Radhika previously worked in content marketing at three technology firms, and graduated from Sri Krishna College Of Engineering And Technology with a degree in Information Technology. According to the National Institute of Mental Health, the United States is currently battling a mental health epidemic. One in every five Americans struggles with mental illness in one form or another. According to the Center for Workplace Mental Health founded by the American Psychiatric Association, up to 7% of full-time workers in the U.S. suffer from major depressive disorder, the economic cost of which is estimated to be $210.5 billion per year. When compared to other developed nations, traditional healthcare in the U.S. is notoriously costly; mental healthcare, even more so.

Behavioral decision theories aim to explain human behavior. Can they help predict it? An open tournament for prediction of human choices in fundamental economic decision tasks is presented. The results suggest that integration of certain behavioral theories as features in machine learning systems provides the best predictions. Surprisingly, the most useful theories for prediction build on basic properties of human and animal learning and are very different from mainstream decision theories that focus on deviations from rational choice. Moreover, we find that theoretical features should be based not only on qualitative behavioral insights (e.g. loss aversion), but also on quantitative behavioral foresights generated by functional descriptive models (e.g. Prospect Theory). Our analysis prescribes a recipe for derivation of explainable, useful predictions of human decisions.

Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning. In recent years, numerous methods have been proposed for continual learning, but due to differences in evaluation protocols it is difficult to directly compare their performance. To enable more structured comparisons, we describe three continual learning scenarios based on whether at test time task identity is provided and--in case it is not--whether it must be inferred. Any sequence of well-defined tasks can be performed according to each scenario. Using the split and permuted MNIST task protocols, for each scenario we carry out an extensive comparison of recently proposed continual learning methods. We demonstrate substantial differences between the three scenarios in terms of difficulty and in terms of how efficient different methods are. In particular, when task identity must be inferred (i.e., class incremental learning), we find that regularization-based approaches (e.g., elastic weight consolidation) fail and that replaying representations of previous experiences seems required for solving this scenario.

Cyber-physical systems, especially in critical infrastructures, have become primary hacking targets in international conflicts and diplomacy. However, cyber-physical systems present unique challenges to defenders, starting with an inability to communicate. This paper outlines the results of our interviews with information technology (IT) defenders and operational technology (OT) operators and seeks to address lessons learned from them in the structure of our notional solutions. We present two problems in this paper: (1) the difficulty of coordinating detection and response between defenders who work on the cyber/IT and physical/OT sides of cyber-physical infrastructures, and (2) the difficulty of estimating the safety state of a cyber-physical system while an intrusion is underway but before damage can be effected by the attacker. To meet these challenges, we propose two solutions: (1) a visualization that will enable communication between IT defenders and OT operators, and (2) a machine-learning approach that will estimate the distance from normal the physical system is operating and send information to the visualization.

What they did: The SDO instrument in question is known as MEGS-A, and it was designed to keep an eye on ultraviolet radiation levels, which correlate with a ballooning of the Earth's outer atmosphere that can harm satellites in near-Earth orbit. A deep-learning network that researchers at NASA Frontier Development Lab created with help from IBM, SETI, and Nimbix in 2018 may soon replace the failed instrument by inferring what ultraviolet radiation levels that instrument would detect based on what the other instruments on SDO are observing at any given time, NASA AI consultant Graham Mackintosh tells Axios. While NASA isn't yet using the fix operationally, the results are promising, Mackintosh added. What to watch: AI models like this could also be used for other future missions, Mackintosh said. Instead of loading 3 instruments on a satellite to measure different aspects of the space environment, you could potentially launch two and use the data collected to infer the information that would have been measured by a third.

On Wednesday 10 April, the first image ever taken of a black hole was released. The picture, which shows a black hole surrounded by a hazy red and yellow circle, provides an unprecedented peek at one of the most mysterious entities in the universe. One of the scientists involved in the development of the picture is Dr Katie Bouman. We'll tell you what's true. You can form your own view.

The black hole that starred in the first ever photo to be taken of its kind has been given a name. The now famous swirling void will be known as Powehi, a Hawaiian word which has been bestowed by a language professor. And the name's meaning, chosen by University of Hawaii-Hilo Hawaiian Professor Larry Kimura, is as fittingly dramatic as the picture and work that produced it. We'll tell you what's true. You can form your own view.

When artificial intelligence systems start getting creative, they can create great things – and scary ones. Take, for instance, an AI program that let web users compose music along with a virtual Johann Sebastian Bach by entering notes into a program that generates Bach-like harmonies to match them. Run by Google, the app drew great praise for being groundbreaking and fun to play with. It also attracted criticism, and raised concerns about AI's dangers. My study of how emerging technologies affect people's lives has taught me that the problems go beyond the admittedly large concern about whether algorithms can really create music or art in general.

When Uber filed the paperwork for its initial public offering on Thursday, the quintessential bad boy startup signaled to the world that it was ready to grow up. In a letter to potential investors, the CEO, Dara Khosrowshahi, acknowledged the "greater responsibilities" the company will take on once it goes public, and promised to act with "passion, humility, and integrity". But references to the company's checkered past are littered throughout the more than 300 pages of public disclosures filed to the Securities and Exchange Commission. Here's a rundown of some of the biggest "risk factors" from Uber's past that may come back to haunt its $100bn future: When Uber's ride-share rival Lyft went public with its own pre-IPO disclosures in March, its "unique culture" was referenced as a positive aspect of the company dozens of times; any possible loss of that culture in the future was identified as a risk factor. For Uber, the challenge is the opposite.

NASA has announced a new round of funding for 18 futuristic projects that could help propel humans further into our solar system and beyond. Many of the ideas'sound like the stuff of science fiction,' the agency acknowledged, but they're not too crazy to one day become a reality. Among those that received funding are micro-probes that take after spiders to safely fly through the air, as well as a futuristic'smart suit' with self-healing skin to protect astronauts. Among those that were funded are micro-probes that take after spiders to safely fly through the air, as well as a futuristic'smart suit' with self-healing skin to protect astronauts (pictured) The cutting edge technologies are part of NASA's Innovative Advanced Concepts (NIAC) Program, which awards applicants up to $500,000 to develop their ideas. There are 12 Phase I ideas, like the smart suit, which are awarded $125,000 over nine months.