Go is the most complex strategy game. There are more possibilities in a game of Go than there are atoms in the universe. Thus, it offers huge challenges for artificial intelligence (AI). Recent successes though give valuable decision making tips for humans. As of this writing, AlphaGo, Google's AI system has beaten a top-50 Go professional. It is now taking on one in the top five.

There is no question that machine learning is at the top of the hype curve. And, of course, the backlash is already in full force: I've heard that old joke "Machine learning is like teenage sex; everyone is talking about it, no one is actually doing it" about 20 times in the past week alone. But from where I sit, running a company that enables a huge number of real-world machine-learning projects, it's clear that machine learning is already forcing massive changes in the way companies operate. And it's not just being done by companies that we normally think of as having huge R&D budgets like Google and Microsoft. In reality, I would bet that nearly every Fortune 500 company is already running more efficiently -- and making more money -- because of machine learning. So where is it happening?

During Chevron Corporation's (NYSE:CVX) Security Analyst meeting on March 8, several big pieces of news came out. A day before the meeting, Chevron issued a press release stating that its 54 billion Gorgon LNG facility in Australia had just started producing LNG (liquefied natural gas) and condensate. After originally estimated to be operational by the end of 2014 for under 30 billion USD, the project was delayed as costs skyrocketed. As the operator with a 47.3% stake, Chevron lost a lot of credibility due to the massive cost of its mishaps, as did its partners ExxonMobil (NYSE:XOM) and Royal Dutch Shell (NYSE:RDS.A) (NYSE:RDS.B), who each own 25% of the venture. The first cargo of LNG is expected to be shipped out very soon, potentially marking the beginning of a strong source of growth after all the headaches it took to get here.

Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls.

Recent advances in cognitive computing componentry combined with other factors are leading to commercially viable cognitive systems. From chips to smart phones to public and private clouds, industrial strength "cognition as a service" is beginning to appear at all scales in business and society. Furthermore, in the age of zettabytes on the way to yottabytes, the designers, engineers, and managers of future smart systems will depend on cognition as a service. Cognition as a service can help unlock the mysteries of big data and ultimately boost the creativity and productivity of professionals and their teams, the productive output of industries and organizations, as well as the GDP (gross domestic product) of regions and nations.

For the vast majority of queries (for example, navigation, simple fact lookup, and others), search engines do extremely well. They also highlight many of the issues that are common to sophisticated AI question-answering systems. Rapid and ready-at-hand access, depth of processing, and the way they enable people to offload some ordinary memory tasks suggest that search engines have become more of a cognitive amplifier than a simple repository or front-end to the Internet. Although search engines are superb at finding and presenting information--up to and including extracting complex relations and making simple inferences--knowing how to frame questions and evaluate their results for accuracy and credibility remains an ongoing challenge.

Rehabilitation robots physically support and guide a patient's limb during motor therapy, but require sophisticated control algorithms and artificial intelligence to do so. This article provides an overview of the state of the art in this area. Furthermore, it describes exercise adaptation algorithms that change the overall exercise intensity based on the patient's performance or physiological responses, as well as socially assistive robots that provide only verbal and visual guidance. The article concludes with a discussion of the current challenges in rehabilitation robot software: evaluating existing control strategies in a clinical setting as well as increasing the robot's autonomy using entirely new artificial intelligence techniques.

The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. In summary, we believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.

This study investigates the content of the published scientific literature in the fields of operations research and management science (OR/MS) since the early 1950s. Our study is based on 80,757 published journal abstracts from 37 of the leading OR/MS journals. We have developed a topic model, using Latent Dirichlet Allocation (LDA), and extend this analysis to reveal the temporal dynamics of the field, journals, and topics. Our analysis shows the generality or specificity of each of the journals, and we identify groups of journals with similar content, which are both consistent and inconsistent with intuition. We also show how journals have become more or less unique in their scope. A more detailed analysis of each journals' topics over time shows significant temporal dynamics, especially for journals with niche content. This study presents an observational, yet objective, view of the published literature from OR/MS that would be of interest to authors, editors, journals, and publishers. Furthermore, this work can be used by new entrants to the fields of OR/MS to understand the content landscape, as a starting point for discussions and inquiry of the field at large, or as a model for other fields to perform similar analyses.

CiteSeerX is a digital library search engine providing access to more than five million scholarly documents with nearly a million users and millions of hits per day. These AI technologies have been developed by CiteSeerX group members over the past 5–6 years. We also present AI technologies implemented in table and algorithm search, which are special search modes in CiteSeerX. While it is challenging to rebuild a system like CiteSeerX from scratch, many of these AI technologies are transferable to other digital libraries and/or search engines.