Google Ventures' Big Data Bet

Quentin Hardy, The New York Times,  April 11, 2012


Google Ventures, the independent venture capital entity financed by Google, is building the internal data sciences team that is at the center of its investment philosophy.

The latest hire, Hazem Adam Ghobarah, is, like many others at the venture firm, a former Google employee. Mr. Ghobarah spent six years at Google, most recently in the quantitative research and modeling part of its advertising business. He will look for investment opportunities in the data analysis business and work with the firm’s existing companies on ways they can gather and make use of lots of information.

“This is part of an entire team we are assembling,” said Bill Maris, the managing partner at Google Ventures. “We have an internal quantitative team. Adam underlines the importance of this.”

It should not be too surprising that a Google-created entity should have this bent. Google, along with Web pioneers like Yahoo and Amazon, was crucial to the creation of the emerging Big Data industry. By tracking things like consumer clicks and the behavior of thousands of computer servers working together, they amassed large volumes of data at a time when collapsing prices for data storage made it attractive to analyze. They also captured information from nontraditional sources, like e-mail, leading them to create so-called “unstructured” database software like Hadoop and MapReduce. Versions of those are now used to store and analyze other kinds of data.

The ways in which Google analyzed Web traffic to predict patterns are increasingly applied to other fields, as that data moves online. Mr. Ghobarah believes that biology is an increasingly attractive area.

“We keep coming back to life sciences” as an investment field, he said. “In a given year, you have 200 million pathology slides. If that gets online, it is a Big Data problem.” One Google Ventures company, called DNA Nexus, uploads and analyzes genetic information. Part of the company’s edge is that it is able to store its ocean of data inside Google, where it most likely takes little space in Google’s data centers.

Google Ventures, of course, is hardly the only venture firm accelerating its Big Data business. Greylock Partners, where LinkedIn’s founder, Reid Hoffman, is a partner, has hired DJ Patil, a former LinkedIn executive, as its data scientist in residence. Another firm, Andreessen Horowitz, has spread money across several Internet sectors, including businesses like Factual that support data science by ensuring data quality.

Intellectual Property and the U.S. Economy: Industries in Focus

Economics and Statistics Administration, U.S. Department of Commerce, April 10, 2012
 
WASHINGTON, April 11, 2012 /PRNewswire via COMTEX/ -- The U.S. Commerce Department today released a comprehensive report, entitled "Intellectual Property and the U.S. Economy: Industries in Focus," which finds that intellectual property (IP)-intensive industries support at least 40 million jobs and contribute more than $5 trillion dollars to, or 34.8 percent of, U.S. gross domestic product (GDP).

"This first of its kind report shows that IP-intensive industries have a direct and significant impact on our nation's economy and the creation of American jobs," said Commerce Secretary John Bryson. "When Americans know that their ideas will be protected, they have greater incentive to pursue advances and technologies that help keep us competitive, and our businesses have the confidence they need to hire more workers. That is why this Administration's efforts to protect intellectual property, and modernize the patent and trademark system are so crucial to a 21st century economy that is built to last."

While IP is used in virtually every segment of the U.S. economy, the report identifies the 75 industries that use patent, copyright, or trademark protections most extensively. These "IP-intensive industries" are the source - directly or indirectly - of 40 million jobs. That's more than a quarter of all the jobs in this country. Some of the most IP-intensive industries include: Computer and peripheral equipment, audio and video equipment manufacturing, newspaper and book publishers, Pharmaceutical and medicines, Semiconductor and other electronic components, and the Medical equipment space.

"Strong intellectual property protections encourage our businesses to pursue the next great idea, which is vital to maintaining America's competitive edge and driving our overall prosperity," said Deputy Commerce Secretary Rebecca Blank. "The report released today shows that wages for jobs in IP-intensive industries are higher than average and continue to increase, meaning that these jobs aren't just important for businesses and entrepreneurs - they are important for working families. The IP protections we put in place today are helping support economic security for America's middle class now and in the years to come."

The report has several important findings, including:

IP-intensive industries contributed $5.06 trillion to the U.S. economy or 34.8 percent of GDP in 2010.

40 million jobs, or 27.7 percent of all jobs, were directly or indirectly attributable to the most IP-intensive industries in 2010.

Between 2010 and 2011, the economic recovery led to a 1.6 percent increase in direct employment in IP-intensive industries, faster than the 1.0 percent growth in non-IP-intensive industries.

Merchandise exports of IP-intensive industries totaled $775 billion in 2010, accounting for 60.7 percent of total U.S. merchandise exports.

"Every job in some way, produces, supplies, consumes, or relies on innovation, creativity, and commercial distinctiveness," said Under Secretary of Commerce for Intellectual Property and USPTO Director David Kappos. "America needs to continue investing in a high quality and appropriately balanced intellectual property system that will promote innovative, open, and competitive markets while helping to ensure that the U.S. private sector remains America's innovation engine."

The report is a joint product of the U.S. Commerce Department's Economics and Statistics Administration (ESA) and the U.S. Patent and Trademark Office (USPTO). Secretary Bryson was joined today by Deputy Secretary Rebecca Blank, Under Secretary for Intellectual Property and USPTO Director David Kappos, U.S. Chamber of Commerce President and CEO Tom Donohue, and American Federation of Labor-Congress of Industrial Organizations (AFL-CIO) President Richard Trumka at a White House event, to unveil the report.

