office where it offers supercomputing power on rent to some of its customers, who are contemplating buying their own setup.

Lodha says that once the cost-value equation becomes clear, more global organizations will come to India to outsource their data crunching requirements. A small trend in this direction is already taking place; Eka has been attracting huge interest from overseas clients interested in using the domain capabilities of the CRL team to test their applications.

“We are aiming to be a catalyst in this space and believe that as we push down the price, and offer it as a utility model, we can effectively remove the capacity constraint of users – physically and mentally. Imagine the impact, when enterprises are given the power to crunch design times, because of advanced computing capability. If more Indian enterprises have the ability to use this capability, one can deliver advanced products faster to the market, and lead the market,” says Krishna, speaking on the strategic impact that supercomputers can make, in generating insights from vast amounts of data, which would not be possible using traditional sources of computing.

By offering the supercomputing platform as a service, and bringing the benefits of a platform that has historically been out of reach for most enterprises, CRL has the potential to accelerate design and research, and revolutionize a new industry. Similar to what Tata Motors has done with the launch of Nano, can another Tata group company blaze a trail and create a new market? Watch this space!

Although the chip has a fraction of the number of neurons or connections found in a brain, its design allows it to be scaled up, says Karlheinz Meier, a physicist at Heidelberg University, in Germany, who has coordinated the Fast Analog Computing with Emergent Transient States project, or FACETS.

The hope is that recreating the structure of the brain in computer form may help to further our understanding of how to develop massively parallel, powerful new computers, says Meier.

This is not the first time someone has tried to recreate the workings of the brain. One effort called the Blue Brain project, run by Henry Markram at the Ecole Polytechnique Fédérale de Lausanne, in Switzerland, has been using vast databases of biological data recorded by neurologists to create a hugely complex and realistic simulation of the brain on an IBM supercomputer.

FACETS has been tapping into the same databases. “But rather than simulating neurons,” says Karlheinz, “we are building them.” Using a standard eight-inch silicon wafer, the researchers recreate the neurons and synapses as circuits of transistors and capacitors, designed to produce the same sort of electrical activity as their biological counterparts.

A neuron circuit typically consists of about 100 components, while a synapse requires only about 20. However, because there are so much more of them, the synapses take up most of the space on the wafer, says Karlheinz.

The advantage of this hardwired approach, as opposed to a simulation, Karlheinz continues, is that it allows researchers to recreate the brain-like structure in a way that is truly parallel. Getting simulations to run in real time requires huge amounts of computing power. Plus, physical models are able to run much faster and are more scalable. In fact, the current prototype can operate about 100,000 times faster than a real human brain. “We can simulate a day in a second,” says Karlheinz.

While it may sound implausible, neurons are actually very slow, at least compared to computers, says Thomas Serre, a computational neuroscience researcher at MIT. “The reason why computers seem much slower is that they are serial machines, while our brains run in parallel,” he says.

FACETS is not the only group taking this approach. Researchers at Stanford University have also been creating neuronal circuits and the Defense Advanced Research Projects Agency recently started funding a similar project.

“Where FACETS is ahead of anybody else is that they use these complex synapses,” says Markram. While the neurons are quite simple, he says, the synapses are designed to use a very powerful distributed algorithm–developed by Markram–called spike-timing dependent plasticity, that allows the device to learn and adapt to new situations.

Building such complex circuits has required close collaboration with neurobiologists, says Markram. In fact, the project, whose current budget is €10.5 million (US$14.1 million), relies upon the contributions of 15 scientific groups from seven different countries. Among the challenges they face is recreating the three-dimensional structure of the brain in a 2-D piece of silicon, he says.

Despite efforts to make the chips as biologically plausible as possible, Markram admits they are still crude compared to what can be achieved in simulation. “It’s not a brain. It’s a more of a computer processor that has some of the accelerated parallel computing that the brain has,” he says.

Because of this, Markram doubts that the hardware approach will offer much insight into how the brain works. For example, unlike Blue Brain, researchers won’t be able to perform “in silico” drug testing, simulating the effects of drugs on the brain. “It’s more a platform for artificial intelligence than understanding biology,” he says.

The FACETS group now plans to further scale up their chips, connecting a number of wafers to create a superchip with a total of a billion neurons and 1013 synapses.

