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As man-made robots get smarter, will they eventually outpace man?

A few of the world's smartest technology leaders certainly think so. In recent days, they've taken to sounding the alarm bell about the potential dangers of Artificial Intelligence (AI).

Tesla CEO Elon Musk called AI "our biggest existential threat" while British scientist Stephen Hawking said AI could "spell the end of the human race." In January, Read MoreMicrosoft co-founder Bill Gates sided with Musk, adding, "[I] don't understand why some people are not concerned."

Read More Think tank: Study AI before letting it take over

Yet on the other side of the argument are people like Microsoft co-founder, Paul Allen. In 2013, he founded the Allen Institute for Artificial Intelligence in Seattle, whose mission is to advance the study of AI. The man who heads the organization thinks the fears are overblown.

"Robots are not coming to get you," said Allen Institute CEO Oren Etzioni. In an interview with CNBC, he said: "We quite simply have to separate science from science fiction."

Etzioni said Elon Musk and others may be missing the distinction between intelligence and autonomy. One implies streamlined computer functions, while the other means machines think and operate independently.

Etzioni offered two Artificial Intelligence examples. In 1997, IBM's Deep Blue chess computer beat then world champion Garry Kasparov. In 2011, IBM's Watson supercomputer beat two champions on the game show "Jeopardy."

"These are highly targeted savants," said Etzioni. "They say Watson didn't even know it won. And Deep Blue will not play another chess game unless you push a button."

Etzioni said that the machines "have no free will, they have no autonomy. They're no more likely to do damage than your calculator is likely to do its own calculations."

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Embrace AI, don't fear it: Expert

Artificial intelligence has gone through some dismal periods, which those in the field gloomily refer to as AI winters. This is not one of those times; in fact, AI is so hot right now that tech giants like Google, Facebook, Apple, Baidu, and Microsoft are battling for the leading minds in the field. The current excitement about AI stems, in great part, from groundbreaking advances involving what are known as convolutional neural networks. This machine learning technique promises dramatic improvements in things like computer vision, speech recognition, and natural language processing. You probably have heard of it by its more layperson-friendly name: Deep Learning.

Few people have been more closely associated with Deep Learning than Yann LeCun, 54. Working as a Bell Labs researcher during the late 1980s, LeCun developed the convolutional network technique and showed how it could be used to significantly improve handwriting recognition; many of the checks written in the United States are now processed with his approach. Between the mid-1990s and the late 2000s, when neural networks had fallen out of favor, LeCun was one of a handful of scientists who persevered with them. He became a professor at New York University in 2003, and has since spearheaded many other Deep Learning advances.

More recently, Deep Learning and its related fields grew to become one of the most active areas in computer research. Which is one reason that at the end of 2013, LeCun was appointed head of the newly-created Artificial Intelligence Research Lab at Facebook, though he continues with his NYU duties.

LeCun was born in France, and retains from his native country a sense of the importance of the role of the public intellectual. He writes and speaks frequently in his technical areas, of course, but is also not afraid to opine outside his field, including about current events.

IEEE Spectrum contributor Lee Gomes spoke with LeCun at his Facebook office in New York City. The following has been edited and condensed for clarity.

Yann LeCun on...

IEEE Spectrum: We read about Deep Learning in the news a lot these days. Whats your least favorite definition of the term that you see in these stories?

Yann LeCun: My least favorite description is, It works just like the brain. I dont like people saying this because, while Deep Learning gets an inspiration from biology, its very, very far from what the brain actually does. And describing it like the brain gives a bit of the aura of magic to it, which is dangerous. It leads to hype; people claim things that are not true. AI has gone through a number of AI winters because people claimed things they couldnt deliver.

Spectrum: So if you were a reporter covering a Deep Learning announcement, and had just eight words to describe it, which is usually all a newspaper reporter might get, what would you say?

LeCun: I need to think about this. [Long pause.] I think it would be machines that learn to represent the world. Thats eight words. Perhaps another way to put it would be end-to-end machine learning. Wait, its only five words and I need to kind of unpack this. [Pause.] Its the idea that every component, every stage in a learning machine can be trained.

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Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter

Artificial intelligence is about to take off in a big way.

According to a new report by Goldman Sachs, AI is defined as "any intelligence exhibited by machines or software." That can mean machines that learn and improve their operations over time, or that make sense of huge amounts of disparate data.

Though it's been almost 60 years since we first heard of the term AI, Goldman believes that we are "on the cusp of a period of more rapid growth in its use and applications."

The reasons? Cheaper sensors leading to a flood of new data, and rapid improvements in technology that allows computers to understand so-called "unstructured" data like conversations and pictures.

Other industry insiders are confident that AI will continue to evolve at a much higher rate while affecting wage growth in many industries. Ray Kurzweil, the director of engineering at Google, believes that human-level AI is coming by 2029.

So who are the players going to be?

First, several big tech companies have been storing up patents related to the field.

IBM is the leader, with about 500 patents related to artificial intelligence. IBM's super-computer Watson is an example of the shift to AI, as it entered the healthcare sector in 2013 and helped lower the error rate in cancer diagnoses by physicians.

Other big patent players in the space include Microsoft, Google, and SAP.

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Who's set to make money from the coming artificial intelligence boom?

Computer algorithms capable ofplaying the perfect game of checkers or Texas Holdem poker have achieved success so far by efficiently calculating the best strategiesin advance. But some computer scientists want to createa different form of artificial intelligence that can playany new game without the benefit ofprior knowledge or strategies. The software would face opponents after having onlyreadthe gamesrulebook. AnAI that can adapt well enough to play new games without prior knowledgecould also potentially do well in adapting to the rules of society in areas such as corporate law orgovernment regulations.

