The Association for Computing Machinery has named Anupam Gupta and Matthew T. Mason 2021 ACM fellows. The ACM recognized Gupta, a professor in the Computer Science Department, for his contributions to approximation algorithms, online algorithms, stochastic algorithms and metric embeddings. Mason, a professor emeritus in the Robotics Institute, was honored for his contributions to robotic manipulation and manipulation path planning. Gupta and Mason were among 70 fellows recognized in 2021. "Computing professionals have brought about leapfrog advances in how we live, work and play," said ACM President Gabriele Kotsis. "New technologies are the result of skillfully combining the individual contributions of numerous men and women, often building upon diverse contributions that have emerged over decades.

Artificial intelligence helps build physical infrastructure like modular housing, skyscrapers, and factory floors. "…many problems that we wrestle with in all forms of engineering and design are very, very complex problems…those problems are beginning to reach the limits of human capacity," says Mike Haley, the vice president of research at Autodesk. But there's hope with AI capabilities, Haley continues "This is a place where AI and humans come together very nicely because AI can actually take certain very complex problems in the world and recast them." And where "AI and humans come together" is at the start of the process with generative design, which incorporates AI into the design process to explore solutions and ideas that a human alone might not have even considered. "You really want to be able to look at the entire lifecycle of producing something and ask yourself, 'How can I produce this by using the least amount of energy throughout?'" This kind of thinking will reduce the impact of, not just construction, but any sort of product creation on the planet. The symbiotic human-computer relationship behind generative design is necessary to solve those "very complex problems"--including sustainability. "We are not going to have a sustainable society until we learn to build products--from mobile phones to buildings to large pieces of infrastructure--that survive the long-term," Haley notes. The key, he says, is to start in the earliest stages of the design process. "Decisions that affect sustainability happen in the conceptual phase, when you're imagining what you're going to create." He continues, "If you can begin to put features into software, into decision-making systems, early on, they can guide designers toward more sustainable solutions by affecting them at this early stage."

Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville and Marc G. Bellemare won an outstanding paper award at NeurIPS2021 for their paper Deep Reinforcement Learning at the Edge of the Statistical Precipice. In this blog post, Rishabh Agarwal and Pablo Samuel Castro explain this work. Reinforcement learning (RL) is an area of machine learning that focuses on learning from experiences to solve decision making tasks. While the field of RL has made great progress, resulting in impressive empirical results on complex tasks, such as playing video games, flying stratospheric balloons and designing hardware chips, it is becoming increasingly apparent that the current standards for empirical evaluation might give a false sense of fast scientific progress while slowing it down. To that end, in "Deep RL at the Edge of the Statistical Precipice", given as an oral presentation at NeurIPS 2021, we discuss how statistical uncertainty of results needs to be considered, especially when using only a few training runs, in order for evaluation in deep RL to be reliable.

A wise old wild snark is pondering the world. Alice has left the tea party in a huff. Gone through the looking glass in my opinion. The white rabbits; who were in fact blue are now more than a little purple. The wild snark blames it on the Ethereum; drinking to much it can do very strange thins to you. One of the last few snarks that live in the wild.

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. Like most sexually healthy couples, my boyfriend and I consume pornography. But I'm worried about the sorts of stuff my boyfriend likes. He prefers pornographic games to videos or pictures; he says they're "more interactive."

Celebrated academic and thought leader in machine learning and AI research, UC Berkeley Distinguished Professor Michael I. Jordan, has been named laureate professor at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI). He was also named honorary director of a new Laureate Faculty Programme, which he will help build with MBZUAI President, Professor Eric Xing. Jordan's appointment brings a wealth of experience to the university, and to the country, in artificial intelligence; in the interface of computer science and statistics; and in computational biology, natural language processing, and signal processing. He is a member of the US National Academy of Sciences, National Academy of Engineering, and Academy of Arts and Sciences, as well as a foreign member of the Royal Society. In 2016, he was cited by Science Magazine as the most influential author in computer science.

Abstract--Deep learning for change detection is one of the current hot topics in the field of remote sensing. However, most endto-end networks are proposed for supervised change detection, and unsupervised change detection models depend on traditional pre-detection methods. Therefore, we proposed a fully convolutional change detection framework with generative adversarial network, to conclude unsupervised, weakly supervised, regional supervised, and fully supervised change detection tasks into one framework. A basic Unet segmentor is used to obtain change detection map, an image-to-image generator is implemented to model the spectral and spatial variation between multi-temporal images, and a discriminator for changed and unchanged is proposed for modeling the semantic changes in weakly and regional supervised change detection task. The iterative optimization of segmentor and generator can build an end-to-end network for unsupervised change detection, the adversarial process between segmentor and discriminator can provide the solutions for weakly and regional supervised change detection, the segmentor itself can be trained for fully supervised task. The experiments indicate the effectiveness of the propsed framework in unsupervised, weakly supervised and regional supervised change detection. This paper provides theorical definitions for unsupervised, weakly supervised and regional supervised change detection tasks, and shows great potentials in exploring end-to-end network for remote sensing change detection. It changes and non-changes by pre-detection, and use aims at finding landscape changes from the multi-temporal the corresponding patches as training samples to build a remote sensing images observing the same study site deep network model to extract better features and discriminate at different time. It has been widely used in land-use/landcover semantic labels [25-27].

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments. This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimize overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This work develops a multi-agent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this paper employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs.

Clinical research and drug development professionals are largely aware of artificial intelligence (AI), machine learning (ML), and other advanced analytical tools. However, many are not yet aware of their full potential, or how to best put such tools to work. Lucas Glass, vice president of the IQVIA Analytics Center of Excellence, spoke with Outsourcing-Pharma about how the adoption of AI/ML is evolving, what people in the field need to understand, and what might lie ahead. OSP: Could you please tell us what the biggest challenge has been facing professionals in your corner of the life-sciences industry? LG: The biggest challenge facing the industry is user empathy between the technologists and the clinical trial professionals.

The field of deep learning has been suffering from what you might call a Big Data fallacy, the belief that more and more data is always a good thing. It may be time to focus on quality rather than just quantity. "There's a very fundamental problem that a lot of AI faces," said Andrew Ng, founder and CEO of Landing AI, a startup working to perfect the technology for industrial uses, in an interview with ZDNet this week. "A lot of AI is focused on maximizing the number of calories, which works up to a certain point," he said. "And sometimes you do have a lot of data, but when you have a small data set, it's more the quality of the data rather than the sheer volume."