Playing against a top Go player, Google DeepMind's AlphaGo artificial-intelligence program has puzzled commentators with moves that are often described as "beautiful," but do not fit into the usual human style of play. Artificial-intelligence experts think these moves reflect a key AI strength of AlphaGo, its ability to learn from its experience. Such moves cannot be produced by just incorporating human knowledge, said Doina Precup, associate professor in the School of Computer Science at McGill University in Quebec, in an email interview. "AlphaGo represents not only a machine that thinks, but one that can learn and strategize," agreed Howard Yu, professor of strategic management and innovation at IMD business school. AlphaGo won three games consecutively against Lee Se-dol last week in Seoul, securing the tournament and US$1 million in prize money that Google plans to give to charities. The program, however, lost the fourth game on Sunday when it made a mistake.

IBM has been encouraging developers to build apps using its Watson artificial intelligence system, which it hopes will be a big moneymaker for the company in the future. On Wednesday it kicked off its latest push, pledging US$5 million in prizes for whoever makes the biggest breakthroughs using Watson by 2020. "We're inviting teams from around the world to develop and demonstrate how humans can collaborate with powerful cognitive A.I. technologies capable of solving some of the world's grand challenges," the company said. And by grand challenges, it's thinking big -- it lists past achievements like the moon landing, mapping the human genome, and addressing climate change. The contest is being launched Wednesday at the TED conference in Vancouver by Peter Diamandis, founder of XPrize, which is partnering with IBM on the project.

It's already sorting your email and translating your voice searches, and machine learning will play a bigger role in Google's services moving forward. Google's parent company, Alphabet, reported its quarterly financial results Thursday, with revenue and profit both up from a year earlier. New Google CEO Sundar Pichai took part in his first earnings call, and in between discussing the numbers he revealed how important Google thinks machine learning is to its future. "Machine learning is a core, transformative way by which we're rethinking everything we're doing," he said. He was putting the spotlight on a branch of artificial intelligence that's getting more attention lately.

I have seen the future, and it is a world of unparalleled convenience, untold marketing opportunities, and zero privacy. IBM held an event in San Francisco Thursday to show off new capabilities in Watson, it's artificial intelligence system that's being made available to developers to let them build smarter, "cognitive" applications. To set the futuristic tone, IBM invited Peter Diamandis, founder of the nonprofit X Prize Foundation, which humbly describes itself as "a catalyst for the benefit of humanity." To give you an idea of Diamandis' interests, he said he is currently "prospecting" asteroids that he plans to mine for resources. He put the value of one asteroid at $5.4 trillion.

Listen to much of the well-peddled advice in the enterprise tech world today, and you'd have to be excused for coming away with the belief that "big data" holds all the answers your company is looking for. Too bad it often can't live up to that promise -- at least, not in its traditional form. Turns out, what's commonly referred to as big data -- all those vast "lakes" of numerical measures captured by the enterprise resource planning (ERP), consumer relationship management (CRM) and other business systems so enthusiastically mined by today's analytics tools -- actually amounts to only 10 percent of the data an average company has at its fingertips, according to IDC. The rest is "unstructured" or "qualitative" data, and it can be messy. Included in this type is information from customer surveys, response forms, online forums, social media, documents, videos, news reports, phone calls to call centers and anecdotal evidence gathered by the sales team, to name just a few examples.

A Google computer recently made headlines for appearing to become agitated and verbally lashing out at the human working with it. Artificial intelligence and machine learning researchers say have no fear. That's not what the computer is doing. "They're using big data for machine learning," said Alan W Black, a professor at Carnegie Mellon University's Language Technologies Institute. "They're probably mining logs of questions from various sites, like Google Groups and mailing lists, and you might have noticed that a lot of people on the net are snarky. Because it's in the training data and the machine doesn't know if it's snarky or not, the machine will just use it. You get that personality coming out in the answers."

Researchers at the University of Amsterdam are using neural networks to help a statistical machine translation systems learn what all human translators know--that the best translation of a word often depends on the context. Such tools are increasingly important as individuals and businesses seek to access information or buy products and services from other countries where different languages are spoken. Statistical machine translation work by breaking sentences into phrase fragments and selecting the most likely translation for each fragment--a process that doesn't always yield the best translation for the sentence as a whole in morphologically rich languages such as those where nouns are inflected for number, case and gender. To improve the word selection of such systems when translating into morphologically rich languages such as Russian, Bulgarian and German, the team used a neural network to analyze the words in context in the source language. Translating sentences into grammatically more complex languages is relatively easy for human translators because they understand the grammatical function of the word in a sentence.

The Turing award, in existence since 1966, comes with a $250,000 prize funded by Google and Intel. Last year's award went to Leslie Valiant, a Harvard University computer scientist. One past winner, Internet pioneer Vinton Cerf, says Pearl's accomplishments have "redefined the term'thinking machine'" over the past 30 years. Pearl's efforts have had "a pervasive influence not only on machine learning but on natural language processing, computer vision, robotics, computational biology, econometrics, cognitive science and statistics," Cerf said in a statement. The UCLA computer science professor is widely credited with coining the term "Bayesian Network," which refers to a statistical model ACM describes as mimicking "the neural activities of the human brain, constantly exchanging messages without benefit of a supervisor."

Last year, the Institute of Robotics and Mechatronics at the German Aerospace Center created the "Rollin' Justin" robot, a technical marvel that could catch a ball through a mix of precision, user input, and motion sensors. It proved to be a success, but there's only so much research you can do with a single-function robot. Enter "Agile Justin," the counterpart machine that can pitch a ball with a great deal of finesse. Huffington Post relayed video footage of the two robots in action, as each unit showed off their respective skills in a short game of catch. It's a neat thing to watch in terms of mechanical detail, especially when you consider that each robot has to mimic the hand-to-eye coordination required for a seemingly simple thing.

When Kenneth Wayne Jennings, noted for holding the record for the longest winning streak of 74 games on the U.S. syndicated game show, bowed to IBM's Watson as the new "Jeopardy!" That was probably one small step for a computer but a giant leap for computing. It's ironic to say that Watson's dominance on the game show didn't come out of the blue. The result was a culmination of over a decade of IBM's research. "It opened up a new chapter in information technology called cognitive computing--based on the idea of a natural interaction between systems and people," says Zachary (Zach) Lemnios, vice president of strategy for IBM Research.