Listen in to this special episode of Charged Up!, taken from a live Facebook broadcast with Jason Schenker, who Bloomberg ranks as the world's foremost financial futurist. In this episode, we talk about Schenker's predictions, laid out in his 2017 book "Jobs for Robots: Between Robocalypse and Robotopia," and how the robot revolution will affect our jobs, our pay and our career prospects. Schenker talks about three industry sectors that are safest from being taken over by technology, what students should study if they're entering school now and what kind of skills will protect you from losing out to robots. So, get Charged Up! about learning how to survive the robot revolution! Jason Schenker: Thank you very much, Jenny. It's a real pleasure to be here. Hoff: So, we're going to talk today about your book, "Jobs for Robots" and this is a live broadcast on Facebook so we're also going to be taking questions from our listeners which I will then later translate for the podcast so we make sure that everybody can hear the questions. But first I want to talk a little bit about how did you get into being a futurist and then where did the interest in robots come from? Schenker: Sure, the most important thing is as a futurist there's three components to it: You're part historian because you need the historical perspective of where we've been.

IBM is working with the U.S. Air Force to improve its TrueNorth line of chips designed to optimize the performance of machine learning models at the hardware level. The new 64-chip array will consist of four boards, each with 16 chips. IBM's chips are still too experimental to be used in mass production, but they've shown promise in running a special type of neural network called a spiking neural network. Though the technology is still in its infancy, IBM believes the low power consumption of its chips could some day bring value in constrained applications like mobile phones and self-driving cars. In an Air Force context, this could include applications in satellites and unmanned aerial vehicles (UAVs).

If we sum up all the available numbers for AI research investments (including other government funding like the $93.5M awarded to IVADO by the Canada Research Excellence Fund last September, as well as private funds invested in public or semi-public labs) we end up with close to $500M in funding across the country. Beyond that, when we look up other domains that work hand in hand with AI, such as Big Data, cloud infrastructure and the like, that number grows even higher. What made Silicon Valley's talent pump work up to now was its ecosystem of large firms and venture capital feeding startups, as well as research who in turn generate the innovations to push the large firms forward. With investments from the federal and provincial governments in research, as well as from Big Tech, the Canadian talent pump is growing quickly.

I've been putting together a map on Canada's AI ecosystem, which I first revealed last week in my keynote on C2 Montreal's main stage. As promised, I'm publishing that map at the bottom of this post. Given the speed at which the industry is progressing, this map is constantly evolving, so I'll be sharing updates as we add them. If you have an addition to make, drop me a line! UPDATE June 13, 2017: Last week I posted V1 of my Map of the Canadian AI Ecosystem, and since then I've been inundated with additions.

Are Detroit and the Silicon Valley the hotbeds for driverless car development? Not necessarily, says Brent Snavely of the Detroit Free Press. Last year, Goldman Sachs projected the market for advanced driver assistance systems and autonomous vehicles would grow from about $3 billion in 2015 to $96 billion in 2025 and $290 billion in 2035. In some cities, automakers, suppliers and technology companies are clustering to test their self-driving vehicles. In others, governors and mayors are beckoning the industry by changing laws or touting other inducements.

Self-driving cars are only starting to navigate the nation's streets, but regulators are scrambling to catch up. In the race to deliver cars that can safely operate themselves, proponents are increasingly concerned about a fast-growing thicket of regulations and laws being proposed by states that could come in conflict with each other and threaten to hold up development. "It could be messier, and it could take longer than we want it to," IHS Automotive analyst Stephanie Brinley says. "We will see some state-by-state and some federal fights happening." Some believe the federal government must step in to create a national standard pertaining to testing, crash liability and design requirements.

Self-driving cars could be on our roads in just 5 years. USA TODAY's Chris Woodyard takes a look at what it's going to take to get the roads ready. Self-driving vehicles are synonymous with sophisticated sensors producing terabytes of data being analyzed by powerful computers. But it seems the success of this transportation revolution hinges on a decidedly low-tech material: Paint. That's because when it comes to getting the nation's infrastructure ready for autonomous traffic, the most critical upgrade amounts to making sure the lines on our 4 million miles of roads are solid, bright and preferably white so they can be picked up by computer vision gear. "The (self-driving car companies) actually said make sure you have really good paint lines," says Kirk Steudle, director of Michigan's Department of Transportation.

Adding artificial intelligence to the machines we send out to explore space makes a lot of sense, as it means they can make decisions without waiting for instructions from Earth, and now NASA scientists are trying to figure out how it could be done. As we send out more and more probes into space, some of them may have to operate completely autonomously, reacting to unknown and unexplained scenarios when they get to their destination – and that's where AI comes in. Steve Chien and Kiri Wagstaff from NASA's Jet Propulsion Laboratory think that these machines will also have to learn as they go, adapting to what they find beyond the reaches of our most powerful telescopes. "By making their own exploration decisions, robotic spacecraft can conduct traditional science investigations more efficiently and even achieve otherwise impossible observations, such as responding to a short-lived plume at a comet millions of miles from Earth," write the researchers. One example they give is AI that can tell the difference between a storm and normal weather conditions on a distant planet, making the readings that are being taken much more useful to scientists back home.

The National Tax Agency has unveiled plans to use artificial intelligence to automate responses to taxpayer inquiries and consultations, and select targets for investigation. The plans were included in the agency's 10-year national tax administration program. While both income tax returns and the number of companies have climbed 30 percent from 1989, staffing at the national tax administration has been dropping. The planned use of AI is aimed at streamlining tax administration and allowing employees to focus on priority issues. Under the AI plan, the agency will handle tax inquiries and consultations via email and social media and use AI to provide the best answers.

One measure of the status of civilization is the complexity of tools used by the society. As societies have progressed, tools and machines used by them have become increasingly complex. Despite their rising complexity the current set of tools and machines still need humans to create and use them and they can only do things what humans have pre-programmed them to do or control them to do. In particular, current set of machines cannot learn and enhance their knowledge. However, a new set of machines are emerging that can learn and they need minimal human intervention to operate.