Depiction of fusion research on a doughnut-shaped tokamak enhanced by artificial intelligence. Artificial intelligence (AI), a branch of computer science that is transforming scientific inquiry and industry, could now speed the development of safe, clean and virtually limitless fusion energy for generating electricity. A major step in this direction is under way at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University, where a team of scientists working with a Harvard graduate student is for the first time applying deep learning -- a powerful new version of the machine learning form of AI -- to forecast sudden disruptions that can halt fusion reactions and damage the doughnut-shaped tokamaks that house the reactions. "This research opens a promising new chapter in the effort to bring unlimited energy to Earth," Steve Cowley, director of PPPL, said of the findings (link is external), which are reported in the current issue of Nature magazine. "Artificial intelligence is exploding across the sciences and now it's beginning to contribute to the worldwide quest for fusion power."

NEW DELHi: The NITI Aayog has drawn up a plan for creating an institutional framework for artificial intelligence (AI) in the country. It has circulated a cabinet note to provide Rs 7,500 crore in funding for creation of cloud computing platform called AIRAWAT and research institutes. A senior government official told ET that the Aayog has already circulated the note for consideration by the Expenditure Finance Committee, which is expected to take it up soon. The note proposes that the new government pump in Rs 7,500 crore initially over a three-year period and set up a high-level taskforce to oversee roll-out and implementation of AI, the official said on condition of anonymity. "A cabinet note is ready... We would present it to the new government as we want an institutional framework as well as a transparent policy in place for AI," said the official.

There's great potential in using both drones and ground-based robots for situations like disaster response, but generally these platforms either fly or creep along the ground. Not the "Flying STAR," which does both quite well, and through a mechanism so clever and simple you'll wish you'd thought of it. Conceived by researchers at Ben-Gurion University in Israel, the "flying sprawl-tuned autonomous robot" is based on the elementary observation that both rotors and wheels spin. So why shouldn't a vehicle have both? Well, there are lots of good reasons why it's difficult to create such a hybrid, but the team, led by David Zarrouk, overcame them with the help of today's high-powered, lightweight drone components.

In early April, the European Commission published guidelines intended to keep any artificial intelligence technology used on the EU's 500 million citizens trustworthy. The bloc's commissioner for digital economy and society, Bulgaria's Mariya Gabriel, called them "a solid foundation based on EU values." One of the 52 experts who worked on the guidelines argues that foundation is flawed--thanks to the tech industry. Thomas Metzinger, a philosopher from the University of Mainz, in Germany, says too many of the experts who created the guidelines came from or were aligned with industry interests. Metzinger says he and another member of the group were asked to draft a list of AI uses that should be prohibited.

Natural disasters aren't just threats to people and buildings, they can also erase history -- by destroying rare archival documents. As a safeguard, scholars in Japan are digitizing the country's centuries-old paper records, typically by taking a scan or photo of each page. But while this method preserves the content in digital form, it doesn't mean researchers will be able to read it. Millions of physical books and documents were written in an obsolete script called Kuzushiji, legible to fewer than 10 percent of Japanese humanities professors. "We end up with billions of images which will take researchers hundreds of years to look through," said Tarin Clanuwat, researcher at Japan's ROIS-DS Center for Open Data in the Humanities.

This essay reviews human observer-based methods employed in shark spotting in Muizenberg Beach. It investigates Machine Learning methods for automated shark detection with the aim of enhancing human observation. A questionnaire and interview were used to collect information about shark spotting, the motivation of the actual Shark Spotter program and its limitations. We have defined a list of desirable properties for our model and chosen the adequate mathematical techniques. The preliminary results of the research show that we can expect to extract useful information from shark images despite the geometric transformations that sharks perform, its features do not change. To conclude, we have partially implemented our model; the remaining implementation requires dataset.

Mixup, a recent proposed data augmentation method through linearly interpolating inputs and modeling targets of random samples, has demonstrated its capability of significantly improving the predictive accuracy of the state-of-the-art networks for image classification. However, how this technique can be applied to and what is its effectiveness on natural language processing (NLP) tasks have not been investigated. In this paper, we propose two strategies for the adaption of Mixup on sentence classification: one performs interpolation on word embeddings and another on sentence embeddings. We conduct experiments to evaluate our methods using several benchmark datasets. Our studies show that such interpolation strategies serve as an effective, domain independent data augmentation approach for sentence classification, and can result in significant accuracy improvement for both CNN and LSTM models.

In this paper, a method for automatically deriving energy-preserving numerical methods for the Euler-Lagrange equation and the Hamilton equation is proposed. The derived energy-preserving scheme is based on the discrete gradient method. In the proposed approach, the discrete gradient, which is a key tool for designing the scheme, is automatically computed by a similar algorithm to the automatic differentiation. Besides, the discrete gradient coincides with the usual gradient if the two arguments required to define the discrete gradient are the same. Hence the proposed method is an extension of the automatic differentiation in the sense that the proposed method derives not only the discrete gradient but also the usual gradient. Due to this feature, both energy-preserving integrators and variational (and hence symplectic) integrators can be implemented in the same programming code simultaneously. This allows users to freely switch between the energy-preserving numerical method and the symplectic numerical method in accordance with the problem-setting and other requirements. As applications, an energy-preserving numerical scheme for a nonlinear wave equation and a training algorithm of artificial neural networks derived from an energy-dissipative numerical scheme are shown.

For machine learning task, lacking sufficient samples mean the trained model has low confidence to approach the ground truth function. Until recently, after the generative adversarial networks (GAN) had been proposed, we see the hope of small samples data augmentation (DA) with realistic fake data, and many works validated the viability of GAN-based DA. Although most of the works pointed out higher accuracy can be achieved using GAN-based DA, some researchers stressed that the fake data generated from GAN has inherent bias, and in this paper, we explored when the bias is so low that it cannot hurt the performance, we set experiments to depict the bias in different GAN-based DA setting, and from the results, we design a pipeline to inspect specific dataset is efficiently-augmentable with GAN-based DA or not. And finally, depending on our trial to reduce the bias, we proposed some advice to mitigate bias in GAN-based DA application.

Estimating symmetric properties of a distribution, e.g. support size, coverage, entropy, distance to uniformity, are among the most fundamental problems in algorithmic statistics. While each of these properties have been studied extensively and separate optimal estimators are known for each, in striking recent work, Acharya et al. 2016 showed that there is a single estimator that is competitive for all symmetric properties. This work proved that computing the distribution that approximately maximizes \emph{profile likelihood (PML)}, i.e. the probability of observed frequency of frequencies, and returning the value of the property on this distribution is sample competitive with respect to a broad class of estimators of symmetric properties. Further, they showed that even computing an approximation of the PML suffices to achieve such a universal plug-in estimator. Unfortunately, prior to this work there was no known polynomial time algorithm to compute an approximate PML and it was open to obtain a polynomial time universal plug-in estimator through the use of approximate PML. In this paper we provide a algorithm (in number of samples) that, given $n$ samples from a distribution, computes an approximate PML distribution up to a multiplicative error of $\exp(n^{2/3} \mathrm{poly} \log(n))$ in time nearly linear in $n$. Generalizing work of Acharya et al. 2016 on the utility of approximate PML we show that our algorithm provides a nearly linear time universal plug-in estimator for all symmetric functions up to accuracy $\epsilon = \Omega(n^{-0.166})$. Further, we show how to extend our work to provide efficient polynomial-time algorithms for computing a $d$-dimensional generalization of PML (for constant $d$) that allows for universal plug-in estimation of symmetric relationships between distributions.