The potential positive impact of autonomous driving and driver assistance technolo- gies have been a major impetus over the last decade. On the flip side, it has been a challenging problem to analyze the performance of human drivers or autonomous driving agents quantitatively. In this work, we propose a generic method that compares the performance of drivers or autonomous driving agents even if the environmental conditions are different, by using the driver behavioral advantage instead of absolute metrics, which efficiently removes the environmental factors. A concrete application of the method is also presented, where the performance of more than 100 truck drivers was evaluated and ranked in terms of fuel efficiency, covering more than 90,000 trips spanning an average of 300 miles in a variety of driving conditions and environments.

Teaching computers to read, think and communicate like humans is a daunting task, but it's one that Dr. Geoff Gordon embraces with enthusiasm and optimism. Moving from an academic role at Carnegie Mellon University, to a new role as Research Director of the Microsoft Research Lab in Montreal, Dr. Gordon embodies the current trend toward partnership between academia and industry as we enter what many believe will be a new era of progress in machine learning and artificial intelligence. Today, Dr. Gordon gives us a brief history of AI, including his assessment of why we might see a break in the weather-pattern of AI winters, talks about how collaboration is essential to innovation in machine learning, shares his vision of the mindset it takes to tackle the biggest questions in AI, and reveals his life-long quest to make computers less… well, less computer-like. Geoff Gordon: You cannot know ahead of time exactly what's going to come out, because if you knew, it wouldn't be research. You don't expect your payoffs to be measured in months or even necessarily a couple of years. But it could be that the things you're doing now pay off ten years later. And so, Microsoft has decided that MSR is in it for the long-term, and that changes the type of research that you can do, right? You can afford to make big bets. Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Teaching computers to read, think and communicate like humans is a daunting task, but it's one that Dr. Geoff Gordon embraces with enthusiasm and optimism. Moving from an academic role at Carnegie Mellon University to a new role as research director of the Microsoft Research Lab in Montreal, Dr. Gordon embodies the current trend toward the partnership between academia and industry, as we enter what many believe will be a new era of progress in machine learning and artificial intelligence. Today, Dr. Gordon gives us a brief history of AI, including his assessment of why we might see a break in the weather pattern of AI winters, talks about how collaboration is essential to innovation and machine learning, shares his vision of the mindset it takes to tackle the biggest questions in AI, and reveals his life-long quest to make computers less… well, less computer-like. Host: Geoff Gordon, thanks for coming all the way from Montreal to join us in the studio today.

Several years ago, Canary Pete's political cartoon flooded email inboxes and social media pages. The humorous illustration showed a middle-aged executive walking into a typical job interview, with the exception that he had to build his own office chair (since he was applying to work at IKEA). Pete's satire might be short-lived, as last week the robotics industry achieved a new milestone – building an IKEA chair in less than 10 minutes! It is unclear how soon bots will be replacing human assemblers like TaskRabbit, but in the the words of Jackie DeChamps, Chief Operating Officer of IKEA USA,"We are always looking at ways we can innovate and help make our customers' lives at home easier." Addressing the elephant in the room, I debated a colleague earlier this month at The Frontier Conference in New Orleans. I expressed that it is critical for mechatronic companies to engage early with organized labor for successful deployments.

Code for this project can be found on: Github. This article can also be found on my website here. As part of completing the second project of Udacity's Self-Driving Car Engineer online course, I had to implement and train a deep neural network to identify German traffic signs. In total, the dataset used consisted of 51,839 RGB images with dimensions 32x32, and is publicly accessible on this website. A validation set was used to assess how well the model is performing.

The project named'Enhancing the Region through New Technology for Unmanned Systems,' will implement a new drone technology training program at Dabney S. Lancaster Community College. This program will open up a career pathway, by enhancing the learning opportunities for high school students and extending to four-year degree attainment through partnerships with other higher-education institutions. This project aims to capitalize on the "Alleghany Highlands Drone Zone Initiative," a business accelerator program to support enterprises in the UAS industry in Alleghany County. "Growth and Opportunity for Virginia (GO Virginia) is inspiring the innovative thinking that will help to push Virginia's economy forward," says Governor, Ralph Northam.

In computer science, you are taught to comment your code. When you learn a new language, you learn the syntax for a comment in that language. Although the compiler or interpreter ignores all comments in a program, comments are valuable. However, there is a recent viewpoint that commenting code is bad, and that you should avoid all comments in your programs. In the 2013 article No Comment: Why Commenting Code Is Still a Bad Idea, Peter Vogel continued this discussion.

