Category Archives: Ai

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Why AI researchers like video games - The Economist

In BriefArtificial intelligence continually becomes moresophisticated, and playing games with people is something it'squite skilled at. Researcher Arend Hintze takes a look at theevolution of AI and games and what's coming next.

Way back in the 1980s, a schoolteacher challenged me to write a computer program that played tic-tac-toe. I failed miserably. But just a couple of weeks ago, I explained to one of my computer science graduate students how to solve tic-tac-toe using the so-called Minimax algorithm, and it took us about an hour to write a program to do it. Certainly my coding skills have improved over the years, but computer science has come a long way too.

What seemed impossible just a couple of decades ago is startlingly easy today. In 1997, people were stunned when a chess-playing IBM computer named Deep Blue beat international grandmaster Garry Kasparov in a six-game match. In 2015, Google revealed that its DeepMind system had mastered several 1980s-era video games, including teaching itself a crucial winning strategy in Breakout (seen below). In 2016, Googles AlphaGo system beat a top-ranked Go player in a five-game tournament.

The quest for technological systems that can beat humans at games continues. In late May, AlphaGo will take on Ke Jie, the best player in the world, among other opponents at the Future of Go Summit in Wuzhen, China. With increasing computing power, and improved engineering, computers can beat humans even at games we thought relied on human intuition, wit, deception or bluffing like poker. I recently saw a video in which volleyball players practice their serves and spikes against robot-controlled rubber arms trying to block the shots. One lesson is clear: when machines play to win, human effort is futile.

This can be great: we want a perfect AI to drive our cars, and a tireless system looking for signs of cancer in X-rays. But when it comes to play, we dont want to lose. Fortunately, AI can make games more fun, and perhaps even endlessly enjoyable.

Todays game designers who write releases that earn more than a blockbuster movie see a problem: creating an unbeatable artificial intelligence system is pointless. Nobody wants to play a game they have no chance of winning.

But people do want to play games that are immersive, complex, and surprising. Even todays best games become stale after a person plays for a while. The ideal game will engage players by adapting and reacting in ways that keep the game interesting, maybe forever.

So when were designing artificial intelligence systems, we should look not to the triumphant Deep Blues and AlphaGos of the world, but rather to the overwhelming success of massively multiplayer online games like World of Warcraft. These sorts of games are graphically well-designed, but their key attraction is interaction.

It seems as if most people are not drawn to extremely difficult logical puzzles like chess and Go, but rather to meaningful connections and communities. The real challenge with these massively multiplayer online games is not whether they can be beaten by intelligence (human or artificial), but rather how to keep the experience of playing them fresh and new every time.

At present, game environments allow people lots of possible interactions with other players. The roles in a dungeon raiding party are well-defined: fighters take the damage, healers help them recover from their injuries, and the fragile wizards cast spells from afar. Or think of Portal 2, a game with a multiplayer aspect focused entirely on collaborating robots puzzling their way through a maze of cognitive tests.

Exploring these worlds together allows you to form common memories with your friends. But any changes to these environments or the underlying plots have to be made by human designers and developers.

In the real world, changes happen naturally, without supervision, design or manual intervention. Players learn, and living things adapt. Some organisms even co-evolve, reacting to each others developments. (A similar phenomenon happens in a weapons technology arms race.)

Computer games today lack that level of sophistication. And for that reason, I dont believe developing an artificial intelligence that can play modern games will meaningfully advance AI research.

A game worth playing is a game that is unpredictable because it adapts, a game that is ever novel because novelty is created by playing the game. Future games need to evolve. Their characters shouldnt just react; they need to explore and learn to exploit weaknesses or cooperate and collaborate. Darwinian evolution and learning, we understand, are the drivers of all novelty on Earth. It could be what drives change in virtual environments as well.

Evolution figured out how to create natural intelligence. Shouldnt we, instead of trying to code our way to AI, just evolve AI instead? Several labs including my own and that of my colleague Christoph Adami are working on what is called neuro-evolution.

In a computer, we simulate complex environments, like a road network or a biological ecosystem. We create virtual creatures and challenge them to evolve over hundreds of thousands of simulated generations. Evolution itself then develops the best drivers, or the best organisms at adapting to the conditions those are the ones that survive.

Todays AlphaGo is beginning this process, learning by continuously playing games against itself, and by analyzing records of games played by top Go champions. But it does not learn while playing in the same way we do, experiencing unsupervised experimentation. And it doesnt adapt to a particular opponent: for these computer players, the best move is the best move, regardless of an opponents style.

