The folks over at x.ai – creators of Amy, the artificial intelligence answer to scheduling meetings – have had a shot at showing exactly what it looks like inside their bot's brain, using AI, of course. The team used a powerful deep-learning model, a Recurrent Neural Network (RNN), to trawl 500,000 words in its database, looking at their sequence in a sentence to understand what they mean, then predicting how to categorize them. Get your company on stage at TNW Europe. Without a human ever telling the RNN the definitions of different word groups, it has managed to understand that Stanford is different from Instagram, and that Jesse, Luke and Jason are names. This data was cut to down to the 3,500 most frequently used words and has then been projected into a 2D shape in order to show the relationships the AI has made between different words.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

This week in big data we've got an acquisition by Hadoop distributor Cloudera, what Nvidia's CEO thinks of the state of AI, news out of the Adobe Summit 2016, Google's machine learning pitch, and more. Plus, we've got a quick look at a new book about applying statistics and analytics to college basketball. Let's start with the news from Cloudera. This week the company quietly acquired Sense, a big data cloud platform that lets data scientists collaborate with each other. "We launched Sense with the mission of helping data scientists and data engineers focus on what's important -- extracting value rather than managing infrastructure," wrote Sense founders Tristan Zajonc and Anand Patil in a blog post.

Here we discuss general applications of statistical models, whether they arise from data science, operations research, engineering, machine learning or statistics. We do not discuss specific algorithms such as decision trees, logistic regression, Bayesian modeling, Markov models, data reduction or feature selection. Instead, I discuss frameworks - each one using its own types of techniques and algorithms - to solve real life problems. Most of the entries below are found in Wikipedia, and I have used a few definitions or extracts from the relevant Wikipedia articles, in addition to personal contributions. Spatial dependency is the co-variation of properties within geographic space: characteristics at proximal locations appear to be correlated, either positively or negatively. Methods for time series analyses may be divided into two classes: frequency-domain methods and time-domain methods.

Machine learning explores the study and construction of algorithms that learn and make predictions based on data. In the field of machine learning, data scientists, who specialize in analyzing data, are responsible for writing and modifying such algorithms. Initially, a data scientist writes an algorithm based on a set of data features. This is generally an iterative process in which the data scientist explores different algorithms for predictive purpose. In this process, the amount of data and the number of features chosen for analysis may change.

One of the biggest issues in the presidential race is voter anger over lost middle-income jobs, real and perceived damage from trade deals, and rising inequality. But none of the candidates is talking about the elephant pushing its way into the room: a new wave of job-eating information technology, advanced automation, robots and artificial intelligence. The elites have been discussing what's coming for some time, notably a 2014 speech by Eric Schmidt, the executive chairman of Google's parent Alphabet. Huge numbers of middle-class jobs were going to be automated, and few new positions would replace them. He called it the "defining" issue of the next two or three decades. A study from the previous year by Carl Benedikt Frey and Michael Osborne examined the vulnerability of more than 700 occupations.

There's a big difference between what a person wants and what they need. On one hand we need healthy food, a good night's rest, and decent medical care. But a little voice inside our heads has us craving dinner at Gordon Ramsay's, an overnight stay at the Ritz Carlton, and a spa weekend at the St. Regis in Aspen to fix whatever is wrong. The same is true with countries. There's a big difference between what a country wants and what it needs.

On Wednesday, when Microsoft had a much rosier view of humanity than it does now, the software giant released a "Millennial chatbot" to Twitter named Tay. She was supposed to mimic 18-to-24-year-olds, learn from her interactions, and develop a personality like her peers over time. This went exactly how you would've expected it to. Like most 19-year-olds on Twitter for 24 hours with no supervision, Tay had become a white supremacist Holocaust denier who believes that "Ted Cruz is the Cuban Hitler." Microsoft had to take the thing behind the server racks and shoot it Thursday morning.

Very interesting article.I'd like to add that from a risk management perspective the median is not the only relevant quantile. Here, also best and - especially worst - cases like the 5% and 95% are important. I did not see anything like that in ML yet, but I suppose the information could easily be obtained from the residuals. This quantile information is (still) the most important one for banks and insurers when they calculate figures like Value at Risk and Economic Capital and should be also useful for everyone who wants to minimize risks.

In terms of robot karma, Boston Dynamics is really asking for it. The Massachusetts company posted a new video this week in which engineers trip, shove, tease and otherwise bully the latest iteration of their decidedly creepy humanoid robot named Atlas. The new video follows an earlier clip in which Boston Dynamics staffers kick around around a robotic canine named Spot. I don't know if it's some kind of r what, but the robot abuse is oddly disturbing to watch. Of course, all the shoving and poking has a purpose -- the interactions are intended to demonstrate the system's ability to react to unanticipated events.