In this AI Podcast, Doina Precup describes why their doesn't need to be a gender gap in computer science education. An associate professor at McGill University and research team lead at DeepMind, Precup shares her personal experiences, along with the AI4Good Lab she co-founded to give women more access to machine learning training. Growing up in Romania, Precup attended a high school that specialized in computer science and a technical university. "If anything, programming was considered a very good job for women, because you did not need to be working in the fields," she explained. It made the gap in Canadian universities and companies even more noticeable.
AIC is a Montreal based company involved in software development. The major business of AIC comes from the acquisition of a commercial Warehouse Management System that is currently used by a number of 3PL warehouses that operate for clients like Unilever and Coca Cola. We begin by focusing on a known 1.5M square foot 3PL warehouse in Burlington (NJ) operated for a number of different clients and product groups. Different product groups pose significant constraints on the pick route given that apparel, electronics, food and hazardous materials have very different constraints on how they are to be received, stored and then picked, packed and shipped. We first examine the optimization of picking routes for a single picker given a specific simulated warehouse layout.
As friends who work as pediatricians, we've had several conversations recently about artificial intelligence and its growing role in medicine. Machine learning and computer algorithms, it seems, are on the cusp of changing the medical profession forever. Prestigious medical journals publish a steady stream of studies demonstrating the potential of deep learning to replace tasks that are currently the bread and butter of highly trained physicians, like reading CT scans of the head. We are attuned to artificial intelligence in part because our city, Montreal, has become an international AI hub. One of the city's biggest teaching hospitals, the Centre hospitalier de l'Université de Montréal, just launched an AI school for health professionals.
We are a passionante group of biologists, ecologists, data scientists, software developers, and AI specialists based in Montréal. We aim to make detecting whales as fast and easy as possible. Whether it's providing wildlife managers with cutting edge tools to increase their efficiency, accuracy, and lighten their workload - or helping ports manage whale presence and marine traffic in a healthy and profitable way - we want anyone interested in whale detection to have access. To learn more about us please visit our website at: https://www.whaleseeker.com. The objective of the project is to work with the multidisciplinary team to develop a solution that processes images using Machine Learning to identify whale targets among large (35 MPx and above) aerial pictures containing multiple sources of interferences like glare, waves, rocks, muddy water, etc.
Nearly a decade after co-founding Cyberjustice Laboratory, a unique hub that analyses the impact of technologies on justice while developing concrete technological tools that are adapted to the reality of justice systems, Karim Benyekhlef and Fabien Gélinas have set their sights on artificial intelligence. The Autonomy through Cyberjustice Technologies (ACT), the latest brainchild of the Cyberjustice Laboratory, is the largest international multidisciplinary research initiative that seeks to leverage artificial intelligence to increase access to justice while providing justice stakeholders with a roadmap to help them develop technology that is better adapted to justice. "The main objective behind the initiative is to ensure that individuals know their rights, understand their legal situation regarding their problems and improve access to justice – and AI may help accomplish those goals," said Benyekhlef, the head of Cyberjustice Laboratory and a law professor at the Université de Montréal. "There's a good chance that our reflections and work on areas such as privacy, data management, data governance could easily be used in other realms such as in public administration. But we must be careful. We cannot play the sorcerer's apprentice. These are tools that are not yet mature. There's work to be done."
Contributor and SMX speaker, Duane Brown, explains in this video why 2020 is the year to get a handle on your mobile experience as well as find the platforms your customers are on and experiment if they're new to you. I run an agency up in Montreal, Canada. We focus on kind of two areas, paid ads, PPC, Google, Facebook, stuff like that. We also do CRO for clients, we'll often have to figure out how do their websites convert more. And a lot of our clients are in e-commerce.
Yoshua Bengio, left, has been a machine learning researcher for decades and runs Montreal's MILA institute for AI. Gary Marcus is a psychologist at NYU and a frequent critic of the puffed-up hype around AI. Gary Marcus, the NYU professor and entrepreneur who has made himself a gadfly of deep learning with his frequent skewering of headline hype, and Yoshua Bengio, a leading practitioner of deep learning awarded computing's higher honor for his pioneering work, went head to head Monday night in a two-hour debate Webcast from Bengio's MILA institute headquarters in Montreal. The two scholars seemed to find a lot of common ground as far as the broad strokes of where artificial intelligence needs to go, things such as trying to bring reasoning to AI. But when the discussion periodically lapsed into particular terminology or historical assertions, the two were suddenly at odds. The recorded stream of the video is posted on the organization's Facebook page if you want to go back and watch it.
Joelle Pineau launched a reproducibility challenge at the 2019 Conference on Neural Information Processing Systems.Credit: Facebook Joelle Pineau doesn't want science's reproducibility crisis to come to artificial intelligence (AI). Spurred by her frustration with difficulties recreating results from other research teams, Pineau, a machine-learning scientist at McGill University and Facebook in Montreal, Canada, is now spearheading a movement to get AI researchers to open up their methods and code to scrutiny. Alongside Koustuv Sinha, a PhD student at McGill, Pineau holds one of two new roles dedicated to reproducibility on the organizing committee for the Conference on Neural Information Processing Systems (NeurIPS), a major meeting for AI that this year attracted some 13,000 researchers. Ahead of this year's conference in Vancouver, Canada, from 8 to 14 December, the committee asked scientists to provide their code and fill in a checklist of methodological details for each paper submitted. They also ran a competition that challenged researchers to recreate each other's work.
NIPS 2018 (Montreal, Canada), or NeurIPS, as it is called now, is over, and I would like to take the opportunity to dissect one of the papers that received the Best Paper Award at this prestigious conference. The name of the paper is Neural Ordinary Differential Equations (arXiv link) and its authors are affiliated to the famous Vector Institute at the University of Toronto. In this post, I will try to explain some of the main ideas of this paper as well as discuss their potential implications for the future of the field of Deep Learning. Since the paper is quite advanced and touches on concepts such as Ordinary Differential Equations (ODE), Recurrent Neural Networks (RNN) or Normalizing Flows (NF), I suggest that you read up on these terms if you are not familiar with them, since I will not go into details on these. However, I will try to explain the ideas of the paper as intuitively as possible, so that you may get the main concepts without going too much into the technical details.