Category Archives: Artificial Intelligence

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Atrios today:

Self-Checkouts

Those still a thing? I mean, I know they are, but around me the 3 major supermarkets within walking distance got rid of them....Anyway, I know they still exist, but I do think our robot future is not quite as inevitable as people think. Worrying about the impact of future automation on jobs seems to be a cool tech away of ignoring the current fucked and bullshit jobs situation. And, yes, automation has been going on for decades, which is actually my point. There's nothing new about it, and I don't know why people think there will be this sudden automation discontinuity. The robots have been here for awhile, and they aren't really going away, but that doesn't mean the sci-fi dystopian workless future is just around the corner. Shit is fucked up and bullshit enough without worrying about things which haven't happened yet, and likely won't.

It really doesn't matter if artificial intelligence is distracting us from whatever you think the "real" problem is. It's coming anyway. The speed of the AI revolution depends solely on fundamental factors (mostly continued reductions in the cost of parallel computing power) and the level of interest in AI software development. The fundamental factors are obviously still barreling ahead, and it sure looks like the free market has a ton of interest too:

Besides, AI is the real problem. As we all know (don't we?), the decline of manufacturing in the US has far more to do with automation than with trade or globalization. That decline set up the conditions for an angry working class in three northwestern states that finally decided it had found a savior in a guy who claimed it was all the fault of a bunch of foreigners. So now Donald Trump is president. How much more real can you get?

And that was just old-fashioned dumb automation. Smart automation is going to have a far bigger and far faster effect. We're not very far off from the first real destruction of an industry (probably long-haul trucking) thanks to smart automation, and after that it's going to come thick and fast.

So what are we going to do? Will our future be in the hands of demagogues who gain power by lashing out at scapegoats while they work hard to make sure that rich people get all the benefits of AI? Or will it be in the hands of people who actually give a damn about the working class and understand that a world of increasing automation requires a dramatic rethink of basic economics? I would sure like it to be the latter.

Unfortunately, like global warming, the effects of AI are slow and invisibleon a human timescale anyway. So it's easy to pretendno matter how idiotic this isthat AI is just a rerun of the Industrial Revolution. It's easy to pretend that each new advance isn't really a step toward true AI. It's easy to pretend that each individual industry to fall is just a special case. It's easy to pretend that something else is always more important.

Is AI coming soon? I find this question too boring to spend much time on anymore. Of course it's coming soon. The only question I'm interested in is what we're going to do about it. I keep pondering this, and I keep failing to come up with any likely answers that are very optimistic in the medium term. Maybe I'm not thinking outside the box enough. But it sure looks like we're determined to keep our collective heads in the sand for a long time. At best, the result is going to be a grim future of plutocracy for some and the dole for everyone else. At worst, it's going to be a future of global genocide (do you think there's enough aid in the world to keep Bangladesh afloat when there's no longer any work there?).

Eventually everything will work out, probably after a lot of suffering and a popular revolt. But wouldn't it be nice to avoid all that?

Oh, and those self-checkout machines? I don't know about Philly, but there's hardly a supermarket within ten miles of me that doesn't have them. Not only are they still a thing, but they're only going to get better. So sorry about all those nice union jobs as checkers and baggers.

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Artificial Intelligence Is Coming Whether You Like It Or Not - Mother Jones

The Quebecois city of Montreal has long been known as a hotbed of creativity -- home of Cirque du Soleil and a hub for companies in the online gaming and special effects industries, not to mention its place as a financial and trade capital.

Creativity played a key role when the city of 2 million (with 4 million regionally) competed against other municipalities globally to win the 2016 title of Intelligent Community of the Year.

And now that commitment to creativity is spurring the city to explore a range of unique new smartphone apps and other startup-generated initiatives that leverage sensors, data collection and analysis, and machine learning to deal with snow removal, ever-increasing traffic and other municipal challenges.

Public Wi-Fi, smart mobility and digital public services are just some of the 70 municipal projects detailed in the city's Smart and Digital City Action Plan, begun in 2015. More than half of the projects are expected to be finished by 2018, though some will take longer.

"Montreal is known as the place 'where Shakespeare meets Moliere.' It's a creativity hub," says Harout Chitilian, the elected official in charge of the city's smart city initiatives and technology. "All these things meshing together make Montreal one of the greatest startup digital ecosystems."

By intent, the government has made that startup ecosystem a key compontent of its smart city push, says Chitilian, who serves as vice president of the city's executive committee, the executive branch of the municipal government that includes Mayor Denis Coderre.

Of the dozens of initiatives currently underway in Montreal, several involve partnerships with the private sector in which the city, Quebec Province and businesses share costs. Those projects range from a high-speed, fiber-optic Scientific Information Network to eight different smart mobility and parking projects.

The principal driver of this partnership is InnoCit MTL, an independent, non-profit tech accelerator that receives both city and business financial support. Housed in the historic Notman House in downtown Montreal, InnoCit MTL has already fostered more than 15 startups in just over a year.

Notman House was alive with activity when Computerworld visited during a cold snap in mid-December, 2016 as part of a three-day tour of this smart city. Here's what we found.

The city government, along with the Province of Quebec and members of the academic community, have put special focus on artificial intelligence. Those efforts meld well with private sector startups that likewise are tapping the power of AI.

One such startup is Infra.AI, which intends to use machine learning and artificial intelligence to scan high resolution images of the city's streets and buildings."The benefits of AI are numerous," says co-founder Franois Maillet. "The fact that Montreal is serious about smart city and investing in it, there's a direct and positive impact in the startup community and the R&D. For the city itself, it provides better services to the citizens."

LIDAR images can help municipalities like Montreal monitor city infrastructure to identify such changes in status as detoriating bridges, broken windows or building code violations.

With digital image information from satellites, low-flying planes and LIDAR-equipped city vehicles, technology under development at Infra.AI will make it possible for Montreal and other cities to provide almost-real-time data on street conditions or the safety of roads and bridges.

That data can be combined with information from traffic video sensors and sensors on buildings, says Maillet, who also co-founded a related startup, MLDB.AI, that is working on a machine-learning database.

The potential applications are far-ranging. A firetruck speeding to a fire might be automatically advised that there's an obstruction in the roadway, allowing it to take another pathway. Or a pothole larger than a foot could be spotted, automatically dispatching a road crew to patch it. AI can even help identify a sagging highway bridge span, noticing a small drop when compared with the previous scans from days or weeks earlier.

Montreal-based Infra.AI is employing pattern recognition intelligence to distinguish a group of pedestrians from vehicles. The software could be used to identify problem locations and develop systems for improved pedestrian safety.

Infra.AI is currently piloting a program that helps identify ailing trees on city streets, a problem plaguing Montreal right now. When the startup's AI system is shown images of healthy trees, it can compare those with recent imagery to identify less-healthy trees with patches and browning leaves that need to be maintained or replaced.

"When you think of the kind of data [already] coming in from LIDAR and cameras, it's huge. The applications are now becoming possible with AI," says Jean-Franois Gagn, CEO of Element AI, a Montreal-based incubator dedicated to matching AI startups with larger companies and with government agencies.

