This is just an image representation. Let's talk about this topic in detail... The immense capabilities artificial intelligence is bringing to the world would have been inconceivable to past generations. But even as we marvel at the incredible power these new technologies afford, we're faced with complex and urgent questions about the balance of benefit and harm. When most people ponder whether AI is good or evil, what they're essentially trying to grasp is whether AI is a tool or a weapon.

In July this year, Holly Ransom (Fulbright Anne Wexler Scholar, CEO Emergent) interviewed former US Ambassador to Australia Jeffrey Bleich (Professorial Fellow at Flinders University) for the launch of JBC and the first instalment in the Jeff Bleich Series – a multimedia platform for the Centre's research, engagement, and education. In a wide-ranging interview, Ambassador Bleich outlines the JBC vision, its values, goals, and aspirations, and addresses the spectrum of challenges and opportunities Australia and the United States confront in the digital age. From the impacts of automation, Artificial Intelligence, 5G & Blockchain, to the need to reinvigorate good governance, democratic participation, civil society and industry collaboration, and community and individual level empowerment, this timely and important discussion is not to be missed.

In the future 6th generation networks, ultra-reliable and low-latency communications (URLLC) will lay the foundation for emerging mission-critical applications that have stringent requirements on end-to-end delay and reliability. Existing works on URLLC are mainly based on theoretical models and assumptions. The model-based solutions provide useful insights, but cannot be directly implemented in practice. In this article, we first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC. The basic idea is to merge theoretical models and real-world data in analyzing the latency and reliability and training deep neural networks (DNNs). Deep transfer learning is adopted in the architecture to fine-tune the pre-trained DNNs in non-stationary networks. Further considering that the computing capacity at each user and each mobile edge computing server is limited, federated learning is applied to improve the learning efficiency. Finally, we provide some experimental and simulation results and discuss some future directions.

Vladimir Vapnik is the co-inventor of support vector machines, support vector clustering, VC theory, and many foundational ideas in statistical learning. He was born in the Soviet Union, worked at the Institute of Control Sciences in Moscow, then in the US, worked at AT&T, NEC Labs, Facebook AI Research, and now is a professor at Columbia University. His work has been cited over 200,000 times. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman

In the early 1980s, the psychologist Harry Heft put a 16 mm camera in the back of a sports car and made a movie. It consisted of a continuous shot of a residential neighborhood in Granville, Ohio, where Heft was a professor at Denison University. It didn't have a plot or actors, but it did have a simple narrative: The car started moving at 5 miles per hour and made nine turns from one street to another and then came to a stop after traveling just under a mile. One showed just the vistas along the route, the expansive layout of environmental features, such as a group of houses or trees seen from a distance. The second film showed the transitions of the route, the parts between each vista where the view is occluded by, say, a turn in the road or the crest of a hill.

Almost three years ago, Carnegie Mellon University Prof. Dave Andersen and I announced the Intel Science and Technology Center for Visual Cloud Systems (ISTC-VCS) at the 2016 NAB Show. Along with Prof. Kayvon Fatahalian at Stanford, Dave has led the center and collaborated closely with other academic and Intel Labs researchers to push the boundaries in visual cloud systems. We set out to study and find solutions for some of the key problems with gathering, storing and analyzing video data in large scale distributed environments. With the completion of the center now drawing near, it's time to take stock of the results and to talk of work yet to be done. The center's approach has been to bring together systems researchers and computer vision, AI and graphics researchers to create prototype systems that allow investigation of these topics.

In this paper, we design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed to improve the data freshness and connectivity to the Internet of Things (IoT) devices. First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy. We also incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. Second, we propose an agile deep reinforcement learning with experience replay model to solve the formulated problem concerning the contextual constraints for the UAV-BS navigation. Moreover, the proposed approach is well-suited for solving the problem, since the state space of the problem is extremely large and finding the best trajectory policy with useful contextual features is too complex for the UAV-BSs. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time. Finally, the simulation results illustrate the proposed approach is 3.6% and 3.13% more energy efficient than those of the greedy and baseline deep Q Network (DQN) approaches.

In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply. However, the risk associated with energy supply is also increased due to unpredictable energy generation from renewable and non-renewable sources. Especially, the risk of energy shortfall is involved with uncertainties in both energy consumption and generation. In this paper, we study a risk-aware energy scheduling problem for a microgrid-powered MEC network. First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the loss of energy shortfall of the MEC networks and we show this problem is an NP-hard problem. Second, we analyze our formulated problem using a multi-agent stochastic game that ensures the joint policy Nash equilibrium, and show the convergence of the proposed model. Third, we derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This method mitigates the curse of dimensionality of the state space and chooses the best policy among the agents for the proposed problem. Finally, the experimental results establish a significant performance gain by considering CVaR for high accuracy energy scheduling of the proposed model than both the single and random agent models.

What do you think of when you hear that term? How do these systems know your name? And what else can they tell you about someone whose image is in the system? These questions and others led the Partnership on AI (PAI) to begin the facial recognition systems project. During a series of workshops with our partners, we discovered it was first necessary to grasp how these systems work.

The stringent requirements of mobile edge computing (MEC) applications and functions fathom the high capacity and dense deployment of MEC hosts to the upcoming wireless networks. However, operating such high capacity MEC hosts can significantly increase energy consumption. Thus, a BS unit can act as a self-powered BS. In this paper, an effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. First, a two-stage linear stochastic programming problem is formulated with the goal of minimizing the total energy consumption cost of the system while fulfilling the energy demand. Second, a semi-distributed data-driven solution is proposed by developing a novel multi-agent meta-reinforcement learning (MAMRL) framework to solve the formulated problem. In particular, each BS plays the role of a local agent that explores a Markovian behavior for both energy consumption and generation while each BS transfers time-varying features to a meta-agent. Sequentially, the meta-agent optimizes (i.e., exploits) the energy dispatch decision by accepting only the observations from each local agent with its own state information. Meanwhile, each BS agent estimates its own energy dispatch policy by applying the learned parameters from meta-agent. Finally, the proposed MAMRL framework is benchmarked by analyzing deterministic, asymmetric, and stochastic environments in terms of non-renewable energy usages, energy cost, and accuracy. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost (with 95.8% prediction accuracy), compared to other baseline methods.