Over the last few decades, research, activity, and funding have been devoted to improving the recruitment, retention, and advancement of women in the fields of science, engineering, and medicine. In recent years the diversity of those participating in these fields, particularly the participation of women, has improved and there are significantly more women entering careers and studying science, engineering, and medicine than ever before. However, as women increasingly enter these fields they face biases and barriers and it is not surprising that sexual harassment is one of these barriers. According to the National Academies of Sciences, Engineering, and Medicine 2018, report, the count of women in science is decreasing since 1990. The report also revealed that till 2015, women made up only 18% of computer science majors in the US -- a decline from a high of 37% in 1984.

My view, and that of the majority of my colleagues in AI, is that it'll be at least half a century before we see computers matching humans. Given that various breakthroughs are needed, and it's very hard to predict when breakthroughs will happen, it might even be a century or more. If that's the case, you don't need to lose too much sleep tonight. One reason for believing that machines will get to human-level or even superhuman-level intelligence quickly is the dangerously seductive idea of the technological singularity. This idea can be traced back to a number of people over fifty years ago: John von Neumann, one of the fathers of computing, and the mathematician and Bletchley Park cryptographer IJ Good. More recently, it's an idea that has been popularised by the science-fiction author Vernor Vinge and the futurist Ray Kurzweil.

Reading minds has just come a step closer to reality: scientists have developed artificial intelligence that can turn brain activity into text. While the system currently works on neural patterns detected while someone is speaking aloud, experts say it could eventually aid communication for patients who are unable to speak or type, such as those with locked in syndrome. "We are not there yet but we think this could be the basis of a speech prosthesis," said Dr Joseph Makin, co-author of the research from the University of California, San Francisco. Writing in the journal Nature Neuroscience, Makin and colleagues reveal how they developed their system by recruiting four participants who had electrode arrays implanted in their brain to monitor epileptic seizures. These participants were asked to read aloud from 50 set sentences multiple times, including "Tina Turner is a pop singer", and "Those thieves stole 30 jewels".

Welcome to the February edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development. This month, we spotted among others, articles about AI that can diagnose breast cancer with higher accuracy than experts! Let's start, as usual, with the comic of the month: A recent evaluation of a AI system for breast cancer screening concludes that it is capable of surpassing human experts in breast cancer prediction. It is essential to identify breast cancer at earlier stages of the disease when treatment can be more successful.

Tech company C3.ai has teamed up with an all-star cast of collaborators, including Microsoft and several prestigious educational institutions. Partnering with Princeton University, Berkley, the Massachusetts Institute of Technology and others, C3.ai has launched two new initiatives to develop research and innovation within the field of artificial intelligence (AI): C3.ai Digital Transformation Institute: Pooling the research assets of several leading institutions, this project will seek to drive the widespread adoption of AI in business, government and wider society. C3.ai DTI First Call for Research Proposals: Designed to create a dialogue between researchers, academics and concept developers, C3.ai is hoping to tackle a timely subject and hopefully accelerate the finding of a solution: the COVID-19 pandemic. Thomas M. Siebel, CEO, presented the projects as a unique opportunity to bring together the best minds from every aspect of the tech industry and unite their abilities for the greater good of society. "We have the opportunity through public-private partnership to change the course of a global pandemic […] I cannot imagine a more important use of AI," he stated.

With novel coronavirus spreading throughout the United States, researchers are turning to social media and artificial intelligence to track the virus as it spreads. A team headquartered at Boston Children's Hospital is implementing machine learning to scour through social posts, news reports, data from official public health channels and information supplied by doctors for warning signs that the virus is taking hold in locations outside of China. "There's incredible data that's locked away in various tools like online news sites, social media, crowdsourcing, data sources, that you wouldn't think of that would be used for public health," said Dr. John Brownstein, chief innovation officer at Boston Children's Hospital. "But actually they have incredible amounts of information that you wouldn't find in any sort of traditional government system." More than 95,000 people around the world have been infected by the outbreak of novel coronavirus, and more than 3,200 have died -- most in China.

You can be addicted to your Artificial Intelligence (AI) software as much as your favored fortune. And you'll feel rewarding being addicted to your AI. Because they replace the extravagance, inefficiency, and endangerment associated with business operations. Tech Oracle if you ask? Employing AI will lessen human error, mundane tasks, in turn, more time for innovation. This means you print money while remaining effortless.

A blood test developed and checked using blood samples from 4000 people can accurately detect more than 50 cancer types, often before any symptoms appear. It was most accurate at identifying 12 especially dangerous types, including pancreatic cancers that are usually diagnosed only at a very late stage. Many groups around the world are trying to develop blood tests for cancer, often referred to as "liquid biopsies". Michael Seiden at US Oncology, a company involved in cancer care, and his team explored several ways of testing for cancer based on sequencing the DNA that dying cells release into the bloodstream. The team found that looking at methylation patterns at around a million sites was the most promising.

Fox News finds the coronavirus outbreak has left San Francisco streets and tourist sites including Chinatown and Fisherman's Wharf largely deserted. Get all the latest news on coronavirus and more delivered daily to your inbox. New drone footage and other video shot by Fox News shows once-busy streets and tourist areas in Los Angeles and San Francisco eerily deserted as the coronavirus has kept people indoors. Fisherman's Wharf, one of San Francisco's busiest tourist areas, once brimming with souvenir shops and seafood stalls and situated near Ghirardelli Square, was shuttered after the city's mayor called for a shelter-in-place, restricting people from leaving their homes except for trips to the grocery store or for medical supplies. The Golden Gate Bridge, which usually has seen over 100,000 cars and other vehicles a day and Alamo Square -- which overlooks the famous "Painted Ladies" -- were surprisingly barren.

We propose to incorporate neural architecture search (NAS) into general-purpose multi-task learning (GP-MTL). Existing NAS methods typically define different search spaces according to different tasks. In order to adapt to different task combinations (i.e., task sets), we disentangle the GP-MTL networks into single-task backbones (optionally encode the task priors), and a hierarchical and layerwise features sharing/fusing scheme across them. This enables us to design a novel and general task-agnostic search space, which inserts cross-task edges (i.e., feature fusion connections) into fixed single-task network backbones. Moreover, we also propose a novel single-shot gradient-based search algorithm that closes the performance gap between the searched architectures and the final evaluation architecture. This is realized with a minimum entropy regularization on the architecture weights during the search phase, which makes the architecture weights converge to near-discrete values and therefore achieves a single model. As a result, our searched model can be directly used for evaluation without (re-)training from scratch. We perform extensive experiments using different single-task backbones on various task sets, demonstrating the promising performance obtained by exploiting the hierarchical and layerwise features, as well as the desirable generalizability to different i) task sets and ii) single-task backbones. The code of our paper is available at https://github.com/bhpfelix/MTLNAS.