Source: http://www.technologyreview.com/computing/22339/

The Unstructured Information Management Architecture (UIMA) is an XML-based data retrieval architecture under development at IBM. UIMA will greatly expand and enhance the retrieval techniques underlying databases, said Alfred Spector, vice president of services and software at IBM’s Research division.

UIMA “is something that becomes part of a database, or, more likely, something that databases access,” he said. “You can sense things almost all the time. You can effect change in automated or human systems much more.”

Once incorporated into systems, UIMA could allow cars to obtain and display real-time data on traffic conditions and on average auto speeds on freeways, or it could let factories regulate their own fuel consumption and optimally schedule activities. Automated language translation and natural language processing also would become feasible.

The theory underlying UIMA is the Combination Hypothesis, which states that statistical machine learning–the sort of data-ranking intelligence behind search site Google–syntactical artificial intelligence, and other techniques can be married in the relatively near future.

“If we apply in parallel the techniques that different artificial intelligence schools have been proponents of, we will achieve a multiplicative reduction in error rates,” Spector said. “We’re beginning to apply the Combination Hypothesis, and that is going to happen a lot this year. I think you will begin to see this rolling out in technologies that people use over the next few years. It isn’t that far away.

“There is more progress in this happening than has happened, despite the fact that the Nasdaq is off its peak,” he added.

The results of current, major UIMA experiments will be disclosed to analysts around March, with public disclosures to follow, sources at IBM said.

Although it’s been alternately touted and debunked, the era of functional artificial intelligence may be dawning. For one thing, the processing power and data-storage capabilities required for thinking machines are now coming into existence.

Researchers also have refined more acutely the algorithms and concepts behind artificially intelligent software.

Additionally, the explosive growth of the Internet has created a need for machines that can function relatively autonomously. In the future, both businesses and individuals simply will own far more computers than they can manage–spitting out more data than people will be able to mentally absorb on their own. The types of data on the Net–audio, text, visual–will also continue to grow.

XML, meanwhile, provides an easy way to share and classify data, which makes it easier to apply intelligence technology into the computing environment. “The database industry will undergo more change in the next three years than it has in the last 20 due to the emergence of XML,” Spector said.

A new order
Artificial intelligence in a sense will function like a filter. Sensors will gather data from the outside world and send it to a computer, which in turn will issue the appropriate actions, alerting its human owners only when necessary.

When it comes to Web searching, humans will make a query, and computers will help them refine it so that only the relevant data, rather than 14 pages of potential Web sites, match.

IBM’s approach to artificial intelligence has been decidedly agnostic. There are roughly two basic schools of thought in artificial intelligence. Statistical learning advocates believe that the best guide for thinking machines is memory.

Based in part on the mathematical theories of 18th century clergyman Thomas Bayes, statistical theory essentially states that the future, or current events, can be identified by what occurred in the past. Google search results, for example, are laundry lists of sites other individuals examined after posing similar queries ranked in a hierarchy. Voice-recognition applications work under the same principle.

By contrast, rules-based intelligence advocates, broken down into syntactical and grammatical schools of thought, believe that machines work better when more aware of context.

A search for “Italian Pet Rock” on a statistically intelligent search engine, for example, might return sites about the 1970s novelty. A rules-based application, by contrast, might realize you mistyped the Italian poet Petrarch. A Google search on UIMA turned up the Ukrainian Institute of Modern Art as the first selection.

“The combination of grammatical, statistical, advanced statistical (and) semantics will probably be needed to do this, but you can’t do it without a common architecture,” Spector said. Thinking in humans, after all, isn’t completely understood.

“It’s not exactly clear how children learn. I’m convinced it’s statistically initially, but then at a certain point you will see…it is not just statistical,” he said. “They are reasoning. It’s remarkable.”

Singularity University is less a traditional university and more an institution that will feature intensive 10-week, 10-day, or 3-day programs examining a set of 10 technologies and disciplines, such as future studies and forecasting; biotechnology and bioinformatics; nanotechnology; AI, robotics, and cognitive computing; and finance and entrepreneurship.

The founders anticipate that students will come from all over the world, and they hope the program results in the founding of new companies, the evolution of scientific and technological thinking, and the solidifying of professional and personal networks among the highly-accomplished students and faculty.

To Kurzweil, Singularity University is a place to problem-solve and talk about the results of the most recent iterations of the exponentially growing technologies that have shaped modern life. Among them, he said, are vacuum tubes, integrated circuits, chips and microprocessors.