Through its Canada First Research Excellence Fund, the Canadian government last year provided about $200 (US) million to three Montreal-based universities for research that Gagn believes will yield sophisticated AI spinoff companies in 2017.

In addtion, both Google and Microsoft have recently made investments in Montreal-based AI.

On a more personal level, another InnoCit MTL startup, Key2Access, is getting ready to test an app to make it safer for disabled people to cross city streets, according to CEO Sophie Aladas. Key2Access's tech is already being piloted in Ottawa, and has been successfully tested there by Richard Marsolais, a man with a vision impairment who is a specialist in independent living for the Canadian National Institute for the Blind.

Marsolais and his guide dog, Ashland, along with Motaz Aladas, head engineer for Key2Access (and CEO Sophie's father), demonstrated for Computerworld at a Montreal intersection how a small handheld device or a smartphone could be used to activate a Bluetooth-enabled crosswalk signal, making it safe for a vision-impaired or disabled person to cross. (See Smart Cities: Montreal for video footage of that demonstration.)

Marsolais says it would be helpful to have a handheld activation device to change the signal, instead of relying only on his guide dog or an audible crossing signal, which isn't always easy to hear. In addition, it isn't always clear in which direction it's safe to cross; Key2Access aims to solve that problem by using audible commands or vibrations to direct the user onto the crosswalk in the proper direction.

For Key2Access to function, traffic engineers in Montreal will need to install a receiver at each intersection to receive the wireless signal from the handheld device, Aladas says. The cost will be comparable to enabling a traditional crosswalk button on a pole, Sophie Aladas says. The city is expected to install the gear on at least one intersection in the spring as part of the testing phase.

A number of initiatives are in the works to help reduce traffic in Montreal in the next two years, including a tripling of the number of intelligent traffic signals to reach 2,200 units.

Data from the 700 existing smart signals installed over the last two years and from 500 surveillance cameras and Bluetooth sensors already helps prioritize buses traveling the streets to lessen commute times by 15% to 20%, the city's Chitilian says, with more improvements expected. Montreal is also in partnership with Waze, Google's crowdsourcing traffic app, to help syphon off driver data for greater intelligence.

In addition to its efforts to lessen traffic congestion and improve the efficiency of public transportation, Montreal heavily promotes bicycle riding. It's not uncommon to see bicyclists pedaling through downtown streets even in the dead of winter.

Bixi, a bike-sharing system, got its start in Montreal in 2009; as of 2015, there were 3.5 million Bixi rides each year in the city, and the service has grown to 45,000 bikes in 15 cities. The Bixi mobile apps for iOS and Android, along with other Bixi add-ins developed by Montreal startups, allow everything from online payments to personal fitness tracking for the bikes.

Separately, Montreal startup SmartHalo is testing technology to turn any bike into a smart bike using a rider's smartphone and its GPS connection.

"We know for a fact that adding preferential lights and dedicated bus lanes increases the speed of going from point A to B and makes the service much more efficient. You can have the same amount of buses and workable hours with better service," Chitilian says.

Sensor data from traffic signals is already being sent to a recently created central command post -- a "decision center," Chitilian calls it -- where technicians pore over dozens of desktop monitors and large wall displays. "The center gives us the ability to have an overall view" of the city, helping if there is an accident or other public safety need, he says.

Montreal also has designated $76 million US to replace 100,000 streetlights in the next five years with more efficient LED lighting that will be equipped with sensor and communications technology to expand the city's ability to manage congestion, pedestrian crowds, accidents and more, according to Chitilian.

With its combination of AI-focused startup innovation, sensor-driven traffic-improvement initiatives and data-driven apps for citizen empowerment, Montreal seems well on its way to furthering its designation as an intelligent city.

"We are trying to build a smart city from the ground up, and are putting in the pillars to do it," Chitilian says. "As politicians, we have to show immediate results, but some of our decisions will have lasting impact beyond our political mandates," he muses.

"We have to make decisions that will look good down the road," Chitilian says. "What we have in Montreal is more than optimism. It is a generational transformation."

Montreal and the Quebec Province have committed to sharingpublicly available data, which private enterpreneurs have put to innovative use via smartphone apps. Here are a few of locals' favorites:

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Montreal sees its future in smart sensors, artificial intelligence (with video) - Computerworld

Babak Hodjat believes humans are too emotional for thestock market. So he's started one of the first hedge funds run completely by artificial intelligence.

"Humans have bias and sensitivities, conscious and unconscious," says Hodjat, a computer scientist who helped laythe groundwork for Apple's Siri. "It's well documented we humans make mistakes. For me, it's scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you."

Babak Hodjat

Photographer: David Paul Morris/Bloomberg

Hodjat, with 21 patents to his name, is co-founder and top scientist of Sentient Technologies Inc., a startup that has spent nearly a decadelargely in secrettraining an AI system that can scour billions of pieces of data, spot trends, adapt as it learns and make money trading stocks. The team of technology-industry vets is betting that softwareresponsible forteaching computers to drive cars, beat the world's best poker players and translate languages will give their hedge fund an edge on Wall Street pros.

The walls of Sentient's San Francisco office are dotted with posters for robots-come-alive movies such as "Terminator." Inside a small windowless trading room, the only light emanates fromcomputer screens and a virtual fire on a big-screen TV. Two guys are quietly monitoring the machine's tradesjust in case the system needs to be shut down.

If all hell breaks loose," Hodjat says, "there is a red button."

Sentient won't disclose its performance or many details about the technology, and the jury is out on the wisdom of handing off trading to a machine. While traditional hedge funds including Bridgewater Associates, Point72 and Renaissance Technologies have poured money into advanced technology, many use artificial intelligence to generate ideasnot to control their entire trading operations.

All the same, Sentient, which currently trades only its own money, is being closely watched by the finance and AIcommunities. The venture capital firm owned by Hong Kong's richest man, Li Ka-shing, and India's biggest conglomerate, Tata Group, are among backers who have given the company $143 million. (Beyond trading, Sentient's AI system is being applied to a separate e-commerce product.)

Trading is "one of the top 10 places that AI can make a difference," says Nello Cristianini, a professor of artificial intelligence at the University of Bristol who has been advising Sentient. "A trading algorithm can look at the data, make a decision, act and repeatyou can have full autonomy."

Sentient's team includes veterans of Amazon, Apple, Google, Microsoft and other technology companies. They're part of a small group in Silicon Valley using expertise in data science and the field of artificial intelligence known as machine learning to try and disrupt financial markets.

AI scientists typically have no interest in working for a hedge fund, says Richard Craib, who started the AI hedge fund Numerai. "But they may want to mess around with data sets." Numerai's system makes trades by aggregating trading algorithms submitted by anonymous contributors who participate in a weekly tournament where prizes are awarded in Bitcoin. It recently raised $6 million from investors including Howard Morgan, the co-founder of the quant investment management firm Renaissance Technologies. "It's entirely a data science problem," Craib says.

Another company, called Emma, started a hedge fund last year based on an artificial intelligence system that can write news articles.

