Are the U.S., China, and Russia recklessly undertaking an "AI arms race"? Clearly, there is military competition among these great powers to advance a range of applications of robotics, artificial intelligence, and autonomous systems. So far, the U.S. has been leading the way. AI and autonomy are crucial to the Pentagon's Third Offset strategy. Its Algorithmic Warfare Cross-Functional Team, Project Maven, has become a "pathfinder" for this endeavor and has started to deploy algorithms in the fight against ISIS.

I make some basic observations about hard takeoff, value alignment, and coherent extrapolated volition, concepts which have been central in analyses of superintelligent AI systems.

Please register here to get Tyche pre-order notification. The purpose of AIBrain is to augment human intelligence with AI by unifying Problem Solving, Learning, and Memory. The power of our practical AI agent comes from two central components, which are a reasoning engine called AICoRE and a human-like memory called Memory Graph. AICoRE (Adaptive Interactive Cognitive Reasoning Engine) is a cognitive reasoning engine by unifying problem solving and learning. AICoRe fully automates the reasoning process from end to end.

This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line , we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.

Interest in emergent communication has recently surged in Machine Learning. The focus of this interest has largely been either on investigating the properties of the learned protocol or on utilizing emergent communication to better solve problems that already have a viable solution. Here, we consider self-driving cars coordinating with each other and focus on how communication influences the agents' collective behavior. Our main result is that communication helps (most) with adverse conditions.

Vital for the building and maintaining of successful business relationships, networking is now easier and more effective than ever before. Invacio Network is a relevancy-driven web desktop that offers a suite of features designed to help you to build meaningful business and personal relationships with the right people, whoever and wherever they may be... Powerful yet extremely elegant, Network gives immediate access to a broad range of Invacio products and services, including Data, Agnes, Invmail, Alise and Consumer, as well as up to the minute content that is tailored exactly to your use by the work of our groundbreaking multi-agent system AI. Whether you wish to follow company and market tickers and charting with one click or to get real time information on the things that matter to you from the entire web, there is never sponsored content, never the clutter of advertising ...only the information you need when you need it, whatever your purpose. The hassle is taken out of arranging business meetings with our Meet Me feature. Simply choose to broadcast the location of your next trip and let other users know when you're in their area seeking business meetings or perhaps even just a friendly face to have dinner with.

The spread of autonomous systems into safety-critical areas has increased the demand for their formal verification, not only due to stronger certification requirements but also to public uncertainty over these new technologies. However, the complex nature of such systems, for example, the intricate combination of discrete and continuous aspects, ensures that whole system verification is often infeasible. This motivates the need for novel analysis approaches that modularise the problem, allowing us to restrict our analysis to one particular aspect of the system while abstracting away from others. For instance, while verifying the real-time properties of an autonomous system we might hide the details of the internal decision-making components. In this paper we describe verification of a range of properties across distinct dimesnions on a practical hybrid agent architecture. This allows us to verify the autonomous decision-making, real-time aspects, and spatial aspects of an autonomous vehicle platooning system. This modular approach also illustrates how both algorithmic and deductive verification techniques can be applied for the analysis of different system subcomponents.

AI will augment people's capabilities but won't take all jobs. However, there will be socioeconomic upheaval, warns PwC's authority on AI, Anand Rao. Anand Rao is PwC global leader for artificial intelligence (AI) and is the consulting giant's innovation lead for the US analytics practice. Rao has 24 years of industry and consulting experience, helping senior executives to structure, solve and manage critical issues facing their organisations. He has worked extensively on business, technology and analytics issues across a wide range of industry sectors including financial services, healthcare, telecommunications, aerospace and defence, across US, Europe, Asia and Australia.

A common mechanism to assess trust in crowdworkers is to have them answer gold tasks. However, assigning gold tasks to all workers reduces the efficiency of the platform. We propose a mechanism that exploits transitivity so that a worker can be certified as trusted by other trusted workers who solve common tasks. Thus, trust can be derived from a smaller number of gold tasks assignment through multiple layers of peer relationship among the workers, a model we call deep trust. We use the derived trust to incentivize workers for high quality work and show that the resulting mechanism is dominant strategy incentive compatible. We also show that the mechanism satisfies a notion of fairness in that the trust assessment (and thus the reward) of a worker in the limit is independent of the quality of other workers.

As learning algorithms are increasingly deployed in markets and other competitive environments, understanding their dynamics is becoming increasingly important. We study the limiting behavior of competitive agents employing gradient-based learning algorithms. Specifically, we introduce a general framework for competitive gradient-based learning that encompasses a wide breadth of learning algorithms including policy gradient reinforcement learning, gradient based bandits, and certain online convex optimization algorithms. We show that unlike the single agent case, gradient learning schemes in competitive settings do not necessarily correspond to gradient flows and, hence, it is possible for limiting behaviors like periodic orbits to exist. We introduce a new class of games, Morse-Smale games, that correspond to gradient-like flows. We provide guarantees that competitive gradient-based learning algorithms (both in the full information and gradient-free settings) avoid linearly unstable critical points (i.e. strict saddle points and unstable limit cycles). Since generic local Nash equilibria are not unstable critical points---that is, in a formal mathematical sense, almost all Nash equilibria are not strict saddles---these results imply that gradient-based learning almost surely does not get stuck at critical points that do not correspond to Nash equilibria. For Morse-Smale games, we show that competitive gradient learning converges to linearly stable cycles (which includes stable Nash equilibria) almost surely. Finally, we specialize these results to commonly used multi-agent learning algorithms and provide illustrative examples that demonstrate the wide range of limiting behaviors competitive gradient learning exhibits.