The Department of Commerce and USPTO are unleashing new innovations and new industries by advancing a robust framework of intellectual property protections for a global economy. The USPTO has already implemented eight provisions of the recently passed America Invents Act, which are enhancing the speed and quality of patent processing, connecting businesses with the tools they need to develop their technologies, and speeding up patent applications. Since President Obama took office, the backlog has been reduced by nearly 15%, from about 750,000 to just under 641,000 today. That reduction has come despite the acceleration of American ingenuity, and patent filings in the U.S. grew 5% in FY 2011. By re-engineering the IP system from the ground up, the USPTO is creating a 21st century innovation architecture that's built to last and will help America remain a global leader going forward.

Patents, trademarks, and copyrights are the principal means for establishing ownership rights to inventions and ideas, and provide a legal foundation by which intangible ideas and creations generate tangible benefits to businesses and employees. IP protection affects commerce throughout the economy, including by: providing incentives to invent and create; protecting innovators from unauthorized copying; facilitating vertical specialization in technology markets; creating a platform for financial investments in innovation; supporting startup liquidity and growth through mergers, acquisitions, and IPOs; making licensing-based technology business models possible; and, enabling a more efficient market for technology transfer and trading in technology and ideas.

The full report can be found online at http://www.esa.doc.gov/Reports/intellectual-property-and-us-economy-industries-focus .

To learn more about the Department of Commerce's efforts to spur innovation and protect IP, visit www.uspto.gov .  News Media Contact:Sarah Horowitz, Department of Commerce Office of Public Affairs, (202) 482-4883 SOURCE U.S. Department of Commerce

How Computers Are Creating a Second Economy Without Workers

Bill Davidow, The Atlantic, April 10, 2012

Call it the post-employee economy: The digital revolution is creating billions of dollars of wealth in a second world without people

When the disappointing jobs numbers were reported last week (employers added 120,000 jobs in March, about half the number reported in the two previous months), analysts tripped over themselves looking for an explanation. Of course, jobs numbers are bound to vary, but in my view the long-term trend calls for more jobs to disappear, and the reason is clear as day: the exploding Second Economy.

The Second Economy -- a term the economist Brian Arthur uses to describe the computer-intensive portion of the economy -- is, quite simply, the virtual economy. One of its main byproducts is the replacement of low-productivity workers with computers. It's growing by leaps and bounds, brimming with optimistic entrepreneurs, and spawning a new generation of billionaires. In fact, the booming Second Economy will probably drive much of the economic growth in the coming decades.

Unfortunately, the Second Economy will not create many jobs.

Arthur's article in the McKinsey Quarterly, titled "The Second Economy," does a wonderful job of explaining it. His Second Economy is made up of large swaths of corporate America, especially the service areas that are being replaced by transactions over the network where -- in many cases -- computers talk only with other computers. Arthur uses the airline check-in process to illustrate the point:

Twenty years ago, if you went into an airport you would walk up to a counter and present paper tickets to a human being. That person would register you on a computer, notify the flight you'd arrived, and check your luggage in. All this was done by humans. Today, you walk into an airport and look for a machine. You put in a frequent-flier card or credit card, and it takes just three or four seconds to get back a boarding pass, receipt, and luggage tag. What interests me is what happens in those three or four seconds. The moment the card goes in, you are starting a huge conversation conducted entirely among machines. Once your name is recognized, computers are checking your flight status with the airlines, your past travel history, your name with the TSA (and possibly also with the National Security Agency). They are checking your seat choice, your frequent-flier status, and your access to lounges.

Nobody knows how big the Second Economy is, and there is no rigorous way of defining it. But in estimating it, I would use fairly expansive parameters for what to include. For example, I would count Amazon's total sales. In the case of Walmart, I would include all of the online sales plus the value added when computers talk to other computers to manage the supply chain, payables, and receivables and inventory. In the case of the airline industry, I would count not only the check-in process Arthur describes but things like the online reservation and ticketing process and the value added by automated systems in running airline operations.

Online retail sales topped $200 billion in 2011. Forrester Research expects them to grow at greater than 10 percent per year for the next few years. Amazon had $48 billion in sales in 2011. Google generates $40 billion in revenue. Large portions of corporations have now replaced workers with Second Economy processes, with computers busily interacting among themselves. Corporate billing system computers are now communicating with bank computers to get bills paid, and portions of the human-operated billing and crediting processes have vanished. Again, it's just computers talking with computers. Once recordings and movies are loaded into virtual space, computers in media distribution centers talk with computers in your iPod, tablet, and home entertainment center. The media manufacturing process, retail video rental stores, and theaters have been sucked into virtual space.
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My New York Times and Wall Street Journal are, increasingly, Second Economy employers. No paper, no paper delivery, no paper bills, no printing presses, no more jobs for people who run printing presses, just the Times and WSJ computers talking to my iPad and billing my credit card.

New economic growth is also occurring. New businesses have been created: Google, Facebook, OpenTable, and Zynga. I, for one, buy more books because shopping at Amazon is so convenient and buying eBooks so easy. Others are watching more movies and TV reruns.

In estimating the size of the Second Economy, it's easy to count the sales of Amazon and the revenue of Google but it is difficult to know what portion of corporate activity has made the transition. For example, how deep into the Second Economy is the $5 trillion retail sales industry and the airline and banking industries? But if just one percent of the 2.5 to 3 percent labor productivity increase since 1995 is attributable to digitization, it would greatly exceed $2 trillion annually. If a $2 trillion Second Economy is growing at 10 percent per year, the same rate at which online sales are growing, then the growth in the Second Economy would account for about half the anticipated increase in GNP in 2012.

Let's do some arithmetic.

