WELLINGTON – Mike Moore, who served as New Zealand's prime minister before leading the World Trade Organization during a tumultuous time when thousands protested in Seattle riots, died early Sunday. He died at his home in Auckland, his wife Yvonne Moore said. He had suffered a number of health complications since having a stroke five years ago. Moore was an advocate for both advancing the rights of blue-collar workers and for expanding international trade, a combination which, to some, seemed at odds with itself. Although he had a long political career in New Zealand, Moore's tenure as prime minister was brief: just two months in 1990 before he was defeated in an election.

Automated driving in urban settings is challenging chiefly due to the indeterministic nature of the human participants of the traffic. These behaviors are difficult to model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when they face unmodeled dynamics. On the other hand, the more recent, end-to-end Deep Reinforcement Learning (DRL) based ADSs have shown promising results. However, pure learning-based approaches lack the hard-coded safety measures of model-based methods. Here we propose a hybrid approach that integrates a model-based path planner into a vision based DRL framework to alleviate the shortcomings of both worlds. In summary, the DRL agent learns to overrule the model-based planner's decisions if it predicts that better future rewards can be obtained while doing so, e.g., avoiding an accident. Otherwise, the DRL agent tends to follow the model-based planner as close as possible. This logic is learned, i.e., no switching model is designed here. The agent learns this by considering two penalties: the penalty of straying away from the model-based path planner and the penalty of having a collision. The latter has precedence over the former, i.e., the penalty is greater. Therefore, after training, the agent learns to follow the model-based planner when it is safe to do so, otherwise, it gets penalized. However, it also learns to sacrifice positive rewards for following the model-based planner to avoid a potential big negative penalty for making a collision in the future. Experimental results show that the proposed method can plan its path and navigate while avoiding obstacles between randomly chosen origin-destination points in CARLA, a dynamic urban simulation environment. Our code is open-source and available online.

The way that people make choices or exhibit preferences can be strongly affected by the set of available alternatives, often called the choice set. Furthermore, there are usually heterogeneous preferences, either at an individual level within small groups or within sub-populations of large groups. Given the availability of choice data, there are now many models that capture this behavior in order to make effective predictions. However, there is little work in understanding how directly changing the choice set can be used to influence a group's preferences or decisions. Here, we use discrete choice modeling to develop an optimization framework of such interventions for several problems of group influence, including maximizing agreement or disagreement and promoting a particular choice. We show that these problems are NP-hard in general but imposing restrictions reveals a fundamental boundary: promoting an item is easier than maximizing agreement or disagreement. After, we design approximation algorithms for the hard problems and show that they work extremely well for real-world choice data.

The history and development of Artificial Intelligence has seen numerous peaks and troughs. Hype around what machines can accomplish lead to boosts in AI funding while unmet expectations cripple the industry until the next breakthrough. The term AI Winter refers to periods in history of reduced funding and interest in artificial intelligence development. During the cold war, there was an increased interest in Machine Translation to automate the translation of Russian documents into English. This time period also coincided with massive strides in linguistic developments and the early career of the famed linguist Noam Chomsky.

Creating a system that uses data, machine learning and state-of-the art software to seamlessly link "sensors to shooters" across all domains – air, land, …

Perth City Council has reportedly been filming and tracking people moving around parts of the city without their knowledge. In what the council calls a trial, a network of 30 cameras with facial recognition technology have been deployed across East Perth. This has quietly gone on for six months. The cameras use deep-learning artificial intelligence (AI) to recognise faces and vehicles, and to count passing people – a form of population control which China widely employs, and is criticised for by human rights groups. But in Perth – the third Australian city to invest in the technology – many residents were unaware of the trial before it started.

A Baidu Apollo autonomous vehicle is on display during the 2nd Digital China Summit & Exhibition at ... [ ] Fuzhou Strait International Conference & Exhibition Center on last year in Fuzhou, Fujian Province of China. As the technology dawned, the predictions were stunning: one 2015 prediction forecast that autonomous vehicles would be piloting humans around U.S. cities in significant numbers as soon as 2018. But here we are in 2020 and AVs have barely shown themselves in the United States, while other countries--notably China--are threatening to take the lead. "Even the manufacturers have taken a much more careful view of it, a much more calculated view in the United States," said Jerry Quandt, executive director of the Illinois Autonomous Vehicle Association, "but we are seeing it happening in China, and there are cities where 20 percent of the vehicles they have on the road are autonomous." It's not entirely clear where the two countries stand in relation to technology development.

Imagine an underwater drone armed with nuclear warheads and capable of operating autonomously. Now imagine that drone has lost its way and wandered into another state's territorial waters. Russia aims to field just such a drone by 2027, CNBC reported last year, citing those familiar with a U.S. intelligence assessment. Known as Poseidon, the drone will be nuclear-armed and nuclear-powered. While the dynamics of artificial intelligence and machine learning, or ML, research remain open and often collaborative, the military potential of AI has intensified competition among great powers.

The majority of Information Security teams' cybersecurity analysts are overwhelmed today analyzing security logs, thwarting breach attempts, investigating potential fraud incidents and more. The following graphic compares the percentage of organizations by industry who are relying on AI to improve their cybersecurity. The bottom line is all organizations have an urgent need to improve endpoint security and resilience, protect privileged access credentials, reduce fraudulent transactions, and secure every mobile device applying Zero Trust principles. Many are relying on AI and machine learning to determine if login and resource requests are legitimate or not based on past behavioral and system use patterns. Several of the top ten companies to watch take into account a diverse series of indicators to determine if a login attempt, transaction, or system resource request is legitimate or not.

China's Xiongan New Area project, near Beijing, is part of the central government's ambitious drive to lead in new technologies like AI and 5G communication. Woven City, near Japan's Mount Fuji, is a much smaller project -- just 175 acres -- that is being led not by the government, but by one of its leading industrial giants, Toyota Motor Corp. If the U.S. were to build a similar prototype city, it would need to invest or direct billions of dollars in advanced technologies like 5G, vehicle-to-vehicle communication, electric charging infrastructure and vehicle automation in an area with a high population density. My thought bubble: Why not San Juan, Puerto Rico? Yes, but: Puerto Rican residents have to want to be test subjects, notes Michelle Avary, head of autonomous mobility at the World Economic Forum.