Employees of Sentient Technologies in San Francisco.

Photographer: David Paul Morris/Bloomberg

Hodjat of Sentient spent much of his career focused on the language-detection technology behind smartphone digital assistants. Several employees from his previous company, Dejima, went on to create Apple's Siri. Rather than join, he chose to focus on advances in artificial intelligence. His career goals didn't include finance, but he sees markets as one of the most promising applications for the technology. The vast amounts of publicly available data, along with stronger computers to analyse it for patterns, make the field an ideal fit. "That is the fuel for AI," he says.

Sentient's system is inspired by evolution. According to patents, Sentient has thousands of machines running simultaneously around the world, algorithmically creating what are essentially trillions of virtual traders that it calls "genes." These genes are tested by giving them hypothetical sums of money to trade in simulated situations created from historical data. The genes that are unsuccessful die off, while those that make money are spliced together with others to create the next generation. Thanks to increases in computing power, Sentient can squeeze 1,800 simulated trading days into a few minutes.

An acceptable trading gene takes a few days and then is used inlive trading. Employees set goals such as returns to achieve, risk level and time horizon, and then let the machines go to work. The AI system evolves autonomously as it gains more experiences.

Sentient typically owns a wide-ranging batch of U.S. stocks, trading hundreds of times per day and holding positions for days or weeks. "We didn't impose that on the system," says Jeff Holman, the company's chief investment officer. "The artificial intelligence seems to agree with what you get from human intelligence that it's better to spread your bets and have a more diversified portfolio."

As impressive as Sentient's technology appears, it's hard to know if it works. The company says the AI system is beating internal benchmarks, but won't disclose what those are. It shares little about the data used for the AI's decision-makingand isn't profitable. The company plans to bring in outside investors later this year. Holman, a Wall Street veteran who joined last year, said thecompany is limited on what it can say by U.S. Securities Exchange Commission rules restricting marketing by hedge funds that are raising money. "The platform is solid," he says. "It doesn't look like any other strategy I've seen."

Anthony Ledford, the chief scientist at the $19 billion hedge fund Man AHL in London, warns of putting too much faith in this branch of artificial intelligence without more evidence. Man AHL uses machine learning for a portion of its clients money, and Ledford is encouraged by the results. While the company is exploring a standalone machine-learning strategy, he says it's too early to declare success."There's a lot of hype and promise," Ledford says. "But when you actually ask people how many hundreds of millions dollars they are trading, many of them don't come back with much at all."

Little performance data is available about AI-focused hedge funds. One index that tracks 12 pools that utilize AI as part of its core strategies, called Eurekahedge AI Hedge Fund Index, returned 5 percent last year. That's slightly better than the average hedge fund, but trailed the S&P 500.

Tristan Fletcher, who wrote his doctoral thesis on machine learning in financial markets and works for a hedge fund, says investors may be reluctant to turn over their money completely to a machine. "I know how conservative investors are and I know of no one who would put their money in asystem that's fully systematic," says Fletcher. "Machine learning isn't a panacea for everything. You need people who have literal thinking."

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Silicon Valley Hedge Fund Takes On Wall Street With AI Trader - Bloomberg

An artificial intelligence called Libratus beats four of the worlds best poker players in Pittsburgh last week. Photograph: Carnegie Mellon University

First it was checkers (draughts to you and me), then chess, then Jeopardy!, then Go and now poker. One after another, these games, all of which require significant amounts of intelligence and expertise if they are to be played well, have fallen to the technology we call artificial intelligence (AI). And as each of these milestones is passed, speculation about the prospect of superintelligence (the attainment by machines of human-level capabilities) reaches a new high before the media caravan moves on to its next obsession du jour. Never mind that most leaders in the field regard the prospect of being supplanted by super-machines as exceedingly distant (one has famously observed that he is more concerned about the dangers of overpopulation on Mars): the solipsism of human nature means that even the most distant or implausible threat to our uniqueness as a species bothers us.

The public obsession with the existential risks of artificial superintelligence is, however, useful to the tech industry because it distracts attention from the type of AI that is now part of its core business. This is weak AI and is a combination of big data and machine-learning algorithms that ingest huge volumes of data and extract patterns and actionable predictions from them. This technology is already ubiquitous in the search engines and apps we all use every day. And the trend is accelerating: the near-term strategy of every major technology company can currently be summarised as AI Everywhere.

The big data/machine-learning combination is powerful and enticing. It can and often does lead to the development of more useful products and services search engines that can make intelligent guesses about what the user is trying to find, movies or products that might be of interest, sources of information that one might sample, connections that one might make and so on. It also enables corporations and organisations to improve efficiency, performance and services by learning from the huge troves of data that they routinely collect but until recently rarely analysed.

Human freedoms and options are increasingly influenced by opaque, inscrutable algorithms

Theres no question that this is a powerful and important new technology and it has triggered a gadarene stampede of venture and corporate capital. We are moving into what one distinguished legal scholar calls the black box society, a world in which human freedoms and options are increasingly influenced by opaque, inscrutable algorithms. Whose names appear on no-fly lists? Who gets a loan or a mortgage? Which prisoners get considered for parole? Which categories of fake news appear in your news feed? What price does Ryanair quote you for that particular flight? Why has your credit rating suddenly and inexplicably worsened?

In many cases, it may be that these decisions are rational and/or defensible. The trouble is that we have no way of knowing. And yet the black boxes that yield such outcomes are not inscrutable to everyone just to those who are affected by them. They are perfectly intelligible to the corporations that created and operate them. This means that the move towards an algorithmically driven society also represents a radical power-shift, away from citizens and consumers and towards a smallish number of powerful, pathologically secretive technology companies, whose governing philosophy seems to be that they should know everything about us, but that we should know as little as possible about their operations.

Whats even more remarkable is that these corporations are now among the worlds largest and most valuable enterprises. Yet, on the whole, they dont receive the critical scrutiny their global importance warrants. On the contrary, they get an easier ride from the media than comparable companies in other industries. If the CEO of an oil company, a car manufacturer or a mining corporation were to declare, for example, that his motto was Dont Be Evil, even the most somnolent journalist might raise a sceptical eyebrow. But when some designer-stubbled CEO in a hoodie proclaims his belief in the fundamental goodness of humanity, the media yawn tolerantly and omit to notice his companys marked talent for tax avoidance. This has to stop: transparency is a two-way process.

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The Observer view on artificial intelligence - The Guardian

New research indicates that Artificial Intelligence, or AI, as it is defined and practised today, has several limits. New buzzwords only serve to mystify the populace, and it is increasingly clear to me that many technologists and information technology (IT) managers are just groping about in the dark. They throw out terms such as neural networks, deep learning, big data, black box systems, and so on, hoping to mask the fact that they know very little of how this technology may evolve over the next several years.

As an observer, I cant help but think there is an important question in front of us: are the ramblings of these pundits in fact a case of the one-eyed man becoming king in the land of the blindor, instead, more akin to the parable of the five blind men, who all encountered an elephant and, after inspecting various parts of the elephant by touch, came away with different definitions of what an elephant is like?

