The promise of artificial intelligence has captured our cultural imagination since at least the 1950s--inspiring computer scientists to create new and increasingly complex technologies, while also building excitement about the future among regular everyday consumers. What if we could explore the bottom of the ocean without taking any physical risks? While our understanding of AI--and what's possible--has changed over the the past few decades, we have reason to believe that the age of artificial intelligence may finally be here. So, as a developer, what can you do to get started? This article will go over some basics of AI, and outline some tools and resources that may help.

Perhaps the flat delivery, the Christmas word salad and the elementary melody tipped you off to the computer-generated nature of this performance. It's from a team at the University of Toronto Computer Science Department, which has been teaching a computer to write sing-along music. Dubbed "neural karaoke," this artificial intelligence system has been fed more than 100 hours of music to learn how to create simple melodies. It was also trained to recognize images and compose related lyrics. Listen: The Music From'Westworld' Is Finally Here Using an algorithm, the AI finds patterns in the data and essentially "learns" music -- including beats and chords. It learned the correlation between lyrics and music notes from around 50 hours of pop songs, says Hang Chu, one of the researchers.

Uber lost $2.2bn (£1.77bn) in the first nine months of the year despite a surge in revenues, according to reports, adding to the taxi-hailing app's woes as it faces a setback in plans to launch self-driving cars. The San Francisco-based firm was valued at more than $65bn in a $12.5bn fundraising effort earlier this year but has remained secretive about its financial performance. People familiar with the matter said Uber had lost $800m in the third quarter and $2.2bn in the first nine months of the financial year, according to Bloomberg and technology site The Information (subscription). The losses came despite its nine-month revenues surging from $3.76bn to $5.5bn, the reports said. Passengers spent $5.4bn in the third quarter on Uber fees, which the company splits with its drivers, up from $5bn in the second quarter and $3.8bn in the first.

This hunger for data, or "statistical inefficiency" is perhaps the most significant practical limitation of current deep learning technology. Many of our clients at Scyfer have problems that could be solved by deep learning, but don't have large annotated datasets. Scyfer Active Learning Platform: once integrated, our system will passively observe the work of a domain expert (whether that's a medical doctor diagnosing patients or a factory worker identifying defective products). As the system is starting to learn how to imitate the expert, it will identify its own weaknesses and ask for guidance from the expert, thereby greatly accelerating its learning without requiring so many examples. Data-efficient deep networks: by building in prior knowledge, like "a rotated teddy bear is still a teddy bear", we can drastically reduce the number of examples required to learn a new concept.

PARIS – Pregnancy causes "long-lasting" physical changes to a woman's brain, with significant, but seemingly beneficial, gray matter loss in parts of the crucial organ, a study said Monday. Some alterations lasted at least two years, they reported, but did not appear to erode memory or other mental processes. The changes "concern brain areas associated with functions necessary to manage the challenges of motherhood," study co-author Erika Barba-Muller of the Autonomous University of Barcelona (UAB) said in a statement. The radical hormone surges and physical changes of pregnancy have long been known and studied, but its effects on the brain have been little understood. The new study, published in Nature Neuroscience, claims to provide the first evidence "that pregnancy confers long-lasting changes in a woman's brain."

Updated 11th November 2016 with the latest artificial intelligence (AI) software from Sentient to undertake complex multivariate testing. A/B and multivariate testing tools are essential for digital marketers as they enable you to deliver and measure the relative performance of different user experiences through robust online controlled experiments. Increasingly they also allow you to personalise your customer experience and allow you to discover new customer segments based upon behaviour rather than just demographics. A/B testing allows you to run an online controlled experiment to measure the difference in performance between an existing webpage (e.g. A/B testing tools randomly select visitors for each design and uses robust statistical analysis to measure the performance between the control and the variant. If there is a statistical difference between the two experiences we can say with a high degree of confidence (normally 99%) that it is down to the design and not other factors (e.g.

The Wisdom of Crowds (WOC), as a theory in the social science, gets a new paradigm in computer science. The WOC theory explains that the aggregate decision made by a group is often better than those of its individual members if specific conditions are satisfied. This paper presents a novel framework for unsupervised and semi-supervised cluster ensemble by exploiting the WOC theory. We employ four conditions in the WOC theory, i.e., diversity, independency, decentralization and aggregation, to guide both the constructing of individual clustering results and the final combination for clustering ensemble. Firstly, independency criterion, as a novel mapping system on the raw data set, removes the correlation between features on our proposed method. Then, decentralization as a novel mechanism generates high-quality individual clustering results. Next, uniformity as a new diversity metric evaluates the generated clustering results. Further, weighted evidence accumulation clustering method is proposed for the final aggregation without using thresholding procedure. Experimental study on varied data sets demonstrates that the proposed approach achieves superior performance to state-of-the-art methods.

The availability of graph data with node attributes that can be either discrete or real-valued is constantly increasing. While existing kernel methods are effective techniques for dealing with graphs having discrete node labels, their adaptation to non-discrete or continuous node attributes has been limited, mainly for computational issues. Recently, a few kernels especially tailored for this domain, and that trade predictive performance for computational efficiency, have been proposed. In this paper, we propose a graph kernel for complex and continuous nodes' attributes, whose features are tree structures extracted from specific graph visits. The kernel manages to keep the same complexity of state-of-the-art kernels while implicitly using a larger feature space. We further present an approximated variant of the kernel which reduces its complexity significantly. Experimental results obtained on six real-world datasets show that the kernel is the best performing one on most of them. Moreover, in most cases the approximated version reaches comparable performances to current state-of-the-art kernels in terms of classification accuracy while greatly shortening the running times.

Abstract--Due to limited metering infrastructure, distribution grids are currently challenged by observability issues. On the other hand, smart meter data, including local voltage magnitudes and power injections, are communicated to the utility operator from grid buses with renewable generation and demand-response programs. This work employs grid data from metered buses towards inferring the underlying grid state. T o this end, a coupled formulation of the power flow problem (CPF) is put forth. Exploiting the high variability of injections at metered buses, the controllability of solar inverters, and the relative time-invariance of conventional loads, the idea is to solve the nonlinear power flow equations jointly over consecutive time instants. An intuitive and easily verifiable rule pertaining to the locations of metered and non-metered buses on the physical grid is shown to be a necessary and sufficient criterion for local observability in radial networks. T o account for noisy smart meter readings, a coupled power system state estimation (CPSSE) problem is further developed. Both CPF and CPSSE tasks are tackled via augmented semi-definite program relaxations. The observability criterion along with the CPF and CPSSE solvers are numerically corroborated using synthetic and actual solar generation and load data on the IEEE 34-bus benchmark feeder . Power flow (PF) and power system state estimation (PSSE) are central to planning, monitoring and control of electricity networks.

Over the last years, huge resources of biological and medical data have become available for research. This data offers great chances for machine learning applications in health care, e.g. for precision medicine, but is also challenging to analyze. Typical challenges include a large number of possibly correlated features and heterogeneity in the data. One flourishing field of biological research in which this is relevant is epigenetics. Here, especially large amounts of DNA methylation data have emerged. This epigenetic mark has been used to predict a donor's 'epigenetic age' and increased epigenetic aging has been linked to lifestyle and disease history. In this paper we propose an adaptive model which performs feature selection for each test sample individually based on the distribution of the input data. The method can be seen as partially blind domain adaptation. We apply the model to the problem of age prediction based on DNA methylation data from a variety of tissues, and compare it to a standard model, which does not take heterogeneity into account. The standard approach has particularly bad performance on one tissue type on which we show substantial improvement with our new adaptive approach even though no samples of that tissue were part of the training data.