The ACM constitution provides that our Association hold a general election in the even-numbered years for the positions of President, Vice President, Secretary/Treasurer, and Members-at-Large. Biographical information and statements of the candidates appear on the following pages (candidates' names appear in random order). In addition to the election of ACM's officers--President, Vice President, Secretary/Treasurer--two Members-at-Large will be elected to serve on ACM Council. Please refer to the instructions posted at https://www.esc-vote.com/acm2018. To access the secure voting site, you will need to enter your email address (the email address associated with your ACM member record) and your unique PIN provided by Election Services Co. Should you wish to vote by paper ballot please contact Election Services Co. to request a paper copy of the ballot and follow the postal mail ballot procedures: [email protected] or 1-866-720-4357. Please return your ballot in the enclosed envelope, which must be signed by you on the outside in the space provided. The signed ballot envelope may be inserted into a separate envelope for mailing if you prefer this method. All ballots must be received by no later than 16:00 UTC on 24 May 2018. Validation by the Tellers Committee will take place at 14:00 UTC on 29 May 2018. Jack Davidson's research interests include compilers, computer architecture, system software, embedded systems, computer security, and computer science education. He is co-author of two introductory textbooks: C Program Design: An Introduction to Object-Oriented Programming and Java 5.0 Program Design: An Introduction to Programming and Object-oriented Design. Professionally, he has helped organize many conferences across several fields.

Whereas people learn many different types of knowledge from diverse experiences over many years, and become better learners over time, most current machine learning systems are much more narrow, learning just a single function or data model based on statistical analysis of a single data set. We suggest that people learn better than computers precisely because of this difference, and we suggest a key direction for machine learning research is to develop software architectures that enable intelligent agents to also learn many types of knowledge, continuously over many years, and to become better learners over time. In this paper we define more precisely this never-ending learning paradigm for machine learning, and we present one case study: the Never-Ending Language Learner (NELL), which achieves a number of the desired properties of a never-ending learner. NELL has been learning to read the Web 24hrs/day since January 2010, and so far has acquired a knowledge base with 120mn diverse, confidence-weighted beliefs (e.g., servedWith(tea,biscuits)), while learning thousands of interrelated functions that continually improve its reading competence over time. NELL has also learned to reason over its knowledge base to infer new beliefs it has not yet read from those it has, and NELL is inventing new relational predicates to extend the ontology it uses to represent beliefs. We describe the design of NELL, experimental results illustrating its behavior, and discuss both its successes and shortcomings as a case study in never-ending learning. NELL can be tracked online at http://rtw.ml.cmu.edu, and followed on Twitter at @CMUNELL. Machine learning is a highly successful branch of artificial intelligence (AI), and is now widely used for tasks from spam filtering, to speech recognition, to credit card fraud detection, to face recognition. Despite these successes, the ways in which computers learn today remain surprisingly narrow when compared to human learning. This paper explores an alternative paradigm for machine learning that more closely models the diversity, competence and cumulative nature of human learning.

Last week, PLOS Medicine and PLOS ONE editors Linda Nevin and Meghan Byrne attended Human Intelligence & Artificial Intelligence (HIAI) in Medicine, a Stanford Presence Center symposium. HIAI brought together thought leaders in medicine, computer science and policy to envisage an inclusive, equitable and humane experience in medicine with AI solutions. A few highlights from the symposium are described here. "Supervised learning is the ultimate example of'garbage in, garbage out'," computer scientist and former Stanford President John L. Hennessy told the audience in his opening remarks at last Tuesday's Human Intelligence & Artificial Intelligence (HIAI) in Medicine Symposium, hosted by the Stanford Presence Center. Dr. Hennessy was honored at the symposium for his recent Turing Award, but his talk stayed true to the Presence mission--championing human intelligence in medicine as artificial intelligence (AI)'s role in the clinic grows.

Two mayors discussed how they are using artificial intelligence and machine learning to improve their cities and prepare for the workforce of the future at a conference held April 23 in Chicago. The event was hosted by news organization Axios and the United States Conference of Mayors and led by Axios Executive Editor Mike Allen. Also joining the discussion was Imir Arifi, head of artificial intelligence and machine learning at Health Care Service Corporation. According to Arifi, the main use of AI and machine learning is through historical data to predict future events. In a city, for example, Arifi said AI can be used to predict how many potholes the city will need to fill in a year based on data from previous years.