The vital premise in todays AI is that the computer program itself learns as it goes along, creating a database of information, and then, uses that database to automatically generate additional computer programming codes as it learns morewithout the need for human programmers. These AI programs then become black boxes, since even their original human programmers have no way of knowing what code the machine has generated on its own.

ALSO READ: The road ahead for AI: engendering trust

These computer programs, however, need copious amounts of carefully categorized data to make themselves smarter. Anything that is sloppily characterized can easily cause the machine to make the wrong conclusions. I have mentioned before in this column that it has been proven that just changing a few pixels on an image can make an AI image-recognition program conclude that a car is in fact an elephantwhich is a mistake that an ordinarily intelligent human eye would never make.

Thus, many firms that are trying to chart out a path in AI are scrambling to go out and acquire vast stores of data that have already been neatly characterized. IBM, for instance, has bought firms that own billions of medico-radiological imagesin the hope of feeding this vast acquired data to the medical diagnosis components of IBMs Watson product. The idea is that this data, collected over many years of digital medico-radiological imaging, will enable Watson to become cannier in diagnosing diseases. When quizzed about these acquisitions, a senior IBM executive said to me recently: If youre not at the table, you can be sure youll be on the menu.

In another example of the use of categorized data, a firm called Cambridge Analytica has recreated a sinister way to profile people, from psychometric tests that show up, ostensibly as harmless quizzes, on Facebook and other social networking sitesluring people into taking them and posting the individual results online. Cambridge Analytica claims it used these psychometric analyses to accurately predict the personality types and preferences of individual voters. The firm was apparently retained by both the Brexit leave and Donald Trumps presidential election campaigns to accurately target voters who were likely to vote for them, and to lure more of these supportive voters out to the polling booths.

Trained psychologists have a dim view of psychometric testing and other personality profiling tests. When I asked my sister, who holds a doctorate from Harvard in Psychology, about the efficacy of such methods, her response was that there are dozens of such psychometric rubrics out there that do have some utility, but are in fact quite flawed; many of them have been debunked for predictive utility.

The accuracy of diagnostics and psychometrics aside, the fact remains that without reams of carefully categorized data, AI as we know it today is dead on arrival. That means that in areas where data is not yet availablefor instance, crash data for self-driving carswe must look elsewhere to create models that mimic large data stores accurately when data is absent. Where does one go to find out under what circumstances self-driving automobiles like the Tesla that killed its occupant in 2016 might have other such accidents? Enough instances of this havent occurred and, therefore, the data doesnt exist. Building predictive models here without data is not neuralits neurotic, and dangerous!

ALSO READ: Why India needs an AI policy

This brings us to the fields of pure mathematics and theoretical physics, which are the way forward. In an informative blog last year, Wale Akinfaderin, a Ph.D. candidate in physics at Florida State University, has enumerated the types of mathematics that an aspiring AI specialist must be familiar with, if not master, to be effective. Here is a partial list from his blog post: Principal Component Analysis, Eigen decomposition, Combinatorics, Bernoulli, Gaussian, Hessian, Jacobian, Laplacian, and Lagragian Distributions, Entropy, and Manifolds. Ill stop hereIm sure you get the idea!

Dont panic, says Neil Sheffield, an AI researcher at Amazon, in a blog. By bringing our mathematical tools to bear on the new wave of deep learning methods, we can ensure they remain mostly harmless.

Time for us amateur pundits and pedestrian programmers to make way for the pure mathematicians and theoretical physicists to lead the charge. They have long used mathematical theory to contemplate the unsolvable where data doesnt exist. Visionaries like Stephen Hawking, Albert Einstein and Srinivasa Ramanujan have been feted for their ability to posit plausible models on hitherto unsolvable problems such as the theory of the universe.

One-eyed they may well be, but all hail the new kings of AI!

Siddharth Pai is a world-renowned technology consultant who has led over $20 billion in complex, first-of-a-kind outsourcing transactions.

First Published: Tue, Feb 07 2017. 12 58 AM IST

Originally posted here:

Allow mathematicians to pierce artificial intelligence frontiers - Livemint

In his relatively short tenure, Pope Francis has been hard at work welcoming spiritual seekers into the Catholic Church. Hes refused to judge LGBT people, sought to integrate divorced couples, and extended priests ability to forgive abortion. But Franciss wide arms have arguably never stretched further than a mass in 2014 when he suggested the church would baptize Martians.

Iffor exampletomorrow an expedition of Martians came and one says, But I want to be baptized! What would happen? Pope Francis asked. When the Lord shows us the way, who are we to say, No, Lord, it is not prudent! No, lets do it this way.

While playful, this odd scenario got at a serious question about just how far the churchs welcome can go. Should Christianity, the worlds largest religion, embrace all intelligent life? Even aliens? Granted, the arrival of green space creatures seeking salvation isnt very likely. But the Popes lesson opens the door to the acceptance of another science-fiction stalwart, tooone thats not so easily dismissed. Namely, hyper-intelligent machines.

While most theologians arent paying it much attention, some technologists are convinced that artificial intelligence is on an inevitable path toward autonomy. How far away this may be depends on whom you ask, but the trajectory raises some fundamental questions for Christianityas well as religion broadly conceived, though for this article Im going to stick to the faith tradition I know best. In fact, AI may be the greatest threat to Christian theology since Charles Darwins On the Origin of Species.

For decades, artificial intelligence has been advancing at breakneck speed. Today, computers can fly planes, interpret X-rays, and sift through forensic evidence; algorithms can paint masterpiece artworks and compose symphonies in the style of Bach. Google is developing artificial moral reasoning so that its driverless cars can make decisions about potential accidents.

AI is already here, its real, its quickening, says Kevin Kelly, a co-founder of Wired magazine and the author of The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future. I think the formula for the next 10,000 start-ups is to take something that already exists and add AI to it.

Despite AIs promise, certain thinkers are deeply concerned about a time when machines might become fully sentient, rational agentsbeings with emotions, consciousness, and self-awareness. The development of full artificial intelligence could spell the end of the human race, Stephen Hawking told the BBC in 2014. Once humans develop artificial intelligence, it would take off on its own, and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded."

This explosion of artificial intelligenceoften referred to as the singularityis one of many futures technologists have envisioned for robots, not all so apocalyptic. But the possibility of any threat to humans, even if small, is real enough that some are advocating for precautionary measures. More than 8,000 people, including Hawking, Noam Chomsky, and Elon Musk, have signed an open letter warning against potential pitfalls of AI development. Ryan Calo, a Washington University law professor, argues for the development of a Federal Robotics Commission to monitor and regulate developments so that we dont innovate irresponsibly.

While concerns mostly center on economics, government, and ethics, theres also a spiritual dimension to what were making, Kelly argues. If you create other things that think for themselves, a serious theological disruption will occur.

History lends credibility to this prediction, given that many major scientific advances have had religious impacts. When Galileo promoted heliocentrism in the 1600s, it famously challenged traditional Christian interpretations of certain Bible passages, which seemed to teach that the earth was the center of the universe. When Charles Darwin popularized the theory of natural selection in the 1800s, it challenged traditional Christian beliefs about the origins of life. The trend has continued with modern genetics and climatology.

