The analytics community has long been discussing whether analytics is about art or science. Analytics is more an art than a science in its ability to form conditions to drive business toward an action that is based on the confidence that the action will improve business performance. This ability to be actionable have recognized recently as the most important aspect in analytics [1]. The concept is known as Prescriptive Analytics [2] shares some similar statements with Actionable Analytics, but some meaningful differences are present as well. Classification rules plays a significant role in practical predictive analytics.

This report is my final project for the MIT Media Lab Class "Integrative Theories of Mind and Cognition" (also known as Future of AI, and New Destinations in Artificial Intelligence) in Spring 2016. Artificial Intelligence performs gradient descent. The AI field discovers a path of success, and then travels that path until progress stops (when a local minimum is reached). Then, the field resets and chooses a new path, thus repeating the process. If this trend continues, AI should soon reach a local minimum, causing the next AI winter. However, recent methods provide an opportunity to escape the local minimum. To continue recent success, it is necessary to compare the current progress to all prior progress in AI. I begin this paper by pointing out a concerning pattern in the field of AI and describing how it can be useful to model the field's behavior. The paper is then divided into two main sections. In the first section of this paper, I argue that the field of artificial intelligence, itself, has been performing gradient descent. I catalog a repeating trend in the field: a string of successes, followed by a sudden crash, followed by a change in direction. In the second section, I describe steps that should be taken to prevent the current trends from falling into a local minimum. I present a number of examples from the past that deep learning techniques are currently unable to accomplish. Finally, I summarize my findings and conclude by reiterating the use of the gradient descent model.

A market basket analysis or recommendation engine [1] is what is behind all these recommendations we get when we go shopping online or whenever we receive targeted advertising. The underlying engine collects information about people's habits and knows that if people buy pasta and wine, they are usually also interested in pasta sauces. So, the next time you go to the supermarket and buy pasta and wine, be ready to get a recommendation for some pasta sauce! A typical analysis goal when applying market basket analysis it to produce a set of association rules in the following form: IF {pasta, wine, garlic} THEN pasta-sauce The first part of the rule is called "antecedent", the second part is called "consequent". A few measures, such as support, confidence, and lift, define how reliable each rule is.

Employer wellness programs can gather medical information from employees and spouses -- so long as financial incentives or penalties don't exceed 30 percent of the annual cost for an individual in the company's group health plan, according to final rules issued by the Equal Employment Opportunity Commission Monday. Although such penalties or incentives could run into the hundreds or even thousands of dollars, the programs are considered voluntary -- and therefore legal, the commission said. The rules seek to ensure "wellness programs actually promote good health and are not just used to collect or sell sensitive medical information about employees and family members or to impermissibly shift health insurance costs to them," the EEOC said. But the final rules drew immediate concern from some groups. Jennifer Mathis, director of programs for the Bazelon Center for Mental Health Law, says the new rule rolls back protections in existing law.

How you "feel" has never been more important in tech. This year, a number of companies are showing an interest in emotions, from Apple's purchase of Emotient for interpreting emotions from facial expressions in January, to IBM's release of new Watson APIs for analyzing emotion and tone in text. At Expert System, we've always believed in the value of a deeper understanding of text. While meaning and context can tell us so much, we're leaving a lot of valuable information on the table if we're not looking at one of the key things that makes us human. So, it's natural that emotion is the next frontier for conquering, albeit one whose subtleties and nuance make it even more difficult to determine with great accuracy.

Association Rules: How do you put these into practice? I've learned how to create product association rules. What are common ways people use these association rules to make business decisions?

Link prediction in large knowledge graphs has received a lot of attention recently because of its importance for inferring missing relations and for completing and improving noisily extracted knowledge graphs. Over the years a number of machine learning researchers have presented various models for predicting the presence of missing relations in a knowledge base. Although all the previous methods are presented with empirical results that show high performance on select datasets, there is almost no previous work on understanding the connection between properties of a knowledge base and the performance of a model. In this paper we analyze the RESCAL method (Nickel et al., 2011) and show that it can not encode asymmetric transitive relations in knowledge bases.

Commonsense knowledge representation and reasoning is key for tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In this paper, we introduce a novel 3D game engine for commonsense knowledge acquisition (GECKA3D) which aims to collect commonsense from game designers through the development of serious games. GECKA3D integrates the potential of serious games and games with a purpose. This provides a platform for the acquisition of re-usable and multi-purpose knowledge, and also enables the development of games that can provide entertainment value and teach players something meaningful about the actual world they live in.

In system Z, reasoning is done with respect to a unique minimal ranking function obtained from a partitioning of the conditionals in a knowledge base. In this paper, we extend system Z-FO, a recent proposal for a system Z-like approch to first-order conditionals. We introduce the notion of tolerance pair and show how sceptical Z-FO-inference can be defined and implemented by taking all minimal tolerance pairs into account.

Subscribe to the O'Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come. In this episode, I chat with Marc Warner, CEO of ASI, a data science and business analytics consultancy and training organization in London. We talk about artificial intelligence, speculating about the future and looking at current real-world business applications of AI. We also talk about a survey Warner recently conducted with data science companies in London, where he uncovered a data scientist skills cap. We see a really interesting cross section of what people are actually doing with machine learning and artificial intelligence right now. Conventionally, software's been used for decades in the form of expert systems where humans would code in explicit sets of rules, and nowadays you just don't need to provide those rules; the machine learning algorithms can pick out the understanding themselves from the data, and it's actually much, much more effective.