Now, he said, we are on the threshold of an explosion of the newest such technology, including 3D and self-organizing molecular circuits. And to Kurzweil, the ability to bring together the leaders in this wide range of fields is a rare opportunity to jump-start the future. (The program’s name is based on the theories Kurzweil popularized in his best-selling book The Singularity is Near.)

For Diamandis, who previously co-founded the International Space University (a space studies program on which Singularity University will be modeled), the idea of building an interdisciplinary academic institution around the concepts of exponentially growing trends seemed natural–and powerful.

So, after bringing together 50 leading thinkers for a founding conference at NASA Ames, Kurzweil, Diamandis, and Ismail got the backing of Ames’ director, Pete Worden, and a commitment of space at the center–a highly visual Silicon Valley landmark along highway 101–for the annual summer programs.

In addition to the core 10-week course, which will be open to graduate and post-graduate students, Singularity University will also offer 3-day and 10-day executive programs. The shorter version will be targeted at CEOs and CTOs, while the 10-day program will be aimed at rising-star executives who want to add to their knowledge and networks.

“These programs are there to give executives a look at what’s in the lab today,” said Diamandis, “and what is likely to hit the marketplace in the next 5 to 10 years.”

This summer, Singularity University will kick off with just 30 or so students and will piggyback on the International Space University, which will host 120 students at NASA Ames. But in following years, the new institution is expected to expand to about 120 students, each of whom could be the next Larry Page or Sergey Brin.

“If we do our job correctly,” Diamandis said, students “will meet, (discover their) common visions, and start companies together. They’ll have a chance to match a nanotech expert from Russia with an AI expert from Silicon Valley and see what magic happens at the boundaries.”

As evidence of how seriously many people in the fields of focus take Singularity University, it has pulled together what can only be described as a very impressive roster of faculty.

Among them are The Sims and Spore creator Will Wright; George Smoot, a professor at the University of California at Berkeley and winner of the 2006 Nobel Prize in Physics; Dan Kammen, co-director of the Berkeley Institute of the Environment and winner of the 2006 Nobel Peace Prize; Vint Cerf, Google’s chief Internet evangelist; and Stephanie Langhoff, NASA Ames’ chief scientist.

Befitting the serious nature of the program, its curriculum is not for the faint of heart. The first phase, said Diamandis, is a series of plenary lectures in which all students take the same coursework and learn together about each of the 10 disciplines.

“It’s about learning the vocabulary” of the disciplines, Diamandis said, “the basic principles, so they can communicate better between themselves.”

In the second phase, students will take deep dives into one of the 10 tracks, typically not one in which they already specialize, learning together in 10-person classes.

And in the final phase, the entire student body will come together to work on a team project.

“This is where the student body will focus as a group in taking on one of the world’s grand challenges,” said Diamandis, dealing “with global hunger, pandemics, climate change,” or something similar.

And while the program’s students can expect to work very hard and be deeply immersed in their studies, the faculty will be equally challenged.

“It caused all of us who were invited to be faculty to pause and think about it,” said Paul Saffo, a Silicon Valley-based forecaster who is teaching in the Singularity University program. “We’re expected to be there for the full nine weeks, which is a breathtaking commitment of time.”

But for Saffo, who is helping to organize the future studies and forecasting track with Kurzweil, being intimately involved with the program at every level is precisely the point.

“The real benefit of teaching is being able to participate,” Saffo said. “It would be a waste of time to just show up, give a couple of lectures, and leave.”

And while their involvement at any level would bring Singularity University the prestige it needs to recruit talented students and faculty, both Kurzweil and Diamandis said they would be teaching each summer.

For Kurzweil, that means teaching some of the future studies and forecasting classes, and for Diamandis, it means helping to build the curriculum and teaching where he is needed.

The students, meanwhile, will need to pony up some serious money to take part in Singularity University. The base fee for the 10-week program is $25,000, though Diamandis said that there will be a significant number of full and partial scholarships available, funded by private companies, and other contributors.

Ultimately, the results of Singularity University won’t be known for some time. But given the people behind it and the likelihood of a steady stream of highly talented students, the odds of it producing the kind of deep thinking and world-changing technology the founders hope for are good.