The creation of non-human autonomous robots would disrupt religion, like everything else, on an entirely new scale. "If humans were to create free-willed beings, says Kelly, who was raised Catholic and identifies as a Christian, absolutely every single aspect of traditional theology would be challenged and have to be reinterpreted in some capacity.

Take the soul, for instance. Christians have mostly understood the soul to be a uniquely human element, an internal and eternal component that animates our spiritual sides. The notion originates from the creation narrative in the biblical book of Genesis, where God created human beings in Gods own image. In the story, God forms Adam, the first human, out of dust and breathes life into his nostrils to make him, literally, a living soul. Christians believe that all humans since that time similarly possess Gods image and a soul.

But what exactly is a soul? St. Augustine, the early Christian philosopher, once observed that I have therefore found nothing certain about the origin of the soul in the canonical scriptures. And Mike McHargue, a self-described Christian mystic and author of Finding God in the Waves: How I Lost my Faith and Found it Again Through Science, believes that the rise of AI would draw out the ambiguities in the ways that many Christians have defined terms like consciousness and soul.

Those in religious contexts dont know precisely what a soul is, McHargue says. Weve understood it to be some non-physical essence of an individual thats not dependent upon or tied to their body. Would AI have a soul by that definition?

If this seems like an absurd question, consider technologies such as in vitro fertilization and genetic cloning. Intelligent life is created by humans in each case, but presumably many Christians would agree that those beings have a soul. If you have a soul and you create a physical copy of yourself, you assume your physical copy also has a soul, says McHargue. But if we learn to digitally encode a human brain, then AI would be a digital version of ourselves. If you create a digital copy, does your digital copy also have a soul?

If youre willing to follow this line of reasoning, theological challenges amass. If artificially intelligent machines have a soul, would they be able to establish a relationship with God? The Bible teaches that Jesuss death redeemed all things in creationfrom ants to accountantsand made reconciliation with God possible. So did Jesus die for artificial intelligence, too? Can AI be saved?

I dont see Christs redemption limited to human beings, Christopher Benek, an associate pastor at Providence Presbyterian Church in Florida with degrees from Princeton Theological Seminary, told Gizmodo in 2015. Its redemption of all of creation, even AI. If AI is autonomous, then we should encourage it to participate in Christs redemptive purposes in the world.

And what about sin? Christians have traditionally taught that sin prevents divine relationship by somehow creating a barrier between fallible humans and a holy God. Say in the robot future, instead of eradicating humans, the machines decideor have it hardwired somewhere deep inside themthat never committing evil acts is the ultimate good. Would artificially intelligent beings be better Christians than humans are? And how would this impact the Christian view of human depravity?

These questions so far concern religious belief, but there is also the many matters related to religious practice. If Christians accept that all creation is intended to glorify God, how would AI do such a thing? Would AI attend church, sing hymns, care for the poor? Would it pray?

James McGrath, a professor of religion at Butler University and the author of Theology and Science Fiction, recently toyed with the prayer question using a strange classroom assignment. He told his religion students to ask Siri, the personal assistant in Apple devices, to pray for them and observe what happened. The students quickly learned that Siri was more comfortable with questions like What is prayer? than commands like Pray for me. When directed to pray, Siri basically responded, Im not programmed to do that. But if a more advanced version Siri were programmed to pray, would such an action be valuable? Does God receive prayers from any intelligent beingor just human intelligence?

There are no easy answers for Christians willing to entertain these questions. And, certainly, theres a case to be made that Christians shouldnt bother in the first place. The Christian Bible never anticipates non-human intelligence, much less addresses the questions and concern it creates. It does, however, teach that God has established a special relationship with humans that is unique among all creatures. Russell Bjork, a professor at the evangelical Gordon College who is cautious about broadening the Christian understanding of personhood to include AI, argues in the journal Perspectives on Science and Christian Faith, What makes humans special is not what humanity is, but rather it is Gods relationship to us based on his purpose for making us.

In addition to the Bible, many Christians look to their ancient creeds for guidance. One of the most popular, the Nicene Creed, speaks of Jesus as the only son of God, begotten, not made. The implicit corollary is that humans are Gods children who are made, not begotten. Christians believe that God makes humans, but humans make machines. By this logic, one might conclude that AI could not be considered Gods children or possess soul.

But this hasnt stopped Kevin Kelly from beginning to advocate for the development of a catechism for robots. A catechism is a statement of faith usually framed in a question-and-answer format that outlines orthodox belief and is typically taught to children in some religious traditions. Kelly says he takes the idea very seriously and even suggested it in a keynote talk at Q conference, an annual gathering of more than 1,000 prominent Christian leaders.

There will be a point in the future when these free-willed beings that weve made will say to us, I believe in God. What do I do? At that point, we should have a response, Kelly says.

Kelly, McHargue, and McGrath all are convinced that most traditional theologians today arent engaged enough in conversations like this because theyre stuck rehashing old questions instead of focusing on the coming ones. McHargue notes that questions about AI and theology are some of the most common that he receives from listeners of his popular Ask Science Mike and The Liturgist podcasts. Any non-biological, non-human intelligence will present a greater challenge to religion and human philosophy than anything else in our entire history combine, he claims. Nothing else will raise that level of upheaval, and collective trauma as the moment we first encounter it.

Despite these pitfalls, McGrath raises one last mischievous point: AI actually could bolster a persons faith. For some people, religion is precisely about recognizing that I, as a human being, am not God and so I don't have all the answers and will inevitably be wrong about things, he says. If that is ones outlook, then finding out you were wrong is a good thing. It simply confirms what you already knew: that life is about trusting God and not trusting in my own understanding.

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Is AI a Threat to Christianity? - The Atlantic

HomeNewsPokerNews Op-Ed

Deep Blue was one hell of a chess player.

It was February 1996 and the machine developed by IBM was locked in battle with Gary Kasparov. Chess was big news as the computer system project originally begun in 1985 at Carnegie Mellon University attempted to do something other chess-playing devices had been unable to do beat a reigning world champion.

Even those with only a passing interest in chess like myself were intrigued by the matchup. Deep Blues designer said the machine could evaluate 200 million positions per second, and at the time, it was the fastest computer to match up with a world chess champion. Reports on the days progress were published in newspapers all across the globe.

Ultimately, the first match of six games was a victory for humanitywith Kasparov notching a 4-2 victory. However, in May the following year, and after some additional re-engineering, it was Deep Blue coming out on top.

The Deep Blue phenomenon has been in my head for the last couple weeks as four top poker players (Jason Les, Daniel McAulay, Jimmy Chou and Dong Kim) squared off against artificial intelligence software at the Rivers Casino in Pittsburgh.

This time the AI came out on top.

As Reuters noted, Libratus [Latin for balance], an AI built by Carnegie Mellon University racked up over $1.7 million worth of chips against four of the top professional poker players in the world in a 20-day marathon poker tournament that ended on Tuesday.

