On the days, driving a vehicle could be one of the most mindful activities; on the worst, it could be the biggest pain. And, when you are in a city driving your own car through busy lanes and negotiating pedestrians on streets, things can turn really nasty. That's why we find autonomous vehicle technology turning the "wheels of innovation" way faster today than any other smart technology. AI and Automation sit at the core of this novel neo-connected technology that promises to make cities and roads smarter, safer and much less crowded. And, in this league of smart technology providers, we have Waymo.

Uber, which hasn't publicly discussed the architecture of its autonomous car platform in great detail, today published a post laying out the technologies that enable engineers within its Advanced Technologies Group (ATG) to test, validate, and deploy AI models to cars. It gives a glimpse into the complexities of self-driving car development generally, and perhaps more importantly, it serves as a yardstick for Uber's driverless efforts, which suffered a setback following an accident in Tempe, Arizona in May 2018. According to Uber, the most important component of the ATG's workflow is VerCD, a set of tools and microservices developed specifically for prototyping self-driving vehicles. It tracks the dependencies among the various codebases, data sets, and AI models under development, ensuring that workflows start with a data set extraction stage followed by data validation, model training, model evaluation, and model serving stages. "VerCD … has become a reliable source of truth for self-driving sensor training data for Uber ATG," wrote Uber.

When we open our eyes, we immediately see our surroundings in great detail. How the brain is able to form these richly detailed representations of the world so quickly is one of the biggest unsolved puzzles in the study of vision. Scientists who study the brain have tried to replicate this phenomenon using computer models of vision, but so far, leading models only perform much simpler tasks such as picking out an object or a face against a cluttered background. Now, a team led by MIT cognitive scientists has produced a computer model that captures the human visual system's ability to quickly generate a detailed scene description from an image, and offers some insight into how the brain achieves this. "What we were trying to do in this work is to explain how perception can be so much richer than just attaching semantic labels on parts of an image, and to explore the question of how do we see all of the physical world," says Josh Tenenbaum, a professor of computational cognitive science and a member of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds, and Machines (CBMM).

The writing is on the wall. Every major global governmental agency is warning of the imminent tipping point towards catastrophic climate change, even the world's largest oil company Saudi Aramco is now talking about reaching peak oil within the next 20 years, and the International Energy Agency projects that it will happen in more like 10. Solar and wind are cheaper than ever, and large-scale solar mega-projects are quickly becoming the norm. It makes sense, then, that even the supermajor oil companies are diversifying their portfolios and investing in their own demise--also known as the renewable energy sector. Way back in July, 2017 Oilprice reported that France's Total S.A. was "leading the charge on renewables". At the time, Total's website boasted: "For Total, contributing to the development of renewable energies is as much a strategic choice as an industrial responsibility. We are doing our part to diversify the global energy mix by investing in renewables, with a strategic focus on solar energy and bioenergies."

Here's the thing: All the top-performing Medicare Advantage plans are using, today, right now, some form of advanced analytics and artificial intelligence (AI) to risk-stratify their populations and predict which members will, without intervention, become high cost in the near term. The idea is then to intervene to mitigate risk and stop bad things from happening--bad things that stink if you're the patient and also cost a lot if you're the plan. That's what population health management is all about, after all. Others using AI, right now, to do the kind of predictive analytics that you need to excel at pop health include PCP groups and other providers, mainly those at risk to manage populations or readmissions. In this health care podcast, I talk with Andrew Eye about AI.

LOS ANGELES – It was an otherwise routine flight until, at an altitude of about 1,100 feet east of this city's downtown, the crew aboard the news chopper heard a loud bang. "The pilot and I just looked at each other. 'What was that?'" reporter Chris Cristi of KABC-TV remembers thinking. Not far from their base, they landed Air 7 HD, as their Eurocopter is known to viewers, and discovered a dent in the horizontal stabilizer and next to it, a gash and one-inch hole. There was no blood or feathers as if they had hit a bird.

Quantum computing isn't yet far enough along that it could have helped curb the spread of this coronavirus outbreak. But this emerging field of computing will almost certainly help scientists and researchers confront future crises. "Can we compress the rate at which we discover, for example, a treatment or an approach to this?" asks Dario Gil, the director of IBM Research. "The goal is to do everything that we are doing today in terms of discovery of materials, chemistry, things like that, (in) factors of 10 times better, 100 times better," And that, he says, "could be game-changing." Quantum computing is the next big thing in computing, and it promises exponential advances in artificial intelligence and machine learning through the next decade and beyond, leading to potential breakthroughs in healthcare and pharmaceuticals, fertilizers, battery power, and financial services.

Download Slides: https://www.datacouncil.ai/talks/chat... WANT TO EXPERIENCE A TALK LIKE THIS LIVE? ABOUT THE TALK Nestlé built its first chatbot in 2016 with the rise of Artificial Intelligence and particularly Machine Learning and Natural Language Processing. Brands are currently using chatbots to communicate with consumers on their preferred messaging apps, such as Facebook Messenger or WhatsApp. Instead of having an army of people, chatbots can hold personalized conversations with multiple consumers 24/7. Since 2016, Nestlé has built and deployed up to 20 bots in production, with more than 15 projects in progress.

The state of Utah has given an artificial intelligence company real-time access to state traffic cameras, CCTV and "public safety" cameras, 911 emergency systems, location data for state-owned vehicles, and other sensitive data. The company, called Banjo, says that it's combining this data with information collected from social media, satellites, and other apps, and claims its algorithms "detect anomalies" in the real world. The lofty goal of Banjo's system is to alert law enforcement of crimes as they happen. It claims it does this while somehow stripping all personal data from the system, allowing it to help cops without putting anyone's privacy at risk. As with other algorithmic crime systems, there is little public oversight or information about how, exactly, the system determines what is worth alerting cops to. In its pitches to prospective clients, Banjo promises its technology, called "Live Time Intelligence," can identify, and potentially help police solve, an incredible variety of crimes in real-time. Banjo says its AI can help police solve child kidnapping cases "in seconds," identify active shooter situations as they happen, or potentially send an alert when there's a traffic accident, airbag deployment, fire, or a car is driving the wrong way down the road. Banjo says it has "a solution for homelessness" and can help with the opioid epidemic by detecting "opioid events." It offers "artificial intelligence processing" of state-owned audio sensors that "include but may not be limited to speech recognition and natural language processing" as well as automatic scene detection, object recognition, and vehicle detection on real-time video footage pulled in from Utah's cameras.

Pathology, the study and diagnosis of disease, is a growth industry. As the global population ages and diseases such as cancer become more prevalent, demand for keen-eyed pathologists who can analyze medical images is on the rise. In the U.K. alone, about 300,000 tests are carried out daily by pathologists. In the U.S., there are only 5.7 pathologists for every 100,000 people. By 2030, this number is expected to drop to 3.7.