AT A time when the country's Information Technology industry is forced to carry out mass layoffs, experts argue that Artificial Intelligence (AI) and Data Science sectors offer a glimmer of hope. While it is feared that IT jobs are being lost to robots, Y S Chowdary, Minister of State for Science & Technology, told The Indian Express that the country lacks skilled personnel in the fields of Data Analytics and Artificial Intelligence. "Softwares are evolving and we need new kinds of skills. There is a huge demand-supply gap in data analytics field," said Chowdary. "Majority of government missions is based on historical data. However, today there is data available even from the unorganised population. We need to concentrate on capacity building to analyse this data to better our governance," said Chowdary, who was the chief guest at a Data Science Congress held in the city earlier this month.

Recitations from Tel-Aviv University introductory course to computer science, assembled as IPython notebooks by Yoav Ram. Exploratory Computing with Python, a set of 15 Notebooks that cover exploratory computing, data analysis, and visualization. No prior programming knowledge required. Each Notebook includes a number of exercises (with answers) that should take less than 4 hours to complete. Developed by Mark Bakker for undergraduate engineering students at the Delft University of Technology.

Some foundations of statistical science have been questioned recently, especially the use and abuse of p-values. See also this article published in FiveThirtyEight.com. Statistical tests of hypotheses rely on p-values and other mysterious parameters and concepts that only the initiated can understand: power, type I error, type II error, or UMP tests, just to name a few. Pretty much all of us have had to learn this old stuff (pre-dating the existence of computers) in some college classes. Sometimes results from a statistical test will be published in a mainstream journal - for instance about whether or not global warming is accelerating - using the same jargon that few understand, and accompanied by misinterpretations and flaws in the use of the test itself.

I have a background in Computer Science and worked with nearly every programming language on the planet. I graduated with highest distinction during my masters program. I've worked on projects ranging from Robotics, Web Apps, Mobile Apps to Embedded Systems. These courses will help you achieve your goals.

Diffusion maps are a nonlinear manifold learning technique based on harmonic analysis of a diffusion process over the data. Out-of-sample extensions with computational complexity $\mathcal{O}(N)$, where $N$ is the number of points comprising the manifold, frustrate applications to online learning applications requiring rapid embedding of high-dimensional data streams. We propose landmark diffusion maps (L-dMaps) to reduce the complexity to $\mathcal{O}(M)$, where $M \ll N$ is the number of landmark points selected using pruned spanning trees or k-medoids. Offering $(N/M)$ speedups in out-of-sample extension, L-dMaps enables the application of diffusion maps to high-volume and/or high-velocity streaming data. We illustrate our approach on three datasets: the Swiss roll, molecular simulations of a C$_{24}$H$_{50}$ polymer chain, and biomolecular simulations of alanine dipeptide. We demonstrate up to 50-fold speedups in out-of-sample extension for the molecular systems with less than 4% errors in manifold reconstruction fidelity relative to calculations over the full dataset.

EARNING a representation for nodes in a graph, also known as node embedding, has been an important tool for extracting features that can be used in machine learning problems involving graph-structured data [1]-[4]. Perhaps the most widely adopted node embedding is the one based on the eigendecomposition of the adjacency matrix or the graph Laplacian [2], [5], [6]. Recent advances in word embeddings for natural language processing such as [7] has inspired the development of analogous embeddings for nodes in graphs [3], [8]. These so-called "neural" node embeddings have been applied to a number of supervised learning problems such us link prediction and node classification and demonstrated stateof-the-art performance [3], [4], [8]. In contrast to applications to supervised learning problems in graphs, in this work we leverage the neural embedding framework to develop an algorithm for the unsupervised community discovery problem in graphs [9]-[12]. The key idea is straightforward: learn node embeddings such that vectors of similar nodes are close to each other in the latent embedding space. Then, the problem of discovering communities in a graph can be solved by finding clusters in the embedding space. We focus on non-overlapping communities and validate the performance of the new approach through a comprehensive set of experiments on both synthetic and real-world data.

Picking things up is such a fundamental skill for robots, and robots have been picking up things for such a long time, that it's sometimes difficult to understand how challenging grasping still is. Robots that are good at grasping things usually depend on high quality sensor data along with some amount of advance knowledge about the things that they're going to be grasping. Where grasping gets really tricky is when you're trying to design a system that can use standardized (and affordable) grippers and sensors to reliably pick up almost anything, including that infinitely long tail of objects that are, for whatever reason, weird and annoying to grasp. One way around this is to design grasping hardware that uses clever tricks (like enveloping grasps or adhesives) to compensate for not really knowing the best way to pick up a given object, but this may not be a long-term sustainable approach: Solving the problem in software is much more efficient and scalable, if you can pull it off. "I've been studying robot grasping for 30 years and I'm convinced that the key to reliable robot grasping is the perception and control software, not the hardware," Ken Goldberg, a professor of robotics and director of the AUTOLAB at UC Berkeley, told us this week.

Carnegie Mellon University's School of Computer Science (SCS) has launched a new initiative, CMU AI, that marshals the school's work in artificial intelligence (AI) across departments and disciplines, creating one of the largest and most experienced AI research groups in the world. "For AI to reach greater levels of sophistication, experts in each aspect of AI, such as how computers understand the way people talk or how computers can learn and improve with experience, will increasingly need to work in close collaboration," said SCS Dean Andrew Moore. "CMU AI provides a framework for our ongoing AI research and education." From self-driving cars to smart homes, AI is poised to change the way people live, work and learn. "AI is no longer something that a lone genius invents in the garage," Moore added.

You see, Mattheij decided he wanted in on the profitable cottage industry of online Lego reselling, and after placing a bunch of bids for the colorful little blocks on eBay, he came into possession of 2 tons (4,400 pounds) of Lego -- enough to fill his entire garage. As Mattheij explains in his blog post, resellers can make up to €40 ($45) per kilogram for Lego sets, and rare parts and Lego Technic can fetch up to €100 ($112) per kg. Instead of spending an eternity sifting through his own, intimidatingly large collection, Mattheij set to work on building an automated Lego sorter powered by a neural network that could classify the little building blocks. "By the end of two weeks I had a training data set of 20,000 correctly labeled images."

Carnegie Mellon University's School of Computer Science (SCS) has launched a new initiative, CMU AI, that marshals the school's work in artificial intelligence (AI) across departments and disciplines, creating one of the largest and most experienced AI research groups in the world. "For AI to reach greater levels of sophistication, experts in each aspect of AI, such as how computers understand the way people talk or how computers can learn and improve with experience, will increasingly need to work in close collaboration," said SCS Dean Andrew Moore. "CMU AI provides a framework for our ongoing AI research and education." From self-driving cars to smart homes, AI is poised to change the way people live, work and learn, Moore said. "AI is no longer something that a lone genius invents in the garage," Moore added.