But a new system designed by Google DeepMind and British doctors goes a crucial step further: It can show users how it reached its conclusions. A study published Monday in Nature Medicine reports that the DeepMind system can identify dozens of diseases and point out the portions of optical coherence tomography scans that it relies upon to make its diagnoses. That's a crucial factor in validating the safety and efficacy of AI technologies being developed for use in diagnosing or recommending treatments for a broad range of diseases, from cancer to neurological and vision problems. The paper states that the system made the right referral recommendation in more than 94 percent of cases based on a review of historic patient scans at Moorfields Eye Hospital in London and performed as good as, or better than, top eye specialists who examined the same scans. Experts said that level of accuracy is impressive on such an open-ended query.

Artificial intelligence (AI) relies on big data and machine learning for myriad applications, from autonomous vehicles to algorithmic trading, and from clinical decision support systems to data mining. The availability of large amounts of data is essential to the development of AI. Given China's large population and business sector, both of which use digitized platforms and tools to an unparalleled extent, it may enjoy an advantage in AI. In addition, it has fewer constraints on the use of information gathered through the digital footprint left by people and companies. India has also taken a series of similar steps to digitize its economy, including biometric identity tokens, demonetization and an integrated goods and services tax. But the recent scandal over the use of personal and social data by Facebook and Cambridge Analytica has brought ethical considerations to the fore.

Artificial Intelligence is the hot topic in the tech world right now. Companies want to include it in some way in their platforms, students are lining up to train in it, and researchers are pushing its limits to see what they can apply it to. And the possibilities are truly endless. At Intel's AI DevCon 2018 in Bengaluru this past week, I got a brief glimpse into AI's transformative potential. Amidst the hype of an upcoming AI-focused chip was the excitement of dozens of developers and researchers gathered to exchange ideas, of which there was plenty on offer.

Artificial Intelligence is the hot topic in the tech world right now. Companies want to include it in some way in their platforms, students are lining up to train in it, and researchers are pushing its limits to see what they can apply it to. And the possibilities are truly endless. At Intel's AI DevCon 2018 in Bengaluru this past week, I got a brief glimpse into AI's transformative potential. Amidst the hype of an upcoming AI-focused chip was the excitement of dozens of developers and researchers gathered to exchange ideas, of which there was plenty on offer.

"The human brain has 100 billion neurons, each neuron connected to 10 thousand other neurons. Sitting on your shoulders is the most complicated object in the known universe," Michio Kaku, Physicist and Futurist The human brain, which not just stores but also computes, is by far the most powerful and complex computers in the world that occupies just 1.3 litres of space and consumes about 20 watts of power. In comparison, the finest supercomputers in the world require gigawatts of power, massive real estate, infrastructure, and dedicated cooling systems while attempting to perform brain-like tasks. Understanding how the human brain functions and replicating it has been a lifelong quest for the scientific and research community. Enter neuromorphic computing, a concept developed by American scientist and researcher Carver Andress Mead in the late 1980s – which tries to emulate certain functions of the human brain in silicon.

Artificial intelligence is one of the most influential forces in information technology. It can help drive cars, fly unmanned aircraft and protect networks. But artificial intelligence also can be a dark force, one that adversaries use to learn new ways to hack systems, shut down networks and deny access to crucial information. The challenge is to prepare for a future where autonomous cyber attacks powered by artificial intelligence (AI) will threaten cyberspace and could endanger human life. This prospect is so significant that the Japanese Cabinet Secretariat tasked with developing the country's cybersecurity initiatives has created a research and development focus group to craft plans to counter cybersecurity threats, including those designed with AI.

Climate change has been bleaching coral reefs, decimating the local marine species that call them home, since at least the first major observations were recorded in the Caribbean in 1980. Thankfully, new A.I. cataloguing designed to identify the geographic regions where coral is still thriving hopes to reverse the trend, saving some of the world's most dense and varied aquatic ecosystems from all-but-certain extinction. There are numerous reasons why we need to care about saving coral reefs, from the ethical to the economic. In addition to housing about a quarter of marine species, these reefs provide $375 billion USD in revenue to the world economy, according to the Guardian, and food security to half a billion people. Without them, researchers say countless species and the entire ocean fishing industry that depends on them would simply evaporate.

Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning str\textbf{a}teg\textbf{y} (J-Play), with its application to multi-label classification. The J-Play learns high-level and semantically meaningful feature representation from high-dimensional data by 1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; 2) progressively learning multi-coupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; 3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.

The understanding of geographical reality is a process of data representation and pattern discovery. Former studies mainly adopted continuous-field models to represent spatial variables and to investigate the underlying spatial continuity/heterogeneity in the regular spatial domain. In this article, we introduce a more generalized model based on graph convolutional neural networks (GCNs) that can capture the complex parameters of spatial patterns underlying graph-structured spatial data, which generally contain both Euclidean spatial information and non-Euclidean feature information. A trainable semi-supervised prediction framework is proposed to model the spatial distribution patterns of intra-urban points of interest(POI) check-ins. This work demonstrates the feasibility of GCNs in complex geographic decision problems and provides a promising tool to analyze irregular spatial data.

Non-Gaussian component analysis (NGCA) is a problem in multidimensional data analysis. Since its formulation in 2006, NGCA has attracted considerable attention in statistics and machine learning. In this problem, we have a random variable $X$ in $n$-dimensional Euclidean space. There is an unknown subspace $U$ of the $n$-dimensional Euclidean space such that the orthogonal projection of $X$ onto $U$ is standard multidimensional Gaussian and the orthogonal projection of $X$ onto $V$, the orthogonal complement of $U$, is non-Gaussian, in the sense that all its one-dimensional marginals are different from the Gaussian in a certain metric defined in terms of moments. The NGCA problem is to approximate the non-Gaussian subspace $V$ given samples of $X$. Vectors in $V$ corresponds to "interesting" directions, whereas vectors in $U$ correspond to the directions where data is very noisy. The most interesting applications of the NGCA model is for the case when the magnitude of the noise is comparable to that of the true signal, a setting in which traditional noise reduction techniques such as PCA don't apply directly. NGCA is also related to dimensionality reduction and to other data analysis problems such as ICA. NGCA-like problems have been studied in statistics for a long time using techniques such as projection pursuit. We give an algorithm that takes polynomial time in the dimension $n$ and has an inverse polynomial dependence on the error parameter measuring the angle distance between the non-Gaussian subspace and the subspace output by the algorithm. Our algorithm is based on relative entropy as the contrast function and fits under the projection pursuit framework. The techniques we develop for analyzing our algorithm maybe of use for other related problems.