WASHINGTON – Neuroscientists acting as cartographers of the human mind have devised the most comprehensive map ever made of the cerebral cortex, the part of the brain responsible for higher cognitive functions such as abstract thought, language and memory. Using MRI images from the brains of 210 people, the researchers said on Wednesday they were able to pinpoint 180 distinct areas in the cerebral cortex, the brain's thin, wrinkly outermost layer made of so-called gray matter. These areas were present in both the left and right hemispheres of the cerebral cortex. More than half, 97 of them, were previously unknown. The researchers nailed down the specific function of some of the areas, but said they were only scratching the surface on understanding what all of the areas did.

On a previous post we learned how to train a machine learning classifier that is able to detect the different aspects mentioned on hotel reviews. With this aspect classifier, we were able to automatically know if a particular review was talking about cleanliness, comfort & facilities, food, Internet, location, staff and/or value for money. We also learned how to combine this classifier with the sentiment analysis classifier to get interesting insights and answer questions like are guests loving the location of a particular hotel but complaining about its cleanliness? These are the kind of questions we aim to answer with this tutorial and that will lead us to some interesting insights. The source code used for this process is available in this repository.

Over the course of the past year or so, there have been a number of incredible tech projects aimed at stopping the spread of Ebola. One approach that we've never come across before, however, involves plotting the possible spread of Ebola and other "filoviruses" of the same family by predicting which bat species they're most likely to be carried by. That's exactly the goal of a team of scientists, who recently used machine learning techniques to build just such a model. Their work may help prevent future spillover events in which it is important to predict which species of wildlife help spread contagion. "This work entailed collecting intrinsic features describing the world's bat species -- 1,116 species altogether -- and training a machine learning algorithm on these data to learn which features best predict the bat species that carry filoviruses," lead author of the study Barbara Han, a disease ecologist at the Cary Institute of Ecosystem Studies, tells Digital Trends.

Google tapped into the superior intelligence of its DeepMind neural network to find ways to vastly reduce the energy it uses in its data centers, which make up 40% of the worldwide Internet. "This will also help other companies who run on Google's cloud to improve their own energy efficiency," Google said in a blog about the achievement. "While Google is only one of many data center operators in the world, many are not powered by renewable energy as we are." Google has set a goal to eventually power its data centers using 100% renewable energy. Today, the company claims, renewable energy is used for 35% of its power needs.

The post highlights points raised by Yoav in his official response to the ASA statement (available as on page 4 in the ASA supplemental tab), as well as offers a list of relevant R resources. It is just as well relevant to the use of most other statistical methods: context matters, no single statistical measure suffices, specific thresholds should be avoided and reporting should not be done selectively. The latter problem is discussed mainly in relation to omitted inferences. We argue that the selective reporting of inferences problem is serious enough a problem in our current industrialized science even when no omission takes place. Many R tools are available to address it, but they are mainly used in very large problems and are grossly underused in areas where lack of replicability hits hard.

The release continues, "PhishAlarm Analyzer scans reported emails and examines them based on the attributes of the email and linguistic characteristics of the text. The emails are then prioritized, and an HTML threat report on the suspicious email is delivered to the security and incident response teams. The research report saves time for the incident response team by performing much of the research in advance so that they respond more quickly to the reported threats. By using various email threat feeds coupled with machine learning, PhishAlarm Analyzer constantly improves as it learns new patterns of email threats. PhishAlarm Analyzer is built to scan emails quickly and prioritize the threat, including identifying zero-hour phishing attacks in real time. By quickly detecting and ranking the most dangerous threats, PhishAlarm Analyzer allows incident response teams to remediate quickly and efficiently."

Earlier today, Daniel An and Pat Meenan from Google shared the results of a recent research project focused on uncovering what influences the bounce and conversion rates for e-commerce sites. Using a machine learning model developed in collaboration with SOASTA, Daniel and Pat identified that the speed and performance of a website can significantly influence the bounce rate of an e-commerce site. To put it simply: the slower and the more complex the page, the higher the bounce rate and the lower the conversion rate. This is consistent with several research studies and shows the promise that AMP adoption in e-commerce sites holds for doing business on the mobile web. To read more about Daniel and Pat's research check out their article on Think with Google.

We first ran into the predictive marketing startup Datatrics from the Netherlands at the PAPI's Connect event in Valencia earlier this year, where they competed in the first ever AI Startup Battle. The Dutch startup offers marketing teams an easy and actionable way to leverage Machine Learning with its innovative data management platform, which we believe sets a great example for other startups in showing how BigML can add to their competitive edge and supercharge their growth. So we interviewed Bas Nieland, CEO and co-founder of Datatrics to find out more. Can you tell us what was the motivation behind starting Datatrics? Bas Nieland: Nowadays digital marketers are awashed with data due to the fragmentation of consumer attention on many more channels.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Automation is the past, current and next big thing. For a long time, getting robots and software to work for us has been the Holy Grail of business. In theory it makes everything cheaper, more reliable, more powerful and it frees humans up to work on creative projects. Ever since the first industrial revolution, capitalists have looked for ways to extract human labour from the means of production and replace it with smart systems. This, of course, was initially driven by greed.