Headlines have trumpeted Libratus accomplishment around the world. Here are just a few examples:

Machine beats humans for the first time in poker (Reuter's) Computer manages to beat 4 of world's best poker players (FOX News) A Computer Just Clobbered Four Pros At Poker (FiveThirtyEight) A Mystery AI Just Crushed the Best Human Players at Poker (Wired magazine) Artificial Intelligence Goes All-in on Texas Holdem (Wall Street Journal)

Developers compared the victory to that of Deep Blue 20 years ago. The team certainly faced a challenge in engineering their AI to adjust to betting differences, imperfect information, unorthodox play, and that unique aspect of poker that differs it from most other games,bluffing.

Players were given a certain amount of play money and Libratus would go on to notch a computer's first victory in the no limit variety of Texas Hold'em (a previous computer had already mastered Limit Hold'em).

Yes, poker is just a game," University of Michigan professor Michael Wellman, who specializes in game theory and closely follows AI poker, said to Wired magazine. "But the game theory exhibited by Libratus could help with everything from financial trading to political negotiations to auctions.

Some have hailed the entire spectacle as great for the game of poker and no doubt there is some nice PR benefit that comes with it. But from a simple poker-playing perspective and in regards to its relevance among poker fans, the whole thing seems a bit too much. As a massive fan of the game of poker, this whole spectacle lacks the impact of Deep Blues win.

To me, this matchup of man versus droid/computer/software/techno-gizmo lacks the one aspect of poker that makes it so unique:risk. Its the reason that playing poker online for free or playing with your grandmother for matchsticks (or cheerios or whatever) is so lame;there is no risk of losing ones own money.

Chess is a game with merely risk of losing one individual match itself. The two combatants may have some kind of extrinsic monetary motivation, such as tournament payouts, appearance fees, etc., but there is not an inherent expected loss of ones own personal earnings.

In poker, players must square off against each other with their (usually) hard-earned money and that risk of ones own cash is a huge part of pokers appeal. Financial risk is inherently about losing money, and if youre not playing with risk in the game, youre not really playing poker.

If youre afraid to lose your money, you cant play to win, said Johnny Moss, a Texas poker legend and winner of the first two WSOP Main Events.

That attitude is something inherently flawed in making so much hoopla about Libratus' accomplishment;a machine/software/robot has no real inherent sense of loss or risk.

And when it comes to the art of the bluff, it seems engineering a machine to make these kinds of moves misses the key component of the risk involved in doing this: the pulse-racing feel of having all your chips in on a pot when you know your hand is squadoosh as ESPN WSOP analyst Norman Chad likes to put it. A highly-engineered AI topped four poker sharks with no real money on the line.

As a poker fan, this whole event doesnt even seem like real poker and just left me asking: So what? Poker is a game that is extremely dependent on human emotion and temperament.

Artificial intelligence has no fears about losing the mortgage payment in a pot.

Artificial intelligence has no fears about losing the mortgage payment in a pot or being down to that last bit of the poker bankroll and having to look for a real job to build it back.

Another aspect of this matchup with Libratus that is really missing for me, and I think for many poker fans, is that the self-reliant, mano-a-mano, battle of minds that takes place at the poker table. Sure I can concede a machine can get the better of humans in this type of setup, but pokers appeal for me is seeing players squaring off against each other and matching skills.

A battle against a computer lacks the panache of seeing real-life humans battling it out for their own cash. Libratus may have massive amounts of computing power, but it lacks the humanity that makes poker great and now watchable on television.

Many poker insiders and those with deep roots in the game may forget that, to casual fans, seeing thousands of dollars won and lost on a single game of cards is extremely bizarre yet extremely appealing. That appeal, along with the games unique characters and history, is the reason poker has grown into the international game it is today.

Poker is great because the human aspect is so important to excelling; it is not simply a series of moves on a game board or your old Commodore 64. Players who master the game can read other players and keep their own emotions in check.

They must master the subtleties and games within the game to excel. They benefit themselves by timing their actions correctly based on other players tendencies, outlooks and general gameplay. Players like Jason Mercier and Daniel Negreanu have mastered these nuances.

Dont read my hand wrong here, I am not a poker pessimist who thinks the game is moving in the wrong direction. Quite the contrary: I think the game is moving in the right direction in general after massive growth in the 2000s.

Libratus is not the next Big Blue and these four players were not Gary Kasparov.

Actual growth of the game depends on continuing presentations of the game in its real context on the felt and focusing on the players.

Some of those include: continued growth of the WSOP and live ESPN broadcasts; the World Poker Tours continued success and international growth; great broadcasts like Poker Centrals Super High Roller Bowl (with great commentary catering to fans and hard-core players alike); progress (thought slow) of state-by-state legalized online poker; the growth of the game by appealing younger players via Twitch; and the success of middle-tier tours catering to average Joe poker players (which are still needed to grow the game) like the Heartland Poker Tour and Mid-States Poker Tour.

The AI win seems like a minute footnote in comparison. Libratus may have won the battle against mankind, but was there ever really a war? Im not sure this is a battle that means a whole lot in the big picture of modern poker.

Libratus may have won the battle against mankind, but was there ever really a war?

Libratus is not the next Big Blue and these four players were not Gary Kasparov. It may have been an interesting technological endeavor, but Im sure these players in the "Brains vs. Artificial Intelligence, as the event came to be known, would much rather bring home a WSOP gold bracelet or WPT title if they had to pick. That hardware (not software) would be tangible and real and it would certainly be a nice real-life check to cash.

Sean Chaffin is a freelance writer in Crandall, Texas, and writes frequently about gambling and poker. If you have any story ideas, please email him at seanchaffin@sbcglobal.net or follow him @PokerTraditions. His poker book is RAISING THE STAKES: True Tales of Gambling, Wagering & Poker Faces and available on amazon.com.

The opinions expressed here are those of the authors and do not necessarily reflect the positions PokerNews

Be sure to complete your PokerNews experience by checking out an overview of our mobile and tablet apps here. Stay on top of the poker world from your phone with our mobile iOS and Android app, or fire up our iPad app on your tablet. You can also update your own chip counts from poker tournaments around the world with MyStack on both Android and iOS.

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Artificial Intelligence Tops Humans in Poker Battle What's the Big Deal? - PokerNews.com

ment" (Nilsson, 1984). Soon, SRI committed itself to the development of an AI-driven robot, Shakey, as a means to achieve its objective. Shakey's development necessitated extensive basic research in several domains, including planning, natural-language processing, and machine vision. SRI's achievements in these areas (e.g., the STRIPS planning system and work in machine vision) have endured, but changes in the funder's expectations for this research exposed SRI's AI program to substantial criticism in spite of these real achievements.

Under J.C.R. Licklider, Ivan Sutherland, and Robert Taylor, DARPA continued to invest in AI research at CMU, MIT, Stanford, and SRI and, to a lesser extent, other institutions.18 Licklider (1964) asserted that AI was central to DARPA's mission because it was a key to the development of advanced command-and-control systems. Artificial intelligence was a broad category for Licklider (and his immediate successors), who "supported work in problem solving, natural language processing, pattern recognition, heuristic programming, automatic theorem proving, graphics, and intelligent automata. Various problems relating to human-machine communicationtablets, graphic systems, hand-eye coordinationwere all pursued with IPTO support" (Norberg and O'Neill, 1996).

