Was Da Vinci born in Florence? Does patient'Johnson' really have 300 heart-beats per minute? Checking the accuracy of facts is vital, for question answering, data cleaning, anomaly detection, fraud detection, and more. The emphasis is on the intuition behind each method, as well as on a practitioner's guide, highlighting the applicability of each method to each setting. A B.Sc. in computer science should suffice.

Summary: IBM's Watson QAM (Question Answering Machine), famous for its 2011 Jeopardy win was supposed to bring huge payoffs in healthcare. Instead both IBM and its Watson Healthcare customers are rapidly paring back these projects that have largely failed to pay off. Watson was the first big out-of-the-box commercial application in ML/AI. I'm sure I'm leaving out many other notable firsts that IBM has scored but since it's Watson we want to talk about, we'll stop there. The remarkable thing about Watson is that in 2011 the other skills that we think of as AI, image and video processing, facial recognition, text and speech processing, game play beyond chess, autonomous vehicles, all these were so primitive they were not yet close to commercial acceptance and wouldn't be for several more years.

The component parts of a successful search engine optimization (SEO) strategy may have remained relatively constant, but their definition and purpose have changed entirely. Driven by trends like visual search and voice search, the industry's scope has expanded and evolved into something more dynamic. This delivers on a genuine consumer need. According to a report from Slyce.it, 74 percent of shoppers report that text-only search is insufficient for finding the products they want. It is unsurprising that Gartner research predicts that by 2021, early adopter brands that redesign their websites to support visual and voice search will increase digital commerce revenue by as much as 30 percent.

Humans gather information by engaging in conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning. We evaluate strong conversational and reading comprehension models on CoQA. The best system obtains an F1 score of 65.1%, which is 23.7 points behind human performance (88.8%), indicating there is ample room for improvement. We launch CoQA as a challenge to the community at http://stanfordnlp.github.io/coqa/

That's the message of a big Wall Street Journal post-mortem on Watson, the IBM project that was supposed to turn IBM's computing prowess into a scalable program that could deliver state-of-the-art personalized cancer treatment protocols to millions of patients around the world. Watson in general, and its oncology application in particular, has been receiving a lot of skeptical coverage of late; STAT published a major investigation last year, reporting that Watson was nowhere near being able to live up to IBM's promises. After that article came out, the IBM hype machine started toning things down a bit. But while a lot of the problems with Watson are medical or technical, they're deeply financial, too. IBM is shrinking: In 2011, when the company first introduced the idea that Watson might be able to one day cure cancer, its revenues were $107 billion. They've gotten smaller every year since, ending up at $79 billion in 2017.

Business leaders understand the advantage of using the power of artificial intelligence and machine learning to stay ahead of their competitors. However, understanding the power of AI is a lot different than actually successfully implementing it in companies. For example, in 2017, Gartner estimated that Big Data projects have a success rate of only 15%. While organizational factors may be a primary reason for this poor success rate, another reason for such a high failure rate could be due to a lack of AI / Machine Learning talent needed to successfully pursue these types of projects. Specifically, it's been shown that there is a lack of advanced machine learning talent among data professionals; less than 20% of surveyed data professionals said they were competent in such areas as Natural Language Processing (19%), Recommendation Engines (14%), Reinforcement Learning (6%), Adversarial Learning (4%) and Neural Networks – RNNs (15%).

We warmly invite you to read the presentation given to the INSEAD alumni à BCG Paris site to understand what is going on!.

I've been using Jupyter Notebooks with great delight for many years now, mostly with Python, and it's validating to see that their popularity keeps growing, both in academia and the industry. I do have a pet peeve though, which is the lack of a first-class visual debugger similar to these available in other IDEs like Eclipse, IntelliJ, or Visual Studio Code. Some would rightfully point out that Jupyter already supports pdb for simple debugging, where you can manually and sequentially enter commands to do things like inspect variables, set breakpoints, etc. -- and this is probably sufficient when it comes to debugging simple analytics. To raise the bar, the PixieDust team is happy to introduce the first (to the best of our knowledge) visual Python debugger for Jupyter Notebooks. As advertised, the PixieDebugger is a visual Python debugger built as a PixieApp, and includes a source editor, local variable inspector, console output, the ability to evaluate Python expressions in the current context, breakpoints management, and a toolbar for controlling code execution.

You've got to love the idea of IBM Watson. The super-computer using advanced AI to learn everything, faster and better than any human being could ever hope to do. The hope is it would help us solve some of our most pressing problems. One of IBM's (IBM) high-profile challenges was their desire to cure cancer. Unfortunately, it has not happened.

We study the problem of finite ontology mediated query answering (FOMQA), the variant of OMQA where the represented world is assumed to be finite, and thus only finite models of the ontology are considered. We adopt the most typical setting with unions of conjunctive queries and ontologies expressed in description logics (DLs). The study of FOMQA is relevant in settings that are not finitely controllable. This is the case not only for DLs without the finite model property, but also for those allowing transitive role declarations. When transitive roles are allowed, evaluating queries is challenging: FOMQA is undecidable for SHOIF and only known to be decidable for the Horn fragment of ALCIF. We show decidability of FOMQA for three proper fragments of SOIF: SOI, SOF, and SIF. Our approach is to characterise models relevant for deciding finite query entailment. Relying on a certain regularity of these models, we develop automata-based decision procedures with optimal complexity bounds.