Machine learning is, regrettably, not one of the day-to-day chores assigned to most programmers. However, with data volumes exploding, and high-profile successes such as IBM's Jeopardy-beating Watson and the recommendation engines of Amazon and Netflix, the odds are increasing that ML's opportunity might knock on your door one day. From the 1960s to the 1980s, the emphasis of artificial intelligence was in "top-down" approaches in which expertise from domain experts was somehow transcribed into a fixed set of rules and their relations. Often, these would be a series of small "if-then" rules, and the "magic sauce" of expert systems was that they could draw conclusions by automatically chaining together the execution of those rules whose "if" parameters were known. The technology for inferencing worked well enough, but it turned out that very large rulebases were hard to debug and maintain, while not very large rulebases didn't produce many compelling applications (for instance, my expert system for identifying seabirds failed to make me a billionaire).

In warehouses, hospitals and retail stores, and on city streets, industrial parks and the footpaths of college campuses, the first representatives of this new invading force are starting to become apparent. "The robots are among us," says Steve Jurvetson, a Silicon Valley investor and a director at Elon Musk's Tesla and SpaceX companies, which have relied heavily on robotics. A multitude of machines will follow, he says: "A lot of people are going to come in contact with robots in the next two to five years." The arrival of the robots -- and their potentially devastating effect on human employment -- has been widely predicted. Now, the machines are starting to roll or walk out of the labs.

JUST as native English-speakers stumble with Japanese, the Japanese struggle mightily with English, not to mention Korean or Chinese. In the widely used TOEFL tests of English as a foreign language, Japan invariably ranks second from bottom among the 29 countries participating in the scheme. Compared with the 150m people around the world who speak English as a second language, there are only 9m non-native speakers of Japanese--and most of those were forced to learn the language during Japan's era of colonial occupation, and are now dying of old age. For those who put their faith in technology, therefore, it was encouraging to hear Shinzo Abe, Japan's prime minister, demonstrate his linguistic skills a few weeks ago with a palm-sized gizmo that provided instantaneous translations of spoken Japanese into near-flawless English and Chinese. Mr Abe can manage perfectly well without such a device, being one of the few Japanese prime ministers in recent years to speak English fluently.

Tim Berry is president and founder of Palo Alto Software and bplans.com, Call it coincidence, serendipity, synchronicity, or just random, but last week I was accidentally exposed to two seemingly unrelated ideas that ended up seeming very related to me. And they gave me a fascinating whack on the side of the head. I thought artificial intelligence had run its course, but computers that learn could be much more important. First, the book Blondie24, by David Fogel, describing how he and his team used evolutionary computing to develop computer programming that taught itself to play checkers.

A consultation program plays the role of an expert consultant in some domain, giving advice or answers to non-experts with problems In the domain. Users will often want to know how the system arrived at its results during a particular consultation. This paper explains how the implementation of such a program as a production system can facilitate program-generated explanations. A production system [2] consists of three basic components: a set of production rules, a data base which is both used and updated by these rules, and a rule interpreter. A production rule often is in the form of a situation-action rule: it describes a situation and a set of actions to be taken if this situation is found to exist.

An object-oriented system for manipulating and analyzing data hosts a rule learning system for non-rectangular dataset. This I2D has learned several hundred pages of empirically interesting rules on the history of international conflict since 1945 from the SHERFACS dataset. This system is occaisionally accessible over the Web to Learn if-then rules. Alternatively, A Common LISP Hypermedia Server offers some description of the application and several screen snapshots of an experiment and several rules learned.

Harold Cohen was already an acclaimed artist when he represented the United Kingdom at the Venice Biennale back in 1966, and his work subsequently appeared in top-ranked galleries and museums around the world. So in 1969, when he began dabbling in computers, his intent was simply for the machines to help him create his drawings and paintings. I thought of designing a program as a kind of assistant, he recalls. I was to think up the heavenly paradigm and it was to do the earthly instantiation. But as Cohen found himself devoting less and less time and energy to his own paintings, his computerized alter ego, dubbed Aaron, began to take on a career of its own. In 1983, Aaron took up a pencil in its robotic hand and tirelessly produced drawing after drawing for an audience of captivated visitors to the Tate Gallery in London. It didn't matter to them that Cohen had to add color to the drawings with his own hand; many an onlooker walked out with one of the new drawings tucked under his arm. By last year, when the Computer Museum in Boston devoted an entire exhibit to Cohen's stepchild, Aaron had mastered paintbrush and palette and, once Cohen set up the apparatus, produced whole paintings, many of them quite pleasant to look at. Cohen's success with his computer program raises the question: Who is the creator of these paintings? The answer is by no means clear. Perhaps the creative intelligence is Cohen's because, after all, Aaron merely does what he programs it to do.

Using humanoid robots to study human behavior.

A list of transactions from a grocery store is shown in the figure above. Frequent items are a list of items that commonly appear together. One example is {wine, diapers, soy milk}. From the data set we can also find an association rule such as diapers - wine. This means that if someone buys diapers, there is a good chance they will buy wine. With the frequent item sets and association rules retailers have a much better understanding of their customers. Although common examples of association rulea are from the retail industry, it can be applied to a number of other categories, such as web site traffic, medicine, etc. How do we define these so called relationships? Who defines what is interesting? When we are looking for frequent item sets or association rules, we must look two parameters that defines its relevance. The support of an itemset, which is defined as the percentage of the data set which containts this itemset.

Last November, detectives investigating a murder case in Bentonville, Arkansas, accessed utility data from a smart meter to determine that 140 gallons of water had been used at the victim's home between 1 a.m. and 3 a.m. It was more water than had been used at the home before, and it was used at a suspicious time--evidence that the patio area had been sprayed down to conceal the murder scene. As technology advances, we have more detailed data and analytics at our fingertips than ever before. It can potentially offer new insights for crime investigators. One area crying out for more insight is cybersecurity.