Doctors often rely on their past experience in order to diagnose patients. For a doctor with enough experience, almost every patient would have similarities to key cases seen in the past, and each new patient could be viewed as a mixture of these key past cases. Because doctors often tend to reason this way, an efficient computationally aided diagnostic tool that thinks in the same way might be helpful in locating key past cases of interest that could assist with diagnosis. This article develops a novel mathematical model to mimic the type of logical thinking that physicians use when considering past cases. The proposed model can also provide physicians with explanations that would be similar to the way they would naturally reason about cases. The proposed method is designed to yield predictive accuracy, computational efficiency, and insight into medical data; the key element is the insight into medical data - in some sense we are automating a complicated process that physicians might perform manually. We finally implemented the result of this work on two publicly available healthcare datasets, for (1) heart disease prediction and (2) breast cancer prediction.

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.

Extracting characteristics from the training datasets of classification problems has proven effective in a number of meta-analyses. Among them, measures of classification complexity can estimate the difficulty in separating the data points into their expected classes. Descriptors of the spatial distribution of the data and estimates of the shape and size of the decision boundary are among the existent measures for this characterization. This information can support the formulation of new data-driven pre-processing and pattern recognition techniques, which can in turn be focused on challenging characteristics of the problems. This paper surveys and analyzes measures which can be extracted from the training datasets in order to characterize the complexity of the respective classification problems. Their use in recent literature is also reviewed and discussed, allowing to prospect opportunities for future work in the area. Finally, descriptions are given on an R package named Extended Complexity Library (ECoL) that implements a set of complexity measures and is made publicly available.

Hollywood producer Harvey Weinstein is seeking to get the criminal case against him thrown out of court. On Friday, his lawyers filed a defence motion citing dozens of "warm" emails they say Mr Weinstein received from one of his accusers after an alleged rape. His team argue prosecutors should have shared the evidence with the Grand Jury that indicted him. Mr Weinstein has pleaded not guilty to six charges involving three different women. The accuser in question has retained her anonymity.

Nothing kills a bad idea faster than good advertising. Yet, the diffusion of information into a system can be essential--especially in medicine. So the balance between the kind of stuff that "sticks to the roof of your customer's brain" and valuable information can be tricky and even contradictory. For most of us, the introduction of Watson's skill set wasn't as a peer-reviewed paper published in a top academic journal--it was a guy name Ken Jennings and the popular TV game show Jeopardy. After a winning streak of 74 shows, Jennings took on IBM Watson and the rest is history.

Today's topics include Google improving its cloud platform security and introducing new IoT options, and Apple issuing a software update to fix a throttling glitch in its new MacBook Pros. At its Google Cloud Next conference on July 25, Google declared security as the top concern of enterprise customers. To that end, Garrick Toubassi, Google's vice president of engineering for G Suite, said his company is using machine learning and artificial intelligence to block so-called "bad messages" and display a warning for suspicious messages. Google is also adding a confidential mode that lets users add restrictions to email. Also at the conference, Google announced an enterprise version of Google Voice to be integrated with G Suite, which lets administrators manage users, provision and port phone numbers, access detailed reports and set up call routing functionality.