High costs of treatment, long waiting time, affordability of airfares to overseas destinations and favorable exchange rate change are crucial factors related to the fast growth of Medical Tourism (Connell, 2006). Rapid development of medical infrastructure with international standards and certification, easy availability of skilled manpower bring South Asian countries like Thailand, Malaysia, and India at the forefront in this area. With current annual growth of 13.0 percent, the Indian health care sector contributes about $ 23 billion (nearly 4 percent of GDP) to the Indian economy, with'foreign exchange earning around $1.8 billion' (Chakraborty, 2006). Although research studies are abundant focusing on social impacts of Medical Tourism, there is no proper methodology for customers, both foreign and domestic, to assess the medical tourist destination in any country. The problem can be solved by taking the interest of stakeholder's in assessing the weights of a multiple criteria set, namely medical infrastructure, logistics service providers, 1 government policy along with city demography. Therefore, assessment of desirable medical destination selection and evaluation problem can be considered decision making problem with multiple attributes varying from consumer demands to resource constraints of medical related industry. In this regard, MCDM has become a very crucial area of management research and decision theory with lots of methods developed, extended and modified in solving problems in the present and past few decades.

The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180,000 randomly selected binary reactions. We show that our data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-) discovering novel transformations (even including transition-metal catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph, and because each single reaction prediction is typically achieved in a sub-second time frame, our model can be used as a high-throughput generator of reaction hypotheses for reaction discovery. Our innate ability to reason beyond established knowledge is one of the main driving forces of Science.

I often come across data science teams that are fascinated by deep learning, compressive classification, and self-driving cars, and that are eager to employ the algorithm du jour. For instance, I was recently working with a large financial institution on increasing its cybersecurity and, before we even got started with basic monitoring, a data scientist on my team was talking about k-means clustering and neural networks. We must always remember to understand the problem and opportunity first, and then apply the right system or algorithm. Sometimes self-learning neural networks may be the best alternative; however, sometimes you'll have to go with a classic: the expert system. An expert system is a rule-based engine based on the collective wisdom of experts.

This paper presents an idea of a creative expert system. It is based on inference and machine learning integration. Execution of learning algorithm is automatic because it is formalized as applying a complex inference rule. Firing such a rule generates intrinsically new knowledge: rules are learned from training data, which consists of facts stored already in the knowledge base. This new knowledge may be used in the same inference chain to derive a decision.

Governor Inslee's op-ed for CNBC published July 12, 2016 America's advantage in the knowledge-based economy is our human capital. Well-educated, highly-trained, creative-thinking people are essential to the most innovative companies and successful entrepreneurs in the world today. Skilled people are the currency of economic development for states in the 21st century. Why are nearly 95 percent of all the commercial aircraft in North America built in Washington state? Why is our Puget Sound region the cloud computing capital?

The other day, a search for "artificial intelligence in law" produced 86,400 results from just the News section of Google's vast index. From the Web as a whole, 32,800,000 results and from Videos – 261,000, beginning with Jude Law's role as Gigolo Joe in the movie A.I. (thank you, RankBrain). The first News story was "Law firm bosses envision Watson-type computers replacing young lawyers," reporting on the answers to one question in the recent Altman & Weil survey of law firm leaders (page 82). As wittily argued by Ryan McClead, "the question is flawed on many levels [and] … it's time to cut the hysteria surrounding artificial intelligence in law." But we need to parse the pile a bit.

People have always dreamed about going beyond the limitations of their bodies: the pain, illness and, above all, death. Now a new movement is dressing up this ancient drive in new technological clothes. Referred to as transhumanism, it is the belief that science will provide a futuristic way for humans to evolve beyond their current physical forms and realise these dreams of transcendence. A new movement is dressing up this ancient drive in new technological clothes. To replicate the mind digitally we would have to map each of these connections, something that is far beyond our current capabilities.

Earlier this week, I found myself answering a question from a new colleague at Finning International that relates both to the research I do in the iSchool at the University of British Columbia, as well as the analytics, engineering & technology work that I lead at Finning. The questions were simple: 1) What is artificial intelligence? As I sat to reflect last evening, it dawned on me that taking time to craft a clear answer to these questions might be extremely beneficial for many. Analytics, data science, and predictive intelligence are hot topics in many communities and business areas. And yet, despite this interest, few folks I have talked to have a clear understanding of the history of the discipline; one, that frames much of the work currently going on within the space.

Defining artificial intelligence isn't just difficult; it's impossible, not the least because we don't really understand human intelligence. Paradoxically, advances in AI will help more to define what human intelligence isn't than what artificial intelligence is. But whatever AI is, we've clearly made a lot of progress in the past few years, in areas ranging from computer vision to game playing. AI is making the transition from a research topic to the early stages of enterprise adoption. Companies such as Google and Facebook have placed huge bets on AI and are already using it in their products. But Google and Facebook are only the beginning: over the next decade, we'll see AI steadily creep into one product after another. We'll be communicating with bots, rather than scripted robo-dialers, and not realizing that they aren't human. We'll be relying on cars to plan routes and respond to road hazards. It's a good bet that in the next decades, some features of AI will be incorporated into every application that we touch and that we won't be able to do anything without touching an application. Given that our future will inevitably be tied up with AI, it's imperative that we ask: Where are we now? What is the state of AI? And where are we heading? Descriptions of AI span several axes: strength (how intelligent is it?), Each of these axes is a spectrum, and each point in this many-dimensional space represents a different way of understanding the goals and capabilities of an AI system.

We propose a general model explanation system (MES) for "explaining" the output of black box classifiers. This paper describes extensions to Turner (2015), which is referred to frequently in the text. We use the motivating example of a classifier trained to detect fraud in a credit card transaction history. The key aspect is that we provide explanations applicable to a single prediction, rather than provide an interpretable set of parameters. We focus on explaining positive predictions (alerts). However, the presented methodology is symmetrically applicable to negative predictions.