FOX Business' Charles Payne covers artificial intelligence and genetic editing and says Hollywood movies often dramatizing these cutting-edge technologies when we should actually be embracing and investing in companies that develop them. Artificial intelligence can help companies increase their efficiency, reduce their carbon footprint and help keep workers safe, according to engineering experts. Now, Microsoft is partnering with energy industry tech company Baker Hughes and AI developer C3.ai to bring enterprise AI technology to the energy industry via its Azure cloud computing platform. The companies said their alliance would allow customers to streamline the adoption of AI designed to address issues like inventory, energy management, predictive maintenance and equipment reliability. "For the energy industry, this is a time of significant transformation and forward-thinking companies are exploring how to leverage technology to make their operations cleaner, safer and more efficient," said Judson Athoff, EVP of Microsoft's worldwide commercial business, in a press release.

So, imagine you are driving along in your fancy high-tech car, minding your own business, with your yet-to-be-feature-complete full self-driving software activated, and suddenly the car slams the brakes to a full stop, like a donkey that had an epiphany that what it was doing just didn't serve any purpose and therefor just refused to move any further. Sure, it's great that something useful is becoming of outdated fossil fuel vehicles. Giants having fun with them riding down hills seem like a good solution, and I guess they don't burn gas in the process, but my car might get nervous around these guys. For the time being, my car didn't stop, but when the brain of the car gets more clever than that of a cricket, it might recognize the car, and panic! It does so already when it sees broken off tree branches laying on the side of the road.

Every part of our daily lives can play a role in causing it, from electricity, to transportation, the homes we live in, the food we eat, even the healthcare services we rely on. And all of those aspects of our lives are also affected by climate change. Much of what we know about the impacts of climate change comes from sophisticated computer models. Now, a group of computer scientists is calling on their colleagues to put advanced computing and artificial intelligence to work to solve the climate problem. "A.I. can help pinpoint where deforestation is happening using satellite imagery or aerial imagery," said David Rolnick, lead author of a new study outlining how artificial intelligence could help with climate change.

Wangchuk, who inspired Aamir Khan's character Phunsukh Wangdu in the film 3 Idiots, is the founding director of the Students' Educational and Cultural Movement of Ladakh (SECMOL). Wangchuk said that SEMCOL was founded in 1988 by students who were, "victims of an alien education system foisted on Ladakh." He has set up the campus in Ladakh that runs on solar energy and uses no fossil fuels for lighting, cooking, or heating. Talking about his courtship with innovation, he said, "I grew up in a place where innovation was all around and without it, life wouldn't have been possible." "I saw how in the trans-Himalayan cold desert of Ladakh where temperatures reach minus 30 degrees, our ancestors not only survived but also contributed to the thriving inter-mingled civilisations. So, the freezing winters were not something to fear but something to look forward to – the hard work of ploughing, farming, winnowing, and thrashing, became in the carrying out of these processes, a community festival," he added.

You don't need to take in Disney's latest Star Wars instalments to see robots battling robots. Just pay a visit to the cyber desk of an energy company. Digital technologies are very democratic -- anyone can access them. All you need is a reasonably advanced smart phone from any of the big phone suppliers, an internet access (free in coffee shops and malls), and a free account on a cloud service. The apps are mostly free too, which tells you they're not costly to make.

This paper is concerned with inverse problems where we intend to match certain given data to data predicted by a (discrete or continuous) mathematical model, often called the forward model.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered great success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Though empirically successful, theoretical foundations for MARL are relatively lacking in the literature. In this chapter, we provide a selective overview of MARL, with focus on algorithms backed by theoretical analysis. More specifically, we review the theoretical results of MARL algorithms mainly within two representative frameworks, Markov/stochastic games and extensive-form games, in accordance with the types of tasks they address, i.e., fully cooperative, fully competitive, and a mix of the two. We also introduce several significant but challenging applications of these algorithms. Orthogonal to the existing reviews on MARL, we highlight several new angles and taxonomies of MARL theory, including learning in extensive-form games, decentralized MARL with networked agents, MARL in the mean-field regime, (non-)convergence of policy-based methods for learning in games, etc. Some of the new angles extrapolate from our own research endeavors and interests. Our overall goal with this chapter is, beyond providing an assessment of the current state of the field on the mark, to identify fruitful future research directions on theoretical studies of MARL. We expect this chapter to serve as continuing stimulus for researchers interested in working on this exciting while challenging topic.