The Gross Domestic Product for the United States in 2011 was around $15 trillion. There are a little over 130 million non-farm employees. So each worker adds a little over $100,000 to the domestic output. The numbers are quite different for a Google employee. Google has a little more than 32,000 employees and its $38 billion in revenues means it generates about $1.2 million per employee. The numbers are similar for Facebook.

Walmart has some two million employees, and annual sales of around $200 billion. Given that many work part-time, I figure that the company has sales of around $100,000 per employee. With 56,000 employees in 2011, Amazon generated a little over $800,000 per employee.

Here's the challenge: In the past, every million-dollar increase in economic output generated on the order of ten jobs. In the future, in the productive Second Economy, it may generate only one or two.

When I listen to politicians and policy makers explain how they will create jobs in the future, I never hear mention of the Second Economy. If a large portion of future economic growth will occur in the rapidly expanding and highly productive Second Economy, where will the jobs come from?

As sobering as the Second Economy scenario is I believe the trend is reversible. In my next post I will sketch out an argument for how that could happen.

IT investments deliver profits, study finds

Spending on revenue-enhancing IT systems more beneficial than focusing on streamlining processes to cut costs

Patrick Thibodeau, Computerworld, April 3, 2012

Computerworld - Investing in IT departments can have a big impact on a firm's profits, more so than similar spending on R&D and some marketing endeavors, according to a team of university researchers. 

The researchers reached their conclusions after examining the IT investments and financial data of about 450 companies in an effort to draw a link between IT spending and profits.

The work, published in the journal, MIS Quarterly, led to several key conclusions.

First, IT investments are "positively associated with profitability," according to the research paper.

The researchers said they were able to look deep enough at these unidentified global firms to draw insights into the type of IT investments that are most helpful. They found that companies are better off spending on projects that create revenue instead of reducing costs.

For instance, a company that builds or buys a business intelligence system that helps sell products will see more bottom line benefits than a company that spends IT dollars to automate internal processes to trim expenses, said the paper's lead author, Sunil Mithas, an associate professor Robert H. Smith School of Business at the University of Maryland.

He noted that cost cutting systems are likely not unique, and thus don't offer a competitive advantage. "Your vendors will sell the same system to your competitors," Mithas said.

But firms that spend IT dollars on revenue generating products may come up with systems that "are highly differentiated from your competition" said Mithas, "It will be very hard for your competition to replicate that."

Mithas said the team was unable to pinpoint an optimum level of IT spending.

Some firms are good at managing IT spending consistently, while others may pull back on IT investments after a failed project, and thus become a laggard.

The team, which also included researchers from the University of Illinois, the University of Texas, and the IE Business School in Madrid, Spain, concluded that that IT investments are more likely to enhance profits than advertising or R&D spending.

Even in R&D and advertising intensive businesses, such as the pharmaceutical industry, strong IT investments can prove fruitful. The paper cited a project Pfizer which "enabled virtual teams from different business units around the world to collaborate in research and develop new drugs."

The researchers examined IT investment and financial data from 1998 to 2003.

Mithas said there is no reason to believe that anything has changed since then -- even with constantly changing technology.

The IT spending data was gleaned from surveys conducted by a consulting firm that the researchers didn't identify due to a non-disclosure agreement. Mithas could only say that the firm is among the top IT consulting companies in the world.

The absence of a identified source for the IT spending data in this paper drew criticism from Paul Strassmann, a former U.S. Department of Defense CIO and acting CIO of NASA who has written about the correlation of IT and profits.

Strassmann said that "unless the authors disclose the origins of the critical IT numbers, the entire paper must be considered speculation and not verifiable analytic proof."

Mithas said the paper went through "a blind peer-review process by top academic scholars familiar with this domain of enquiry and such relevant data related details were shared with them."

The reviewers "appeared satisfied with the data quality and procedures we used for analyses."

Mithas said the findings are strong and said a characterization of the study's findings as speculation "is a little extreme."

Patrick Thibodeau covers SaaS and enterprise applications, outsourcing, government IT policies, data centers and IT workforce issues for Computerworld. Follow Patrick on Twitter at @DCgov, or subscribe to Patrick's RSS feed . His e-mail address is pthibodeau@computerworld.com.

Data 2.0 Summit Explores the Big Data, Social Data, and Open Data Revolution

Microsoft, Google, HP, Salesforce, and speakers from over 80 companies discuss why cloud data is the next $100 billion dollar industry.

 PRWEB, San Francisco, CA, April 02, 2012

The Data 2.0 Summit on Tuesday, April 3, in San Francisco invites over 500 executives, entrepreneurs, investors, and thought leaders across business and technology to discuss why Big Data, Social Data, and Open Data are driving the next technology revolution in Cloud Data. The one-day conference will take place at Mission Bay Conference Center with over 100 speakers from Microsoft, Google, HP, Salesforce, Data.com, Mozilla and LinkedIn.

The Data 2.0 Summit aims to explore topics around why Fortune 500 companies are investing hundreds of billions of dollars in their big data strategies and why data companies such as Kaggle, RadiumOne, Vertica (acquired by HP), Quova (acquired by Neustar), or Jigsaw (acquired by Salesforce) are raising tens of millions of dollars, even in this current economy. Companies will learn the new business models, data strategies, and open data initiatives revolutionizing business and technology in 2012 at this year's summit.

The City of San Francisco, in partnership with Socrata, will present the San Francisco Open Data Cloud and executives from the National Conference on Citizenship will launch their Civic Data Challenge onstage at the Data 2.0 Summit.

