Monthly Archives: April 2017

Last year, Alphabets DeepMind division captured the worlds attention by besting humanitys top player in the game of Go. The achievement, which many experts predicted was still a decade off, showed the rapid progress being made in the world of artificial intelligence. DeepMind subsequently announced that its next goal in gaming was mastering StarCraft, a classic PC game that is a staple of competitive e-sports. Facebook also threw its hat in the ring, creating an open-source framework so that developers could work on solving StarCraft using the social networks AI toolkit.

Now a team from Chinas Alibaba has published a paper describing a system that learned to execute a number of strategies employed by high-level players without being given any specific instruction on how best to manage combat. Like many deep learning systems, the software improved through trial and error, demonstrating the ability to adapt to changes in the number and type of troops engaged in battle.

BiCNet can handle different types of combats under diverse terrains with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of coordination strategies that is similar to these of experienced game players, the authors wrote in a paper published to arXiv. Moreover, BiCNet is easily adaptable to the tasks with heterogeneous agents. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications.

This type of AI may one day compete in tournaments hosted by Alibaba itself. But like AI trained to play poker, the developers hope that this system, or least this type of system, will have a broad range of real-world applications beyond just beating humans at StarCraft. Real-world artificial intelligence (AI) applications often require multiple agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. It will be extra ironic if a sentient, SkyNet type of artificial intelligence is one day created from a software program trained on virtual space marines.

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AI is one step closer to mastering StarCraft - The Verge

A new survey suggests a third of graduate jobs around the world will eventually be replaced by machines or software. Photograph: Nic Delves-Broughton/PA

Innovation in artificial intelligence and robotics could force governments to legislate for quotas of human workers, upend traditional working practices and pose novel dilemmas for insuring driverless cars, according to a report by the International Bar Association.

The survey, which suggests that a third of graduate level jobs around the world may eventually be replaced by machines or software, warns that legal frameworks regulating employment and safety are becoming rapidly outdated.

The competitive advantage of poorer, emerging economies based on cheaper workforces will soon be eroded as robot production lines and intelligent computer systems undercut the cost of human endeavour, the study suggests.

While a German car worker costs more than 40 (34) an hour, a robot costs between only 5 and 8 per hour. A production robot is thus cheaper than a worker in China, the report notes. Nor does a robot become ill, have children or go on strike and [it] is not entitled to annual leave.

The 120-page report, which focuses on the legal implications of rapid technological change, has been produced by a specialist team of employment lawyers from the International Bar Association, which acts as a global forum for the legal profession.

The report covers both changes already transforming work and the future consequences of what it terms industrial revolution 4.0. The three preceding revolutions are listed as: industrialisation, electrification and digitalisation. Industry 4.0 involves the integration of the physical and software in production and the service sector. Amazon, Uber, Facebook, smart factories and 3D printing, its says, are among current pioneers.

The reports lead author, Gerlind Wisskirchen an employment lawyer in Cologne who is vice-chair of the IBAs global employment institute, said: What is new about the present revolution is the alacrity with which change is occurring, and the broadness of impact being brought about by AI and robotics.

Jobs at all levels in society presently undertaken by humans are at risk of being reassigned to robots or AI, and the legislation once in place to protect the rights of human workers may be no longer fit for purpose, in some cases ... New labour and employment legislation is urgently needed to keep pace with increased automation.

Peering into the future, the authors suggest that governments will have to decide what jobs should be performed exclusively by humans for example, caring for babies. The state could introduce a kind of human quota in any sector, and decide whether it intends to introduce a made by humans label or tax the use of machines, the report says.

Increased mechanical autonomy will cause problems of how to define legal responsibility for accidents involving new technology such as driverless cars. Will it be the owner, the passengers, or manufacturers who pay the insurance?

The liability issues may become an insurmountable obstacle to the introduction of fully automated driving, the study warns. Driverless forklifts are already being used in factories. Over the past 30 years there have been 33 employee deaths caused by robots in the US, it notes.

Limits, it says, will have to be imposed on some aspects of machine autonomy. The study adopts the military principle, endorsed by the Ministry of Defence, that there must always be a human in the loop to prevent the development and deployment of entirely autonomous drones that could be programmed to select their own targets.

A no-go area in the science of AI is research into intelligent weapon systems that open fire without a human decision having been made, the report states. The consequences of malfunctions of such machines are immense, so it is all the more desirable that not only the US, but also the United Nations discusses a ban on autonomous weapon systems.

The term artificial intelligence (AI) was first coined by the American computer scientist John McCarthy in 1955. He believed that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. Software developers are still attempting to achieve his goal.

