Currently, Artificial Intelligence (AI) and Machine Learning are being used, not only as personal assistants for internet activities, but also to answer phones, drive vehicles, provide insights through Predictive and Prescriptive Analytics, and so much more. Artificial Intelligence can be broken down into two categories: Strong (also known as General or Broad) AI and Weak (Applied or Narrow) AI. According to a recent DATAVERSITY interview with Adrian Bowles, the lead analyst at Aragon Research, Strong AI is the goal of achieving intelligence equal to a human's, and continues to evolve in that direction. The debate on the differences between Artificial Intelligence vs. Machine Learning are more about the particulars of use cases and implementations of the technologies, than actual real differences – they are allied technologies that work together, with AI being the larger concept that Machine Learning is a part of. Deep Learning also fits into this debate and is a more distinct usage of Machine Learning.

NVIDIA (NVDA) is moving fast in its mission to make AI (artificial intelligence) affordable. The company is expanding its efforts to create more AI researchers and developers through its DLI (Deep Learning Institute). Don't miss the next report. You are now receiving e-mail alerts for new research. A temporary password for your new Market Realist account has been sent to your e-mail address.

As Russia's government develops a digital economy, organisations are stepping up the use of artificial intelligence (AI) and machine learning technologies. Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address.

The new Amazon Echo is cheaper, smaller and has a less imposing stature, but is it still the best smart speaker going? Amazon's voice assistant Alexa has improved greatly since the Echo's introduction to the UK at the end of last year, altered behind the scenes without users needing to do anything thanks the virtue of being a cloud-powered product. It has gained new skills, routines and other smart home control abilities. Its voice recognition and understanding has improved, and it is now a little more conversational, remembering certain topics that you're talking about the way a human would. But the outside of the speaker has not changed, until now.

Louis Buckley, Content Developer at London's Science Museum, plays rock, paper, scissors with Berti the Robot, London, UK, February, 2009. Sign up for Take Action Now, our newsletter that connects busy people to the resistance. Thank you for signing up. The Nation is reader supported: Chip in $10 or more to help us continue to write about the issues that matter. Be the first to hear about Nation Travels destinations, and explore the world with kindred spirits.

Police were forced to break into a German man's house after his Alexa device decided to hold a 2am party on its own while he was out with friends. Oliver Haberstroh said the Amazon personal assistant suddenly started playing deafening music without having received instructions at his flat in Hamburg, northern Germany. Neighbours called police and officers had to break in to turn off the voice-controlled device. Amazon describes Alexa as an'intelligent' personal assistant that enables owners to control lighting and thermostats in their homes or to play music. It can also answer questions using the internet.

At Moorfields Eye Hospital in London, consultants are facing a familiar problem. Age-related eye diseases are becoming more and more common, and as the British demographic gets ever older, numbers are predicted to increase by between a third and one half. "We have enormous numbers of patients, we can barely cope," says Professor Peng Tee Khaw, a consultant ophthalmic surgeon. "We need to look at new ways to deal with the issue." When a patient arrives at Moorfields, doctors will likely perform an eye scan that captures a 3D cross-section of the person's retina.

We propose a new randomized coordinate descent method for a convex optimization template with broad applications. Our analysis relies on a novel combination of four ideas applied to the primal-dual gap function: smoothing, acceleration, homotopy, and coordinate descent with non-uniform sampling. As a result, our method features the first convergence rate guarantees among the coordinate descent methods, that are the best-known under a variety of common structure assumptions on the template. We provide numerical evidence to support the theoretical results with a comparison to state-of-the-art algorithms.

One iteration of $k$-means or EM for Gaussian mixture models (GMMs) scales linearly with the number of data points $N$, the number of clusters $C$, and the data dimensionality $D$. In this study, we explore whether one iteration of $k$-means or EM for GMMs can scale sublinearly with $C$ at run-time, while the increase of the clustering objective remains effective. The tool we apply for complexity reduction is variational EM, which is typically applied to make training of generative models with exponentially many hidden states tractable. Here, we apply novel theoretical results on truncated variational EM to make tractable clustering algorithms more efficient. The basic idea is the use of a partial variational E-step which reduces the linear complexity of $\mathcal{O}(NCD)$ required for a full E-step to a sublinear complexity. Our main observation is that the linear dependency on $C$ can be reduced to a dependency on a much smaller parameter $G$, related to the cluster neighborhood relationship. We focus on two versions of partial variational EM for clustering: variational GMM, scaling with $\mathcal{O}(NG^2D)$, and variational $k$-means, scaling with $\mathcal{O}(NGD)$ per iteration. Empirical results then show that these algorithms still require comparable numbers of iterations to increase the clustering objective to the same values as $k$-means. For data with many clusters, we consequently observe reductions of the net computational demands between two and three orders of magnitude. More generally, our results provide substantial empirical evidence in favor of clustering to scale sublinearly with $C$.

Motivated by the need for low-complexity algorithms, we propose a new approach, dubbed "joint screening test", allowing to screen a set of atoms by carrying out one single test. The approach is particularized to two different sets of atoms, respectively expressed as sphere and dome regions. After presenting the mathematical derivations of the tests, we elaborate on their relative effectiveness and discuss the practical use of such procedures.