From left to right, Zongfu Yu, Ang Chen and Efram Khoram developed the concept for a "smart" piece of glass that recognizes images without any external power or circuits. The sophisticated technology that powers face recognition in many modern smartphones someday could receive a high-tech upgrade that sounds -- and looks -- surprisingly low-tech. This window to the future is none other than a piece of glass. University of Wisconsin–Madison engineers have devised a method to create pieces of "smart" glass that can recognize images without requiring any sensors or circuits or power sources. "We're using optics to condense the normal setup of cameras, sensors and deep neural networks into a single piece of thin glass," says UW-Madison electrical and computer engineering professor Zongfu Yu.

The #AI Supremacy: Who Will Take the Lead in This Global Race https://t.co/rYBYqcYnil Think of the #AI journey as having four steps: Discovery, Data, Develop, and Deploy. Clearview AI #facialrecognition system has received plenty of bad press recently. Let's understand the actual functionality and utility from a criminal investigator. A new technique for teaching a machine-learning algorithm increased image classification accuracy up to 7%. USPTO Rules #artificialintelligence Cannot Be Named As Inventor for Patent Application USPTO Rules #artificialintelligence Cannot Be Named As Inventor for Patent Application Embed To embed, copy and paste .. https://t.co/8lXVoTQWn1

"Cells move and change in very weird ways during the five days the embryo is in an incubator and it's completely beyond any human to work out what all that means," Dr Peter Illingworth, medical director at IVF Australia, who is leading the trial, said. "What the AI tool can do is analyse all the embryos. The embryo with the highest score can then be selected for transfer by the embryologist with the aim of accelerating the chance to a successful pregnancy." Dr Illingworth said the purpose of the study was to determine whether the technology can shorten the time it takes a woman to fall pregnant, ultimately saving aspiring parents thousands of dollars in fertility treatment. "It's a very promising tool, but does it really help women, that's the question," he said.

The proliferation of Artificial Intelligence (AI) in the Healthcare sector is one advancement that is worth a watch. Several major companies including big techs are moving forward in the same direction to revolutionize how care is being given to those in need. Recently, a collaboration between Google's DeepMind and Moorfields Eye Hospital NHS Foundation Trust has come up with a development of an AI model that has the potential to predict whether a patient will develop wet AMD within six months. In the future, this system could potentially help doctors plan studies of earlier intervention, as well as contribute more broadly to the clinical understanding of the disease and disease progression. Age-related macular degeneration (AMD) is the biggest cause of sight loss in the UK and the USA and is the third-largest cause of blindness across the globe.

In recent years, we have witnessed the success of modern machine learning (ML) models. Many of them have led to unprecedented breakthroughs in a wide range of applications, such as AlphaGo beating a world champion human player or the introduction of autonomous vehicles. There has been continuous effort, both from industry and academia, to extend such advances to solving real-life problems. However, converting a successful ML model into a real-world product is still a nontrivial task. Firstly, modern ML methods are known for being data-hungry and inefficient.

This article is part of Privacy in the Pandemic, a Future Tense series. Since the pandemic began, authorities in New Delhi, Italy, Oman, Connecticut, and China have begun to experiment with fever-finding drones as a means of mass COVID-19 screening. They're claiming the aircraft can be used to better understand the health of the population at large and even to identify potentially sick individuals, who can then be pulled aside for further diagnostic testing. In Italy, police forces are reportedly using drones to read the temperatures of people who are out and about during quarantine, while officials in India are hoping to use thermal-scanner-equipped drones to search for "temperature anomalies" in people on the ground. A Lithuanian drone pilot even used a thermal-scanning drone to read the temperature of a sick friend who didn't own a thermometer. Unfortunately, there's almost no evidence that these fever-detecting drones actually work.

The Association for Computing Machinery has announced that Carnegie Mellon University's Phillip Gibbons, professor in the Computer Science and the Electrical and Computer Engineering Departments, will receive the Paris Kanellakis Theory and Practice Award. Gibbons will share the award with Noga Alon of Princeton University and Tel Aviv University, Yossi Matias of Google and Tel Aviv University and Mario Szegedy of Rutgers University. The award recognizes them for their seminal work on the foundations of streaming algorithms and their application to large-scale analytics. In a series of papers published in the late 1990s, Gibbons and his colleagues pioneered a framework for algorithmic treatment of streaming massive datasets, the ACM said. Their algorithms remain the core approach for streaming big data and constitute an entire subarea of the field of algorithms.

The Fannie and John Hertz Foundation announced today that Maxwell Wang is one of the recipients of the 2020 Hertz Fellowship. Wang, a M.D./Ph.D. student at Carnegie Mellon University and the University of Pittsburgh, is one of 16 researchers to receive the prestigious award, chosen from more than 800 applicants from 24 universities across the nation. Hertz Fellows receive up to five years of research funding, giving them the freedom to pursue innovative ideas. At CMU, Wang is studying machine learning and neuroscience, working with mentors Avniel Ghuman, Max G'Sell and Rob Kass. He is conducting research to understand how brain networks change during neuro-interventions, such as deep brain stimulation, and to link these changes to endpoints such as symptom improvement and adverse side-effect profiles.

CS Assistant Professor Alexander Sasha Rush has successfully moved the International Conference on Learning Representations (ICLR) to an entirely online environment. From today, all content for the ICLR 2020 Virtual Conference is available in open-access for anyone across the world to learn from. A public archive of the virtual conference site is now available for everyone to explore the 2020 conference proceedings, and to get a sense of the virtual conference portal and its flow. The registered participants site remains available. Organising the 8th international conference on learning representations (ICLR 2020) was highly challenging, but ultimately, highly rewarding for our organising committees.

Gaussian processes~(Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems. These optimization problems are nonconvex and global optimization is desired. However, previous literature observed computational burdens limiting deterministic global optimization to Gaussian processes trained on few data points. We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the branch-and-bound solver branches only on the degrees of freedom and McCormick relaxations are propagated through explicit Gaussian process models. The approach also leads to significantly smaller and computationally cheaper subproblems for lower and upper bounding. To further accelerate convergence, we derive envelopes of common covariance functions for GPs and tight relaxations of acquisition functions used in Bayesian optimization including expected improvement, probability of improvement, and lower confidence bound. In total, we reduce computational time by orders of magnitude compared to state-of-the-art methods, thus overcoming previous computational burdens. We demonstrate the performance and scaling of the proposed method and apply it to Bayesian optimization with global optimization of the acquisition function and chance-constrained programming. The Gaussian process models, acquisition functions, and training scripts are available open-source within the "MeLOn - Machine Learning Models for Optimization" toolbox~(https://git.rwth-aachen.de/avt.svt/public/MeLOn).