These categories were sufficiently broad that researchers like McCarthy, Minsky, and Newell could view their institutions' research, during the first 10 to 15 years of DARPA's AI funding, as essentially unfettered by immediate applications. Moreover, as work in one problem domain spilled over into others easily and naturally, researchers could attack problems from multiple perspectives. Thus, AI was ideally suited to graduate education, and enrollments at each of the AI centers grew rapidly during the first decade of DARPA funding.

DARPA's early support launched a golden age of AI research and rapidly advanced the emergence of a formal discipline. Much of DARPA's funding for AI was contained in larger program initiatives. Licklider considered AI a part of his general charter of Computers, Command, and Control. Project MAC (see Box 4.2), a project on time-shared computing at MIT, allocated roughly one-third of its $2.3 million annual budget to AI research, with few specific objectives.

The history of speech recognition systems illustrates several themes common to AI research more generally: the long time periods between the initial research and development of successful products, and the interactions between AI researchers and the broader community of researchers in machine intelligence. Many capabilities of today's speech-recognition systems derive from the early work of statisticians, electrical engineers,

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9 Development in Artificial Intelligence | Funding a ...

Which is Which?

It all started out as science fiction: machines that can talk, machines that can think, machines that can feel. Although that last bit may be impossible without sparking an entire world of debate regarding the existence of consciousness, scientists have certainly been making strides with the first two.

Over the years, we have been hearing a lot about artificial intelligence, machine learning, and deep learning. But how do we differentiate between these three rather abstruse terms, and how are they related to one another?

Artificial intelligence (AI) is the general field that covers everything that has anything to do with imbuing machines with intelligence, with the goal of emulatinga human beings unique reasoning faculties. Machine learning is a category within the larger field of artificial intelligence that is concerned with conferring uponmachines the ability to learn. This is achieved by using algorithms that discoverpatterns and generate insights from the data they are exposed to, for application to future decision-making and predictions, a process that sidesteps theneed to be programmed specifically for every single possible action.

Deep learning, on the other hand, is a subset of machine learning: its the most advanced AI field, one that brings AI the closest to thegoal of enabling machines to learn and think as much like humans as possible.

In short, deep learning is a subset of machine learning, and machine learning falls within artificial intelligence. The followingimage perfectly encapsulatesthe interrelationship of the three.

Heres a little bit of historical background to better illustrate the differences between the three, and how each discovery and advance has paved the way for the next:

Philosophers attempted to make sense of human thinking in the context of a system, and this idea resulted in the coinage ofthe term artificial intelligence in 1956. And its stillbelieved that philosophy has an important role to play in the advancement of artificial intelligence to this day. Oxford University physicist David Deutsch wrote in an article how he believes that philosophy still holds the key to achieving artificial general intelligence (AGI), the level of machine intelligence comparable to that of the human brain, despite the fact that no brain on Earth is yet close to knowing what brains do in order to achieve any of that functionality.

Advancements in AI have given rise to debates specifically about them being a threat to humanity, whether physically or economically (for which universal basic income is also proposed, and is currently being tested in certain countries).

Machine learning is just one approach to reifyingartificial intelligence, and ultimately eliminates (or greatly reduces) the need to hand-code the software with a list of possibilities, and how the machine intelligence ought toreact to each of them. Throughout 1949 until the late 1960s, American electric engineer Arthur Samuel worked hard onevolving artificial intelligence from merely recognizing patterns to learning from the experience, making him the pioneer of the field. He used a game of checkers for his research while working with IBM, and this subsequently influenced the programming of early IBM computers.

Current applications are becoming more and more sophisticated, making their way into complex medical applications.

Examples include analyzing large genome sets in an effort to prevent diseases, diagnosing depression based on speech patterns, and identifying people with suicidal tendencies.

As we delve into higher and evenmore sophisticated levels of machine learning, deep learning comes into play. Deep learning requires a complex architecture that mimics a human brains neural networks in order to make sense of patterns, even with noise, missing details, and other sources of confusion. While the possibilities of deep learning are vast, so are its requirements: you need big data, and tremendous computing power.

It means not having to laboriously program a prospective AI with that elusive quality of intelligencehowever defined. Instead, all the potential for future intelligence and reasoning powers are latent in the program itself, much like an infants inchoate but infinitely flexible mind.

Watch this video for a basic explanation of how it all works:

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Artificial Intelligence: What It Is and How It Really Works

Artificial intelligence is a fast emerging technology, dealing with development and study of intelligent machines and software. This software is being used across various applications such as manufacturing (assembly line robots), medical research, and speech recognition systems. It also enables in-build software or machines to operate like human beings, thereby allowing devices to collect, analyze data, reason, talk, make decisions and act The global artificial intelligence market was valued at US$ 126.24 Bn in 2015 and is forecast to grow at a CAGR of 36.1% from 2016 to 2024 to reach a value of US$ 3,061.35 Bn in 2024.

The global artificial intelligence market is currently witnessing healthy growth as companies have started leveraging the benefits of such disruptive technologies for effective customer reach and positioning of their services/solutions. Market growth is also supported by an expanding application base of artificial intelligence solutions across various industries. However, factors such as low funding access or high upfront investment, and demand for skilled resources (workforce) are presently acting as major deterrents to market growth.

On the basis of types of artificial intelligence systems, the market is segmented into artificial neural network, digital assistance system, embedded system, expert system, and automated robotic system. Expert system was the most adopted or revenue generating segment in 2015. This was mainly due to the extensive use of artificial intelligence across various sectors including diagnosis, process control, design, monitoring, scheduling and planning.

Based on various applications of artificial intelligence systems, the market has been classified into deep learning, smart robots, image recognition, digital personal assistant, querying method, language processing, gesture control, video analysis, speech recognition, context aware processing, and cyber security. Image recognition is projected to be the fastest growing segment by application in the global artificial intelligence market. This is due to the growing demand for affective computing technology across various end-use sectors for better study of systems that can recognize, analyze, process, and simulate human effects.

North America was the leader in the global artificial intelligence market in 2015, holding approximately 38% of the global market revenue share, and is expected to remain dominant throughout the forecast period from 2016 to 2024. High government funding and a strong technological base have been some of the major factors responsible for the top position of the North America region in the artificial intelligence market over the past few years. Middle East and Africa is expected to grow at the highest CAGR of 38.2% throughout the forecast period. This is mainly attributed to enormous opportunities for artificial intelligence in the MEA region in terms of new airport developments and various technological innovations including robotic automation.

The key market players profiled in this report include QlikTech International AB, MicroStrategy Inc., IBM Corporation, Google, Inc., Brighterion Inc., Microsoft Corporation, IntelliResponse Systems Inc., Next IT Corporation, Nuance Communications, and eGain Corporation.