“I have no doubt that society gets ever more complex, and the consequences of ever-growing technology become ever more difficult to anticipate and respond to,” said Saffo. “So having a 10-week program of smart, committed people looking at the challenges from an interdisciplinary point of view can only be a good thing.”

Tononi, professor of psychiatry at the UW-Madison School of Medicine and Public Health and an internationally known expert on consciousness, is part of a team of collaborators from top institutions who have been awarded a $4.9 million grant from the Defense Advanced Research Projects Agency (DARPA) for the first phase of DARPA’s Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project.

Tononi and scientists from Columbia University and IBM will work on the “software” for the thinking computer, while nanotechnology and supercomputing experts from Cornell, Stanford and the University of California-Merced will create the “hardware.” Dharmendra Modha of IBM is the principal investigator.

“Every neuron in the brain knows that something has changed,” Tononi explains. “It tells the brain, ‘I got burned, and if you want to change, this is the time to do it.’’

Thus, a cat landing on a hot stovetop not only jumps off immediately, it learns not to do that again.

The idea is to create a computer capable of sorting through multiple streams of changing data, to look for patterns and make logical decisions.

There’s another requirement: The finished cognitive computer should be as small as a the brain of a small mammal and use as little power as a 100-watt light bulb. It’s a major challenge. But it’s what our brains do every day.

“Our brains can do it, so we have proof that it is possible,” says Tononi. “What our brains are good at is being flexible, learning from experience and adapting to different situations.”

While the project will take its inspiration from the brain’s architecture and function, Tononi says it isn’t possible or even desirable to recreate the entire structure of the brain down to the level of the individual synapse.

“A lot of the work will be to determine what kinds of neurons are crucial and which ones we can do without,” he says.

It all comes down to an understanding of what is necessary for teaching an artificial brain to reason and learn from experience.

“Value systems or reward systems are important aspects,” he said. “Learning is crucial because it needs to learn from experience just like we do.”

So a system modeled after the neurons that release neuromodulators could be important. For example, neurons in the brain stem flood the brain with a neurotransmitter during times of sudden stress, signaling the “fight-or flight” response.

“Every neuron in the brain knows that something has changed,” Tononi explains. “It tells the brain, ‘I got burned, and if you want to change, this is the time to do it.’”

Thus, a cat landing on a hot stovetop not only jumps off immediately, it learns not to do that again.

Tononi says the ideal artificial brain will need to be plastic, meaning it is capable of changing as it learns from experience. The design will likely convey information using electrical impulses modeled on the spiking neurons found in mammal brains. And advances in nanotechnology should allow a small artificial brain to contain as many artificial neurons as a small mammal brain.

It won’t be an easy task, says Tononi, a veteran of earlier efforts to create cognitive computers. Even the brains of the smallest mammals are quite impressive when you consider what tasks they perform with a relatively small volume and energy input.

“I would be happy to create a mouse brain,” Tononi says. “A mouse brain is quite remarkable. And from there, it shouldn’t be too hard to scale up to a rat brain, and then a cat or monkey brain.”

Source: http://www.sciencedaily.com/releases/2008/12/081221215537.htm
Adapted from materials provided by University of Wisconsin-Madison. Original article written by Susan Lampert Smith.

Atomic-scale computing, in which computer processes are carried out in a single molecule or using a surface atomic-scale circuit, holds vast promise for the microelectronics industry. It allows computers to continue to increase in processing power through the development of components in the nano- and pico scale. In theory, atomic-scale computing could put computers more powerful than today’s supercomputers in everyone’s pocket.

“Atomic-scale computing researchers today are in much the same position as transistor inventors were before 1947. No one knows where this will lead,” says Christian Joachim of the French National Scientific Research Centre’s (CNRS) Centre for Material Elaboration & Structural Studies (CEMES) in Toulouse, France.

Joachim, the head of the CEMES Nanoscience and Picotechnology Group (GNS), is currently coordinating a team of researchers from 15 academic and industrial research institutes in Europe whose groundbreaking work on developing a molecular replacement for transistors has brought the vision of atomic-scale computing a step closer to reality. Their efforts, a continuation of work that began in the 1990s, are today being funded by the European Union in the Pico-Inside project.

In a conventional microprocessor – the “motor” of a modern computer – transistors are the essential building blocks of digital circuits, creating logic gates that process true or false signals. A few transistors are needed to create a single logic gate and modern microprocessors contain billions of them, each measuring around 100 nanometres.