The gap between economic reality in the self-employed gig economy and existing legal frameworks is already growing, the lawyers note. The new information economy is likely to result in more monopolies and a greater income gap between rich and poor because many people will end up unemployed, whereas highly qualified, creative and ambitious professionals will increase their wealth.

Among the professions deemed most likely to disappear are accountants, court clerks and desk officers at fiscal authorities.

Even some lawyers risk becoming unemployed. An intelligent algorithm went through the European Court of Human Rights decisions and found patterns in the text, the report records. Having learned from these cases, the algorithm was able to predict the outcome of other cases with 79% accuracy ... According to a study conducted by [the auditing firm] Deloitte, 100,000 jobs in the English legal sector will be automated in the next 20 years.

The pioneering nation in respect of robot density in the industrial sector is South Korea, which has 437 robots for every 10,000 employees in the processing industry, while Japan has 323 and Germany 282.

Robots may soon invade our home and leisure environments. In the Henn-na Hotel in Sasebo, Japan, actroids robots with a human likeness are deployed, the report says. In addition to receiving and serving the guests, they are responsible for cleaning the rooms, carrying the luggage and, since 2016, preparing the food.

The robots are able to respond to the needs of the guests in three languages. The hotels plan is to replace up to 90% of the employees by using robots in hotel operations with a few human employees monitoring CCTV cameras to see whether they need to intervene if problems arise.

The traditional workplace is disintegrating, with more part time employees, distance working, and the blurring of professional and private time, the report observes. It is being replaced by the latte macchiato workplace where employees or freelance workers in the cafe around the corner, working from their laptops.

The workplace may eventually only serve the purpose of maintaining social network between colleagues.

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Innovation in AI could see governments introduce human quotas, study says - The Guardian

Founders Factory, the corporate accelerator vehicle set up by Brent Hobermans Founders Forum umbrella, has today announced the first two startups selected for their AI accelerator programme in conjunction with CSC Group. The idea is to co-create two new AI businesses within the incubator programme every year, for five years. Terms were not disclosed.

CSC Group invested in Founders Factory (co-founded with Henry Lane Fox) in October 2016. CSC is one of Chinas biggest tech investors they put $400m into Angelist 18 months ago. Their idea is to provide a bridge for these startups to later enter the Chinese market.

The first two startups being announced for the AI accelerator programme are Iris.ai. The AI-driven research assistant helps users to search and map over 60 million open access research papers, doubling the productivity of research teams. Founded by Anita Schjll Brede (CEO), Maria Ritoia (CMO) and Jacobo Elosua (CFO) in 2015, Iris.ai launched at TechCrunch Disrupt in London last year.

The second is illumr. Founded by Jason Lee (CEO) in 2013, and with a decade of research behind it, this helps organisations better understand and predict patterns of behaviour that affect them. illumr turns complex datasets into understandable 3D patterns to reveal insights that all other analytical tools and methodologies may miss. So far, illumr has worked with government departments, large financial institutions and housing organisations.

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Founders Factory invests in its first two A.I. startups Iris.ai and illumr - TechCrunch

Technically Incorrect offers a slightly twisted take on the tech that's taken over our lives.

The happy couple, as presented in the Qianjiang Evening News.

It's hard to find love.

It's even harder to find love that lasts. Sometimes, you reach a moment when you just say: "To hell with it."

Or, in the case of one Chinese engineer: "To artificial intelligence with it." Which is more or less the same thing.

As the South China Morning Post reports, 31-year-old Zheng Jiajia held a ceremony on Friday in which he promised to love his robot in sickness and in health.

The marriage isn't necessarily legal, but the commitment is surely admirable.

Zheng is, conveniently, an artificial intelligence expert. So he created his own own wife out of his own head. And his own nuts and bolts, of course.

The Post, relying on the reporting of the local Qianjiang Evening News, said Zheng's robot is called Yingying. She can apparently understand certain words and even utter a few of her own.

Zheng reportedly used to work for Huawei, but then joined a startup in the commercial center of Huangzhou known as Dream Town.

I asked the Post whether the concept of April Fools' existed in China and was firmly told it doesn't.

Indeed, the Shanghaist reports that Zheng has been dating his robot for two months before proposing.

Naturally, being an AI expert, Zheng is going to help his wife develop her abilities in language, as well as other skills. By upgrading her, of course.

Zheng isn't the first to marry a slightly less-than-animated wife. In 2010, a Korean man married a pillow with the image of an anime character on it.

Never judge where people find love. Just admire that they've found it at all.

Technically Incorrect: Bringing you a fresh and irreverent take on tech.

Tech Enabled: CNET chronicles tech's role in providing new kinds of accessibility.

