Report on the 24th International Conference on Case-Based Reasoning Research and Development (ICCBR-2016)

Computational analogy and case-based reasoning (CBR) are closely related research areas. Both employ prior cases to reason in complex situations with incomplete information. Analogy research often focuses on modeling human cognitive processes, the structural alignment between a base/source and target, and adaptation/abstraction of the analogical source content. While CBR research also deals with alignment and adaptation, the field tends to focus more on retrieval, case-base maintenance, and pragmatic solutions to real-world problems. However, despite their obvious overlap in research goals and approaches, cross communication and collaboration between these areas has been progressively diminishing. CBR and computational analogy researchers stand to benefit greatly from increased exposure to each other's work and greater cross-pollination of ideas. The objective of this workshop is to promote such communication by bringing together researchers from the two areas, to foster new collaborative endeavors, to stimulate new ideas and avoid reinventing old ones.

Pablo Gervás's talk, How Creative Can Reuse Be? pointed up CBR as a favored The main conference program comprised 31 contributions between presentations and posters from 144 authors on technical and applied CBR papers. The origins of the Conference on Case-Based Reasoning The accepted papers were of very high quality, and date from the first European workshop on provided many new insights across a wide range of CBR (EWCBR) held in Kaiserslautern, Germany, in CBR issues. Topics in recent CBR research included in 1993. Since then many European and international the presentations and discussions at ICCBR 2016 conferences on CBR have been held in different parts included novel approaches to similarity and retrieval; of the world. The European conference on CBR advances in adaptation strategies; case generation; representation and knowledge discovery; CBR as a (ECCBR) and the International Conference on CBR cognitive approach to big data; AI with large-scale (ICCBR) were held in alternating years.

This demo shows how to control a Smart Home using IBM Watson and Augmented Reality technologies.

Last week, hundreds of digital health entrepreneurs, investors, and executives met in Boston for the annual Digital Healthcare Innovation Summit. Among the more frequently discussed trends and technologies was artificial intelligence -- its promise, its stumbles, and how it should be implemented to best serve healthcare. "I started marketing Watson for Oncology in January of 2016. I'm almost approaching two years," Deborah DiSanzo, general manager at IBM Watson Health, said during a session. "By the end of this year, I will have over 20,000 patients [and] over 120 hospitals using it, and really seeing helping oncologists all over the world. Nothing that I have done in my life in healthcare technology has gone as fast as that, and that is not hype."

Since its creation, artificial intelligence (AI) has found use in many different industries, including healthcare. The amount of medical data is astronomically huge and the problem of systematizing, storing, and, above all, using such data is of the utmost importance. People have long hoped that someday, computers will make accurate diagnoses and eliminate medical errors. But no one has created an effective AI doctor yet. The Skychain project promises to revolutionize the healthcare industry, using AI and blockchain technology.

Before scientists can effectively capture and deploy fusion energy, they must learn to predict major disruptions that can halt fusion reactions and damage the walls of doughnut-shaped fusion devices called tokamaks. Today, researchers at the U.S. Department of Energy's Princeton Plasma Physics Laboratory (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability....

Another important distinction is Skychain's use of distributed computing technologies based on the blockchain principles. Many thousands of crypto miners will provide their computational resources to Skychain to get a reward each time an independent neural network performs calculations at the request of an end user, or each time a neural network is trained at the request of its developer. It means that Skychain's end users and neural network developers won't need to bother about obtaining any special hardware or computational resources.

Machine learning has been successfully applied to demand planning, but leading suppliers of supply chain planning are beginning to work on using machine learning to improve production planning. But architecturally and culturally, this is a much tougher problem than machine learning applied to demand planning. In the $2 billion-plus supply chain planning market, ARC Advisory Group's latest market study shows production planning as being a critical application SCP solution representing over 25 percent of the total market. Production planning applications are used for both planning daily production at a factory to creating weekly or monthly plans to divvy up the production tasks that need to be accomplished across multiple factories. Machine learning is a form of continuous improvement.

Machine learning has been successfully applied to demand planning, but leading suppliers of supply chain planning are beginning to work on using machine learning to improve production planning. But architecturally and culturally, this is a much tougher problem than machine learning applied to demand planning. In the $2 billion-plus supply chain planning market, ARC Advisory Group's latest market study shows production planning as being a critical application SCP solution representing over 25 percent of the total market. Production planning applications are used for both planning daily production at a factory to creating weekly or monthly plans to divvy up the production tasks that need to be accomplished across multiple factories. Machine learning is a form of continuous improvement.