Transistors have continued to shrink in size since Intel co-founder Gordon E. Moore famously predicted in 1965 that the number that can be placed on a processor would double roughly every two years. But there will inevitably come a time when the laws of quantum physics prevent any further shrinkage using conventional methods. That is where atomic-scale computing comes into play with a fundamentally different approach to the problem.

“Nanotechnology is about taking something and shrinking it to its smallest possible scale. It’s a top-down approach,” Joachim says. He and the Pico-Inside team are turning that upside down, starting from the atom, the molecule, and exploring if such a tiny bit of matter can be a logic gate, memory source, or more. “It is a bottom-up or, as we call it, ‘bottom-bottom’ approach because we do not want to reach the material scale,” he explains.

Joachim’s team has focused on taking one individual molecule and building up computer components, with the ultimate goal of hosting a logic gate in a single molecule.

How many atoms to build a computer?

“The question we have asked ourselves is how many atoms does it take to build a computer?” Joachim says. “That is something we cannot answer at present, but we are getting a better idea about it.”

The team has managed to design a simple logic gate with 30 atoms that perform the same task as 14 transistors, while also exploring the architecture, technology and chemistry needed to achieve computing inside a single molecule and to interconnect molecules.

They are focusing on two architectures: one that mimics the classical design of a logic gate but in atomic form, including nodes, loops, meshes etc., and another, more complex, process that relies on changes to the molecule’s conformation to carry out the logic gate inputs and quantum mechanics to perform the computation.

The logic gates are interconnected using scanning-tunnelling microscopes and atomic-force microscopes – devices that can measure and move individual atoms with resolutions down to 1/100 of a nanometre (that is one hundred millionth of a millimetre!). As a side project, partly for fun but partly to stimulate new lines of research, Joachim and his team have used the technique to build tiny nano-machines, such as wheels, gears, motors and nano-vehicles each consisting of a single molecule.

“Put logic gates on it and it could decide where to go,” Joachim notes, pointing to what would be one of the world’s first implementations of atomic-scale robotics.

The importance of the Pico-Inside team’s work has been widely recognised in the scientific community, though Joachim cautions that it is still very much fundamental research. It will be some time before commercial applications emerge from it. However, emerge they all but certainly will.

“Microelectronics needs us if logic gates – and as a consequence microprocessors – are to continue to get smaller,” Joachim says.

The Pico-Inside researchers, who received funding under the ICT strand of the EU’s Sixth Framework Programme, are currently drafting a roadmap to ensure computing power continues to increase in the future.

Moments later, a machine gun rings out, and you are cut down in a shower of bullets. Then, as you lie dying, you glimpse the soldier who flanked you from behind while his two buddies drew your attention.

Thankfully, it was only a video game, so in fact you’re not mortally wounded. Still, your ego might well be bruised, because you were not only outgunned but also outsmarted by artificial intelligence (AI).

The game is called F.E.A.R. , short for First Encounter Assault Recon, and its use of AI, along with its impressive graphics, are its prime attractions. The developer, Monolith Productions of Kirkland, Wash., released it in 2005 to rave reviews, including the GameSpot Web site’s Best Artificial Intelligence award. Such recognition means a lot to the game’s creators, who face stiff competition in what has become a multibillion-dollar industry.

The game is a far cry from the traditional diversions that AI researchers like ourselves have long studied, such as chess and checkers. Whereas the goal in the past was to write computer programs capable of beating expert players at such board games, now the metric of success for AI is whether it makes video games more entertaining.

Because a high fun factor is what sells, the video-game industry has become increasingly keen to make use of developments in AI research—and computer scientists have taken notice. A watershed came in 2000, when John E. Laird, a professor of engineering at the University of Michigan, and Michael van Lent, now chief scientist at Soar Technology, in Ann Arbor, Mich., published a call to arms that described commercial video games as “AI’s killer application.” Their point was that research to improve AI for such games would create spin-offs in many other spheres.

The main challenge is to make computer-generated characters—dubbed bots—act realistically. They must, of course, look good and move naturally. But, ideally, they should also be able to engage in believable conversations, plan their actions, find their way around virtual worlds, and learn from their mistakes. That is, they need to be smart.

