Last month data scientists, analysts, executives, engineers, developers, and AI researchers from a wide range of industries flew in the city of seven hills, San Francisco, California. Each one of over 1000 attendees was super pumped to share and learn emerging trends that are transforming data and businesses. I was one of them and I would like to share some key takeaways with the data science community around the globe. In my opinion, at Strata Data Conferences one could see a perfect intersection of cutting-edge science and evolving business models. The conference featured more than 300 speakers, 10 keynotes, 10 tutorials, and 150 technical sessions.

Asked what is the biggest misconception about AI, Yoshua Bengio answered without hesitation "AI is not magic." Winner of the 2018 Turing Award (with the other "fathers of the deep learning revolution," Geoffrey Hinton and Yann LeCun), Bengio spoke at the EmTech MIT event about the "amazing progress in AI" while stressing the importance of understanding its current limitations and recognizing that "we are still very far from human-level AI in many ways." Deep learning has moved us a step closer to human-level AI by allowing machines to acquire intuitive knowledge, according to Bengio. Classical AI was missing this "learning component," and deep learning develops intuitive knowledge "by acquiring that knowledge from data, from interacting with the environment, from learning. That's why current AI is working so much better than the old AI."

I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel Distribution for Python*, both from Intel, to discuss the Intel Distribution for Python (IDP): targeted classes of developers, use with commonly used Python packages for data science, benchmark comparisons, the solution's use in scientific computing, and a look to the future with respect to IPD. This Q&A is a follow-up to a previous sponsored post, "Supercharge Data Science Applications with the Intel Distribution for Python." Terry specializes in developer tools and the developer community. Terry has covered a wide variety of tools for Intel from the highly popular XDK to the industry-standard Media Server Studio. He currently covers Intel's machine learning tool such as Intel Data Analytics Acceleration Library (Intel DAAL), Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) and Intel Distribution for Python.

When I left a "regular" job some time ago to become an entrepreneur and adviser to multiple companies, a senior leader in the company observed encouragingly: "Instead of one company-many employees, work is moving to one employee-many companies. You are going to be a part of this future of work." As if to prove his point, the very next day I settled down at my "hot desk" in the spanking new WeWork that had opened in my city and contributed my bit to its ever-expanding valuation. Coworking spaces are mushrooming all around us in every city of the world. JLL estimates that coworking space in India rose three-fold to 3.4 million sq.

The human brain is a funny thing. Certain memories can stick with us forever: the birth of a child, a car crash, an election day. But we only store some details--the color of the hospital delivery room or the smell of the polling station--while others fade, such as the face of the nurse when that child was born, or what we were wearing during that accident. For Google CEO Sundar Pichai, the day he watched AI rise out of a lab is one he'll remember forever. "This was 2012, in a room with a small team, and there were just a few of us," he tells me. An engineer named Jeff Dean, a legendary programmer at Google who helped build its search engine, had been working on a new project and wanted Pichai to have a look. "Anytime Jeff wants to update you on something, you just get excited by it," he says. Pichai doesn't recall exactly which building he was in when Dean presented his work, though odd details of that day have stuck with him. He remembers standing, rather than sitting, and someone joking about an HR snafu that had designated the newly hired Geoffrey Hinton--the "Father of Deep Learning," an AI researcher for four decades, and, later, a Turing Award winner--as an intern. The future CEO of Google was an SVP at the time, running Chrome and Apps, and he hadn't been thinking about AI.

Today's artificial intelligence systems, including the artificial neural networks broadly inspired by the neurons and connections of the nervous system, perform wonderfully at tasks with known constraints. They also tend to require a lot of computational power and vast quantities of training data. That all serves to make them great at playing chess or Go, at detecting if there's a car in an image, at differentiating between depictions of cats and dogs. "But they are rather pathetic at composing music or writing short stories," said Konrad Kording, a computational neuroscientist at the University of Pennsylvania. "They have great trouble reasoning meaningfully in the world."

Illinois, known as a national test bed for regulation, has struck again in an unlikely area; the use of artificial intelligence (AI) in job interviews. On 11 August, Democratic Governor JB Pritzker signed the Illinois Artificial Intelligence Video Interview Act (AIVI Act) into law. Illinois is now the first state to regulate the use of algorithms (interview bots) and other forms of AI to analyse applicants during video interviews from the beginning of 2020. AI in video interviews is a growing trend in hiring practices across the US. Companies rely on video interviews to reduce the stages of these processes to get a sense for candidates, who are asked to answer questions and present themselves just as they would in an in-person interview. Companies typically use the services of third-party platforms such as Hire Vue to conduct video interviews.

Yolanda Gil, a research director at the USC Viterbi Information Sciences Institute (ISI), co-authored a new 20-year Artificial Intelligence Roadmap. An outbreak of a highly contagious mosquito-borne virus in the U.S. has spread quickly to major cities around the world. It's all hands on deck to stop the disease from spreading–and that includes the deployment of artificial intelligence (AI) systems, which scour online news and social media for relevant data and patterns. In consultation with human scientists, AI systems could help contain infectious diseases and identify effective vaccines. Working with these results, and data gathered from numerous hospitals around the world, scientists discover an interesting link to a rare neurological condition and a treatment is developed.

In this special edition of the Futurum Tech Podcast, Daniel Newman takes a look at some of the trends and challenges in the future of work with interviewees Edward Bagden, Associated Director of Flight Operations and Safety at LIFT Academy, and Greg Bowen, CTO and SVP of Digital Acceleration at Dell Digital, the IT organization that supports Dell. In the next decade, there are many exciting ways that AI and other emerging technologies will change the future of work. One of the ways AI will--and actually already is--changing the workplace is as it relates to inclusive talent. Removing bias from the talent recruitment processes and creating a culture of diversity and inclusivity are areas where AI-powered technology can help companies make significant inroads. On that topic, on the podcast today my guests and I took a dive into LIFT Academy, a company that is on the cutting edge of using AI for recruiting and retaining talent for the commercial piloting industry. LIFT's Edward Bagden shared that the company's goal is to remove the socio-economic bias historically associated with gaining piloting skills. Rather than limiting the company's talent pool to those who can afford to fly, they focus first on finding people who would be good, solid team players for their company, and then provide the training they need to be successful. Finding people who have a passion for a job, then training them to do the job makes a lot of sense. Based on an algorithm the company has developed to determine what will make the most successful piloting candidate, the company guarantees a piloting job to every candidate it accepts to its training program. Another way AI is changing the future of work relates to the ability of an organization to empower workers. Dell Digital's Greg Bowen discussed the concept of supporting distributed and remote workers by creating platforms that enable real-time collaboration. The future of work is both employee and values-driven, he says, and AI is part of that shift. In terms of challenges AI faces, there are many things that make tech adoption and rollout a challenge, the most important of which is culture.

What began as a way to increase public safety has turned into a civil rights concern. Some residents of San Diego, California are demanding the removal of some 4,000 'Smart Streetlights' which they claim are an invasion of privacy. The devices use sensor nodes to gather a range of information, such as weather and parking counts, but also uses facial recognition technology to count pedestrians. Some residents of San Diego, CA are demanding the removal of some 4,000 'Smart Streetlights' which they claim are an invasion of privacy. The San Diego City Council approved the installation of the Smart StreetLights in December 2016 - and now approximately 4,200 are in place.