"A lot of companies have been using the term 'Big Data' to define a revolution of finding value in the new abundance of data, " said Geoff Domoracki, co-founder of the Data 2.0 Summit, "What the business and technology community needs to realize is that 'Big Data' is not just about analyzing private company data but about an avalanche in 'Cloud Data' from social sharing activity and tweets to open government initiatives and data cooperatives. We are all becoming members of a larger online data economy."

Highlighting the Data 2.0 Summit are speakers including: Jim Fowler, Co-Founder of Jigsaw.com (now Data.com); Bram Cohen, founder of BitTorrent; Gil Elbaz, founder and CEO of Factual; as well as executives from PARC (a Xerox Company), 10gen, Hadoop, DataStax, Intuit, Dun & Bradstreet, RapLeaf, GNIP, Mashery, RadiumOne, Kaggle, Cloudera, MTV Networks, Experian, Bizo, BlueKai, and Walmart Labs. The featured "How Open is the Open Web" panel will also explore whether the rise of popular web applications like Facebook or Twitter are undermining the openness data accessibility across the web.

As a conference precursor, on Monday, April 2, ten data startups will compete for over $20,000 in products and services at the Data 2.0 Summit Startup Pitch Competition, judged by investors from August Capital, Lightspeed Venture Partners, True Ventures, among other VC firms.

For those that cannot attend the event in person, Data 2.0 Summit will be livestreamed at the following link: www.ustream.tv/channel/data-2-0-summit

About the Data 2.0 Summit

Data 2.0 is the next step in the evolution of the Internet, creating new channels for facilitating connectivity and communication between websites. While corporations and websites each possess their own 'silos' of data, a new marketplace of information has opened up for safely and transparently sharing data on people, locations and products. Offering a glimpse of the future, the Data 2.0 Summit will give attendees the unique experience of hearing what industry leaders have to say about the future of data marketplaces and the pricing of information.

The Data 2.0 Conference is produced by Data 2.0 Labs. For more information, visit www.data2summit.com

For the original version on PRWeb visit: www.prweb.com/releases/prweb2012/4/prweb9360277.htm

Restoring Contemplation

How Disconnecting Bolsters the Knowledge Economy

Jessie Mannisto, American Library Association Office of Information Technology Policy, March 2012

While constant access to information enabled by digital devices has done much to improve our lives, it also exacts costs with respect to our attention and productivity that are especially harmful in a knowledge based economy. Increased public awareness of the impact of our information consumption habits—and ways to develop a healthier “information diet”—will help mitigate the negative impacts of constant connectivity.

To build this awareness, librarians and educators can teach information consumers to differentiate actively between gathering and processing information and help them understand when and how each of these modes of thought will benefit them. Libraries also can provide services and spaces that promote contemplation within the modern information infrastructure. Software developers and system engineers can contribute

by creating products and services that promote contemplation. Researchers can help us better understand the costs of constant connectivity and tailor an information infrastructure that better supports creative and analytical thought—and, ultimately, a higher quality of life.

Social Networking Heads to the Office

New business applications allow far-flung workers to collaborate in a way they often can't with email

Shayndi Raice, The Wall Street Journal, April 2, 2012

Facebook Inc. has changed the way people socialize on the Web. Now the same concept is changing the way people interact at work.

New social-networking applications aimed at the workplace borrow from Facebook in that they enable workers to set up profiles, form groups and "follow" each other's status updates. But the purpose isn't just social connection; rather, it is to increase productivity by making it easier for employees to identify who does what within an organization and to share their knowledge.

"It's very difficult to collaborate on email and figure out who the experts are," says Rob Koplowitz, an analyst at Forrester Research Inc. "A lot of organizations are finding that working more socially…magnifies the value of information."

These new business tools are based on the idea that email isn't always the best method of communication for widely dispersed workers who need to collaborate on projects or stay abreast of other initiatives within their organizations.

"Email is a 50-year-old technology" and an inefficient way to get work done, says Tony Zingale, chief executive of Palo Alto, Calif.-based Jive Software Inc., which went public in December and whose software allows organizations to maintain their own social networks, among other things. "Collaborative communications are more effective," he says.

Among the other companies selling social-networking applications for businesses are Yammer Inc., Tibco Software Inc. and Salesforce.com Inc., which offers a service called Chatter.

Rob Zell, a leadership-development specialist with Dallas-based 7-Eleven Inc., says the company has about 2,000 employees using Yammer. The convenience-store operator says it deployed the application in May 2011 to help field consultants, who work with local franchise owners, share their knowledge and learn best practices from one another.

Someone might post a picture of a display that worked particularly well in one franchise location, so that others can see it and try the same approach in their areas.

"I use the analogy of the virtual water cooler," Mr. Zell says. "People talk about what's going on in an informal way and have some formal documentation to keep track of best practices."

The companies selling these applications recommend that users create virtual groups so employees can follow a specific topic, branch of the company or type of job.

"Groups are the key," says David Sacks, chief executive of San Francisco-based Yammer, which has four million users. Information-technology professionals can create the groups, but employees should be permitted to create them, as well, so that people can follow topics they are interested in without having to sort through a flood of information, Mr. Sacks says.

In January 2011, Tibco Software launched a service called tibbr that not only enables users to follow what colleagues are doing, it also allows them to follow data, such as the status of an order or invoice.

The service automatically surfaces data so that those interested in keeping track of, for example, orders are notified every time one is filled, rather than having to go look for that information.

"We can bring that information and post it on your proverbial wall," says Ram Menon, the president of social computing for Palo Alto, Calif.-based Tibco, which says it has 70 customers and close to a million users.