Programs that learn from experience are the next step in AI. They would make computer games much more interesting, and enable robots to not only function better in the real world, but to adapt to it on the fly.

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AI Opponents Can Beat Humans at Chess, Go, and Even Poker - Futurism

Embrace it and get used to it AI is here to stay.

While some robots may be out to take our jobs, theres a big skills gap in the AI-fueled services industry thats just waiting to be filled.

There will be two major drivers around the jobs of the future. The first will be what can be automated, and the second will be what level of comfort do we have with thingsbeing automated.

However, while everyone likes to talk about widespread fear that automation and artificial intelligence (AI) will make human workers redundant, it seems people are actually becoming more comfortable with the idea of automation and AI in the workplace. Recent research conducted by Adecco Group reveals that many employees feel AI will have a positive impact in creating a future workplace with myriad opportunities for more flexible, rewarding work.

So if our current roles in the workplace are set to be replaced, what will we be doing instead? Here are five new jobs that are likely to see the light as a result of AI.

Robots are to be deployed everywhere from the classroom to hospitals, but who will teach this new wave of bots the skills they are meant to apply so effortlessly, and, more importantly, who will teach robots how to teach each other so that AI can continue to scale? While robotic programming automation (RPA) requires an intensive focus on programming repeatable tasks, AI is here to provide structured outputs from unstructured inputs. Teacher training is about to take on a whole new meaning.

In the U.K. and Europe, regulations are being drafted to govern the use and creation of robots and AI. This includes an electronic personhood status assigned to address the rights and responsibilities of AI-programmed robots and to cover all acts carried out by them. What does it mean? Well, in short it means that governments are waking up to the fact that AIs can cause real harm to people. It also means that robots, along with their owners and creators, may be sued.

Regardless of the form electronic personhood laws take, its safe to say there will be a new generation of legal professionals dedicated to this subject, as well as expert law makers focusing on the area.

Doing business today is not a case of choosing between human or machine. Weve reached an age of human and machine. Orchestration will be key in implementing the right technology, or the right person, for the job. The variable now is how the division of laborwill be managed.

Orchestration is already in place on the assembly line. For example, we have beautifully orchestrated factories with a mixture of human and robotic workers. Orchestration of service delivery is an entirely different matter, however. Robotic service orchestration (RSO) platforms are already in place in more advanced shared services centers, but as the requirement for more orchestration between automation, humans, and AI continues to grow, so too will the requirements for experts in this field.

There are a million different companies and technology-driven solutions aiming to make digital transformation happen quickly and seamlessly, and with the convergence of AI and big data, data scientists will be able to extract business-changing information on a daily basis.

It wont be the robots making the actual business-critical decisions, though. These will no doubt be left up to a human expert in a pivotal transformation role. While its not a new role, the head of transformation will now need both cutting edge skills and experience from previous roles involving automation, product and process simplification, and service excellence.

Over the next few years, a seismic shift in the automotive industry will continue to deliver the power of AI and IoT to vehicles. These vehicles will be synced to more content-aware, location-based apps, which will enable cars within the same pool to communicate and share data, such as road conditions or speed. Vehicles will also be able to tap into AI to learn routes and routines, and even to upgrade while on the move.

This shift will see the widespread adoption of autonomous vehicles. While cars may be able to navigate themselves, a person (in the form of an autonomous vehicle fleet manager) will still need to be positioned in case of any anomalies, particularly as fleets go through the various stages to becoming fully automated. And, of course, there will need to be a human representative that interacts with more complex passenger and customer requests.

Amid the doom and gloom created by the robots will steal our jobs messaging that has circulated frequently over the past year, a considerable percentage of people are finding the silver lining in the automation and AI situation we find ourselves in. While robots will render some occupations obsolete, there is a huge opportunity here for humans and robots to become more collaborative and for new positions to be created in an industry that nurtures our newfound AI colleagues. Its time to get used to the change and embrace it, for you may find yourself working alongside a robot teacher sooner than you think.

Kit Cox is thefounder and CEO at Enate Limited, a business process management company.

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5 new jobs of the robot generation - VentureBeat

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The Surprising Repercussions of Making AI Assistants Sound Human - WIRED

Hundreds of thousands of people live in the city of Las Vegas. But the citys information security team is made up of just three employees and one intern, so the chief information officer of Las Vegas relies on artificial intelligence to keep the citys data and tech secure.

