IMAGE: Yoshua Bengio, Co-recipient of the ACM A.M. Turing Award, will present his Turing Lecture at the Heidelberg Laureate Forum on September 23, 2019. ACM, the Association for Computing Machinery, today announced that Yoshua Bengio, co-recipient of the 2018 ACM A.M. Turing Award, will present his Turing Award Lecture, "Deep Learning for AI," at the Heidelberg Laureate Forum on September 23 in Heidelberg, Germany. Bengio is a professor at the University of Montreal and Scientific Director at Mila, Quebec's Artificial Intelligence Institute. He received the 2018 ACM A.M. Turing Award with Geoffrey Hinton, VP and Engineering Fellow of Google, and Yann LeCun, VP and Chief AI Scientist at Facebook. Bengio, Hinton and LeCun were recognized for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.

In a recent post, we covered five of our favorite artificial intelligence (AI) applications that you can see in action today. It delved into how organizations around the world are leveraging Narrow AI, data mining, computer vision, and more. In case you missed it, you can read it here. But one article certainly wasn't enough to cover all that AI is capable of accomplishing! For this reason, we've decided to revisit this topic with another round of AI applications being used right now. Machine learning is a subset of AI that arose from the idea that computers could learn how to perform specific tasks without being explicitly programmed to do so.

ORLANDO, Fla. - What do you think of when you hear the term Artificial Intelligence? More often than not, you might think of a movie with an evil robot intent on destroying the world. Hollywood films often present a frightening image of Artificial Intelligence (AI), but the reality is that we use AI in our daily lives without even knowing it. During its world premiere at Orlando Science Center this fall, Artificial Intelligence: Your Mind & The Machine will show visitors exactly what AI is, how it works, and what it might do in the future. From illusions that trick our brains to machines that can identify your emotions and translate languages, Artificial Intelligence: Your Mind & The Machine shows numerous ways that the human mind and thinking machines work.

New Tool: TF-Explain to Help DNN Interpretability What Do You Think? Despite advances in our ability to solve new problems with deep neural nets, these models are consistently challenged by questions of interpretability. Can TF explain give ML Engineers and Scientists the long sought after tools needed to fight this challenge? New Tool: TF-Explain to Help DNN Interpretability What Do You Think?

S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE).

Is emotional AI ready to be a key component of our cars and other devices? Analysts are predicting huge growth for emotional AI in the coming years, albeit with widely differing estimates. A 2018 study by Market Research Future (MRFR) predicted that the "emotional analytics" market, which includes video, speech, and facial analytics technologies among others, will be worth a whopping $25 billion globally by 2025. Tractica has made a more conservative estimate in its own analysis, but still predicted the "emotion recognition and sentiment analysis" market to reach $3.8 billion by 2025. Researchers at Gartner have predicted that by 2022 10 percent of all personal electronic devices will have emotion AI capabilities, either on the device itself or via cloud-based services.

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.