Zubaida starts her day at eight in the morning, sorting discarded plastics, glass, and chemicals with her bare hands, to collect items she can sell. With waste-segregation centers in this part of East Delhi currently shut down, she and other waste-pickers from the Seemapuri slum work outside by a dusty road through the hottest hours of the day, under the blazing sun. There is no fan or shade. With Delhi's heat wave season here, they are constantly exposed to intense high temperatures. On June 11, the India Meteorological Department (IMD) issued a red alert for Delhi, warning of a high risk of heat illness and heat stroke.

When Vinod Khosla had a skiing accident in 2011 that led to an ACL injury in his knee, doctors gave conflicting opinions over his treatment. Frustrated with the healthcare system, the leading venture capitalist proffered, in a hotly debated article, that AI algorithms could do the job better than doctors. Since then, Khosla's firm has invested in a number of robotics and medtech companies, including Rad AI, a radiology tech company. The self-professed techno-optimist still stands by his assertions a decade later. "Almost all expertise will be free in an AI model, and we'll have plenty of these for the benefit of humanity," he told TIME in an interview in August.

Graph Machine Learning (GraphML), whereby classical machine learning is generalized to irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of models and their applications in several domains. With its growing applicability to sensitive domains and regulations by governmental agencies for trustworthy AI systems, researchers have started looking into the issues of transparency and privacy of graph learning. However, these topics have been mainly investigated independently. In this position paper, we provide a unified perspective on the interplay of privacy and transparency in GraphML. In particular, we describe the challenges and possible research directions for a formal investigation of privacy-transparency tradeoffs in GraphML.

Various work has suggested that the memorability of an image is consistent across people, and thus can be treated as an intrinsic property of an image. Using computer vision models, we can make specific predictions about what people will remember or forget. While older work has used now-outdated deep learning architectures to predict image memorability, innovations in the field have given us new techniques to apply to this problem. Here, we propose and evaluate five alternative deep learning models which exploit developments in the field from the last five years, largely the introduction of residual neural networks, which are intended to allow the model to use semantic information in the memorability estimation process. These new models were tested against the prior state of the art with a combined dataset built to optimize both within-category and across-category predictions. Our findings suggest that the key prior memorability network had overstated its generalizability and was overfit on its training set. Our new models outperform this prior model, leading us to conclude that Residual Networks outperform simpler convolutional neural networks in memorability regression. We make our new state-of-the-art model readily available to the research community, allowing memory researchers to make predictions about memorability on a wider range of images.

Vinod Khosla, a co-founder of the former Sun Microsystems and a longtime technology entrepreneur, venture capitalist and IT sage, makes billions of dollars betting on new technologies. Khosla shared some of his technology and investment thoughts at a recent tech conference about the future of AI in business, AI chip design and quantum computing -- and even gave some advice to AI developers and companies about how they can successfully navigate the tumultuous times of the COVID-19 pandemic. Khosla gave his remarks at an "Ask Me Anything" Industry Luminary Keynote at the virtual AI Hardware Summit earlier in October. The Q&A was hosted by Rene Haas, the president of Arm's IP products group, and a former executive with AI chipmaker Nvidia. Khosla, who is ranked #353 on the Forbes 400 2020 list, has a net worth today of $2.6 billion, largely earned through his investment successes in the tech field. He founded his VC firm, Khosla Ventures, in 2004. Rene Haas: What has been the most significant technological advancement in AI in the last year or two?

As a child, Neal Khosla became engrossed by the Oakland Athletics baseball team's "Moneyball" approach of using data analytics to uncover the value and potential of the sport's players. A few years ago, the young engineer began pursuing similar techniques to improve medical decision-making. It wasn't long after Khosla met Xavier Amatriain, who was looking to apply his engineering skills to a higher mission, that the pair founded Curai. The three-year-old startup, based in Palo Alto, Calif., is using AI to improve the entire process of providing healthcare. The scope of their challenge -- transforming how medical care is accessed and delivered -- is daunting.

Sarmishta Neogy, a fitness enthusiast from Delhi, uses the HealthifyMe app to log and track her calorie intake. Neogy recently upgraded from the company's free service to a paid tier, which gives her access to an artificial intelligence (AI)-based assistant called Ria. However, Neogy says she still uses the app mostly for their recipes, tips and to document food. She found the AI's tips generic and not very helpful. "The Ria service is very basic, so I don't know if I will benefit from it. For instance, if you ask Ria what is missing from my diet, it will tell you what is missing but nothing more," she added.

Self-driving cars have countless obstacles to contend with on the road, chiefly drivers who don't always act predictably -- or responsibly. There's inclement precipitation and wind to worry about, plus jaywalking pedestrians and zippy electric bikes and scooters. That's not to mention alleyways and busy intersections that no amount of Google Maps data can elucidate. Perhaps it's not surprising, then, that Ghost Locomotion, a stealthy startup headed by former Yahoo CTO and Pure Storage cofounder John Hayes, isn't tackling a full stack autonomous car platform just yet. Instead, it's honing in on the task that constitutes two-thirds of all miles driven in the U.S.: highway driving.

The founders of Vise AI met when they were 13, a couple of teenagers more interested in applied artificial intelligence than English class. Fast-forward several years and the pair has relocated from the Midwest to San Francisco to raise money for a financial technology business they've been self-funding since 2016. As teenagers with an inordinate amount of AI knowledge, Samir Vasavada and Runik Mehrotra proved to be quite useful to large businesses, investment bankers and other financiers. Leveraging their AI know-how, they were paid $700 per hour by a consulting firm to teach financial "experts" about AI. Mehrotra, according to Vasavada, is a mathematical prodigy: "And that translates extremely well to AI, right, because what underlies AI is math," Vasavada, co-founder and chief executive officer of Vise AI, tells TechCrunch.

The founders of Vise AI met when they were 13, a couple of teenagers more interested in applied artificial intelligence than English class. Fast-forward several years and the pair has relocated from the Midwest to San Francisco to raise money for a financial technology business they've been self-funding since 2016. As teenagers with an inordinate amount of AI knowledge, Samir Vasavada and Runik Mehrotra proved to be quite useful to large businesses, investment bankers and other financiers. Leveraging their AI know-how, they were paid $700 per hour by a consulting firm to teach financial "experts" about AI. Mehrotra, according to Vasavada, is a mathematical prodigy: "And that translates extremely well to AI, right, because what underlies AI is math," Vasavada, co-founder and chief executive officer of Vise AI, tells TechCrunch.