Brian Frezza, co-founder and chief executive of Silicon Valley-based biotech start-up Emerald Therapeutics, says that before he started using software from San Francisco-based Asana that allows people associated with tasks to see what others are doing and comment on it, he spent most of his time in 30-minute meetings with his eight employees. There was no easy way for people to know what others had accomplished or had placed as their top priorities, he says.

Mr. Frezza says he now keeps his Asana window open all day, so he can follow what his workers are doing and how they are progressing on various projects. "From the management side, it's staying on track," he says. "It puts things into coherent threads."

Making It Fun

Getting employees to rely more on these social-networking tools for communication and less on email can be a challenge, according to company executives. They say the key to pushing adoption is making sure that senior managers are using the tools. Employees will use a social-networking service if they know their chief executive is on there, listening to their feedback and input, they say.

Making the tools fun to use also helps.

Melissa Madura-Altmann, a vice president of communications at Prudential Real Estate Investors, a unit of Prudential Financial Inc., says she tried to get employees excited about using Jive's software by assigning points to people when they posted on the site.

Employees reacted very competitively, she says, trying to rack up points even though there was no prize involved. "It's fun to see that kind of reaction," she says.

Fostering Unity

Wayne Shurts, chief information officer at Supervalu Inc., a grocery giant with stores in 48 states, says the idea to install Yammer for the company's 15,000 associates came from Chief Executive Craig Herkert, who returned from a Microsoft Corp. CEO conference in Seattle in the fall of 2010 inspired by the idea of using social-media tools to unify employees at his various companies.

Some of Supervalu's workers were already making use of a free version of Yammer's product, so "it was sort of like the top met the bottom in this beautiful way," says Mr. Shurts.

Supervalu, which owns brands such as Albertson's in the U.S. Northwest and Shaw's in New England, was created through a series of acquisitions, so the company was looking for a way to foster better communication and a sense of shared culture.

"The real benefit for us was to break down those walls and start to act and operate as Supervalu," says Mr. Shurts.

Ms. Raice is a staff reporter in The Wall Street Journal's San Francisco bureau. She can be reached at shayndi.raice@wsj.com.

AI robot: how machine intelligence is evolving

No computer can yet pass the 'Turing test' and be taken as human. But the hunt for artificial intelligence is moving in a different, exciting direction that involves creativity, language – and even jazz

Marcus du Sautoy, The Observer, March 31, 2012

'I propose to consider the question "Can machines think?"' Not my question but the opening of Alan Turing's seminal 1950 paper which is generally regarded as the catalyst for the modern quest to create artificial intelligence. His question was inspired by a book he had been given at the age of 10: Natural Wonders Every Child Should Know by Edwin Tenney Brewster. The book was packed with nuggets that fired the young Turing's imagination including the following provocative statement:

"Of course the body is a machine. It is vastly complex, many times more complicated than any machine ever made with hands; but still after all a machine. It has been likened to a steam machine. But that was before we knew as much about the way it works as we know now. It really is a gas engine; like the engine of an automobile, a motor boat or a flying machine."

If the body were a machine, Turing wondered: is it possible to artificially create such a contraption that could think like he did? This year is Turing's centenary so would he be impressed or disappointed at the state of artificial intelligence? Do the extraordinary machines we've built since Turing's paper get close to human intelligence? Can we bypass millions of years of evolution to create something to rival the power of the 1.5kg of grey matter contained between our ears? How do we actually quantify human intelligence to be able to say that we have succeeded in Turing's dream? Or is the search to recreate "us" a red herring? Should we instead be looking to create a new sort of machine intelligence different from our own?

Last year saw one of the major landmarks on the way to creating artificial intelligence. Scientists at IBM programmed a computer called Watson to compete against the best the human race has to offer in one of America's most successful game shows: Jeopardy! It might at first seem a trivial target to create a machine to compete in a general knowledge quiz. But answering questions such as: "William Wilkinson's An account of the principalities of Wallachia and Moldavia inspired this author's most famous novel" requires a very sophisticated piece of programming that can return the answer quickly enough to beat your rival to the buzzer. This was in fact the final question in the face-off with the two all-time champions of the game show. With the answer "Who is Bram Stoker?" Watson claimed the Jeopardy! crown.

Watson is not IBM's first winner. In 1997 IBM's super computer Deep Blue defeated reigning world chess champion Garry Kasparov. But competing at Jeopardy! is a very different test for a computer.

Playing chess requires a deep logical analysis of the possible moves that can be made next in the game. Winning at Jeopardy! is about understanding a question written in natural language and accessing quickly a huge database to select the most likely answer in as fast a time as possible. The two sorts of intelligence almost seem perpendicular to each other. The intelligence involved in playing chess feels like a vertical sort of intelligence, penetrating deeply into the logical consequences of the game, while Jeopardy! requires a horizontal thought process, thinking shallowly but expansively over a large data base.

The program at the heart of Watson's operating system is particularly sophisticated because it learns from its mistakes. The algorithms that select the most likely answers are tweaked by Watson every time it gets an answer wrong so that next time it gets a similar question it has a better chance of getting it right. This idea of machine learning is a powerful new ingredient in artificial intelligence and is creating machines that are quickly doing things that the programmers hadn't planned for.

Despite Watson's win, it did make some very telling mistakes. In the category 'US cities' contestants were asked: "Its largest airport is named for a world war two hero; its second largest for a world war two battle." The humans responded correctly with "Where is Chicago?" Watson went for Toronto, a city that isn't even in the United States.

