We examine which top tools are "friends", their Python vs R bias, and which work well with Spark/Hadoop and Deep Learning, and identify an emerging Big Data Deep Learning ecosystem.

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.

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer. There's no doubt that Canada could lead the planet in artificial intelligence (AI). Canadian academics such as Geoffrey Hinton and Yoshua Bengio essentially created the field of deep learning and put Canada on the map; today, Edmonton, Toronto and Montreal are globally important centres of AI research. The best AI talent in the world is also increasingly coming to Canada to launch AI businesses such as integrate.ai

Entity resolution (ER) presents unique challenges for evaluation methodology. While crowdsourcing platforms acquire ground truth, sound approaches to sampling must drive labelling efforts. In ER, extreme class imbalance between matching and non-matching records can lead to enormous labelling requirements when seeking statistically consistent estimates for rigorous evaluation. This paper addresses this important challenge with the OASIS algorithm: a sampler and F-measure estimator for ER evaluation. OASIS draws samples from a (biased) instrumental distribution, chosen to ensure estimators with optimal asymptotic variance. As new labels are collected OASIS updates this instrumental distribution via a Bayesian latent variable model of the annotator oracle, to quickly focus on unlabelled items providing more information. We prove that resulting estimates of F-measure, precision, recall converge to the true population values. Thorough comparisons of sampling methods on a variety of ER datasets demonstrate significant labelling reductions of up to 83% without loss to estimate accuracy.

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer. There's no doubt that Canada could lead the planet in artificial intelligence (AI). Canadian academics such as Geoffrey Hinton and Yoshua Bengio essentially created the field of deep learning and put Canada on the map; today, Edmonton, Toronto and Montreal are globally important centres of AI research. The best AI talent in the world is also increasingly coming to Canada to launch AI businesses such as integrate.ai

Levine explains what deep learning is and he discusses the challenges of using deep learning in robotics. Lastly, Levine speaks about his collaboration with Google and some of the surprising behavior that emerged from his deep learning approach (how the system grasps soft objects). In addition to the main interview, Audrow interviewed Levine about his professional path. They spoke about what questions motivate him, why his PhD experience was different to what he had expected, the value of self-directed learning, work-life balance, and what he wishes he'd known in graduate school. Sergey Levine is an assistant professor at UC Berkeley.

GoGuardian, a Silicon Beach startup launched their new AI platform Admin 2.0. The new artificial intelligence platform will enhance the safety of students with real time learning of behaviors to properly filter content on and off campus to keep students on task and engaged. GoGuardian's cloud-based software allows educators flexible control over their school district's 1:1 program. GoGuardian provides Chromebook management solutions that help protect students online. Their Head of Innovation, Tyler Shaddix, said "Smart Alerts uses A.I. to help educators truly grasp the content of the web page. When you fully understand the context of the behavior, the insights into student needs become more actionable. Smart Alerts' A.I. technology provides educators with proper context and the most accurate information available…accuracy is clearly of dire importance for educators facing issues like student suicide and self-harm."

Skilled, scrubbed-up surgeons huddle around a patient in a brightly lit operating theatre. The chief physician makes the first incision, calmly explaining his every move to a captive audience of more than one million people. He deftly removes a cancerous tumour and the scene is all too real. You might feel like you're there. But chances are you're safe at home, learning about surgery through your VR headset.

His impact on artificial intelligence research has been so deep that some people in the field talk about the "six degrees of Geoffrey Hinton" the way college students once referred to Kevin Bacon's uncanny connections to so many Hollywood movies. Dr. Hinton's students and associates are now leading lights of artificial intelligence research at Apple, Facebook, Google and Uber, and run artificial intelligence programs at the University of Montreal and OpenAI, a nonprofit research company. "Geoff, at a time when A.I. was in the wilderness, toiled away at building the field and because of his personality, attracted people who then dispersed," said Ilse Treurnicht, chief executive of Toronto's MaRS Discovery District, an innovation center that will soon house the Vector Institute, Toronto's new public-private artificial intelligence research institute, where Dr. Hinton will be chief scientific adviser. Dr. Hinton also recently set up a Toronto branch of Google Brain, the company's artificial intelligence research project. His tiny office there is not the grand space filled with gadgets and awards that one might expect for a man at the leading edge of the most transformative field of science today.

Memory and context play a huge role in helping humans interpret the world. If you encounter a word like "spring" while reading, you don't usually have to ask yourself whether it's a verb meaning "to jump," or a noun referring to the season after winter, or another noun referring to a coil of metal, because the context of the sentence makes it clear. But as with many tasks, what's effortless for humans can be incredibly difficult for computers. We've spent a good deal of time looking at various types of neural networks and their applications. We started with feedforward networks and their ability to sort and label objects based on shared characteristics, and then we explored convolutional networks, which are particularly well-suited to decoding images.