A friend's birthday barbecue is coming up in a few days and we decide to surprise our friend with a new grill. Online, a manufacturer's website allows us to customize the grill. Unknown to us, the design space consists of billions of grills and we create a one-of-a-kind design that has never been produced before. Designing, procuring, producing, and delivering a unique product in a short time, at an affordable price, with minimal human intervention, requires complex interactions across software-hardware, systems, and time scales. This process, known as lot size one production, is only possible through the use of autonomous production systems. Manufacturing is a cornerstone of a country's innovation pipeline and many companies are reshoring to keep manufacturing and R&D as close as possible. While most people think manufacturing is low-tech and boring, I must disagree!

The ACM constitution provides that our Association holds 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--five Members-at-Large will be elected to serve on ACM Council. Please refer to the instructions posted at https://www.esc-vote.com/acm. 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. 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 22 May 2020. Validation by the Tellers Committee will take place at 14:00 UTC on 26 May 2020. Elizabeth Churchill is a Director of User Experience at Google. Her field of study is Human Computer Interaction (HCI) and User Experience (UX), with a current focus on the design of effective designer and developer tools. Churchill has built research groups and led research in a number of well-known companies, including as Director of Human Computer Interaction at eBay Research Labs in San Jose, CA, as a Principal Research Scientist and Research Manager at Yahoo! in Santa Clara, CA, and as a Senior Scientist at the Palo Alto Research Center (PARC) and FXPAL, Fuji Xerox's Research lab in Silicon Valley. Working across a number of research areas, she has over 100 peer reviewed top-tier journal and conference publications in theoretical and applied psychology, cognitive science, human-computer interaction, mobile and ubiquitous computing, computer-mediated communication, and social media, more than 50 patents granted or pending, and 7 academic books. Her team produces research that impacts a large number of Google's products (by shaping Google's Flutter and Material Design), influencing the work of hundreds of thousands of designers and developers globally, and thus affecting the user experience of millions of end-users. She continues to guest lecture at universities and to mentor early stage career professionals and students.

This paper has been accepted by IEEE Transactions on Automation Science and Engineering. 1 This preprint is an accepted version, not the IEEE published version. Abstract--In the machine learning domain, active learning is an iterative data selection algorithm for maximizing information acquisition and improving model performance with limited training samples. It is very useful, especially for the industrial applications where training samples are expensive, time-consuming, or difficult to obtain. Existing methods mainly focus on active learning for classification, and a few methods are designed for regression such as linear regression or Gaussian process. Uncertainties from measurement errors and intrinsic input noise inevitably exist in the experimental data, which further affects the modeling performance. The existing active learning methods do not incorporate these uncertainties for Gaussian process. In this paper, we propose two new active learning algorithms for the Gaussian process with uncertainties, which are variance-based weighted active learning algorithm and D-optimal weighted active learning algorithm. Through numerical study, we show that the proposed approach can incorporate the impact from uncertainties, and realize better prediction performance. This approach has been applied to improving the predictive modeling for automatic shape control of composite fuselage. I. INTRODUCTION Active learning is a type of iterative supervised learning which focuses on maximizing information acquisition with limited samples. In statistics literature, this process is also called optimal experimental design, or sequential design. The main idea of active learning is to iteratively pose "query" or "design" to explore the most informative new experimental samples according to the information obtained from the current samples. In many machine learning applications, especially in some industrial systems, the explanatory data are rich and easy to get, but the response data are very expensive, time-consuming, or difficult to obtain. For example, when training autonomous driving algorithms, a lot of media (e.g., images, videos) require that oracle users mark them with particular labels, such as "vehicle", "street sign" or "road lines". It can be tedious, redundant and time-consuming to annotate lots of these instances.