Today many video games create only an illusion of intelligence, using a few programming tricks. But in the not-so-distant future, game bots will routinely use sophisticated AI techniques to shape their behavior. We and our colleagues in the University of Alberta GAMES (Game-playing, Analytical methods, Minimax search and Empirical Studies) research group, in Edmonton, Canada, have been working to help bring about such a revolution.

The AI of F.E.A.R. is based loosely on an automated planner called STRIPS (for STanford Research Institute Problem Solver), which Richard E. Fikes and Nils J. Nilsson, both now of Stanford University, developed way back in 1971. The general idea of STRIPS was to establish one or more goals along with a set of possible actions, each of which could be carried out only when its particular preconditions were satisfied. The planning system kept track of the physical environment and determined which actions were allowed. Carrying out one of them in turn modified the state of the environment, which therefore made other actions possible.

The designers of F.E.A.R. gave its soldiers such goals as patrolling, killing the player’s character, and taking cover to protect their own virtual lives. The makers of the game also gave each kind of bot a set of possible actions with which to accomplish each of its goals. One advantage of this approach is that it saves the developers the burden of trying to specify a response to every situation that might arise. Further, it allows seemingly intelligent behaviors to appear almost magically—such as the maneuver described above.

In that instance, the three attackers were carrying out two types of basic actions. One is to move to covered positions that are as close as possible to the player’s character. The other is simply to move around obstacles. The combination creates something that was not explicitly programmed into the game at all: a devastating flanking maneuver.

The spontaneous emergence of such complex behaviors is important because it provides a sense of deeper intelligence. That’s really what gets your heart pounding when you play the game. But you’d also like your adversaries to become more cunning over time, and F.E.A.R. has no mechanism for accomplishing that.

Why do bots need to get smarter? Imagine a game of badminton in which your opponent always reacts to your serves in the same way, always falls for your drops, and never attempts to anticipate your smashes. It would be a boring match. Up until recently, AI had been able to offer video gamers no better: the imps of Doom, released in 1993, never shoot their fireballs preemptively, and the civil-protection officers in Half‑Life 2 (2004) always take the nearest cover while reloading their weapons—to mention just a couple of things players experience with two well-known releases.

The standard solution is to add an element of randomness to the code that controls a bot’s decision making. Doing so varies a player’s experience, but the result does not necessarily come across as being intelligent.

A better approach is for the computer to learn about the player and to adapt a bot’s tactics and strategy appropriately. Of course, you don’t want the bot to become so good that it will win all the time; you just want it to give the human player a good run for the money. This capability, known as machine learning, is found in very few commercial games: Creatures, from the now-defunct Creature Labs, employed machine learning as early as 1997, as did Black & White, developed by the UK-based Lionhead Studios a few years later. But most video games are not able to “learn” on the fly or otherwise adapt to the person playing. Our group is hoping to push things forward in this regard using a system we’ve created for research purposes called PaSSAGE, which stands for Player-Specific Stories via Automatically Generated Events.

PaSSAGE, as its name implies, is all about storytelling, which has long been a staple of various role-playing games. But video games of all types rely to some extent on engaging storytelling. You can categorize such games by the way they vary their repertoire to appeal to different people.

Some games— Half-Life (2001), for example—are immensely popular even though they feature just a single linear story. So good scriptwriting can clearly go a long way. Other games, such as Star Wars: Knights of the Old Republic (2003), offer several alternatives to the main plot. This gives you the impression that you can shape your virtual fate—what psychologists call a sense of agency. That feeling of being in control is usually limited, however, because the branching plot lines often merge later on.

Titles like The Elder Scrolls IV: Oblivion (2006) and S.T.A.L.K.E.R.: Shadow of Chernobyl (2007) work similarly, taking one main story and complementing it with episodes drawn from a library of side quests. Other games, such as The Sims 2 (2005), go a step further by dispensing with a scripted plot altogether and creating an open-ended world in which players can effectively design their own happenings.

Although each of these techniques has enjoyed success, they all force the designer to make a trade-off between scriptwriter expressiveness and player agency. The approach we’ve taken with PaSSAGE avoids that conundrum by having the computer learn players’ interests and preferences and mold the story to suit them as the game progresses.

