Data analysis in high-dimensional spaces aims at obtaining a synthetic description of a data set, revealing its main structure and its salient features. We here introduce an approach for charting data spaces, providing a topography of the probability distribution from which the data are harvested. This topography includes information on the number and the height of the probability peaks, the depth of the "valleys" separating them, the relative location of the peaks and their hierarchical organization. The topography is reconstructed by using an unsupervised variant of Density Peak clustering exploiting a non-parametric density estimator, which automatically measures the density in the manifold containing the data. Importantly, the density estimator provides an estimate of the error. This is a key feature, which allows distinguishing genuine probability peaks from density fluctuations due to finite sampling.

PARSE representations have become popular in several applications of signal, image and video processing, such as denoising [1], [2], super-resolution, inpainting, compression [3]-[6] or classification. While it was common to analyze and reconstruct signals based on representations over predefined bases such as wavelets and DCT, research in the recent years has shown that learning overcomplete dictionaries adapted to the structure of the treated signals can significantly improve the representation quality. Observing that learning redundant dictionaries from collections of data samples under sparsity priors leads to models that fit and approximate well the characteristics of signals [7], [8], the learning of dictionaries in a supervised setting for the discrimination of different classes of signals has also become a popular research problem [9]. In this work, we propose a method to learn multilevel structured dictionaries with high discrimination capability for the problem of pixelwise image classification. We consider a supervised classification setting where the classes are known and exemplars are available for each class. In particular, we are interested in image classification problems with a large amount of variability between data samples of the same class, resulting from e.g., dominant presence

Complex network data may be analyzed by constructing statistical models that accurately reproduce structural properties that may be of theoretical relevance or empirical interest. In the context of the efficient fitting of models for large network data, we propose a very efficient algorithm for the maximum likelihood estimation (MLE) of the parameters of complex statistical models. The proposed algorithm is similar to the famous Metropolis algorithm but allows a Monte Carlo simulation to be performed while constraining the desired network properties. We demonstrate the algorithm in the context of exponential random graph models (ERGMs) - a family of statistical models for network data. Thus far, the lack of efficient computational methods has limited the empirical scope of ERGMs to relatively small networks with a few thousand nodes. The proposed approach allows a dramatic increase in the size of networks that may be analyzed using ERGMs. This is illustrated in an analysis of several biological networks and one social network with 104,103 nodes.

Fish farming is big business - the industry now produces about 100 million tonnes a year - and with salmon prices soaring, producers are turning to lasers, automation and artificial intelligence to boost production and cut costs. How do you know if farmed salmon have had enough to eat? Well, according to Lingalaks fish farms in Norway, which produce nearly three million salmon each year, the fish make less noise once the feeding frenzy is over. The firm knows this thanks to a new hydro-acoustic system it has installed at one of its farms. The system listens to the salmon sloshing loudly about as they feed in a cluster. When the fish have had enough, they swim off and the noise lessens.

Huawei unveiled in November 2017 the world's first smartphone-driven car using the Mate 10 Pro handset that takes control of the car using an artificial intelligence chip. The MWC presents the latests advances in mobile technologies and will be held from 26 February to 01 March 2018.

A team of international researchers recently taught AI to justify its reasoning and point to evidence when it makes a decision. The'black box' is becoming transparent, and that's a big deal. Figuring out why a neural network makes the decisions it does is one of the biggest concerns in the field of artificial intelligence. The black box problem, as it's called, essentially keeps us from trusting AI systems. The team was comprised of researchers from UC Berkeley, University of Amsterdam, MPI for Informatics, and Facebook AI Research.

When the world shifted from personal computers to smartphones, websites had to slim down to work on smaller screens and slower wireless connections. A similar shift to voice-centric services is again forcing businesses to rethink how they present information to consumers -- and spurring new efforts to help them do so. The software company Adobe, for instance, announced on Tuesday a new suite of tools that could help airlines, retailers and other companies create simple voice interfaces for travel and shopping. That means companies have to figure out how to winnow down those choices to the travel options or products people are most likely to want -- an inherently fraught undertaking. The technology is still in its infancy, and Adobe doesn't have any actual corporate partners to showcase yet.

If you can't win, kill yourself or cheat. That's the strategy invented by an artificial intelligence trained to play old Atari video games. Patryk Chrabaszcz of the University of Freiburg, Germany and his colleagues created an AI to play eight Atari games, including the arcade classic Q*bert, in which players must navigate a strange orange character around a pyramid and dodge enemies. Rather than mastering the game like a human gamer might, their algorithm came up with two particularly unusual ways to play.

A new report details a dystopian future for humans, as we have created a technology that will soon create an unreality that will be difficult for our cognitive abilities to discern from reality. Titled "The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation," the report was authored by 26 experts from 14 institutions, including Oxford University's Future of Humanity Institute, Cambridge University's Centre for the Study of Existential Risk, Elon Musk's OpenAI, and the Electronic Frontier Foundation. They only looked at the near-future. This isn't some Jetsons-style society that our grandchildren will have to deal with, but an evolving threat that everyone will soon be fighting back against. For the purposes of this report, we only consider AI technologies that are currently available (at least as initial research and development demonstrations) or are plausible in the next 5 years, and focus in particular on technologies leveraging machine learning.

The idea of an artificial mind that can think by itself has always loomed large in the human imagination – the ancient Greeks told myths of mechanical men. As an academic discipline, the field of Artificial Intelligence (AI) has been studied since the 1950s, after computer scientist Alan Turing first asked, "can machines do what we, as thinking entities, do?" But in 2018, AI is firmly out of the realms of science fiction or academic theory. Last week UK prime minister Theresa May stood up in her keynote address to the World Economic Forum and announced her ambition to establish the UK as a "world leader" in AI, alongside plans for its ethical oversight. These days, voice assistants like Google Now and Microsoft's Cortana are in every smart device and computer, and smart speakers like the Amazon Echo and Google Home are selling in their tens of millions.