It's this strange answer that gives away that it is a probably a machine rather than a person answering the question. Getting a machine to pass itself off as human was one of the key hurdles that Turing believed a machine would need to pass in order to successfully claim the realisation of artificial intelligence. With the creation of the Loebner prize in 1991, monetary prizes were offered for anyone who could create a chatbot that judges could not distinguish from the chat of a human being. Called the Turing test, many working in AI regard the challenge as something of a red herring. The Loebner prize, in their opinion, has distorted the quest and has proved a distraction from a more interesting goal: creating machine intelligence that is different from our own.

The AI community is beginning to question whether we should be so obsessed with recreating human intelligence. That intelligence is a product of millions of years of evolution and it is possible that it is something that will be very difficult to reverse engineer without going through a similar process. The emphasis is now shifting towards creating intelligence that is unique to the machine, intelligence that ultimately can be harnessed to amplify our very own unique intelligence.

Already the descendants of Deep Blue are performing tasks that no human brain could get anywhere near. Blue Gene can perform 360 trillion operations a second, which compares with the 3 billion instructions per second that an average desktop computer can perform. This extraordinary firepower is being used to simulate the behaviour of molecules at an atomic level to explore how materials age, how turbulence develops in liquids, even the way proteins fold in the body. Protein folding is thought to be crucial to a number of degenerative diseases so these computer simulations could have amazing medical benefits.

But isn't this number-crunching rather than the emergence of a new intelligence? The machine is just performing tasks that have been programmed by the human brain. It may be able to completely outperform my brain in any computational activity but when I'm doing mathematics my brain is doing so much more than just computation. It is working subconsciously, making intuitive leaps. I'm using my imagination to create new pathways which often involve an aesthetic sensibility to arrive at a new mathematical discovery. It is this kind of activity that many of us feel is unique to the human mind and not reproducible by machines.

For me, a test of whether intelligence is beginning to emerge is when you seem to be getting more out than you put in. Machines are human creations yet when what they produce is beginning to surprise the creators then I think you're getting something interesting emerging.

Exciting new research is currently exploring how creative machines can be in music and art. 
Stravinsky once wrote that he could only be creative by working within strict constraints: "My freedom consists in my moving about within the narrow frame that I have assigned myself for each one of my undertakings." By understanding the constraints that produce exciting music, computer engineers at Sony's Computer Science Laboratory in Paris are beginning to produce machines that create new and unique forms of musical composition. One of the big successes has been to produce a machine that can do jazz improvisation live with human players. The result has surprised those who have trained for years to achieve such a facility.

Other projects have explored how creative machines can be at producing visual art. The Painting Fool is a computer program written by Simon Colton of Imperial College. Not everyone likes the art produced by the Painting Fool but it would be anaemic art if they did. What's extraordinary is that the programmes in these machines are learning, and changing and evolving so that very soon the programmer no longer has a clear idea of how the results are being achieved and what it is likely to do next. It is this element of getting more out than you put in that represents something approaching emerging intelligence.

For me one of the most striking experiments in AI is the brainchild of the director of the Sony lab in Paris, Luc Steels. He has created machines that can evolve their own language. A population of 20 robots are first placed one by one in front of a mirror and they begin to explore the shapes they can make using their bodies in the mirror. Each time they make a shape they create a new word to denote the shape. For example the robot might choose to name the action of putting the left arm in a horizontal position. Each robot creates its own unique language for its own actions.

The really exciting part is when these robots begin to interact with each other. One robot chooses a word from its lexicon and asks another robot to perform the action corresponding to that word. Of course the likelihood is that the second robot hasn't a clue. So it chooses one of its positions as a guess. If they've guessed correctly the first robot confirms this and if not shows the second robot the intended position.

The second robot might have given the action its own name, so it won't yet abandon its choice, but it will update its dictionary to include the first robot's word. As the interactions progress the robots weight their words according to how successful their communication has been, downgrading those words where the interaction failed. The extraordinary thing is that after a week of the robot group interacting with each other a common language tends to emerge. By continually updating and learning, the robots have evolved their own language. It is a language that turns out to be sophisticated enough to include words that represent the concept of "left" and "right". These words evolve on top of the direct correspondence between word and body position. The fact that there is any convergence at all is exciting but the really striking fact for me is that these robots have a new language that they understand yet the researchers at the end of the week do not comprehend until they too have interacted and decoded the meaning of these new words.

Turing might be disappointed that in his centenary year there are no machines that can pass themselves off as humans but I think that he would be more excited by the new direction artificial intelligence has taken. The AI community is no longer obsessed with reproducing human intelligence, the product of millions of years of evolution, but rather in evolving something new and potentially much more exciting.

Marcus du Sautoy is Simonyi professor for the public understanding of science and a professor of mathematics at the University of Oxford.

The Human Voice, as Game Changer

Natasha Singer, The New York Times, March 31, 2012

VLAD SEJNOHA is talking to the TV again.

O.K., maybe you’ve done that, too. But here’s the weird thing: His TV is listening.

“Dragon TV,” Mr. Sejnoha says to the screen, “find movies with Meryl Streep.” Up pops a list of films like “Out of Africa” and “It’s Complicated.”

“Dragon TV, change to CNN,” he says. Presto — the channel flips to CNN.

Mr. Sejnoha is sitting in what looks like a living room but is, in fact, a sort of laboratory inside Nuance Communications, the leading force in voice technology, and the speech-recognition engine behind Siri, the virtual personal assistant on the Apple iPhone 4S.

