Artificial intelligence may be one of the technology world's current obsessions, but many people find it scary, envisioning robots taking over the world. Two top experts in the field-- Andrew Ng, a Stanford University adjunct professor and former AI scientist at Alphabet Inc.'s Google and Chinese internet giant Baidu Inc., and Tong Zhang, executive director of the AI Lab at Tencent Holdings Ltd. --sat down with The Wall Street Journal's global technology editor, Jason Dean, to explain why they believe the opportunities associated with this technology far outweigh the bad. The title of this panel refers to "the singularity," or the idea that artificial intelligence will become so powerful that robots will take over. Andrew, I know you're skeptical of that. What should we be worried about with AI and where are the biggest opportunities?

The experts go on to predict a 50 percent chance that AI will be better than humans at more or less everything in about 45 years. Artificial intelligence is changing the world and doing it at breakneck speed. The promise is that intelligent machines will be able to do every task better and more cheaply than humans. Rightly or wrongly, one industry after another is falling under its spell, even though few have benefited significantly so far. And that raises an interesting question: when will artificial intelligence exceed human performance?

In the hit movie Avengers: Age of Ultron, the Iron Man shows the'brains' of a computer system to his colleague, the Incredible Hulk. "I mean, look at this! They're like neurons firing," the Hulk exclaims, pointing to a pulsating, blue orb which represented super baddie Ultron's consciousness. We'd like to think that's what neural networks look like too. They are a rising field of artificial intelligence, and a new trend that is coming to a government near you. Neural networks describe a computing technique that closely imitates human brain functions. "By using neural networks, we try to mimic nature's ability to learn how certain things work," Associate Professor Andy Chun from City University of Hong Kong's Department of Computer Science tells GovInsider.

Bike Sharing System (BSS) is a green mode of transportation that is employed extensively for short distance travels in major cities of the world. Unfortunately, the users behaviour driven by their personal needs can often result in empty or full base stations, thereby resulting in loss of customer demand. To counter this loss in customer demand, BSS operators typically utilize a fleet of carrier vehicles for repositioning the bikes between stations. However, this fuel burning mode of repositioning incurs a significant amount of routing, labor cost and further increases carbon emissions. Therefore, we propose a potentially self-sustaining and environment friendly system of dynamic repositioning, that moves idle bikes during the day with the help of bike trailers. A bike trailer is an add-on to a bike that can help with carrying 3-5 bikes at once. Specifically, we make the following key contributions: (i) We provide an optimization formulation that generates “repositioning” tasks so as to minimize the expected lost demand over past demand scenarios; (ii) Within the budget constraints of the operator, we then design a mechanism to crowdsource the tasks among potential users who intend to execute repositioning tasks; (iii) Finally, we provide extensive results on a wide range of demand scenarios from a real-world data set to demonstrate that our approach is highly competitive to the existing fuel burning mode of repositioning while being green.

Multi-agent forward search (MAFS) is a state-of-the-art privacy-preserving planning algorithm. We describe a new variant of MAFS, called multi-agent forward-backward search (MAFBS) that uses both forward and backward messages to reduce the number of messages sent and obtain new privacy properties. While MAFS requires agents to send a state s produced by an action a to all agents that can apply any action in s, MAFBS sends such messages forward only to agents that have an action that requires one of the effects of a. To achieve completeness, it sends messages backward to agents that can supply a missing precondition. This more focused message passing scheme reduces states exchanged, and requires that agents be aware only of other agents that they directly interact with, leading to agent privacy.

In many robot motion planning problems such as manipulation planning for a personal robot in a kitchen or an industrial manipulator in a warehouse, all motion planning queries are in an environment that is largely static. Consequently, one should be able to improve the performance of a planning algorithm by training on this static environment ahead of operation time. In this work, we propose a method to improve the performance of heuristic search-based motion planners in such environments. The first, learning, phase of our proposed method analyzes search performance on multiple planning episodes to infer local minima zones, that is, regions where the existing heuristic(s) are weakly correlated with the true cost-to-go. Then, in the planning phase of the method, the learnt local minima are used to modify the original search graph in a way that improves search performance. We prove that our method preserves guarantees on completeness and bounded suboptimality with respect to the original search graph. Experimentally, we observe significant improvements in success rate and planning time for challenging 11 degree-of-freedom mobile manipulation problems.

The problem of finding conflict-free trajectories for multiple agents of identical circular shape, operating in shared 2D workspace, is addressed in the paper and decoupled, e.g., prioritized, approach is used to solve this problem. Agents' workspace is tessellated into the square grid on which any-angle moves are allowed, e.g. each agent can move into an arbitrary direction as long as this move follows the straight line segment whose endpoints are tied to the distinct grid elements. A novel any-angle planner based on Safe Interval Path Planning (SIPP) algorithm is proposed to find trajectories for an agent moving amidst dynamic obstacles (other agents) on a grid. This algorithm is then used as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical side, we show that AA-SIPP(m) is complete under well-defined conditions. On the experimental side, in simulation tests with up to 250 agents involved, we show that our planner finds much better solutions in terms of cost (up to 20%) compared to the planners relying on cardinal moves only.

The deployment of robots in public environments is gaining more and more attention and interest both for the research opportunities and for the possibility of developing commercial applications over it. In these scenarios, proper definitions and implementations of human-robot interactions are crucial and the specific characteristics of the environment (in particular, the presence of untrained users) makes the task of defining and implementing effective interactions particularly challenging. In this paper, we describe a method and a fully implemented robotic system using conditional planning for generating and executing short-term interactions by a robot deployed in a public environment. To this end, the proposed method integrates and extends two components already successfully used for planning in robotics: ROSPlan and Petri Net Plans. The contributions of this paper are the problem definition of generating short-term interactions as a conditional planning problem and the description of a solution fully implemented on a real robot. The proposed method is based on the integration between a contingent planner in ROSPlan and the Petri Net Plans execution framework, and it has been tested in different scenarios where the robot interacted with hundreds of untrained users.

We consider the motion-planning problem of planning a collision-free path of a robot in the presence of risk zones. The robot is allowed to travel in these zones but is penalized in a super-linear fashion for consecutive accumulative time spent there. We suggest a natural cost function that balances path length and risk-exposure time. Specifically, we consider the discrete setting where we are given a graph, or a roadmap, and we wish to compute the minimal-cost path under this cost function. Interestingly, paths defined using our cost function do not have an optimal substructure. Namely, subpaths of an optimal path are not necessarily optimal. Thus, the Bellman condition is not satisfied and standard graph-search algorithms such as Dijkstra cannot be used. We present a path-finding algorithm, which can be seen as a natural generalization of Dijkstra’s algorithm. Our algorithm runs in O ((n B · n) log(n B · n) + n B · m) time, where n and m are the number of vertices and edges of the graph, respectively, and n B is the number of intersections between edges and the boundary of the risk zone. We present simulations on robotic platforms demonstrating both the natural paths produced by our cost function and the computational efficiency of our algorithm.

This paper develops an effective, cooperative, and probabilistically-complete multi-robot motion planner. The approach takes into account geometric and differential constraints imposed by the obstacles and the robot dynamics by using sampling to expand a motion tree in the composite state space of all the robots. Scalability and efficiency is achieved by using solutions to a simplified problem representation that does not take dynamics into account to guide the motion-tree expansion. The heuristic solutions are obtained by constructing roadmaps over low-dimensional configuration spaces and relying on cooperative multi-agent graph search to effectively find graph routes. Experimental results with second-order vehicle models operating in complex environments, where cooperation among the robots is required to find solutions, demonstrate significant improvements over related work.