The things that keep me up most are ransomware and phishing, Vegas CIO Michael Sherwood tells TechCrunch. Theyre some of the simplest attacks but the hardest to defend against. In order to rest easy at night, Sherwood relies on AI security solutions from Darktrace to support his small team.

Artificial intelligence is becoming buzzy throughout the tech industry, and cybersecurity is no different. Enterprise security firms are adding AI features to their products to detect anomalies on customers networks no human intervention required.

But Darktrace, which launched in 2013, says its been using AI since the beginning. Wehave a three-plus year lead on anyone else, says CEO Nicole Eagan. Alot of companies are messaging machine learning. I always ask, What is it doing? The way we are using it is quite different.

While some vendors use machine learning to teach their products to recognize malware, Eagan says her team uses machine learning to give enterprise networks a sense of self so they can detect intrusions. She likens it to a human immune system, which detects infections and responds automatically.

Detection has been part of Darktraces product for a while now, but automated response is new and its a crucial feature for small teams like Sherwoods. With Darktrace, a lot of the worry is taken out of certain components, he says. As it tells us what it would like to do, we can say those are good responses and implement these controls immediately.

The goal is for Darktrace to eventually take over, making decisions about its responses without approval from a human. The AI takeover might sound intimidating, but Sherwood is bullish on the idea. He compared his all-in approach to Uber and Lyft, which fought regulators and the taxi industry to deploy on Vegas streets. Do you go in all the way or do you not?, he asks. I wouldnt live without artificial intelligence. Humans make wrong decisions every day.

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Las Vegas taps AI for cybersecurity help - TechCrunch

Although it seems that, for now, Amazons Echo and Googles Google Home are dominating the market for in-home AI assistants, player three, a.k.a. Microsoft, has yet to enter the game. Although if it wanted to, the tech giant could make a late entrance in style, taking inspiration from software and web developer Jarem Archers real-life version of Cortana from Halo. And yes, you could probably get her to call you Master Chief.

Jarem, whos not only a programmer, but also an artist with the ability to defuse a microwave with 1-second to go, turned histech talents toward building a concept Cortana appliance for his latest build on his YouTube channel, untitled network. Theres only one other build up there as of this writing, but its great too, and involves some very sensual lighting.

For those unaware, Microsoft did indeed name its AI assistant after Cortana from the Halo game franchise, although the former generally spends much less time helping you defeat The Covenant than the latter. With Archers build however, the next time you ask your intelligent personal assistant for the best local spot for Pokmon burgers, youll feel like youve been dropped into the Outer Colonies ready to do combat.

Cortana from the Halo game series. Image: Flickr / Bungie via Ben Darlow

In the video, Archer notes that the build utilizes a Windows 10 device with 4GB of RAM, and a built-in Arduino used for the platform lights. Archer 3D printed the base, and also gave his real-life Cortana a little pyramid made of three panes of mirrored glass. The holographic Cortana also utilizes real-time face tracking via a front-facing camera, which Archer says moves the rendered camera perspective relative to a single viewers head position. As for who dared to step into the virtual shoes of Cortana, that was Archers wife, whohad to undergo many takes of motion captureto develop the basis for the holograms movements.

It should also be noted that Microsoft may already have createdits first version of its competitor for this marketwith the Harman Kardon + Cortana appliance they teased back in December of last year:

Archer provides a far more in-depth explanation of the build on his blog, and he also says that in the future,he may add home automation and music tasks. No word yet on whether or not his Cortana will play a critical role in the Human-Covenant War.

What do you think about this real-life Cortana? Say Halo in the comments below!

Images: YouTube / untitled network

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Holographic AI Assistant Brings HALO's Cortana to Life - Nerdist

In Brief Following up last year's release of the first film written by an artificial intelligence, director Oscar Sharp has upgraded the writing software to craft a new short starring David Hasselhoff.

Its scary to think about the rise of automation as it is developing so quickly. Science-fiction, and even some real-life experts, have primed us to fear the potential dangers of artificial intelligence (AI).

Much of this concern regarding automation is becauseof impending job loss. The demographic that these losses will likelyhit first, and perhaps the hardest, are lower-skilled positions. However, the automation revolution isnt likely going to stop with factory work, other professions may soon have to contend with automated competition as well. AI has been used in place of lawyers, to recommend cancer treatments, and to report on major events like the Rio Olympics and the last US election.