Here, Mr. Sejnoha, the company’s chief technology officer, and other executives are plotting a voice-enabled future where human speech brings responses from not only smartphones and televisions, cars and computers, but also coffee makers, refrigerators, thermostats, alarm systems and other smart devices and appliances.

It is a wildly disruptive idea. But such systems are already beginning to change the way we interact with the world and, for better and worse, how we think about technology. Until now, after all, we’ve talked only to one another. What if we begin talking to all sorts of machines, too — and, like Siri, those machines respond as if they were human?

Granted, people have been talking into machines and at machines since the days of Edison’s phonograph. By the 1980s, commercial speech recognition systems had become sophisticated enough to transcribe spoken words into text. Today, voice technology is a fixture of many companies’ customer-service operations, albeit an occasionally maddening one.

But now the race is on to make the voice the sought-after new interface between us and our technology. The results could rival innovations like the computer mouse and the graphic icon and, some experts say, eventually pose challenges for giants like Google by bypassing their traditional search engines.

No player is bigger in voice technology than Nuance, of Burlington, Mass., an industry pioneer that has acquired more than 40 companies in the field and today employs 7,300 people. It is one of the companies that helped make a big technological leap from programs that take dictation to systems that actually extract meaning from words and respond to them. Now it wants to push far beyond that.

“They are the equivalent of Microsoft, Google or Amazon in a very niche technological space,” says Andrew Rosenberg, an assistant professor of computer science at Queens College.

Like many new technologies, sophisticated voice systems have potential drawbacks. Some experts worry about privacy invasions, others about our ever-deepening attachment to devices like smartphones.

Humans are wired for speech and tend to respond to talking devices as if they were kindred spirits, says Sherry Turkle, a professor of the social studies of science and technology at the Massachusetts Institute of Technology.

“I’m not saying voice recognition is bad,” Professor Turkle says. “I’m saying it’s part of a package of attachments to objects where we should tread carefully because we are pushing a lot of Darwinian buttons in our psychology.”

ONLY a decade ago, voice-enabled virtual assistants seemed more science fiction than business fact. But in 2000, Paul Ricci, a former executive at Xerox, concluded that voice software could one day disrupt the marketplace the way the mouse and the icon had in the 1980s.

“We had to decide early on where there were markets where we could successfully deploy the technology,” says Mr. Ricci, Nuance’s chief executive.

Nuance, then known as ScanSoft, went on an aggressive acquisition spree. It bought a desktop dictation system called Dragon NaturallySpeaking, as well as dozens of small companies that had carved niches in medical dictation, automated voice-response systems and speech research. Its most significant acquisition was Nuance, a rival that had been spun off from S.R.I. International of Menlo Park, Calif. The combined company took the Nuance name. (S.R.I. International later developed and spun off Siri, which was acquired by Apple in 2010.)

“They have literally tried to buy every good asset out there, or build it themselves, knit it all together and augment it,” Richard Davis, an analyst at Canaccord Genuity, says of Nuance.

Nuance reported revenue of about $1.3 billion for 2011, with $515 million of that coming from its health care technology business.

The stock market seems to like what it sees: Nuance’s share price touched a record high of $31.15 on Feb. 9, about double a level of $15.59 last August; it closed on Friday at $25.58.

Not everyone is as enamored with voice technology. Some privacy advocates worry that it adds an audio track to the digital trail that people leave behind when they use the Web or apps, potentially exposing them to more data mining.

Voice recognition software works by sending speech to processors that break down spoken words into sound waves and use algorithms to identify the most likely words formed by the sounds. The system typically records and stores speech so it can teach itself to become more accurate over time. Nuance, for example, believes that, aside from the federal government, it has amassed the largest archive of recorded speech in the United States.

Nuance says it is impossible to identify consumers from the recordings, because the company’s system recognizes people’s voices only by unique codes on their devices, rather than by their names. The company’s privacy policy says it uses the voice data of consumers only to improve its own internal systems.

“We have no idea who you are today,” says Peter Mahoney, the company’s chief marketing officer.

Such assurances aside, voice recognition software could conceivably pose enough of a risk to people’s privacy that regulators in Washington are watching.

“Just as we are concerned about the possible applications of facial recognition, there are other forms of biometric identification, like voice, that pose the same kind of problems,” says David C. Vladeck, the director of the Bureau of Consumer Protection at the Federal Trade Commission. He was speaking about voice technology in general, not about Nuance in particular.

“DRAGON GO,” Mr. Sejnoha says into his iPhone, “I want to make reservations for three tomorrow night at Craigie on Main.”

Dragon Go is Nuance’s own virtual assistant, an app that has been downloaded several million times since its introduction last summer.

Unlike Siri, however, Dragon Go doesn’t talk back. Mr. Sejnoha was asking for a reservation at a restaurant in Cambridge, Mass., and the app went directly to OpenTable and displayed his reservation options.

Ask Dragon Go for tickets to, say, “The Hunger Games,” and it typically displays a listing of showtimes at the nearest cinema from Fandango. A query about a particular spa might elicit reviews from Yelp.

Dragon Go, Nuance’s first direct-to-consumer app, is part of a push to build the brand’s visibility and demonstrate Nuance’s technological advances to business customers. Its real goal is even bigger: to disrupt the role of search engines as gatekeepers to the Web.

For the most common queries, Dragon Go usually bypasses search engines by taking users directly to Web sites of companies like Amazon, Expedia and OpenTable, which are Nuance partners on the app. If people don’t find what they’re looking for there, Dragon Go offers traditional Web search.