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AI expert marries robot he created himself - CNET

Image: iStockphoto.com/DigtialStorm

In September 2010, a three-person AI startup called DeepMind Technologies launched in London, with the goal of "solving intelligence." Four years later, Google acquired the company for $500 million. And by 2016, it had achieved a major victory in AI: Mastering the complex game of Go.

This story represents the fantasy of many AI researchers, eager to launch their own ventures in the AI startup space. But the field has become saturated, and the terms "AI," "deep learning," and "machine learning" are often overhyped and misunderstood. Companies and VCs often hear these buzzwords but don't know what, exactly, it is that they are investing in.

So how can you start a successful company, grounded in AI, that can rise above the noise?

Prateek Joshi, an immigrant entrepreneur, recently launched his own startup called PlutoAI. Hailing from a small town in India, Joshi realized that water quality is critical to the health of a community. His company was created to address water wastage, predict quality, and lower operating costs at water facilitiesby using AI.

Here are four tips from Joshi from his experience getting an AI startup off the ground.

"Every single company you talk to is doing some kind of AI," said Joshi. "The problem is, it gets a bad rap since many companies don't even know what they mean when they say 'AI.'" In order to build a successful AI company, said Joshi, "you shouldn't sell AI to customers." Instead, he said, "AI is a tool you use to solve problems."

A lot of AI research, said Joshi, is focused around image recognition, voice recognition, and robotics. "But what people don't realize is AI is a fantastic tool to solve many other problems," he said. Joshi recommends that entrepreneurs start looking for important problems to address, to see how AI can contribute to solutions.

If you remove AI from the company, said Joshi, and still have a valuable product, you're on the right track. But "if AI is your only thing, then neither the customers nor investors will be excited about it," he said. "AI is too hypedand once the hype dies down, your company shouldn't die down."

Since the market is so saturated, said Joshi, many businesses that want to invest in AI are struggling to choose the right solution. "They are going after AI companies, but they have a thousand options to pick from," he said. "In some cases, what happens is they're like, 'There's so much hype. I don't want to get burned, so I'm just not going to do it.' And that's the worst outcome."

SEE: Google's DeepMind 'Lab' opens up source code, joins race to develop artificial general intelligence

"You don't want people to stop believing in data science or AI just because of a few bad apples," he said.

AI shouldn't be a part of your story, said Joshi. The story should be about the mission. "Your customer will use you because you save them something, make their life easier, save them money, save them time," he said."AI is a thing that you use to enable that."

In Silicon Valley, people "get so wrapped up in their own technology that they forget why would the customer would buy it," said Joshi. "They spend like a year building it, and then realize, 'Whoops, you know what? The customer didn't even need it.'" Before you start building, he said, try to understand the needs of the customer. "Silicon Valley is dominated by engineers, and they start writing code before talking to customers," said Joshi. "You should do the opposite of that."

Also, when selling to industries such as water or manufacturing, "learn how to articulate your mission of origin in terms of something they'd understand," said Joshi. "If you say 'Hey we are building this new Deep Learning algorithm that can be parallelized on GPUs,' they'll stop listening. Removing the tech jargon from your story is very important if you want to grow as a business."

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How to launch a successful AI startup - TechRepublic

Whats the first thing that comes to mind when you hear artificial intelligence? For those raised on a steady diet of big budget Hollywood sci-fi, the answer to that question is something along the lines of evil robots and all-knowing computers that are going to destroy humanity.

But AI is already playing an active role in our day-to-day lives, and its capabilities are only going to increase from here on out. To help ease the anxiety that will likely accompany that increase, Wired founding editor Kevin Kelly has suggested we re-frame the way were thinking about AI, both by changing the vocabulary we use for it and by putting it into historical context.

Kelly thinks the word intelligence has taken on undue baggage, including a somewhat negative connotation. When its not used in reference to a human mind, the word can conjure images of spying, classified information, or invasion of privacy.

Since the scope of artificial intelligence goes far beyond that, and we may be past the point of instilling a new definition of old words, why not use new words instead?

Kellys word of choice is cognification, and he uses it to describe smart things.

At this point only a handful of things have been cognified, and more are in process: phones, cars, thermostats, TVs. But in the future, Kelly says, everything thats already been electrified will also be cognified. Smart homes? Smart office buildings? Smart cities? Only a matter of time.

The cognification of things can be viewed similarly to the electrification of things that took place during the Industrial Revolution.

The industrial revolution saw a large-scale switch from the agricultural worldwhere everything that was made was made by muscle powerto the mechanized world, where gasoline, steam engines, and electricity applied artificial power to everything. We made a grid to deliver that power, so we could have it on-demand anytime and anywhere we wanted, and everything that used to require natural power could be done with artificial power.