This idea of general game-playing AI has gotten a big boost from the International General Game Playing Competition, a US $10,000 challengethat has been heldas an annual eventsince 2005. The AI competitorsmust analyze the unfamiliar game at handsay,somevariant of chesswithina start clock time of 5 or 10 minutes. Then they each have a playclock of just one minute to make their move within each turn of play. Its a challenge that requires a very different approach to AI than the specialized algorithms that exhaustively analyze almostevery possible playover days or weeks.

This raises the question of where is the intelligence in artificial intelligence, saysMichael Genesereth, a computer scientist at Stanford University.Is it inthe program which is following a recipe,or is it in the programmer who invented the recipe and understands the rules for playing the games?

Geneserethrecently presentedthe latest advances in general game-playing AI at the29thAssociation for the Advancement of Artificial Intelligence conferenceheld from25-30Januaryin Austin, Texas. The latest champions of the General Game Playingcompetition represent the thirdgeneration of AI to have emerged since the first competition in 2005.

But the idea of general game-playing AIgoes all the way back tothe original 1958 vision of John McCarthy, the computer scientist who coined the term artificial intelligence.McCarthyenvisioned an advice taker AI that didnt need to rely upon a programmers step-by-step recipe to tackle new scenarios, but instead could adapt its behavior based on statements about its environment and goals. To paraphrase science fiction writer Robert Heinlein,such AI could behave more like an adaptable human who can write a sonnet, balance accounts, build a wall, set a bone,rather than just performa single tasklike a specialized insect.

Computer scientists usecompetitions based on games such as tic-tac-toe and chess as benchmarks of their progress. But general game-playing AI would not likely compete with specialized algorithms tofind the best solutions to the ancient game of Goor heads-up nolimit Texas Holdem. Thosespecialized algorithms are programmed to crunch all the information sets about possible moves made by game opponents at each stage of play. Such anexhaustive approach often requires intensive supercomputing resources.

By comparison, general game-playing AI can easily learn toplay new games on its ownby doing the equivalent of translating a games rulebookintoGame Description Language, a computer programming language it can understand. That means general game playing AI can rely upon just one page ofrules to learn games involving thousands of information states; chess, for instance, can be described through just four pages of such rules.

Most examples of AI tend to fall in the category of specialized algorithms following preprogrammed instructions. Some AI uses the popular approach known as machine learning to slowly adapt to new scenarios; they are, in a sense, virtual newborns that knownothing and must learn everything for themselves. General game-playing AI provides an alternative approach, incorporatingexisting knowledge rather than having to learn everything on its own.

I think there needs to be some balance between a machine that knows nothing to start and learns about world,Genesereth says, andamachine that is told everything about human knowledge and startsfrom there.

The first generation of general game-playing AI focused on maximizing the moves available to itself and limiting the moves available to opponents. Such an approach had onlylimited success;computer programs still struggled to beat humans during the first Carbon versus Silicon competition held alongside the General Game Playing competition in2005. Since that time,humans have never again beaten their silicon counterparts.

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Building AI to Play by Any Rules

What do you get if you take a cute humanoid robot and pair it with one of the worlds best artificial intelligence programs? A smarter humanoid with few practical skills.

Japanese mobile carrier SoftBank introduced a talking household machine called Pepper last year, and its now hooking up with Watson, IBMs artificial intelligence platform.

The prospect of the AI, a winner on the Jeopardy! quiz show, becoming embodied in the doe-eyed droid may be frightening or enchanting depending on your perspective. But its unlikely to make the robot any more useful due to its physical limitations.

SoftBank and IBM are planning to use Watson with Pepper, but they havent specified how. The quiz show champ is a cloud-based platform that can be used to sift through large volumes of data and answer questions posed in natural language. IBM has billed cognitive computing as a nimble form of AI that will be able to look at and use unstructured data. Watson has been deployed in the U.S. to help doctors identify treatment options for cancer patients.

SoftBank is rolling out Watson in Japan and wants to apply it to fields such as education, banking, retail and medicine. In banking, for instance, Watson could be used to answer questions about asset management, according to SoftBank.

With a constant high-speed Internet connection, Pepper could channel Watson and become something more sophisticated than what it does now: sing songs, help sell mobile phones and appear in ads for canned coffee with actor Tommy Lee Jones. Like Hondas Asimo robot, Pepper is a corporate ambassador.

At a demo held last September, Pepper was linked to Watson when it answered a moderators question, What weapon was invented by Ernest Swinton and used in 1916? Pepper seemed to think for a moment before saying, Tank.

Even though Pepper didnt respond with a question, it was another version of Watsons Jeopardy! routine, one that evoked super-intelligent robots of science fiction such as C-3PO or Data. But few people will be willing to shell out 198,000 (US$1,653), Peppers planned sale price, for a quiz master that cant do much else.

Constrained by its hardware limitations, Pepper cant cook or clean, as developer Aldebaran Robotics of France readily admits at the top of a promotional webpage. Its rubber-tipped fingers could probably grasp clothing, an Aldebaran researcher speculated via email, but it clearly wasnt designed to carry things. Pepper is an empathy robot.

SoftBank CEO Masayoshi Son unveiled Pepper in a dramatic press event as the worlds first robot that can read human emotions. It can read facial expressions, tone of voice and body language to better communicate with people.

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Even with Watson brains, robots need practical skills