Vinod Iyengar is Director of Marketing at California-based developer H2O.ai. H2O.ai are the makers behind H2O, the leading open source machine learning platform for smarter applications and data products. They work across a number of mission critical applications, including predictive maintenance, operational intelligence, security, fraud, auditing, credit scoring, user based insurance, ICU monitoring and more in over 5,000 organizations. And with customers including Capital One, PricewaterhouseCoopers, Comcast, Nielsen Catalina Solutions, Macy's and Aetna – to name just a select few – they are clearly in a prominent position in this space. Vinod details some key use cases of artificial intelligence in specific industries, while also sharing H2O.ai's vision for AI and the challenges we face in adopting it… Across industries and business disciplines, businesses use artificial intelligence to increase revenue or reduce costs by performing tasks more efficiently than humans could do unaided. With more than 150 million active digital wallets than 200 billion in annual payments, PayPal leads the online payments industry.

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.

With rapid growth of medical informatics technology, a large number of electronic health records (EHRs) have been available in recent years, including a huge mass of data, such as clinical narratives. They have been being used not only to support computerized clinical systems (e.g., computerized clinical decision support systems [1][2]), but also to help the development of clinical and translational research [3]. One of the challenges to use them is that much information is embedded in clinical notes, but cannot be directly accessible for computerized clinical systems which rely on structured information. Therefore, natural language processing (NLP) technologies, which can extract structured information from narrative text, have received great attention in medical domain [4], and many clinical NLP systems have been developed for different applications [5]. Clinical concept recognition (CCR) as a fundamental task of clinical NLP has also attracted great attention, and a large number of systems have been developed to recognize clinical concepts from various types of clinical notes in last two decades.

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.

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?

Large-scale classification, where we need to classify hundreds of thousands or millions of items into thousands of classes, is becoming increasingly common in this age of Big Data… So far, however, very little has been published on how large-scale classification has been carried out in practice, even though there are many interesting questions about such cases. Today's paper is a case study on large-scale classification of products at Walmart. The requirement is to classify 10M products into 5000 categories based on fairly minimal product descriptions. Oh, and new products turn up all the time, and the set of categories is continuously evolving. Many learning solutions assume that we can take a random sample from the universe of items, manually label the sample to create training data, then train a classifier.

For years, machine learning has been used for image, video, and text recognition, as well as serving as the power behind recommendation engines. Today, it's being used to fortify cybersecurity, ensure public safety, and improve medical outcomes. It can also help improve customer service and make automobiles safer. "Machine learning allows you to look at volumes of data and do volumes of calculations that a person really can't do," said Lisa Dolev, founder and CEO of operational intelligence solutions provider Qylur, in an interview. Machine learning can identify patterns that humans tend to overlook or may be unable to find as fast in vast amounts of data.

This paper addresses the problem of detecting information about events from unstructured text. An event-detection system, DISCERN, is presented; its three variants DISCERN- R (rule-based), DISCERN-ML (machine-learned), and DISCERN-C (combined), were evaluated in the NIST TAC KBP 2015 Event Nugget Detection and Event Argument Extraction and Linking tasks. Three contributions of this work are: (a) an approach to collapsing support verb and event nominals that improved recall of argument linking, (b) a new linguist-in-the-loop paradigm that enables quick changes to linguistic rules and examination of their effect on pre- cision and recall at runtime, (c) an analysis of the synergy between the semantic and syntactic features. Results of experimentation with event-detection approaches indicate that linguistically-informed rules can improve precision and machine-learned systems can improve recall. Future refinements to the combination of linguistic and machine learning approaches may involve making better use of the complementarity of these approaches.

TokensRegex is a generic framework included in Stanford CoreNLP for defining patterns over text (sequences of tokens) and mapping it to semantic objects represented as Java objects. TokensRegex emphasizes describing text as a sequence of tokens (words, punctuation marks, etc.), which may have additional attributes, and writing patterns over those tokens, rather than working at the character level, as with standard regular expression packages. TokensRegex was used to develop SUTime, a rule-based temporal tagger for recognizing and normalizing temporal expressions. An included set of slides and the javadoc for TokenSequencePattern provide an overview of this package. Some additional information is available in some older slides.

In today's post, we dive into understanding Association Rules for Market Basket Analysis and discuss three numeric measures that should be considered before deciding to act on / make a business decision based on associations that have been observed in the data: (1) Support (2) Confidence and (3) Lift. Association rules are typically written in the format: Left hand side Implies Right hand side The left hand side is referred to as the Antecedent and the right hand side is the Consequent. The Antecedent means a thing that logically precedes another while a Consequent means a thing that follows as a result. For example, in the association rule: {Butter, Eggs} Implies {Bread} Butter and eggs are the Antecedent while Bread is the Consequent. What this rule means that if you were to pick a shopping cart at random and find butter and eggs in there, there is a chance that you are also likely to find bread.