Amazon.com (AMZN) on Thursday blamed human error for an outage at its cloud-services unit that caused widespread disruption to internet traffic across the U.S. earlier this week. In a post on its website, Amazon said the outage started with a typo at Amazon's North Virginia data centers Tuesday. An employee trying to speed up the company's S3 cloud-storage billing system tried to take a few servers offline. The employee mistyped the command, however, affecting more servers than intended, which led to a cascade of failures that ultimately knocked out S3 and other Amazon services. It also took longer than expected to restart certain services, Amazon said.

Words may literally have the power to heal now. An Israeli company by the name of Beyond Verbal is launching a platform solely committed to analyzing emotions from vocal intonations. The Beyond Health Research platform is paving its way into the world of healthcare by identifying the distinctive vocal features that are, in every way, imperceptible to humans, and are referred to as vocal "biomarkers". These vocal biomarkers can serve as a diagnostic tool for your physician to indicate signs of illnesses ranging from stress and depression to cardiovascular diseases. An earlier diagnosis could essentially be the difference between life and death. This breakthrough is being backed up by 21 years of thorough research encompassing over 2.5 million emotion-labeled voices in over 40 different languages.

The last post in the Machine Learning algorithm frenzy that I'm currently on was based on Support Vector Machines. This post is going to look at another classification algorithm called a Decision Tree and again see if this can improve on our classification problem of predicting whether lines from Simpson's episodes are said by Bart or Homer. Again, a Decision Tree is a supervised learning algorithm that can be used to classify data based on a model it has built on training data. Like the other models, it tries to split data into two or more sets but the most significant variable that creates the best split is calculated by the algorithm. To distinguish between Homer and Bart from a set of images the decision tree would split the data on each of them and choose which performs best.

A decision tree is one of the widely used algorithms for building classification or regression models in data mining and machine learning. A decision tree is so named because the output resulting from it is the form of a tree structure. Consider a sample stock dataset as shown in the table below. The dataset comprises of Open, High, Low, Close Prices and Volume indicators (OHLCV) for the stock. Let us add some technical indicators (RSI, SMA, LMA, ADX) to this dataset.

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.