The benefit for consumers, Nuance executives say, is faster answers in fewer steps. In many cases, Nuance collects a small fee from partner sites when people make restaurant reservations or complete purchases. The app could be construed as a challenge to the likes of Google and Microsoft, which have their own voice products — such as Google Voice Actions and Microsoft Tellme — as well as search engines.

“If you are Google,” says Mr. Davis, the analyst, “you are saying, ‘Holy smokes, we are about to get cut out of the equation.’ ”

Christopher Katsaros, a Google spokesman, declined to comment. The company has recently updated Google Voice Actions, its voice-command system for Android phones, with a feature that continuously converts people’s speech to text, making it faster and smoother to dictate and send text messages, search Google aloud, or ask for directions.

Lezli Goheen, a spokeswoman for Microsoft, said that the company had addressed consumers’ expectations for easier access to information through several means. In addition to Tellme, a program included in all new Windows products that lets people dictate text messages and commands to applications like calendars, she said, the company has introduced Bing Voice Search, a program that lets people speak their Bing searches.

Nuance, meanwhile, has similarly ambitious plans for its health care business. In collaboration with I.B.M., the company is developing analytics to scour the medical notes that doctors dictate after they see patients. The idea is to search the text for common red flags — like medicines that interact dangerously — and automatically alert doctors, hopefully reducing problems and health care costs.

MEMBERS of US Airways’ frequent-flier program who have registered their mobile phone numbers are greeted by name by “Wally,” an interactive voice system that Nuance created for the airline.

One day last month, Wally was talking to Kerry Hester, a senior vice president at US Airways, who had called to check on her own flight.

“Hello, Kerry, I’ve matched your mobile number to your Dividend mileage account,” Wally said. Her flight from Phoenix to Los Angeles, Wally reported, unprompted, was “still scheduled to depart on time at 11:20 from Gate A23.”

If Wally’s voice sounds familiar, that’s because it belongs to Wally Wingert, the announcer on “The Tonight Show With Jay Leno,” who prerecorded all the words that callers hear.

US Airways introduced Wally last summer, as part of a relocation of its offshore customer service call-in operations back to the United States. Nuance designed the system to anticipate callers’ requests. Wally, for example, can automatically tell frequent-flier members their seat assignments or report whether they have received upgrades. It also converts people’s speech to text, so that, should customers ask to speak a live operator, they don’t have to repeat their original requests.

Wally, Ms. Hester says, has reduced the number of customers who ask to speak with agents, as well as the average length of customer calls. “Without the system, we would have had to hire a couple hundred more agents,” she says.

Wally, which never lets on that it is an automated system, seems so personable that many people say “thank you” before hanging up, Ms. Hester says.

“I think that tells us that they were satisfied,” she says. “I think it tells us that they felt they were interacting with a person.”

But the lack of disclosure bothers Professor Turkle of M.I.T. As voice-enabled systems become more sophisticated, she says, they create the illusion that we are interacting with other people, rather than with machines. In the long term, she says, the systems’ sleekness and ease of use could end up diminishing the value of slower, messier, real human connections. Reminding users that they are talking to a machine can make them more conscious of the superficiality of the exchange.

“We need to make a cultural decision,” Professor Turkle says. “Either we want to alert people when they are talking to a machine, or we don’t.”

IN 2008, Nuance sued a fierce rival in the voice technology market, contending patent infringement. The company, Vlingo, which markets its own virtual-assistant apps for Android phones, BlackBerries and iPhones, countersued, making similar allegations.

Last year, a court found that Vlingo had not infringed. On several other lawsuits, including the Vlingo countersuit, the parties have agreed to stays. That is because, last December, Nuance agreed to buy Vlingo for an undisclosed sum. It plans to complete the acquisition in the first half of this year.

“From our standpoint, the ability to compete with Google, that owns half the smartphone market, and Microsoft, that bundles voice with their products, that’s the real business logic behind merging with Nuance,” says Dave Grannan, the chief executive of Vlingo, which is based in Cambridge, Mass.

Nuance and Vlingo share a vision of a world populated by cloud-based, voice-enabled virtual assistants that move seamlessly from one device to another.

One afternoon earlier this year, a team of Vlingo executives demonstrated their own TV voice-command system to a New York Times reporter. The executives also showed a short animated video in which a fictional couple merrily conversed with their smartphones, tablet computer, TV and car — and the devices replied in kind, alerting the male character that his car needed gas and the woman that her flight that day had been canceled because of bad weather.

“More proactively alerting you with voice, telling you something about your car or an accident ahead, a personal assistant thinking about your needs and keeping you connected to other people is where we think this technology is really going,” Mr. Grannan says.

BACK in Nuance’s mock living room, Mr. Sejnoha is finishing his demo of Dragon TV, the company’s new software that can be built into Web-connected TVs. With it, viewers can use voice commands not only to find programs but also to make Skype calls or shop on Amazon via the TV. The technology is scheduled to hit the market shortly: LG Electronics plans to introduce a smart TV powered by Nuance software that lets viewers update Facebook and Twitter accounts by speaking into a special remote control.

Soon, Mr. Sejnoha predicts, many other devices, not just televisions, will be taking voiced commands, and talking back. In Germany, people can already ask a Nuance-powered coffee maker — marketed as “the first fully automatic machine that obeys” speech — to make cappuccino. The machine, called the Jura Impressa Z7 One Touch Voice, speaks both English and German.

Dragon TV, meanwhile, is already available in about two dozen languages.

“Dragon TV, mute,” Mr. Sejnoha says.

Silence.

“See,” he says, “it’s useful.”