The conflict between increasing demand for computing and sluggish grow of hardware capability triggers the heated development of approximate computing, which has achieved massive success in both industry and research community. Many applications that do not require utterly accurate computation can achieve tremendous acceleration and drastic reduction of the energy consumption by leveraging approximate computing, especially in domains that call for real-time calculation, fast response and low power consumption such as learning [27], image processing [19] and scientific computation [24]. Approximation computing can be conduct in different hierarchies, such as hardware [6], [18], system and software levels. Various approximate computing architectures [17], [19], [27] are advocated. Neural network (NN) based approximate computing focus on the acceleration in software-level and has many advantages when compared to previous methods. First, neural networks are proved to be able to fit any continuous function [12], and thus this method can universally be adopted by different tasks. Second, enormous parallelism in the neural networks is exploited by the rapid advancement of various neural network accelerators.

The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.

PwC has released its latest MoneyTree report for Q2 2018, founding that the AI sector continued to thrive in Canadian venture capital. AI experienced a 104 percent funding increase in Q2 2018 compared to the last quarter, with $222 million CAD ($169 million USD) invested across 13 deals. Total quarterly deals and investment reached an all-time high this quarter; the second highest quarter was Q2 2017, when $209 million ($159 million USD) was invested across 12 deals, boosted by Element AI's historic Series A. "Approximately half of the deal volume this quarter went to businesses that provide analytics tools to their customers," said Dave Planques, national deals leader at PwC Canada. "These companies are supporting enterprises in making better data-driven decisions." "If you think of this industry as analytics on one end of the spectrum to artificial intelligence on the other, the Canadian tech sector is firing on all of those cylinders."

Machine learning methods have shown promise in predicting molecular properties, and given sufficient training data ML may surpass density functional theory in computational speed and chemical accuracy. However, the most accurate machine learning methods require optimized 3D molecular geometries, limiting their applicability for high-throughput screening. We show that near-optimal results for large polymeric molecules can be obtained without optimized 3D geometry, and that trained model weights can be used to improve performance on related tasks.

This paper proposes the first step towards a novel unified framework for the analysis of perturbations occurring in nuclear reactors in both Time and Frequency domain. The identification of type and source of such perturbations is fundamental for monitoring core reactors and guarantee safety even while running at nominal conditions. A 3D Convolutional Neural Network (3D-CNN) was employed to analyse perturbations happening in the frequency domain, such as the alteration of an absorber of variable strength or propagating perturbation. Recurrent neural networks (RNN), specifically Long Short-Term Memory (LSTM) was used to study signal sequences related to perturbations induced in the time domain, including the vibrations of fuel assemblies and the fluctuation of thermalhydraulic parameters at the inlet of the reactor coolant loops. 512-dimensional representations were extracted from the 3D-CNN and LSTM architectures, and used as input to a fused multi-sigmoid classification layer to recognise the perturbation type. If the perturbation is frequency domain related, a separate fully-connected layer utilises said representations to regress the coordinates of its source. The results showed that perturbation type can be recognised with high accuracy in both domains, and frequency domain scenario sources can be localised with high precision.

AI is continually improving climate models. As the planet continues to warm, climate change impacts are worsening. In 2016, there were 772 weather and disaster events, triple the number that occurred in 1980. Twenty percent of species currently face extinction, and that number could rise to 50 percent by 2100. And even if all countries keep their Paris climate pledges, by 2100, it's likely that average global temperatures will be 3 C higher than in pre-industrial times.

The Mystic is designed to give the ultimate aerial video and photography experience, creating breathtaking imagery without the need to learn complicated film techniques. The Mystic automatically detects objects and avoids obstacles using the cutting-edge motion intelligence similarly found in the self-driving car. With gesture interaction, you can take stunning aerial selfies, using poses to control the drone. The Mystic recognizes each pose as a specific command and will follow your instructions, moving forward and backward, side to side, and taking photos. The Mystic is the first drone to support up to 6 different gestures, all of which can be customized to your personal preference.

HOUSTON--A Google executive wearing white jeans and a navy T-shirt stood before a roomful of suit-clad oil executives here last month and delivered a blunt sales pitch: We can manage your data better than you. Darryl Willis, part of a new group Google has created to court the oil and gas industry, said energy companies have reams of data but only use 5% of it, a serious problem in the digital economy. Signing a cloud deal with Google, part of Alphabet Inc., GOOGL 3.89% could solve that, he argued. "Companies in the oil and gas industry will either be a catalyst for change or they will be a casualty of change," he said during a presentation at the Unify Conference, an industry forum on digital technology put on by Baker Hughes, a part of General Electric Co. Silicon Valley has come to Houston, as tech companies push to sign oil and gas companies to lucrative cloud and artificial intelligence deals. In recent months, companies including Chevron Corp. CVX 2.08%, Equinor AS EQNR 1.00% A, Total SA TOT 1.14% and Repsol SA REP -0.06% have entered into contracts with companies such as Google and Microsoft Corp. MSFT -0.29% collectively worth billions of dollars.

Echo State Networks (ESN) are a class of Recurrent Neural Networks (RNN) that has gained substantial popularity due to their effectiveness, ease of use and potential for compact hardware implementation. An ESN contains the three network layers input, reservoir and readout where the reservoir is the truly recurrent network. The input and reservoir layers of an ESN are initialized at random and never trained afterwards and the training of the ESN is applied to the readout layer only. The alternative of Recursive Neural Gas (RNG) is one of the many proposals of fully-trainable reservoirs that can be found in the literature. Although some improvements in performance have been reported with RNG, to the best of authors' knowledge, no experimental comparative results are known with benchmarks for which ESN is known to yield excellent results. This work describes an accurate model of RNG together with some extensions to the models presented in the literature and shows comparative results on three well-known and accepted datasets. The experimental results obtained show that, under specific circumstances, RNG-based reservoirs can achieve better performance.

We consider the exploration-exploitation trade-off in reinforcement learning and we show that an agent imbued with a risk-seeking utility function is able to explore efficiently, as measured by regret. The parameter that controls how risk-seeking the agent is can be optimized exactly, or annealed according to a schedule. We call the resulting algorithm K-learning and show that the corresponding K-values are optimistic for the expected Q-values at each state-action pair. The K-values induce a natural Boltzmann exploration policy for which the `temperature' parameter is equal to the risk-seeking parameter. This policy achieves an expected regret bound of $\tilde O(L^{3/2} \sqrt{S A T})$, where $L$ is the time horizon, $S$ is the number of states, $A$ is the number of actions, and $T$ is the total number of elapsed time-steps. This bound is only a factor of $L$ larger than the established lower bound. K-learning can be interpreted as mirror descent in the policy space, and it is similar to other well-known methods in the literature, including Q-learning, soft-Q-learning, and maximum entropy policy gradient, and is closely related to optimism and count based exploration methods. K-learning is simple to implement, as it only requires adding a bonus to the reward at each state-action and then solving a Bellman equation. We conclude with a numerical example demonstrating that K-learning is competitive with other state-of-the-art algorithms in practice.