Chapter 1 Preface 1.1 Research Scope 1.2 Market Segmentation 1.3 Research Methodology

Chapter 2 Executive Summary 2.1 Market Snapshot: Global Artificial Intelligence Market, 2015 & 2024 2.2 Global Artificial Intelligence Market Revenue, 2014 2024 (US$ Bn) and CAGR (%)

Chapter 3 Global Artificial Intelligence Market Analysis 3.1 Key Trends Analysis 3.2 Market Dynamics 3.2.1 Drivers 3.2.2 Restraints 3.2.3 Opportunities 3.3 Value Chain Analysis 3.4 Global Artificial Intelligence Market Analysis, By Types 3.4.1 Overview 3.4.2 Artificial Neural Network 3.4.3 Digital Assistance System 3.4.4 Embedded System 3.4.5 Expert System 3.4.6 Automated Robotic System 3.5 Global Artificial Intelligence Market Analysis, By Application 3.5.1 Overview 3.5.2 Deep Learning 3.5.3 Smart Robots 3.5.4 Image Recognition 3.5.5 Digital Personal Assistant 3.5.6 Querying Method 3.5.7 Language Processing 3.5.8 Gesture Control 3.5.9 Video Analysis 3.5.10 Speech Recognition 3.5.11 Context Aware Processing 3.5.12 Cyber Security 3.6 Competitive Landscape 3.6.1 Market Positioning of Key Players in Artificial Intelligence Market (2015) 3.6.2 Competitive Strategies Adopted by Leading Players

Chapter 4 North America Artificial Intelligence Market Analysis 4.1 Overview 4.3 North America Artificial Intelligence Market Analysis, by Types 4.3.1 North America Artificial Intelligence Market Share Analysis, by Types, 2015 & 2024 (%) 4.4 North America Artificial Intelligence Market Analysis, By Application 4.4.1 North America Artificial Intelligence Market Share Analysis, by Application, 2015 & 2024 (%) 4.5 North America Artificial Intelligence Market Analysis, by Region 4.5.1 North America Artificial Intelligence Market Share Analysis, by Region, 2015 & 2024 (%)

Chapter 5 Europe Artificial Intelligence Market Analysis 5.1 Overview 5.3 Europe Artificial Intelligence Market Analysis, by Types 5.3.1 Europe Artificial Intelligence Market Share Analysis, by Types, 2015 & 2024 (%) 5.4 Europe Artificial Intelligence Market Analysis, By Application 5.4.1 Europe Artificial Intelligence Market Share Analysis, by Application, 2015 & 2024 (%) 5.5 Europe Artificial Intelligence Market Analysis, by Region 5.5.1 Europe Artificial Intelligence Market Share Analysis, by Region, 2015 & 2024 (%)

Chapter 6 Asia Pacific Artificial Intelligence Market Analysis 6.1 Overview 6.3 Asia Pacific Artificial Intelligence Market Analysis, by Types 6.3.1 Asia Pacific Artificial Intelligence Market Share Analysis, by Types, 2015 & 2024 (%) 6.4 Asia Pacific Artificial Intelligence Market Analysis, By Application 6.4.1 Asia Pacific Artificial Intelligence Market Share Analysis, by Application, 2015 & 2024 (%) 6.5 Asia Pacific Artificial Intelligence Market Analysis, by Region 6.5.1 Asia Pacific Artificial Intelligence Market Share Analysis, by Region, 2015 & 2024 (%)

Chapter 7 Middle East and Africa (MEA) Artificial Intelligence Market Analysis 7.1 Overview 7.3 MEA Artificial Intelligence Market Analysis, by Types 7.3.1 MEA Artificial Intelligence Market Share Analysis, by Types, 2015 & 2024 (%) 7.4 MEA Artificial Intelligence Market Analysis, By Application 7.4.1 MEA Artificial Intelligence Market Share Analysis, by Application, 2015 & 2024 (%) 7.5 MEA Artificial Intelligence Market Analysis, by Region 7.5.1 MEA Artificial Intelligence Market Share Analysis, by Region, 2015 & 2024 (%)

Chapter 8 Latin America Artificial Intelligence Market Analysis 8.1 Overview 8.3 Latin America Artificial Intelligence Market Analysis, by Types 8.3.1 Latin America Artificial Intelligence Market Share Analysis, by Types, 2015 & 2024 (%) 8.4 Latin America Artificial Intelligence Market Analysis, By Application 8.4.1 Latin America Artificial Intelligence Market Share Analysis, by Application, 2015 & 2024 (%) 8.5 Latin America Artificial Intelligence Market Analysis, by Region 8.5.1 Latin America Artificial Intelligence Market Share Analysis, by Region, 2015 & 2024 (%)

Chapter 9 Company Profiles 9.1 QlikTech International AB 9.2 MicroStrategy, Inc. 9.3 IBM Corporation 9.4 Google, Inc. 9.5 Brighterion, Inc. 9.6 Microsoft Corporation 9.7 IntelliResponse Systems Inc. 9.8 Next IT Corporation 9.9 Nuance Communications 9.10 eGain Corporation

The Artificial Intelligence Market report provides analysis of the global artificial intelligence market for the period 20142024, wherein the years from 2016 to 2024 is the forecast period and 2015 is considered as the base year. The report precisely covers all the major trends and technologies playing a major role in the artificial intelligence markets growth over the forecast period. It also highlights the drivers, restraints, and opportunities expected to influence the market growth during this period. The study provides a holistic perspective on the markets growth in terms of revenue (in US$ Bn), across different geographies, which includes Asia Pacific (APAC), Latin America (LATAM), North America, Europe, and Middle East & Africa (MEA).

The market overview section of the report showcases the markets dynamics and trends such as the drivers, restraints, and opportunities that influence the current nature and future status of this market. Moreover, the report provides the overview of various strategies and the winning imperatives of the key players in the artificial intelligence market and analyzes their behavior in the prevailing market dynamics.

The report segments the global artificial intelligence market on the types of artificial intelligence systems into artificial neural network, digital assistance system, embedded system, expert system, and automated robotic system. By application, the market has been classified into deep learning, smart robots, image recognition, digital personal assistant, querying method, language processing, gesture control, video analysis, speech recognition, context aware processing, and cyber security. Thus, the report provides in-depth cross-segment analysis for the artificial intelligence market and classifies it into various levels, thereby providing valuable insights on macro as well as micro level.

The report also provides the competitive landscape for the artificial intelligence market, thereby positioning all the major players according to their geographic presence, market attractiveness and recent key developments. The complete artificial intelligence market estimates are the result of our in-depth secondary research, primary interviews, and in-house expert panel reviews. These market estimates have been analyzed by taking into account the impact of different political, social, economic, technological, and legal factors along with the current market dynamics affecting the artificial intelligence markets growth.

QlikTech International AB, MicroStrategy Inc., IBM Corporation, Google, Inc., Brighterion Inc., Microsoft Corporation, IntelliResponse Systems Inc., Next IT Corporation, Nuance Communications, and eGain Corporation are some of the major players which have been profiled in this study. Details such as financials, business strategies, recent developments, and other such strategic information pertaining to these players has been provided as part of company profiling.

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Artificial Intelligence Market Size and Forecast by 2024