We propose a novel accelerated exact k-means algorithm, which performs better than the current state-of-the-art low-dimensional algorithm in 18 of 22 experiments, running up to 3 times faster. We also propose a general improvement of existing state-of-the-art accelerated exact k-means algorithms through better estimates of the distance bounds used to reduce the number of distance calculations, and get a speedup in 36 of 44 experiments, up to 1.8 times faster. We have conducted experiments with our own implementations of existing methods to ensure homogeneous evaluation of performance, and we show that our implementations perform as well or better than existing available implementations. Finally, we propose simplified variants of standard approaches and show that they are faster than their fully-fledged counterparts in 59 of 62 experiments.

Consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes. In the framework of the COSA method of Friedman and Meulman, subsequently improved in the form of the Sparse K-means method of Witten and Tibshirani, a natural and simpler hill-climbing approach is introduced. The new method is shown to be competitive with these two methods and others. Keywords: Sparse Clustering, Hill-climbing, High-dimensional, Feature Selection 1. Introduction Consider a typical setting for clusteringn items based on pairwise dissimilarities, withδ(i,j) denoting the dissimilarity between itemsi,j [n ] {1,...,n } . For concreteness, we assume thatδ(i,j) 0 and δ(i,i) 0 for all i,j [n ] . In principle, if we want to delineateκ clusters, the goal is (for example) to minimize the average within-cluster dissimilarity. Let C n κ denote the class of clusterings ofn items intoκ groups. For C C n κ, its average within-cluster dissimilarity is defined as [C ] k [κ ] 1 C 1 (k) i,j C 1 (k)δ(i,j). If under the Euclidean setting, we further define cluster centers µ k 1 n i C 1 (k)x i with k [κ ], (2) then the within-cluster dissimilarity can be rewritten as follows, [C ] k [κ ] 1 C 1 (k) i,j C 1 (k) x i x j 2 k [κ ] i C 1 (k) x i µ k 2 . The resulting optimization problem is the following: Given (δ(i,j) i,j [n ]), minimize [C ] over C C n κ .

Google has reached a milestone in its DeepMind artificial intelligence (A.I.) project with the successful development of technology that can mimic the sound of human voice. Dubbed as WaveNet, the breakthrough was described as a deep neural network that can generate raw audio wave forms to generate speech. It can reportedly beat existing Text-to-Speech systems. According to researchers in the Britain-based WaveNet unit, the gap in human performance, which could be demonstrated in an actual A.I. -- human conversation -- is reduced by as much as 50 percent. What is also interesting about the WaveNet technology is that it is capable of learning different voices and speech patterns to the point that it can even simulate mouth movements and artificial breaths in addition to emotions, language inflections and accents.

Apologizing for delays and an "unusually difficult couple of weeks," Elon Musk tweeted Saturday that he finally will provide more details about promised updates to Tesla's Autopilot program. The Tesla CEO said he will do an hourlong Q&A with reporters, then publish information about Autopilot on Sunday to the company's blog. Musk had said on Aug. 31 that "major improvements" to Autopilot were coming and would be announced that day. But the next morning, a SpaceX rocket exploded in a fireball on the launch pad, putting the announcement on hold. Musk is the chief executive of both Tesla and SpaceX.

This is the last of three articles about performance measures and graphs for binary learning models in Azure ML. Binary learning models are models which just predict one of two outcomes: positive or negative. These models are very well suited to drive decisions, such as whether to administer a patient a certain drug or to include a lead in a targeted marketing campaign. This final article will cover the threshold setting, and how to find the optimal value for it. As you will learn, this requires a good understanding of error cost, that is, the cost of inaccurate predictions.

Artificial intelligence (or AI) has permeated most facets of our lives. Algorithms suggest our social media mates. But could the arrival of the robots be applied to education? Jozef Misik, managing director of Knowble, a language tech start-up whose products are built on AI, believes so: "Most educational technology products will have an AI or deep learning component in future," he says. Already, AI is able to address common learning challenges.

"AI ...surely will be a trend at least on the size of big data. It almost certainly will be a trend on the size of mobile. It might be a trend on the size of the internet. And maybe, just maybe, it'll be a trend on the size of software; that the software before machine intelligence and after will be two worlds that are very different from each other." It's undeniable that artificial intelligence (AI) is one of tech's hottest topics and a trend that is permeating every part of our our world.

I don't think the future of VR looks like video games; I don't think it looks like cinematic VR; I think it looks like stories from our real lives. It's the most amazing afternoon you've ever had. For one person, it might be what we call a rom-com, for another it might be an action movie. For another, it might be something we don't have a movie genre preexisting for. It might be just exploring.

Infosys is a global leader in consulting, information technology, outsourcing and next-generation services with clients in more than 50 countries. With 9.02 billion in Q2 FY16 revenues and more than 193,000 employees, the Indian multinational is helping enterprises redefine their present and future in a world where innovative solutions in mobility, sustainability, big data and cloud computing are required. Founded in 1981 by seven engineers with 250, Infosys is the second-largest Indian IT services company by 2016 revenues and was the fifth largest employer of H-1B visa professionals in the US in 2013. America is also home to its Global Head of Digital, Scott Sorokin, who has been a strategist and digital partner for senior-level executives at Fortune 100 companies for over 25 years. Formerly the the chief strategy officer at Publicis.Sapient/Razorfish and Rosetta, the New York-based Sorokin combines CXO-level business strategy, technology and marketing experience in a fast-changing global market to spur digital innovation at Infosys.

Artificial intelligence (AI) is poised to have a transformative effect on consumer, enterprise, and government markets around the world. An umbrella term that refers to information systems inspired by biological systems, AI encompasses multiple technologies including machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI. According to a new report from Tractica, these technologies have use cases and applications in almost every industry and promise to significantly change existing business models while simultaneously creating new ones. The market intelligence firm forecasts that annual worldwide AI revenue will grow from 643.7 million in 2016 to 36.8 billion by 2025. In sizing and forecasting the total global AI market, Tractica has identified 191 real-world use cases for AI, organized into 27 different industry sectors and corresponding with six major technology categories, plus multiple combinations of technologies.