Deep reinforcement learning for high dimensional, hierarchical control tasks usually requires the use of complex neural networks as functional approximators, which can lead to inefficiency, instability and even divergence in the training process. Here, we introduce stacked deep Q learning (SDQL), a flexible modularized deep reinforcement learning architecture, that can enable finding of optimal control policy of control tasks consisting of multiple linear stages in a stable and efficient way. SDQL exploits the linear stage structure by approximating the Q function via a collection of deep Q sub-networks stacking along an axis marking the stage-wise progress of the whole task. By back-propagating the learned state values from later stages to earlier stages, all sub-networks co-adapt to maximize the total reward of the whole task, although each sub-network is responsible for learning optimal control policy for its own stage. This modularized architecture offers considerable flexibility in terms of environment and policy modeling, as it allows choices of different state spaces, action spaces, reward structures, and Q networks for each stage, Further, the backward stage-wise training procedure of SDQL can offers additional transparency, stability, and flexibility to the training process, thus facilitating model fine-tuning and hyper-parameter search. We demonstrate that SDQL is capable of learning competitive strategies for problems with characteristics of high-dimensional state space, heterogeneous action space(both discrete and continuous), multiple scales, and sparse and delayed rewards.

Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the load-modulated multiple-input multiple-output (MIMO) transmitters with central power amplifiers (CP A). However, it is difficult to obtain the precise channel state information (CSI), and the traditional optimal maximum likelihood (ML) detection scheme incurs high complexity which increases exponentially with the number of antennas and the number of bits carried per antenna in the PMH modulation. To detect the PMH signals without knowing the prior CSI, we first propose a signal detection scheme, termed as the hypersphere clustering scheme based on the expectation maximization (EM) algorithm with maximum likelihood detection (HEM-ML). By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector. To further reduce the computational complexity in the ML detection in HEM-ML, we also propose the second signal detection scheme, termed as the hypersphere clustering scheme based on the EM algorithm with KD-tree detection (HEM-KD). The CSI obtained from the EM algorithm is used to build a spatial KD-tree receiver codebook and the signal detection problem can be transformed into a nearest neighbor search (NNS) problem. The detection complexity of HEM-KD is significantly reduced without any detection performance loss as compared to HEM-ML. Extensive simulation results verify the effectiveness of our proposed detection schemes. I NTRODUCTION The fifth generation (5G) wireless communication network is forecasted to provide over 1000 times higher capacity than the current system. In addition to dramatically expanding the available bandwidth, multiple-input multiple-output (MIMO) technology is playing a key role in improving the spectral efficiency (SE) and enhancing the throughput in the future wireless cellular communication systems [1]. This ambitious goal will however cause an inevitable energy consumption problem, thus limiting the number of the antennas at the base station (BS) and the user terminals in practice [2]. In the traditional design of the MIMO transceivers, each antenna is connected with one distinct radio frequency (RF) chain which includes a power amplifier (P A). This kind of structure enables the power consumption of the transmission to grow linearly with the number of the antennas. In addition, the use of Orthogonal Frequency Division Multiplexing (OFDM) signals in massive MIMO systems leads to a high peak-to-average power ratios (P APR) and exacerbates the costs of P As, thus reducing the power efficiency. On the other hand, to alleviate the effects of mutual coupling and correlated fading, the antennas should be set at least half of a wavelength apart from each other, which will inevitably cause the size problem [3].

A major wildfire spread through Colorado, and I spent long hours locating shelters, identifying evacuation routes and piecing together satellite imagery. As the Fourmile Canyon Fire devastated areas to the west of Boulder, ultimately destroying 169 homes and causing $217 million in damage, my biggest concerns were ensuring that people could safely evacuate and first responders had the best chance of keeping the fire at bay. I spent it sitting comfortably in my home in Bloomington, Indiana, a thousand miles away from the action. I was a volunteer, trying to help fire victims. I had created a webpage to aggregate data about the fire, including the location of shelters and the latest predictions of fire spread.