PaSSAGE uses the same game engine as Neverwinter Nights, a fantasy adventure set in medieval times, produced by BioWare of Edmonton. With PaSSAGE, scriptwriters determine only the most general arc to the story and provide a library of possible encounters the player’s character may have. The computer studies the player as he or she progresses and cues in the kinds of experiences that are most desired. For instance, if you like fighting, the game will provide ample opportunities for combat. If you prefer to amass riches, the game will conjure up ways for you to be rewarded for your actions. The software is able to make the sequence of events globally consistent by maintaining a history of the virtual world’s changing state and modifying the player’s future encounters appropriately. The game will therefore always appear to make sense, even though it unfolds quite differently for different people—or even for the same person as his moods and tastes change.

Machine learning can also be used to formulate the tactics that bots use, a job that now must be handcrafted by a game’s designers. Pieter Spronck and his colleagues, of the University of Tilburg, in the Netherlands, demonstrated this ability in 2005 using Neverwinter Nights. Spronck had one computer play against computerized opponents, programming it to get better over time by choosing the combat tactics that most often led to victory.

Members of our research group have been following through on Spronck’s work with Neverwinter Nights, using a different learning algorithm. Other colleagues of ours at the University of Alberta aim to do something similar with a multiplayer online game called Counter-Strike (2003), which pits a group of terrorists against a squad of antiterrorist commandos. Each character can be controlled either by a person or by the computer. As with F.E.A.R., players view the virtual world from the perspective of the characters they manipulate, making Counter-Strike an example of what’s known as a first-person-shooter game.

This project has so far produced a formal system for analyzing and classifying a team’s opening moves. That may not sound like much, but this task proved immensely challenging, because positions and actions are not nearly as constrained as they are in a game like chess. Researchers in our group have used this formalism to analyze computer logs of more than 50 hours of tournament-level play between seasoned Counter-Strike teams. Soon, we expect, computer bots programmed to learn tactics from such logs will play reasonably well—doing things a person might do. It’ll be a long time before these bots will be able to beat expert human players, though. But that’s not the objective, after all—they just need to make for entertaining adversaries.

Jeff Orkin and Deb Roy of MIT are undertaking a similar effort with something they call The Restaurant Game, for which they are applying machine learning to the task of making bots speak and act believably in social settings. In this case, the bots’ behaviors are based on observations gleaned from more than 10 000 sessions of human play.

Machine learning can also pay off for poker, which has become an especially hot game in recent years with the explosion of opportunities for playing it online. The strongest programs for two-player fixed-bet-size poker attempt to calculate the mathematically optimal solution for winning each hand. It turns out that finding such solutions is computationally infeasible, at least right now—there are just too many possible combinations of cards and betting sequences. But members of our research group have devised ways to calculate near-optimal strategies using certain simplifying assumptions. For example, instead of allowing four rounds of betting—which is permitted in competition poker—the program sets the limit at three. By further reducing the complexity of the game in clever ways, the computational burden can be reduced to a reasonable level. BioTools, a commercial spin-off of our research group in Edmonton, has incorporated some of our group’s work in this area in its Poker Academy software.

Although this program plays poker pretty well, it can’t yet do what is most required—spot and exploit the other player’s weaknesses. Figuring out how to program a computer to do that is extraordinarily hard. Why so? Studying an opponent should be easy, after all—and it is, but only if you have thousands of poker hands to analyze. What do you do if you have only a few? To make matters worse, human poker players make a point of changing their style so as to be hard to predict.

Right now, the best poker-playing programs to come out of our research group will make money off your average human player, and they are beginning to beat even some of the best in the world in organized competitions. This suggests that poker is just now joining the ranks of chess and checkers—games at which computers have trounced even world champions.

One lesson that computer scientists learned from working on chess and checkers is that programs must strike a balance in how they decide what move to make next. At one extreme, the computer can look all the way to the end of a game, examine every possible final position, and evaluate whether each one constitutes a win, a draw, or a loss. Then it can work backward from those possibilities, assuming best play by both sides at every stage, to select the optimal move. But searching that far ahead would take a great deal of time—for chess, enough for the sun to burn out.

The alternative is to use an evaluation function that incorporates knowledge of the game, enough to go beyond just recognizing an outright win to sense, rather, the slightest inkling of an advantage. In the ideal case, such a program would play perfectly while looking only a single move ahead. Of course, such a sophisticated evaluation would also require a lot of computational power.

