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.

Fraugster, a German and Israeli startup that has developed Artificial Intelligence (AI) technology to help eliminate payment fraud, has raised $5 million in funding. Earlybird led the round, alongside existing investors Speedinvest, Seedcamp and an unnamed large Swiss family office. The new capital will be used to add to Fraugster's headcount as it expands internationally. Founded in 2014 by Max Laemmle, who previously co-founded payment gateway company Better Payment, and Chen Zamir, who I'm told has spent more than a decade in different analytics and risk management roles including five years at PayPal, Fraugster says it's already handling almost $15 billion in transaction volume for "several thousand" international merchants and payment service providers, including (and most notably) Visa. Its AI-powered fraud detection technology learns from each transaction in real-time and claims to be able to anticipate fraudulent attacks even before they happen.

Concerned that President-elect Trump's tweets could knock down the share price of your favorite stock? If you are an investor in (or an executive or employee at) a publicly traded company, then there is a new app to help you navigate the potentially choppy social media waters. It's called Trigger Finance, and it is the brainchild of three Cornell computer science engineers who want to level the playing field between institutional and do-it-yourself investors. Founded in 2015, Trigger is a financial technology mobile platform that provides free real-time data to help retail investors invest more rationally through an event-driven, rules-based approach. "Our mission is to build the next generation mobile investing platform that uses natural language, a wealth of data and artificial intelligence to help investors invest more rationally through rules and discipline," said Rachel Mayer, Trigger's co-founder and chief executive officer.

The development of problem solvers for configuration tasks is one of the most successful and mature application areas of artificial intelligence. The provision of tailored products, services, and systems requires efficient engineering and design processes where configurators play a crucial role. Because one of the core competencies of Siemens is to provide such highly engineered and customized systems, ranging from solutions for medium-sized and small businesses up to huge industrial plants, the efficient implementation and maintenance of configurators are important goals for the success of many departments. For more than 25 years the application of constraint-based methods has proven to be a key technology in order to realize configurators at Siemens. This article summarizes the main aspects and insights we have gained looking back over this period. In particular, we highlight the main technology factors regarding knowledge representation, reasoning, and integration which were important for our achievement. Finally we describe selected key application areas where the business success vitally depends on the high productivity of configuration processes.