Even creative endeavors are starting to be encroached upon by AI. Software has begun to compose music, and even write screenplays. Last year, the first film written by AI debuted, Sunspring, starringThomas Middleditch (from HBOs Silicon Valley). The team behind the film has followed it up with a new short called Its No Game whichstars David Hasselhoff and Thomas Payne (from The Walking Dead).

The film was written by Benjamin 2.0 in conjunction with human intelligenceOscar Sharp, and Benjamin 2.0s writer Ross Goodwin.

Similar to what Sunspring did with X-Files scripts, the film mashes up scripts from a variety of sources including Shakespeare, Knight Rider, Aaron Sorkin, and Baywatchto craft a farcical narrative that asks existential questions about what it means to be an artist in the age of automation.

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AI Wrote This Short Sci-Fi Film Starring David Hasselhoff - Futurism

Many worry that artificial intelligence and robots could begin taking jobs away from humans. But in the federal government, these new technologies could mean billions in savings for agencies, according to a new report.

A28-page reportfrom Deloitte,titled AI-augmented Government,examines several case studies, provides a taxonomy of AI systems, and concludes that in the federal government alone, automation with high investment could free up as many as 1.2 billion hours of work and save up to $41.1 billion annually. Through the use of rules-based systems, machine translation, computer vision, machine learning, robotics and natural language processing, the report notes the unusual but tantalizing paradigm presented by AI in which speed is increased, quality is improved, and cost is reduced all in parallel.

And this reality is not so far out as many may think, according toWilliam Eggers, a co-author of the report andexecutive director of the Deloitte Center for Government Insights.

Even when you see some of the amazing advances in machine translation over the last year, what that shows is that the technologies are getting better very, very quickly and at the same time, theyre falling in cost in terms of deployment, Eggers said. When you see that sort of thing occurring, you start seeing adoption increasing.

Find the report and more about it in Colin Woods coverage on StateScoop.

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Report: AI could save agencies $41B annually - FedScoop

TechCrunch

Cisco acquires conversational AI startup MindMeld for $125 million TechCrunch MindMeld, originally called Expect Labs, was launched on the stage of TechCrunch Disrupt SF 2012. At that time the startup wanted to build an iPad app that could listen in on your conversations and provide relevant contextual information. Since then ... Cisco taps into AI, collaboration with $125M MindMeld buyNetwork World Cisco To Acquire AI Startup MindMeld For $125 Million To Boost Spark UC ProductsCRN Cisco Adds AI in MindMeld BuyNo Jitter The Register -ZDNet -CNET all 16 news articles »

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Cisco acquires conversational AI startup MindMeld for $125 million - TechCrunch

World Chess champion Garry Kasparov (left) ponders a chess move during the sixth and final game of his match with IBM's Deep Blue computer on May 11, 1997.

When the IBM computer Deep Blue beat the world's greatest chess player, Garry Kasparov, in the last game of a six-game match on May 11, 1997, the world was astonished. This was the first time any human chess champion had been taken down by a machine.

That win for artificial intelligence was historic, not only for proving that computers can outperform the greatest minds in certain challenges, but also for showing the limitations and shortcomings of these intelligent hunks of metal, experts say.

Deep Blue also highlighted that, if scientists are going to build intelligent machines that think, they have to decide what "intelligent" and "think" mean. [Super-Intelligent Machines: 7 Robotic Futures]

During the multigame match that lasted days at the Equitable Center in Midtown Manhattan, Deep Blue beat Kasparov two games to one, and three games were a draw. The machine approached chess by looking ahead many moves and going through possible combinations a strategy known as a "decision tree" (think of each decision describing a branch of a tree). Deep Blue "pruned" some of these decisions to reduce the number of "branches" and speed the calculations, and was still able to "think" through some 200 million moves every second.

Despite those incredible computations, however, machines still fall short in other areas.

"Good as they are, [computers] are quite poor at other kinds of decision making," said Murray Campbell, a research scientist at IBM Research. "Some doubted that a computer would ever play as well as a top human.

"The more interesting thing we showed was that there's more than one way to look at a complex problem," Campbell told Live Science. "You can look at it the human way, using experience and intuition, or in a more computer-like way." Those methods complement each other, he said.

Although Deep Blue's win proved that humans could build a machine that's a great chess player, it underscored the complexity and difficulty of building a computer that could handle a board game. IBM scientists spent years constructing Deep Blue, and all it could do was play chess, Campbell said. Building a machine that can tackle different tasks, or that can learn how to do new ones, has proved more difficult, he added.

