Even though Samsung has established a cause for those Galaxy Note 7 flare-ups, the device's story is not over. Korean outlet Hankyung reports that the company will sell the "refurbished" phones, but with smaller, less-explodey batteries inside. It doesn't sound like the devices will be returning to US or European markets (it's tough to imagine regulators reversing course on bans after the first recall and reissue), but they could be sold in India or Vietnam instead. According to the report, Samsung has some 2.5 million Galaxy Note 7s left over after using 20,000 or so up in testing to determine the cause of the problem. The refurbished devices will have new cases, and batteries with a capacity between 3,000 and 3,200mAh (the phones initially contained a 3,500mAh battery).

Persuasion is an activity that involves one party (the persuader) trying to induce another party (the persuadee) to believe or do something. For this, it can be advantageous forthe persuader to have a model of the persuadee. Recently, some proposals in the field of computational models of argument have been made for probabilistic models of what the persuadee knows about, or believes. However, these developments have not systematically harnessed established notions in decision theory for maximizing the outcome of a dialogue. To address this, we present a general framework for representing persuasion dialogues as a decision tree, and for using decision rules for selecting moves. Furthermore, we provide some empirical results showing how some well-known decision rules perform, and make observations about their general behaviour in the context of dialogues where there is uncertainty about the accuracy of the user model.

It's scary enough making a doctor's appointment to see if a strange mole could be cancerous. Imagine, then, that you were in that situation while also living far away from the nearest doctor, unable to take time off work and unsure you had the money to cover the cost of the visit. In a scenario like this, an option to receive a diagnosis through your smartphone could be lifesaving. A dermatologist uses a dermatoscope, a type of handheld microscope, to look at skin. Computer scientists at Stanford have created an artificially intelligent diagnosis algorithm for skin cancer that matched the performance of board-certified dermatologists.

It's scary enough making a doctor's appointment to see if a strange mole could be cancerous. Imagine, then, that you were in that situation while also living far away from the nearest doctor, unable to take time off work and unsure you had the money to cover the cost of the visit. In a scenario like this, an option to receive a diagnosis through your smartphone could be lifesaving. A dermatologist uses a dermatoscope, a type of handheld microscope, to look at skin. Computer scientists at Stanford have created an artificially intelligent diagnosis algorithm for skin cancer that matched the performance of board-certified dermatologists.

A US study ran a head-to-head comparison between doctors and a series of symptom checkers using what are known as clinical vignettes. Clinical vignettes have been used for many years to help hone trainee doctors' diagnostic skills. They are essentially diagnostic puzzles based on real-life case reports designed to test training and clinical knowledge. The researchers provided 45 clinical vignettes to more than 200 doctors. They found doctors were twice as likely to diagnose accurately first time compared with online symptom-checking applications.

When a clogged artery landed Peter Szolovits in the hospital for a coronary bypass operation in mid-October, he noticed a few incongruities other patients might not have. Machines that performed intertwined functions--dosing and delivering medication, for example--did not communicate with one another, and patient statistics detailed on paper were not in the hospital's electronic medical records. As head of the Massachusetts Institute of Technology's Clinical Decision Making Group, which works to apply artificial intelligence (AI) to medicine, Szolovits knew that intelligent systems could optimize care by working together better to eliminate errors as well as avoid repetition of medical tests. Indeed, in the midst of the U.S. health care debate, some experts say that AI could lift some of the burden on physicians by helping them diagnose conditions and choose treatments. Of course, the same claim echoed in the 1970s and 1980s, when a media blitz put medical AI on the cover of newsweeklies.

The London Ambulance Service is investigating whether computer failure in the early hours of New Year's Day may have contributed to the death of a patient. BBC News can reveal that at least one 999 patient died during the period that the computers were down. A major investigation is being carried out to determine the cause of the problems and the full clinical impact. The Care Quality Commission said it would inspect the trust next month. The computer-aided dispatch system, which logs emergencies and allocates ambulances, failed just after midnight.

In this article we will discuss the machine learning method called "decision trees", moving quickly over the usual "how decision trees work" and spending time on "why decision trees work." We will write from a computational learning theory perspective, and hope this helps make both decision trees and computational learning theory more comprehensible. The goal of this article is to set up terminology so we can state in one or two sentences why decision trees tend to work well in practice. Newcomers to data science are often disappointed to learn that the job of the data scientist isn't tweaking and inventing new machine learning algorithms. In the "big data" world supervised learning has been a solved problem since at least 1951 (see [FixHodges1951] for neighborhood density methods, see [GordonOlshen1978] for k-nearest neighbor and decision tree methods).

More than six billion dollars: That's how much health care providers and consumers will be spending every year on artificial intelligence tools by 2021--a tenfold increase from today--according to a new report from research firm Frost & Sullivan. AI will be everywhere--from diagnosing cancer to providing weight-loss coaching, says Venkat Rajan, who has the great title of global director for the company's Visionary Healthcare Program. "Prior to 2015, most of what was happening was sort of academic: pilot programs, exploratory, proof of concept-type stuff," he says. AI's ability to sort through scads of information, and remember everything it has ever seen, could enable a digital (and congenial) version of Dr. House, the brilliant diagnostician from the eponymous TV show, says Rajan. "At first, it's a complete mystery, it could be one of ten different things," he says, about the process in the show, and real life, called differential diagnosis. "And then he's able to sort through various issues, you know, illuminate certain factors on why it's not one of these other conditions, and he's able to pull something from memory that figures out ultimately what it is, and they can provide the appropriate treatment." Robots won't steal doctors' jobs, says Rajan, but they will spare overworked docs some of the dangerous fatigue that can lead to mistakes.

Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and model interpretability. With the emergence of big data, there is an increasing need to parallelize the training process of decision tree. However, most existing attempts along this line suffer from high communication costs. In this paper, we propose a new algorithm, called \emph{Parallel Voting Decision Tree (PV-Tree)}, to tackle this challenge. After partitioning the training data onto a number of (e.g., $M$) machines, this algorithm performs both local voting and global voting in each iteration. For local voting, the top-$k$ attributes are selected from each machine according to its local data. Then, the indices of these top attributes are aggregated by a server, and the globally top-$2k$ attributes are determined by a majority voting among these local candidates. Finally, the full-grained histograms of the globally top-$2k$ attributes are collected from local machines in order to identify the best (most informative) attribute and its split point. PV-Tree can achieve a very low communication cost (independent of the total number of attributes) and thus can scale out very well. Furthermore, theoretical analysis shows that this algorithm can learn a near optimal decision tree, since it can find the best attribute with a large probability. Our experiments on real-world datasets show that PV-Tree significantly outperforms the existing parallel decision tree algorithms in the tradeoff between accuracy and efficiency.