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.

Today though we remember another member of Bletchley Park, Joan Clarke, born one hundred years ago today, five years and a day after Turing. Clarke became one of the leading cryptoanalysts at Bletchley Park during the second World War. She mastered the technique of Banburismus developed by Alan Turing, the only woman to do so, to help break German codes. Bletchley Park promoted her to linguist, even though she didn't know any languages, to partially compensate for a lower pay scale for woman at the time. Keira Knightly played Joan Clarke in The Imitation Game.

What will the world of technology look like 30 years from now? Megatech: Technology In 2050 tries to tackle this question. Edited by The Economist's executive editor Daniel Franklin, the book is a collection of essays by eminent personalities like Frank Wilczek, Alastair Reynolds, Nancy Kress and Melinda Gates--each one of whom tells their version of the future. An essay by Luciano Floridi, professor of philosophy and ethics of information at the University of Oxford in the UK, talks about Artificial Intelligence (AI). In "The Ethics Of Artificial Intelligence", he says the threat of monstrous machines dominating humanity is imaginary, but the risk of humanity misusing its machines is real. In an email interview, Prof. Floridi talks about how real, or not, the threat of AI is.

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.

As members of the deep learning R&D team at SVDS, we are interested in comparing Recurrent Neural Network (RNN) and other approaches to speech recognition. Until a few years ago, the state-of-the-art for speech recognition was a phonetic-based approach including separate components for pronunciation, acoustic, and language models. Typically, this consists of n-gram language models combined with Hidden Markov models (HMM). We wanted to start with this as a baseline model, and then explore ways to combine it with newer approaches such as Baidu's Deep Speech. While summaries exist explaining these baseline phonetic models, there do not appear to be any easily-digestible blog posts or papers that compare the tradeoffs of the different freely available tools.

With artificial intelligence advancing at break-neck speed, it is only a matter of time before robots start taking over jobs. But it seems that androids may well take our household chores too, as scientists have developed a robot that can neatly iron clothes. The TEO robot, which uses a camera in its head to create a 3D model of the garment and board, weighs 80 kilograms (175 lbs) and stands 1.8 metres tall (6 ft). In March, a robot that can lay bricks six times faster than a builder started work on a building site in the US. The Semi-Automated Mason, nicknamed Sam, can lay 3,000 bricks a day, while a builder's average is 500.

Physicists have wondered for decades whether infinitely dense points known as singularities can ever exist outside black holes, which would expose the mysteries of quantum gravity for all to see. Singularities--snags in the otherwise smooth fabric of space and time where Albert Einstein's classical gravity theory breaks down and the unknown quantum theory of gravity is needed--seem to always come cloaked in darkness, hiding from view behind the event horizons of black holes. The British physicist and mathematician Sir Roger Penrose conjectured in 1969 that visible or "naked" singularities are actually forbidden from forming in nature, in a kind of cosmic censorship. But why should quantum gravity censor itself? Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

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.

PwC is on the cusp of launching a robo-economist that could make the company the "most accurate" economic forecaster on the market. The professional services firm has developed a form of artificial intelligence (AI) with a 92% strike rate when it comes to predicting the result of UK gross domestic product (GDP). It discovered the AI's "incredible accuracy" after testing to see if the machine could pinpoint historic GDP results without knowing the outcome. But while Jonathan Gillham, PwC's director of economics, joked that the AI had already started to supersede his job, the firm said there were no plans to replace staff with automation and the program would work alongside human economists. He said: " We have been using an AI technique to forecast the UK economy and we will be launching that (...) in July. "Each quarter, the Office for National Statistics publishes its estimate for GDP and we have been able to use an AI technology base to get that right 92% of the time for the last five years.

We have discussed some AI topics in the previous posts, and it should seem now obvious the extraordinary disruptive impact AI had over the past few years. However, what everyone is now thinking of is where AI will be in five years time. I find it useful then to describe a few emerging trends we start seeing today, as well as make few predictions around machine learning future developments. The following proposed list does not want to be either exhaustive or truth-in-stone, but it comes from a series of personal considerations that might be useful when thinking about the impact of AI on our world. Companies like Vicarious or Geometric Intelligence are working toward reducing the data burden needed to train neural networks.