At the time Deep Blue was built, the field of machine learning hadn't progressed as far as it has now, and much of the computing power wasn't available yet, Campbell said. IBM's next intelligent machine, named Watson, for example, works very differently from Deep Blue, operating more like a search engine. Watson proved that it could understand and respond to humans by defeating longtime "Jeopardy!" champions in 2011.

Machine learning systems that have been developed in the past two decades also make use of huge amounts of data that simply didn't exist in 1997, when the internet was still in its infancy. And programming has advanced as well.

The artificially intelligent computer program called AlphaGo, for example, which beat the world's champion player of the board game Go, also works differently from Deep Blue. AlphaGo played many board games against itself and used those patterns to learn optimal strategies. The learning happened via neural networks, or programs that operate much like the neurons in a human brain. The hardware to make them wasn't practical in the 1990s, when Deep Blue was built, Campbell said.

Thomas Haigh, an associate professor at the University of Wisconsin-Milwaukee who has written extensively on the history of computing, said Deep Blue's hardware was a showcase for IBM's engineering at the time; the machine combined several custom-made chips with others that were higher-end versions of the PowerPC processors used in personal computers of the day. [History of A.I.: Artificial Intelligence (Infographic)]

Deep Blue also demonstrated that a computer's intelligence might not have much to do with human intelligence.

"[Deep Blue] is a departure from the classic AI symbolic tradition of trying to replicate the functioning of human intelligence and understanding by having a machine that can do general-purpose reasoning," Haigh said, hence the effort to make a better chess-playing machine.

But that strategy was based more on computer builders' idea of what was smart than on what intelligence actually might be. "Back in the 1950s, chess was seen as something that smart humans were good at," Haigh said. "As mathematicians and programmers tended to be particularly good at chess, they viewed it as a good test of whether a machine could show intelligence."

That changed by the 1970s. "It was clear that the techniques that were making computer programs into increasingly strong chess players did not have anything to do with general intelligence," Haigh said. "So instead of thinking that computers were smart because they play chess well, we decided that playing chess well wasn't a test of intelligence after all."

The changes in how scientists define intelligence also show the complexity of certain kinds of AI tasks, Campbell said. Deep Blue might have been one of the most advanced computers at the time, but it was built to play chess, and only that. Even now, computers struggle with "common sense" the kind of contextual information that humans generally don't think about, because it's obvious.

"Everyone above a certain age knows how the world works," Campbell said. Machines don't. Computers have also struggled with certain kinds of pattern-recognition tasks that humans find easy, Campbell added. "Many of the advances in the last five years have been in perceptual problems," such as face and pattern recognition, he said.

Another thing Campbell noted computers can't do is explain themselves. A human can describe her thought processes, and how she learned something. Computers can't really do that yet. "AIs and machine learning systems are a bit of a black box," he said.

Haigh noted that even Watson, in its "Jeopardy!" win, did not "think" like a person. "[Watson] used later generations of processors to implement a statistical brute force approach (rather than a knowledge-based logic approach) to Jeopardy!," he wrote in an email to Live Science. "It again worked nothing like a human champion, but demonstrated that being a quiz champion also has nothing to do with intelligence," in the way most people think of it.

Even so, "as computers come to do more and more things better than us, we'll either be left with a very specific definition of intelligence or maybe have to admit that computers actually are intelligent, but in a different way from us," Haigh said.

Because humans and computers "think" so differently, it will be a long time before a computer makes a medical diagnosis, for example, all by itself, or handles a problem like designing residences for people as they age and want to remain in their homes, Campbell said. Deep Blue showed the capabilities of a computer geared to a certain task, but to date, nobody has made a generalized machine learning system that works as well as a purpose-built computer.

For example, computers can be very good at crunching lots of data and finding patterns that humans would miss. They can then make that information available to humans to make decisions. "A complementary system is better than a human or machine," Campbell said.

It's also probably time to tackle different problems, he said. Board games like chess or Go allow players to know everything about their opponent's position; this is called a complete information game. Real-world problems are not like that. "A lesson we should have learned by now There's not that much more that we can learn from board games." (In 2017, the artificially intelligent computer program called Libratus beat the best human poker players in a 20-day No-Limit Texas Hold 'em tournament, which is considered a game of incomplete information.)

As for Deep Blue's fate, the computer was dismantled after the historic match with Kasparov; components of it are on display at the National Museum of American History in Washington, D.C., and the Computer History Museum in Mountain View, California.

Original article on Live Science.

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What Is Intelligence? 20 Years After Deep Blue, AI Still Can't Think Like Humans - Live Science