Tay isn't the first chatbot in history but it became the most prominent because of its ties with Microsoft. Then it became one of the most notorious chatbot in less than 24 hours after it switched from well-meaning teen to offensive, pro-Nazi, anti-feminist rebel. Naturally, Microsoft shut it down, "putting it to sleep", so to speak. Now the company has come out with a statement clarifying that Tay's words do not reflect the company's principles and values at all. They do own up to the "slight" oversight in protecting Tay from attacks.

In April last year, the Chinese scientist announced they have successfully used CRISPR/Cas9 technology to change human embryo's genome. It sparked a worldwide discussion on designer babies. A discussion, that is still going on. The crazy and ambitious guys at DARPA are thinking how to increase the neural plasticity of the brain to increase the rate of learning "beyond normal levels", reducing the time it is needed to master foreign languages or other skills. Nowadays, even not having one arm is not a good excuse to not hit the gym.

An important step in data analysis is data exploration and representation. In this tutorial we will see how by combining a technique called Principal Component Analysis (PCA) together with Cluster Analysis we can represent in a two-dimensional space data defined in a higher dimensional one while, at the same time, being able to group this data in similar groups or clusters and find hidden relationships in our data. More concretely, PCA reduces data dimensionality by finding principal components. These are the directions of maximum variation in a dataset. By reducing a dataset original features or variables to a reduced set of new ones based on the principal components, we end up with the minimum number of variables that keep the maximum amount of variation or information about how the data is distributed. If we end up with just two of these new variables, we will be able to represent each sample in our data in a two-dimensional chart (e.g. a scatterplot). As an unsupervised data analysis technique, clustering organises data samples by proximity based on its variables.

Top Machine Learning & Data Mining Books - for this post, we have scraped various signals (e.g. We have combined all signals to compute a Quality Score for each book and publish the list of top Machine Learning and Data Mining books. The readers will love the list because it is data-driven & objective. This book is very well rated on Amazon website and is written by three professors from USC, Stanford and University of Washington. The three authors: Gareth James, Daniela Witten, & Trevor Hastie all have backgrounds in statistics.

An Iranian charged with hacking the computer system that controlled a New York dam used a readily available Google search process to identify the vulnerable system, according to people familiar with the federal investigation. The process, known as "Google dorking," isn't as simple as an ordinary online search. Yet anyone with a computer and Internet access can perform it with a few special techniques. Federal authorities say it is...

This paper tackles the problem of endogenous link prediction for knowledge base completion. Knowledge bases can be represented as directed graphs whose nodes correspond to entities and edges to relationships. Previous attempts either consist of powerful systems with high capacity to model complex connectivity patterns, which unfortunately usually end up overfitting on rare relationships, or in approaches that trade capacity for simplicity in order to fairly model all relationships, frequent or not. In this paper, we propose Tatec, a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and then combined. We present several variants of this model with different kinds of regularization and combination strategies and show that this approach outperforms existing methods on different types of relationships by achieving state-of-the-art results on four benchmarks of the literature.

Estimating divergences in a consistent way is of great importance in many machine learning tasks. Although this is a fundamental problem in nonparametric statistics, to the best of our knowledge there has been no finite sample exponential inequality convergence bound derived for any divergence estimators. The main contribution of our work is to provide such a bound for an estimator of R\'enyi-$\alpha$ divergence for a smooth H\"older class of densities on the $d$-dimensional unit cube $[0, 1]^d$. We also illustrate our theoretical results with a numerical experiment.

Qatar Computing Research Institute (QCRI), one of Hamad Bin Khalifa University's (HBKU's) national research institutes, has partnered with Qatar Living, a rich source of non-official information on Qatar, to incorporate natural language understanding technology on the Qatar Living website. QCRI's Arabic Language Technology Group has worked on the advancement of the technology for over two years. In collaboration with a team of engineers from Qatar Living, the development is now ready for industry application, allowing visitors to the website the ability to easily and quickly find answers to their questions on the forum to save time. QCRI joined forces with researchers from the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory (MIT CSAIL)to develop novel algorithms for language processing on the data provided by Qatar Living. "The collaboration with Qatar Living provides an excellent opportunity to demonstrate our technology, and acquire insights from these kinds of real-world applications," said Dr Alessandro Moschitti, principal scientist at QCRI and principal investigator on the QCRI side of the project.

Google's artificial intelligence program AlphaGo's overwhelming win over South Korean go grandmaster Lee Sedol in a five-game tournament this month has shown that machine intelligence is rapidly evolving and underlined the possibility that it will catch up with and eventually surpass human intelligence. The time has come for us to think how best to use AI in ways that will contribute to -- and not detract from -- our well-being. In the tournament held in Seoul, the program built by a Google subsidiary DeepMind defeated Lee, a 33-year-old 9-dan professional go player with 18 world titles, in a 4-1 victory. Google had chosen Lee as an opponent in view of his impressive records, considering him as the world's strongest player of the board game. The outcome has stunned go players, professional programmers and the public alike -- given that experts had previously expected it would take more than 10 years for an AI program to beat a world-class professional go player.

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.