"Through making our large datasets and systems publicly available, we've enabled research groups around the world to make significant progress on building machines that can automatically answer questions about visual content," she highlights. When Thomas first started researching deep neural networks a few years ago, virtually no educational resources existed online. Suchi Saria, Assistant Professor at Johns Hopkins University, believes computational modeling of data from sensor platforms and electronic medical records presents "a tremendous opportunity for high impact work." Since receiving her PhD in Computer Science from Stanford, Shubha Nabar has built data products and data science teams at Microsoft, LinkedIn, and now Salesforce.
Jane Wang started out as an applied physicist modeling the complex network dynamics of memory systems in the brain before moving into experimental cognitive neuroscience as a postdoc at Northwestern. Since joining DeepMind two years ago, her non-machine learning background has equipped her with a unique set of tools and perspectives for tackling the hardest AI problems. "It's exhilarating to formulate theories of human brain function as powerful deep reinforcement learning models that can solve similarly complex tasks," she shares. Though Wang has been successful without a formal AI background, she's concerned the steep learning curve and hypercompetitive atmosphere of AI research can discourage diverse participation. "Although competitiveness drives the field forward, it also discourages those who wish to work in more inclusive, cooperative environments," she warns.
As a slew of Silicon Valley companies confront accusations of unfair treatment of women and minorities, Stanford University's Graduate School of Business is trying to help would-be entrepreneurs create more conscientious companies. The 10-week course is the first of its kind at the nation's most selective M.B.A. program and prime breeding ground for tech startups. Students enrolled in "Building Diverse and Inclusive Organizations," slated to launch this spring, will examine research on how to prevent bias from creeping into job descriptions and managers' feedback--and how to promote stronger feelings of belonging, which can enhance employee performance and retention of women and underrepresented minorities. Students will also review case studies of companies' efforts to improve diversity with expanded parental leave and job promotion policies and critique recruitment and retention reports from firms like Facebook Inc., FB -0.06% Apple Inc. AAPL -0.05% and Procter & Gamble Co. PG 0.01% "We're at a tipping point, as people begin to realize that inclusion has to be built into the fabric of the company," says Fern Mandelbaum, a Silicon Valley venture capitalist leading the course. Though other courses at Stanford touch on how diversity impacts a company's business performance, this is the first specifically evaluating the policies of startups and early-stage companies.
"You could say it was bold. You could say it was crazy; maybe even arrogant. But I decided that if Georgia State was going to do something really big, this was the goal whose achievement would allow us to change the world." Mark Becker, president of Georgia State University, is reflecting on his decision, soon after taking up the position in 2009, to reword and prioritise an objective in the university's mission statement about student retention and graduation rates. At this point, he had absolutely no idea how he was going to achieve the objective, which stated that the university would "become a national model for undergraduate education by demonstrating that students from all backgrounds can achieve academic and career success at high rates".
Harvey Mudd computer science professor Jim Boerkoel works with a student in his robotics lab, where ... [ ] he focuses on using AI to develop human-robot teamwork. The rapid expansion of artificial intelligence from facial recognition and self-driving cars to understanding human speech is having a major impact on business and society, which is why the lack of diversity among the people developing AI tools is so troubling. A recent study published by the AI Now Institute of New York University concluded that a "diversity disaster" has resulted in flawed AI systems that perpetuate gender and racial biases. The report found that more than 80 percent of AI professors are men and only 15% of AI researchers at Facebook and 10 percent of AI researchers at Google are women. The numbers reflect a larger issue facing the computer sciences where, in 2018, less than 25 percent of PhDs were awarded to females and/or minorities, who are historically underrepresented in computing. Industry and academia are taking steps to increase diversity among AI researchers through steps designed to ensure that future technology benefits all people and not just a homogenous group of white males.