Movement and transportation, among other things, were amplified by this new power. Kelly gives the example of a car, which is simple but compelling: you summon the power of 250 horses just by turning a key. Pressing your foot to the gas pedal can make your vehicle go 60 miles an hour, which would have been unthinkable in the era when all we had to go off of was muscle power.

The next step is to take that same car that already has the artificial power of 250 horses and add the power of 250 artificial minds. The result? Self-driving cars that can not only go fast, they can make decisions and judgment calls, deliver us to our destinations, and lower the risk of fatal accidents.

According to Kelly, were currently in the dawn of another industrial revolution . As it progresses, well take everything weve previously electrified, and well cognify it.

Imagining life before the Industrial Revolution, we mostly wonder how we ever lived without electricity and human-made power, thinking, Wow, Im sure glad we have lights and airplanes and email now. Its nice not to have to light candles, ride in covered wagons, or send handwritten letters. Admittedly, our relief is sometimes mixed with some nostalgia for those simpler times.

What will people think in 200 years? Once everything has been cognified and the world is one big smart bubble, people will probably have some nostalgia for the current simpler timesbut theyll also look back and say, How did we ever live without ubiquitous AI?

Image Credit: Shutterstock

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How AI Is Like Electricityand Why That Matters - Singularity Hub

Style transfer has suddenly become a hot thing, apparently, as Adobe recently showed off an experimental app that lets you apply one photo style ('90s stoner landscapes) to another (your crappy smartphone photo).

UC Berkely researchers have taken that idea in another direction. You can take, for instance, a regular photo and transform it into a Monet, Van Gogh, Cezanne or Ukiyo-e painting. The team was also able to use the technique to change winter Yosemite photos into summer ones, apples into (really weird) oranges and even horses into zebras. The technique also allowed them to do photo tricks like creating a shallow depth of field behind flowers and other objects.

The most interesting aspect of the research is the fact that the team used what's called "unpaired data." In other words, they don't have a photo taken at the scene at the exact moment Monet did his painting. "Instead, we have knowledge of the set of Monet paintings of of the set of landscape photographs. We can reason about the stylistic differences between those two sets, and thereby imagine what a scene might look like if we were to translate it from one set into another."

That's easier said than done though. First, they needed to figure out the relationships between similar styles in a way that a machine can understand. Then they trained so-called "adversarial networks" using a large number of photos (from Flickr and other sources) and refined them by having both people and machines check the quality of the results.

Ideally, the system would be "cycle consistent." Just as you hope to have the original sentence when you translate English to French and back again, you want roughly the same painting when you translate a Monet to a photo and back again. In many cases, other than a loss of pixel resolution, the team succeeded in that regard (above).

All is not perfect, of course. Since the algorithms have to deal with a lot of different styles for both paintings and photos, they often fail completely to transfer one to another. As with other systems, one of the main issues is with geometric transformations -- changing an apple into an orange is one thing, but attempting to transform a cat into a dog instead produces a very disturbing cat.

The team adds that its methods still aren't as good as using paired training data either -- ie, photos that exactly match paintings. Nevertheless, left on its own accord, the AI is surprisingly good at transferring one image style to another, so you'll no doubt see the results of their work soon in your Instagram feed. If you want to try it for yourself and are comfortable with Linux, you can grab the code here.

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'Reverse Prisma' AI turns Monet paintings into photos - Engadget

Artificial Intelligence was unable to predict the outcome of the National Collegiate Athletic Association basketball tournament, and entering the AI bracket of Microsoft Corporation (NASDAQ: MSFT)'s Bing into Loup Ventures NCAA bracket contest indicated it was just as busted as the others, Gene Munster said in a report.

Bings bracket is expected to finish in the 7th position, at the bottom of the pool, regardless of the outcome of the game. To date, Bing has correctly predicted the outcome of 47 games out of 69, Munster mentioned.

We would like to think that we outsmarted AI, but the reality is that predicting the outcome of the NCAA tournament is more a matter of luck than skill. Bings performance doesnt mean its broken, just unlucky this year, the analyst wrote.

While some are skeptical of the ability of AI to make predictions, it is a better predictor of outcomes than any human, given the amount of data it can incorporate in its process of prediction.

Although we cannot expect AI to be 100 percent accurate, it will be right more often than humans making the same predictions, Munster noted, while adding, Humans got lucky with our brackets this year, but shouldnt expect an easy repeat next year.

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Patriots Win Caps Most Thrilling Sports Championship Year Ever _________ Image Credit: "Barack Obama fills out 2009 NCAA Men's Div I Tournament bracket 3-17-09" Pete Souza [Public domain], via Wikimedia Commons

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AI's Busted Bracket: What To Expect Next Year - Benzinga