Goto

Collaborating Authors

An Introduction to Deep Learning and it's role for IoT/ future cities

#artificialintelligence

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid). Deep learning is often thought of as a set of algorithms that'mimics the brain'. A more accurate description would be an algorithm that'learns in layers'.


An Introduction to Deep Learning and it's role for IoT/ future cities

#artificialintelligence

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid). Deep learning is often thought of as a set of algorithms that'mimics the brain'. A more accurate description would be an algorithm that'learns in layers'.


Deep Learning frameworks: a review before finishing 2016

#artificialintelligence

I love to visit Machine Learning meetups organized in Madrid (Spain) and I'm a regular attendant to Tensorflow Madrid and Machine Learning Spain groups. At least I was until the begining of the Self-Driving Car course, but that is another story. The fact is that too often, during "pizza & beer" time or networking I heard people talking about Deep Learning. Sentences like "where should I begin? Tensorflow is the most popular, isn't it?",


What's Next for Artificial Intelligence

#artificialintelligence

The best minds in the business--Yann LeCun of Facebook, Luke Nosek of the Founders Fund, Nick Bostrom of Oxford University and Andrew Ng of Baidu--on what life will look like in the age of the machinesThe traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.


Can This Man Make AIMore Human?

#artificialintelligence

Like any proud father, Gary Marcus is only too happy to talk about the latest achievements of his two-year-old son. More unusually, he believes that the way his toddler learns and reasons may hold the key to making machines much more intelligent. Sitting in the boardroom of a bustling Manhattan startup incubator, Marcus, a 45-year-old professor of psychology at New York University and the founder of a new company called Geometric Intelligence, describes an example of his boy's ingenuity. From the backseat of the car, his son had seen a sign showing the number 11, and because he knew that other double-digit numbers had names like "thirty-three" and "seventy-seven," he asked his father if the number on the sign was "onety-one." "He had inferred that there is a rule about how you put your numbers together," Marcus explains with a smile.