It's only been a few years since Amazon unveiled the Alexa-powered Echo, but since then, smart speakers have become a major consumer-electronics category. Key to its success is the notion of the always-on virtual assistant, which other companies like Apple and Google have adopted as well. In fact, not only has Google made Assistant the driving force behind its Android smartphones, it has launched its own line of Echo rivals. But underneath all of this technology is the potential risk to your privacy. In just the past few months, news reports have uncovered a series of alarming revelations that companies like Amazon, Google and even Apple have been listening in on conversations without permission.

Policymakers must have a sound understanding of the objectives that AI seeks to achieve in the strategic context of India to disseminate artificial intelligence in defence. What kind of AI do we want? The possibility of AI-ushered advancements has opened the scope of an arms race. Artificial Intelligence (AI), also dubbed as the Industrial Revolution 4.0, has been making giant strides in scientific and technological innovation across varying fields. It is capable of bringing significant transformations in the way civilian activities and military operations are conducted. Till now, the idea of attaining military superiority was conceivable only to a few countries like the US, China and Russia, who maintain large armed forces.

There is still a long way to go before we see 5G's real power. But its technological force is such that it's invoked trade wars between the US and China, both vying to be the leading edge in 5G deployment. The potential of 5G hasn't been met, but it's tipped to provide lightning-fast connectivity required for smart city infrastructure and autonomous vehicles, among a raft of next-gen use cases. But as we ponder 5G's future applications, some are choosing to look even further ahead. In Finland, the University of Oulu in announced project "6Geneis"-- the first research programs that focused on developing the future of communication.

China just stepped up its effort to use technology to police the country deploying traffic robots to help law enforcement in the city of Handan in China's Hebei province. Xinhua, the state-sponsored news agency reported a team of robots has been deployed to assist traffic police in patrolling, providing citizens with information and offering up accident alerts. The artificial intelligence robots have sensors that enable them to move autonomously in every direction similar to a human. These robots can take photos of cars that violated parking rules, verify driver's licenses and even direct traffic. The government plans to use the robots 24 hours a day, deploying them in public locations including train stations and airports.

Imagine if you had a version of Amazon's Alexa or Google Assistant inside your head, capable of feeding you external information whenever you required it, without you needing to say a single word and without anyone else hearing what it had to say back to you. An advanced version of this idea is the basis for future tech-utopian dreams like Elon Musk's Neuralink, a kind of connected digital layer above the cortex that will let our brains tap into hitherto unimaginable machine intelligence. Arnav Kapur, a postdoctoral student with the MIT Media Lab, has a similar idea. And he's already shown it off. The current AlterEgo device prototype looks a bit like one of those popstar Britney mics, as imagined by the designers of the Star Trek: The Next Generation TV show.

We propose a predictive neural network architecture that can be utilized to update reference velocity models as inputs to full waveform inversion. Deep learning models are explored to augment velocity model building workflows during 3D seismic volume reprocessing in salt-prone environments. Specifically, a neural network architecture, with 3D convolutional, de-convolutional layers, and 3D max-pooling, is designed to take standard amplitude 3D seismic volumes as an input. Enhanced data augmentations through generative adversarial networks and a weighted loss function enable the network to train with few sparsely annotated slices. Batch normalization is also applied for faster convergence. Moreover, a 3D probability cube for salt bodies is generated through ensembles of predictions from multiple models in order to reduce variance. Velocity models inferred from the proposed networks provide opportunities for FWI forward models to converge faster with an initial condition closer to the true model. In each iteration step, the probability cubes of salt bodies inferred from the proposed networks can be used as a regularization term in FWI forward modelling, which may result in an improved velocity model estimation while the output of seismic migration can be utilized as an input of the 3D neural network for subsequent iterations.

Deep Reinforcement Learning has made significant progress in multi-agent systems in recent years. In this review article, we have mostly focused on recent papers on Multi-Agent Reinforcement Learning (MARL) than the older papers, unless it was necessary. Several ideas and papers are proposed with different notations, and we tried our best to unify them with a single notation and categorize them by their relevance. In particular, we have focused on five common approaches on modeling and solving multi-agent reinforcement learning problems: (I) independent-learners, (II) fully observable critic, (III) value function decomposition, (IV) consensus, (IV) learn to communicate. Moreover, we discuss some new emerging research areas in MARL along with the relevant recent papers. In addition, some of the recent applications of MARL in real world are discussed. Finally, a list of available environments for MARL research are provided and the paper is concluded with proposals on the possible research directions.

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

With the proliferation of social media platforms and e-commerce sites, several cross-domain collaborative filtering strategies have been recently introduced to transfer the knowledge of user preferences across domains. The main challenge of cross-domain recommendation is to weigh and learn users' different behaviors in multiple domains. In this paper, we propose a Cross-Domain collaborative filtering model following a Translation-based strategy, namely CDT. In our model, we learn the embedding space with translation vectors and capture high-order feature interactions in users' multiple preferences across domains. In doing so, we efficiently compute the transitivity between feature latent embeddings, that is if feature pairs have high interaction weights in the latent space, then feature embeddings with no observed interactions across the domains will be closely related as well. We formulate our objective function as a ranking problem in factorization machines and learn the model's parameters via gradient descent. In addition, to better capture the non-linearity in user preferences across domains we extend the proposed CDT model by using a deep learning strategy, namely DeepCDT. Our experiments on six publicly available cross-domain tasks demonstrate the effectiveness of the proposed models, outperforming other state-of-the-art cross-domain strategies.

Acquiring new robot motor skills is cumbersome, as learning a skill from scratch and without prior knowledge requires the exploration of a large space of motor configurations. Accordingly, for learning a new task, time could be saved by restricting the parameter search space by initializing it with the solution of a similar task. We present a framework which is able of such knowledge transfer from already learned movement skills to a new learning task. The framework combines probabilistic movement primitives with descriptions of their effects for skill representation. New skills are first initialized with parameters inferred from related movement primitives and thereafter adapted to the new task through relative entropy policy search. We compare two different transfer approaches to initialize the search space distribution with data of known skills with a similar effect. We show the different benefits of the two knowledge transfer approaches on an object pushing task for a simulated 3-DOF robot. We can show that the quality of the learned skills improves and the required iterations to learn a new task can be reduced by more than 60% when past experiences are utilized.