In actuality, chess-playing programs operate somewhere between these two extremes. The computer typically examines all the possibilities several moves ahead and evaluates each, say, by tallying points, having assigned a different number of points to a pawn, a knight, a rook, and so forth. The computer then works backward to the current board position. The result is a ranking of all the available next moves, making it easy to pick the best one.

The trade-off between blind searching and employing specialized knowledge is a central topic in AI research. In video games, searching can be problematic because there are often vast sets of possible game states to consider and not much time and memory available to make the required calculations. One way to get around these hurdles is to work not on the actual game at hand but on a much-simplified version. Abstractions of this kind often make it practical to search far ahead through the many possible game states while assessing each of them according to some straightforward formula. If that can be done, a computer-operated character will appear as intelligent as a chess-playing program—although the bot’s seemingly deft actions will, in fact, be guided by simple brute-force calculations.

Take, for example, the problem of moving around intelligently in a virtual world—such as finding the shortest path to take from one spot to another. That’s easy enough to figure out if you can fly like a crow. But what if you’re earthbound and there are obstacles to contend with along the way?

A general algorithm for determining the best route between two points on a map has been around since the late 1960s. The problem with this scheme—known as A*—is that the amount of time the solution takes to compute scales with the size of the territory, and the domains of video games are normally quite large. So there isn’t time to calculate the optimal path in this way. In some games, the computer needs to move hundreds—or even thousands—of bots around their virtual stomping grounds without the action grinding to a crawl, which means that computation times must often be kept to just a few milliseconds per bot.

To address this issue, our research group has developed a series of pathfinding algorithms that simplify the problem. Rather than considering each of the vast number of possible positions each bot can take, these algorithms seek good paths by using coarser versions of the game map. Some of these algorithms can use a set amount of time for planning each move, no matter how vast the playing field, so they can be applied to game worlds of any size and complexity. They are also suitable for environments that change frequently, for instance when paths are blocked, bridges destroyed, doors closed, and so forth. BioWare will be using some of our group’s pathfinding algorithms in its forthcoming Dragon Age: Origins.

This same general approach can help computers master real-time strategy games, such as the Warcraft series, introduced in 1994, which was developed by Blizzard Entertainment of Irvine, Calif. In this popular genre, players control armies of game characters that work together to gather resources and battle enemies on uncharted terrain. The fast pace and large numbers of bots make these games too complex for today’s AI systems to handle, at least at a level that would challenge good human players.

Our research tries to address this problem by considering only the relatively small set of high-level strategies each player can follow, such as having your army of characters rush the opponent or expand aggressively so as to take over more territory. The computer simulates what the outcome would be, given the current state of play, if each side picked one of these strategies and kept to it for the duration of the game. By taking into account whether its human opponent is using all or just a few particular strategies, the computer can choose the counterstrategy that is most likely to succeed. This approach works better than the scripted maneuvers computers now employ in real-time strategy games when pitted against a human player.

The need for better AI in commercial video games is readily apparent—especially to the people playing them. And their thirst for more computer-generated intelligence will only continue to grow. Yet game makers rarely have the time or resources to conduct the research required to solve the many thorny problems involved, which is why they have come to recognize the value of engaging the scholarly community—a community that is hard at work in such places as Georgia Tech; Simon Fraser University, in Burnaby, B.C., Canada; the University of Teesside, in the UK; and the Technical University of Lisbon, to name but a few of the many research centers around the world involved in this kind of work.

With the increased participation of academics in game-related AI research, it will not be long before major improvements are apparent in the quality of the games entering the market. But there is a more significant reason to applaud the growing interest of AI researchers in the video-game industry—something Laird and van Lent pointed out to us and other computer scientists nearly a decade ago. The work we must do to make games feel more realistic will also take us a long way toward our ultimate goal of developing general-purpose machine intelligence. Now that sounds like a smart move.

About the Author

VADIM BULITKO, JONATHAN SCHAEFFER, and MICHAEL BURO are all part of the GAMES group at the University of Alberta, in Canada. They describe how they are using artificial intelligence to develop the next generation of interactive video games in “Bots Get Smart” [p. 48]. As its acronym suggests, their research group creates software for games, with the goal of beating—or at least seriously challenging—the human competitor. In 1994, Chinook, the team’s checkers program, became the first game software to win a championship against humans, earning it a place in Guinness World Records.

Source:
http://www.spectrum.ieee.org/dec08/7011/2