A team of Italian mathematicians, including one who is also a neuroscientist from the Champalimaud Centre for the Unknown (CCU), in Lisbon, Portugal, has shown that artificial vision machines can learn to recognize complex images spectacularly faster by using a mathematical theory that was developed 25 years ago by one of this new study's co-authors. Their results have been published in the journal Nature Machine Intelligence. During the last decades, machine vision performance has exploded. For example, these artificial systems can now learn to recognise virtually any human face – or to identify any individual fish moving in a tank, in the midst of a large number of other almost identical fish which are also moving. The machines we're talking about are, in fact, electronic models of networks of biological neurons, and their aim is to simulate the functioning of our brain, which is as good as it gets at performing these visual tasks – and this, without any conscious effort on our part.

A study by researchers from the University of Rochester found an exodus of artificial intelligence (AI) professors from North American universities to the private sector has reduced the prospect that graduate students will found new AI companies. Those graduates who did start a company usually attracted less venture capital, with the field of deep learning especially affected, according to "Artificial Intelligence, Human Capital, and Innovation," by Michael Gofman and Zhao Jin. This academic attrition could hinder innovation and economic expansion over time, the researchers suggest. The technology industry mostly ignored deep learning's potential until 2010, but interest grew as the Internet produced more data and new computer chips reduced the analytical burden. Large tech companies have hired many academic specialists, including two recent recipients of the ACM A.M. Turing Award honored for their work on neural networks.

Since late last year, a group of workers within Amazon have been organizing to push the company to radically reduce its carbon emissions. Yesterday, they announced a major new action: on 20 September, Amazon workers around the world will walk out of their offices to join the Global Climate Strike. So far, over 1,000 workers have pledged to participate. The organizers have three demands. They want the company to commit to zero emissions by 2030, to have zero custom cloud computing contracts with fossil fuel companies, and to spend zero dollars on funding climate-denying lobbyists and politicians. I spoke to one of the walkout's organizers, a 28-year-old Amazon employee in Seattle named Rebecca Sheppard.

Stanford professor Fei-Fei Li is a pioneer in artificial intelligence. Her research helped lead to breakthroughs like allowing computers to recognize images. Now, AI has spread to every economic sector. This episode, hear Fei-Fei's thoughts on how humans can play a compassionate role in shaping AI's future. Plus, Caroline Fairchild brings reporting on some surprising jobs in this emerging industry. JESSI HEMPEL: From the editorial team at LinkedIn, I'm Jessi Hempel, and this is Hello Monday, a show where I investigate the changing nature of work, and how that work is changing us. Last year, I got to test-drive a self-driving car, which of course means I got to sit behind the wheel and not drive. In this one test, a human-size dummy walked out onto the track, imitating a pedestrian, jaywalking. SELF-DRIVING CAR TAPE: So here it comes...so we pass this trigger…do we see him? The car saw the pedestrian and slowed down to let him pass. This is just one of the many, many things that have become possible now that computers can recognize images. That's why this week, I wanted to talk to Fei-Fei Li.

What do you think when you think of artificial intelligence (AI)? Until recently, generations of people only viewed AI technology through the lens of popular culture depictions. And one major touchstone in their experiences was the movie 2001: A Space Odyssey, which celebrates its 50th anniversary this year. It was the highest-grossing film in North America in 1968, selected to be preserved in the U.S. Library of Congress National Film Registry, won an Academy Award for visual effects and a Hugo Award--one of science fiction's highest honors--for Best Dramatic Presentation. Despite its accolades, the movie drew a mixed reception of awe and confusion over its depiction of evolution and the meaning of existence.

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Combining state-of-the-art optimization and machine learning techniques with high-speed electromagnetic solvers offers a new approach to "inverse" design and implement classical and quantum photonic circuits with superior properties, including robustness to errors in fabrication and environment, compact footprints, novel functionalities, and high efficiencies. In this plenary talk, Jelena Vuckovic of Stanford University illustrates this with a number of demonstrated devices in silicon, diamond, and silicon carbide, including wavelength and polarization splitters and converters, power splitters, couplers, nonlinear optical isolators, on-chip laser driven particle accelerators, and efficient quantum emitter-photon interfaces. Jelena Vuckovic is a professor of electrical engineering at Stanford, where she leads the Nanoscale and Quantum Photonics Lab. Vuckovic has won numerous awards including the Humboldt Prize and the Presidential Early Career Award for Scientists and Engineers. She is a Fellow of APS, OSA, and IEEE.

The latest research from MeriTalk finds what while artificial technology (AI) development is viewed by many in government and industry as still in the blush of youth, the technology is poised to rapidly outgrow its present suit of clothes. The research project – conducted by MeriTalk and underwritten by Pure Storage – polled government and industry executives and IT decision makers at this summer's AI World Government event, who said among other top-line findings that hybrid cloud infrastructure is a key enabler for AI adoption, and that the military and intelligence agencies are expected to lead Federal government's push into AI tech adoption. We sat down last week with Nick Psaki, Federal CTO at Pure Storage, to get his thoughts on the research results, and his longer-term outlook on how AI adoption is likely to gain momentum in the coming years before becoming an ultimately ubiquitous technology layer that will leave an indelible mark on society. MeriTalk: In examining the AI study results, what are the most important findings for Federal government officials that are currently undertaking AI or robotic process automation (RPA), or are considering doing so? Psaki: Two things jump out – the current feelings about AI maturity, and then the time to breakthrough to mission-critical tasks. On the first one, if you add respondents who said AI development is between "a little" (42 percent) and "moderately" (48 percent) mature, combined that's a big number (90 percent).

The Freeman Spogli Institute for International Studies (FSI) and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) are pleased to announce that Marietje Schaake has been named to international policy roles in each of their organizations. At FSI, Schaake will serve as the first international policy director of the Cyber Policy Center. With a focus on cybersecurity, disinformation, digital democracy and election security, the Cyber Policy Center's research, teaching and policy engagement aims to bring new insights and solutions to national governments, international institutions and industry. Schaake will also be an international policy fellow at Stanford HAI, which seeks to advance artificial intelligence (AI) research, education, policy and practice to improve the human condition. The university-wide institute is committed to working with industry, governments and civil society organizations that share the goal of a better future for humanity through AI.

Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning. He is probably best known as the founder of convolutional neural networks, in particular their early application to optical character recognition. This conversation is part of the Artificial Intelligence podcast.