Language is central to human intelligence. We review recent breakthroughs in machine language processing and consider what remains to be achieved. Recent approaches rely on domain general principles of learning and representation captured in artificial neural networks. Most current models, however, focus too closely on language itself. In humans, language is part of a larger system for acquiring, representing, and communicating about objects and situations in the physical and social world, and future machine language models should emulate such a system. We describe existing machine models linking language to concrete situations, and point toward extensions to address more abstract cases. Human language processing exploits complementary learning systems, including a deep neural network-like learning system that learns gradually as machine systems do, as well as a fast-learning system that supports learning new information quickly. Adding such a system to machine language models will be an important further step toward truly human-like language understanding.

Humans engage in informal debates on a daily basis. By expressing their opinions and ideas in an argumentative fashion, they are able to gain a deeper understanding of a given problem and in some cases, find the best possible course of actions towards resolving it. In this paper, we develop a methodology to verify debates formalised as abstract argumentation frameworks. We first present a translation from debates to transition systems. Such transition systems can model debates and represent their evolution over time using a finite set of states. We then formalise relevant debate properties using temporal and strategy logics. These formalisations, along with a debate transition system, allow us to verify whether a given debate satisfies certain properties. The verification process can be automated using model checkers. Therefore, we also measure their performance when verifying debates, and use the results to discuss the feasibility of model checking debates.

Machine learning-based lithography hotspot detection has been deeply studied recently, from varies feature extraction techniques to efficient learning models. It has been observed that such machine learning-based frameworks are providing satisfactory metal layer hotspot prediction results on known public metal layer benchmarks. In this work, we seek to evaluate how these machine learning-based hotspot detectors generalize to complicated patterns. We first introduce a automatic layout generation tool that can synthesize varies layout patterns given a set of design rules. The tool currently supports both metal layer and via layer generation. As a case study, we conduct hotspot detection on the generated via layer layouts with representative machine learning-based hotspot detectors, which shows that continuous study on model robustness and generality is necessary to prototype and integrate the learning engines in DFM flows. The source code of the layout generation tool will be available at https://github. com/phdyang007/layout-generation.

Recent studies in using deep learning to solve the Travelling Salesman Problem (TSP) focus on construction heuristics, the solution of which may still be far from optimal-ity. To improve solution quality, additional procedures such as sampling or beam search are required. However, they are still based on the same construction policy, which is less effective in refining a solution. In this paper, we propose to directly learn the improvement heuristics for solving TSP based on deep reinforcement learning. We first present a reinforcement learning formulation for the improvement heuristic, where the policy guides selection of the next solution. Then, we propose a deep architecture as the policy network based on self-attention. Extensive experiments show that, improvement policies learned by our approach yield better results than state-of-the-art methods, even from random initial solutions. Moreover, the learned policies are more effective than the traditional handcrafted ones, and robust to different initial solutions with either high or poor quality. 1 Introduction The Travelling Salesman Problem (TSP) is a typical combinatorial optimization problem that has extensive applications in the real world. The problem statement is straightforward: given a set of locations, find the salesman a shortest tour that traverses each location exactly once and returns to the original one. Although having been widely studied for decades, achieving satisfactory performance is still challenging due to its NPhard complexity.

A new online version of the game Othello has become a hit in Japan because the AI has been designed to always lose, and players love it. The game, called'The weakest AI Othello,' was released in August and has since attracted over 400,000 players for more than 1.29 million games. It was developed by Takuma Yoshida, who works at Avilen,a Tokyo firm that designs AI and machine learning tools for businesses. 'The Weakest AI Othello' is an online version of the popular board game, in which the computer AI has been designed to always lose to the human player One day at work, Yoshida began to question why he was spending so much time trying to engineer software to outperform humans. He wondered whether human attitudes toward AI and robotics might be different if humans didn't always expect to be beaten by them, according to a report in the Asahi Shimbun.

High-resolution radar and night vision cameras may help scientists protect bats from untimely deaths at wind farms, according to new research. Researchers are using these technologies to provide more specific details about the number of bats killed by wind turbines in Iowa. These details will improve scientists' understanding of bat activity and potentially save their lives, said Jing Teng, a graduate researcher at the University of Iowa who presented the work this week at the 2019 American Geophysical Union Fall Meeting in San Francisco. This work has broad impacts, according to Teng. "The more bats you kill, the more insects you have on farms; then, farmers will put more pesticides; and then, people will eat more pesticides," he said.

Machine learning is a complex process. You build a model, test it in laboratory conditions, then put it out in the world. After that, how do you monitor how well it's tracking what you designed it to do? Arthur wants to help, and today it emerged from stealth with a new platform to help you monitor machine learning models in production. The company also announced it had closed a $3.3 million seed round, which closed in August.

California-based startup Plus.ai claims to have completed a cross-country trip with a prototype autonomous truck. While a human backup driver and a safety engineer were onboard for the entire 2,800-mile trip, Plus.ai claims the truck was in autonomous mode most of the time. This wasn't just a test run, either: the truck hauled a refrigerated trailer loaded with cargo for Land O'Lakes. Perishable cargo gave Plus.ai an added incentive to ensure its tech worked. The company couldn't simply abort the trip, and a human driver taking over would have been a public relations nightmare.

The need for reskilling and retraining due to the impact of artificial intelligence (AI) and automation technology will be massive, affecting more than 120 million workers across the world's 12 largest economies, according to IBM's Institute for Business Value. In a report entitled The enterprise guide to closing the skills gap, the institute indicated that while only 41% of employers have the required people, skills and resources in place to execute their business strategies effectively today, the situation will only get worse as demand for new – particularly soft – skills continues and expertise focused around repetitive, rules-based activities becomes progressively obsolete. "By 2030, the global talent shortage could reach more than 85 million people," the study says. "To be clear, the issue is not a shortage of workers, but a shortage of workers with the right skills." To make matters worse, although the so-called "half-life" of professional skills was formerly estimated at between 10 and 15 years, the half-life of a learned skill today is estimated to be a mere five years, and is potentially even less for technical expertise. So skills learned now will only be half as valuable in five years' time, which means that finding ways to continually update and refresh them will become an increasing imperative.

Artificial intelligence is here, and here to stay. Whether you realize it or not, you feel its impact through the marketing appeals you receive online or in the mail; in the placement, packaging, and pricing of items in a supermarket; and in a myriad of other ways. AI is also embedded in life insurance operations, helping agents match products with prospective clients with a precision that was previously unimaginable. It's understandable, however, that some life agents might be apprehensive about the growth of AI in a field that prides itself on providing thoughtful, individualized solutions to the unique situation of each household. Things will certainly change as AI advances in life insurance, but agents that embrace AI and the changes it brings will actually find themselves to be more valuable to the carriers and customers who rely on them.