There are already networks in place that can detect seismic activity and send an alert as soon as an earthquake is underway. But the current technology doesn't actually send the alert until all of the sensors in the network covering a given area have detected seismic waves. And it could take about a minute from the moment activity is initially detected until an alert hits the wire. A minute is a long time in an emergency. Government agencies, public works, and local utilities ideally need to alert the populace and do things like halt trains and shut off power lines to potentially mitigate damage – every second counts.

Deep learning involves a lot of matrix math, and it's important for you to understand the basics before diving into building your own neural networks. These lessons provide a short refresher on what you need to know for this course, along with some guidance for using the NumPy library to work efficiently with matrices in Python. Python is convenient, but it can also be slow. However, it does allow you to access libraries that execute faster code written in languages like C. NumPy is one such library: it provides fast alternatives to math operations in Python and is designed to work efficiently with groups of numbers -- like matrices. NumPy is a large library and we are only going to scratch the surface of it here.

Canada has a rich history of innovation, but in the next few decades, powerful technological forces will transform the global economy. Large multinational companies have jumped out to a headstart in the race to succeed, and Canada runs the risk of falling behind. At stake is nothing less than our prosperity and economic well-being. The Financial Post set out explore what is needed for businesses to flourish and grow. You can find all of our coverage here.

Every ten to 15 years there is a technology breakthrough that really changes what it means to be human. The internet, mobile phones, social media and, most recently, AI voice assistance: all of these amplify the human experience. And with each technological game-changer we go through much of the same series of questions and anxieties. We worry both that it's all too much, and too little. With the recent advances in artificial intelligence we leap ahead to the existential dangers, and at the same time wonder whether there aren't more pressing issues to discuss: healthcare, climate change, education, the economy. Well, they are pressing issues -- but AI has an impact on all of them.

Me: VGGs suck because they are computationally inefficient, and because they are a naive adoption of a computer vision architecture. Random person on Internet: Jordi, you might be wrong. People use VGGs a lot! No more introduction is required, this series of posts is about that: I want to share my honest thoughts regarding this discussion, for thinking which is the role of the computer vision deep learning architectures in the audio field. In these posts, I'm centering my discussion around the VGG model -- which is a computer vision architecture that is widely used by audio researchers.

I've spent a majority of my adult life in investing. Recently I became more interested in approaching the topic from a quantitative angle. The promise of automating an investment approach whilst I sit on a beach sipping sangria's was all too compelling to ignore. With Sir Isaac's expression in my mind I thought what better place to start than existing research papers. I thought hopefully they'll give me some unique knowledge that I can build up on when I write my own strategies.

This sponsored post explores one of the solutions making computer vision a reality today for more applications: Intel's Distribution of OpenVINO (Open Visual Inference and Neural Network Optimization) toolkit. With the demand for intelligent vision solutions increasing everywhere from edge to cloud, enterprises of every type are demanding visually-enabled – and intelligent – applications for surveillance, retail, manufacturing, smart cities and homes, office automation, autonomous driving, and more coming every day. Increasingly, AI applications are powered by smart vision inputs. OpenVINO includes Intel's deep learning deployment toolkit, which includes a model optimizer that imports and trains models from a number of frameworks (Caffe, Tensoflow, MxNet, ONNX, Kaiai). Up till now, most intelligent computer vision applications have required a wealth of machine learning, deep learning, and data science knowledge to enable simple object recognition, much less facial recognition or collision avoidance.

Edgar Perez is a great business speaker, a confident communicator and a world class motivator. Global executives have come to appreciate his wide-ranging insights on how they can better position their organizations for success through strong leadership and a comprehensive approach that links business strategy and disruptive technologies including artificial intelligence and deep learning, quantum computing and cyber security. A published author, keynote speaker and business consultant for private equity and hedge funds, he is Council Member at the Gerson Lehrman Group, Guidepoint Global Advisors and Internal Consulting Group. Mr. Perez is author of The AI Breakthrough, How Artificial Intelligence is Advancing Deep Learning and Revolutionizing Your World (2018), Knightmare on Wall Street, The Rise and Fall of Knight Capital and the Biggest Risk for Financial Markets (2013), and The Speed Traders, An Insider's Look at the New High-Frequency Trading Phenomenon That is Transforming the Investing World, published in English by McGraw-Hill Inc. (2011), 交易快手, published in Mandarin by China Financial Publishing House (2012), and Investasi Super Kilat, published in Bahasa Indonesia by Kompas Gramedia (2012). Mr. Perez has addressed thousands of top executives around the world through keynote speeches and corporate training programs on quantum computing, artificial intelligence, deep learning, cybersecurity and financial trading.

Human activity on Earth has detrimentally affected the Earth's climate, which has led to whole nations melting away, animal extinction, and potentially the disappearance of clouds. But there's a possibility that the severity of climate change could can be mitigated, if we act fast and leverage another human inventions: artificial intelligence. Google's London-based, A.I. subsidiary DeepMind announced this week its most recent accomplishment, using machine learning to help make wind energy more valuable to the power grid. The company's algorithm was able to predict how much power its wind turbines would generate 36 hours ahead of time, the company explains in a blog post. This would allow wind farms to reliably delivery exact amounts of power to meet electricity demand.

It's obviously important to Australians to make sure their koala population is closely tracked -- but how can you do so when the suckers live in forests and climb trees all the time? A new project from Queensland University of Technology combines some well-known techniques in a new way to help keep an eye on wild populations of the famous and soft marsupials. They used a drone equipped with a heat-sensing camera, then ran the footage through a deep learning model trained to look for koala-like heat signatures. It's similar in some ways to an earlier project from QUT in which dugongs -- endangered sea cows -- were counted along the shore via aerial imagery and machine learning. But this is considerably harder.