In the recent years, there has been so much progress in the field of artificial intelligence. Developments in the field have produced countless innovations that help us better in understanding speech, and images, as well as improving how games are played. Now the company who has contributed a lot in this field has created a chip to keep this going. Nvidia announced their new chip, the Tesla P100. It's a chip that is designed to add more power to "deep learning".

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

Crescent Dunes looks and sounds a bit like an invention lifted from a science fiction novel. Deep in the Nevada desert more than 10,000 mirrors--each the size of a highway billboard--neatly encircle a giant 640-foot tower. It looks like it might be used to communicate with aliens in deep space. But the engineers and financiers behind the facility, located in the desert about halfway between Las Vegas and Reno, say the power plant's promise is anything but fiction. The solar power facility built and operated by the company SolarReserve can power 75,000 homes.

Bagging and Boosting are both ensemble methods in Machine Learning, but what is the key behind them? Bagging and Boosting are similar as they are both ensemble techniques, where a set of weak learners are combined to create a strong learner that obtains better performance than a single one. So, let's start from the beginning: Ensemble is a Machine Learning concept in which the idea is to train multiple models using the same learning algorithm. The ensembles take part in a bigger group of methods, called multiclassifiers where a set of hundreds or thousands of learners with a common objective are fused together to solve the problem. In the second group of multiclassifiers are the hybrid methods.

Thanks to Zalando who kindly offer to be our Host for this meeting. "With four parameters I can fit an elephant, and with five I can make him wiggle his trunk." Predicting the past is easy. With enough model complexity we can create elaborate functions that perfectly explain our training data, even the noise. The learning part of ML is all about generalizing to new situations and new data.

As I mentioned yesterday, Microsoft R Server now available for HDInsight, which means that you can now run R code (including the big-data algorithms of Microsoft R Server) on a managed, cloud-based Hadoop instance. Debraj GuhaThakurta, Senior Data Scientist, and Shauheen Zahirazami, Senior Machine Learning Engineer at Microsoft, demonstrate some of these capabilities in their analysis of 170M taxi trips in New York City in 2013 (about 40 Gb). Their goal was to show the use of Microsoft R Server on an HDInsight Hadoop cluster, and to that end, they created machine learning models using distributed R functions to predict (1) whether a tip was given for a taxi ride (binary classification problem), and (2) the amount of tip given (regression problem). The analyses involved building and testing different kinds of predictive models. Debraj and Shauheen uploaded the NYC Taxi data to HDFS on Azure blob storage, provisioned an HDInsight Hadoop Cluster with 2 head nodes (D12), 4 worker nodes (D12), and 1 R-server node (D4), and installed R Studio Server on the HDInsight cluster to conveniently communicate with the cluster and drive the computations from R. To predict the tip amount, Debraj and Shauheen used linear regression on the training set (75% of the full dataset, about 127M rows).

NEW YORK, NY - NOVEMBER 10: United States Attorney for the Southern District of New York Preet Bharara speaks at a news conference where he announced charges against three individuals for offenses related to the computer hacking of numerous financial institutions, financial news publishers, and other companies on November 10, 2015 in New York City. If companies don't adequately protect their data, cyber attacks can do a lot of damage. Fortunately, a solution to this issue can be found in new applications of the artificial intelligence for predicting hacker attacks. From the security breach leaving VTech toys vulnerable to ransomeware holding hospital records hostage, lately cyber attacks have been in the news a lot. Companies make efforts to better protect their data, but oftentimes they cannot detect that a system is compromised until it's too late.

PA's Nick Kotsis, a business intelligence expert, is quoted in an article on big data and the use of machine learning to spot anomalous behaviour. The article explains that'machine learning' techniques are being developed as data that is being captured by businesses is becoming far too expansive for humans to analyse for unusual activity. Big data systems can pick up on attacks in real time and analyse data into something meaningful that can be interpreted by businesses. The article goes on to explain that a machine learning system has the ability to alert a human to take action. Nick says: "This happens via pattern cognition allowing the system to discriminate between a typical action and an abnormality."

Cyber security is a major challenge in today's world, as government agencies, corporations and individuals have increasingly become victims of cyber attacks that are so rapidly finding new ways to threaten the Internet that it's hard for good guys to keep up with them. A group of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) are working with machine-learning startup PatternEx to develop a line of defense against such cyber threats.The team has already developed an Artificial Intelligence system that can detect 85 percent of attacks by reviewing data from more than 3.6 Billion lines of log files each day and informs anything suspicious. The new system does not just rely on the artificial intelligence (AI), but also on human input, which researchers call Analyst Intuition (AI), which is why it has been given the name of Artificial Intelligence Squared or AI2. The system first scans the content with unsupervised machine-learning techniques and then, at the end of the day, presents its findings to human analysts. The human analyst then identifies which events are actual cyber attacks and which aren't. This feedback is then incorporated into the machine learning system of AI2 and is used the next day for analyzing new logs.

HBO's Game of Thrones is notorious for killing off its major characters in various grisly ways, but it is often impossible to predict who will die next - unless you're an AI algorithm, it seems. Researchers at the Technical University of Munich (TUM) have put together a set of machine learning algorithms, which trawl through data from the books and TV show in order to predict who will be next for the chop. The team have set up a website - A Song of Ice and Data - where you can look over the list of most likely deaths, as well as looking at more in-depth statistics, such as whether men, women, lords or lowborn are more likely to die. The top two entries might not come as a surprise to many; toddler-king Tommen Baratheon has a 97 per cent probability of croaking, while there's a 96 per cent chance that his quasi-uncle Stannis Baratheon will be off to meet the Lord of Light this season. Some predictions are more surprising.