The fatal crash of a Tesla Motors Inc Model S in Autopilot mode has turned up pressure on auto industry executives and regulators to ensure that automated driving technology is deployed safely. On July 1, the US National Highway Traffic Safety Administration (NHTSA) said US traffic deaths rose by 7.7% to 35,200 in 2015 – the highest annual tally since 2008 and biggest single-year jump since 1966. In March, 20 automakers agreed with regulators to make automatic emergency braking standard on nearly all US vehicles by 2022, a move that could prevent thousands of rear-end crashes annually. Hours before the crash became public knowledge on June 30, US National Transportation Safety Board Chairman Christopher Hart said driverless cars will not be perfect.

This article reviews the last two IEEE Conference on Computational Intelligence and Games (CIG) StarCraft Artificial Intelligence (AI) Competitions organized by the authors; these were the fourth and fifth in a series of annual competitions initiated in 2011. StarCraft AI Competitions have been hosted in conjunction with three different events: the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), CIG, and Student StarCraft AI Tournament (SSCAIT). The purpose of these competitions is to design bots that are able to autonomously and successfully play the StarCraft game by implementing real-time strategies. Recent results reveal the promising use of AI techniques in creating successful AI entries, but there is room for improvement with respect to the bots' ability to adapt and learn to defeat humans and scripted AI bots.

Artificial intelligence in Korea is currently prospering. The media is regularly reporting AI-enabled products such as smart advisors, personal robots, autonomous cars, and human-level intelligence machines. The Ministry of Science, ICT, and Future Planning (MSIP) has launched new funding programs in AI and cognitive science to implement the government's newly adopted endeavor of building a "Creative Economy" and "Software Centric Society". Similar to the history of AI worldwide, AI research and industry in Korea have faced both the ups and downs in its history.

This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.

Recent years have seen significant technical progress on robot planning, enabling robots to compute actions and motions to accomplish challenging tasks involving driving, flying, walking, or manipulating objects. However, robots that have been commercially deployed in the real world typically have no or minimal planning capability. Although these robots are highly successful in their respective niches, a lack of planning capabilities limits the range of tasks for which currently deployed robots can be used. In this article, we highlight key conclusions from a workshop sponsored by the National Science Foundation in October 2013 that summarize opportunities and key challenges in robot planning and include challenge problems identified in the workshop that can help guide future research towards making robot planning more deployable in the real world.

Detection of fraud, waste, and abuse (FWA) is an important yet challenging problem. In this article, we describe a system to detect suspicious activities in large healthcare datasets. Each healthcare dataset is viewed as a heterogeneous network consisting of millions of patients, hundreds of thousands of doctors, tens of thousands of pharmacies, and other entities. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous network structure.

If you have feedback, please let us know in the ads subreddit. Who would like to start a collaborative Youtube channel that provides an explanation of various research papers? Aside from the Deep Learning Hype, What are some other interesting research topics for grad students coming into the field of statistics/machine learning? If a binary classifier (neural network model) achieves 99% training accuracy with 65% validation accuracy, what to do next?

If you so choose, you can delete voice items one at a time or purge all of them from the same page, which can be found in the depths of your Google account online. Actually, deleting all your voice clips doesn't purge them from Google's system. On my Voice & Activity page, I find each spoken item presented with a button next to it, allowing me to play the voice command back or delete it entirely. I can see why companies like Apple and Google want to work with spoken commands because speech recognition can only get better when computers confront more and more speech.

Called the structure-mapping engine (SME), the new model is capable of analogical problem solving, including capturing the way humans spontaneously use analogies between situations to solve moral dilemmas. Previous models of analogy, including prior versions of SME, have not been able to scale to the size of representations that people tend to use. Forbus's new version of SME can handle the size and complexity of relational representations that are needed for visual reasoning, cracking textbook problems, and solving moral dilemmas. To encourage research on analogy, Forbus's team is releasing the SME source code and a 5,000-example corpus, which includes comparisons drawn from visual problem solving, textbook problem solving, and moral decision making.

The first ever death in an autonomous car happened in May this year, the US road safety administration revealed yesterday. In a press release, Tesla said the incident was a tragic loss, but noted that it was the first fatality in 130 million miles of Autopilot driving. "There will be questions as to why these semi-autonomous driving features are allowed in beta testing mode into consumers' hands, and whether they have been adequately developed and certified before being added as an option in vehicles – even with the disclaimers which drivers have to accept before activating the feature," he says. Instead, the software will run in the background, jumping in to prevent accidents that come from human error.