When data is plentiful, the loss achieved by well-trained neural networks scales as a power-law $L \propto N^{-\alpha}$ in the number of network parameters $N$. This empirical scaling law holds for a wide variety of data modalities, and may persist over many orders of magnitude. The scaling law can be explained if neural models are effectively just performing regression on a data manifold of intrinsic dimension $d$. This simple theory predicts that the scaling exponents $\alpha \approx 4/d$ for cross-entropy and mean-squared error losses. We confirm the theory by independently measuring the intrinsic dimension and the scaling exponents in a teacher/student framework, where we can study a variety of $d$ and $\alpha$ by dialing the properties of random teacher networks. We also test the theory with CNN image classifiers on several datasets and with GPT-type language models.

This thesis contributes to the mathematical foundation of domain adaptation as emerging field in machine learning. In contrast to classical statistical learning, the framework of domain adaptation takes into account deviations between probability distributions in the training and application setting. Domain adaptation applies for a wider range of applications as future samples often follow a distribution that differs from the ones of the training samples. A decisive point is the generality of the assumptions about the similarity of the distributions. Therefore, in this thesis we study domain adaptation problems under as weak similarity assumptions as can be modelled by finitely many moments.

Intel and Udacity announced that their Intel Edge AI for IoT Developer Nanodegree program is open for enrollment. The collaboration, announced on Thursday, aims to train developers in deep learning and computer vision to help facilitate the deployment of artificial intelligence (AI) at the edge. "We didn't build this program just for hobbyists; we built this for practitioners," said Alper Tekin, CPO at Udacity. "At the end of these courses, you have to write actual code that works in a production environment." Internet of Things (IoT) and edge computing were areas Udacity had its sights set on because of the technology's popularity, as well as the skills gap that exists in the field.

Free Coupon Discount - Artificial Intelligence: Reinforcement Learning in Python, Complete guide to Artificial Intelligence, prep for Deep Reinforcement Learning with Stock Trading Applications Created by Lazy Programmer Inc. Students also bought Data Science: Deep Learning in Python Recommender Systems and Deep Learning in Python PyTorch: Deep Learning and Artificial Intelligence Advanced AI: Deep Reinforcement Learning in Python Deep Learning Prerequisites: Logistic Regression in Python Preview this Udemy Course GET COUPON CODE Description When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go.

From chatbots to handle common queries to predictive search engines, artificial intelligence is making things happen that were once the stuff of science fiction. Here are some of the most awesome ways in which AI is changing life as we know it. When that happens, we need to make sure the computers have goals aligned with ours." The prediction seems gloomy and still far from the truth. But the digital era is rapidly evolving into the Artificial Intelligence (AI) era.

With lockdown entering its fifth week, Minecraft is proving a useful venue for friends and families to meet up, play together and work on collaborative projects. The game is widely used in schools throughout the world to teach everything from sustainable farming to the history of art, and Microsoft recently made the many lessons and exercises in its Minecraft Education programme available to everyone with an Office 365 account. If you have the game at home and are looking for new projects to attempt – maybe as part of your home schooling timetable – here are eight ideas that will test and expand your modelling skills. And we'd love to see how you get on! We'll run a gallery of our favourite examples.

Sandra Aistars is a Clinical Professor at Antonin Scalia Law School, George Mason University, leading the law school's Arts & Entertainment Advocacy Program. Throughout her career she has served in positions that required mastery of intellectual property issues, federal policy process and development, and the ability to understand and manage the implications of intellectual property policies across a portfolio of businesses. In addition, Aistars has a wealth of experience working with policy makers in Washington and internationally. She has served on trade missions and been an industry advisor to the Department of Commerce on intellectual property implications for international trade negotiations; worked on legislative and regulatory matters worldwide; frequently testified before Congress and federal agencies regarding intellectual property matters; chaired cross-industry coalitions and technology standards efforts; and is regularly tapped by government agencies to lecture in U.S. government-sponsored study tours for visiting legislators, judges, prosecutors, and regulators. Aistars has also previously served as Vice President and Associate General Counsel at Time Warner Inc.