A leading centre for AI education will open up to the world, as Imperial launches its first Massive Open Online Courses with Coursera. The move allows anyone with an internet connection to learn from some of the world's top researchers in artificial intelligence (AI), machine learning and mathematics. Professor Alice Gast, President of Imperial, said: "AI has the potential to transform many sectors. It is wonderful to have world-leading Imperial experts providing this opportunity to such a broad audience. Many will benefit from this exciting curriculum on the machine learning and mathematics underpinning the rapid advances in AI."

In the coming months, NYC Mayor Bill de Blasio will announce a new task force on "Automated Decision Systems" -- the first of its kind in the United States. The task force will recommend how each city agency should be accountable for using algorithms and other advanced computing techniques to make important decisions. As a first step toward this goal, we urge the task force to consider a framework structured around Algorithmic Impact Assessments (AIAs). Automated decision systems are here, and are already being integrated across many core social institutions, reshaping how our criminal justice system works via risk assessment algorithms and predictive policing systems, optimizing energy use in critical infrastructure through AI-driven resource allocation, and changing our educational system through new teacher evaluation tools and student-school matching algorithms. And these are merely what journalists, researchers, and the public record expose -- to date, no city in the US has explicitly mandated that its agencies disclose anything about the automated decision systems they have in place or are planning to use.

We introduce a general method for improving the convergence rate of gradient-based optimizers that is easy to implement and works well in practice. We demonstrate the effectiveness of the method in a range of optimization problems by applying it to stochastic gradient descent, stochastic gradient descent with Nesterov momentum, and Adam, showing that it significantly reduces the need for the manual tuning of the initial learning rate for these commonly used algorithms. Our method works by dynamically updating the learning rate during optimization using the gradient with respect to the learning rate of the update rule itself. Computing this "hypergradient" needs little additional computation, requires only one extra copy of the original gradient to be stored in memory, and relies upon nothing more than what is provided by reverse-mode automatic differentiation.

Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervised algorithms to achieve accurate predictions. The accuracy achieved by top weakly supervised algorithms is still significantly lower than their fully supervised counterparts. In this paper, we propose a novel weakly supervised curriculum learning pipeline for multi-label object recognition, detection and semantic segmentation. In this pipeline, we first obtain intermediate object localization and pixel labeling results for the training images, and then use such results to train task-specific deep networks in a fully supervised manner. The entire process consists of four stages, including object localization in the training images, filtering and fusing object instances, pixel labeling for the training images, and task-specific network training. To obtain clean object instances in the training images, we propose a novel algorithm for filtering, fusing and classifying object instances collected from multiple solution mechanisms. In this algorithm, we incorporate both metric learning and density-based clustering to filter detected object instances. Experiments show that our weakly supervised pipeline achieves state-of-the-art results in multi-label image classification as well as weakly supervised object detection and very competitive results in weakly supervised semantic segmentation on MS-COCO, PASCAL VOC 2007 and PASCAL VOC 2012.

Do you know what a "Hippodamian Grid Plan" is? I didn't until I played Assassin's Creed: Origins's new Discovery Tour mode. Turns out Hippodamus of Miletus is known as "the father of European urban planning," a Greek born in 498 B.C.E. Most important: His ideas were adopted by Deinokrates, principle planner for the city of Alexandria. And the rest, as they say, is history.

What Tomorrow's Business Leaders Need to Know About Machine Learning Sometimes I write a blog just to formulate and organize a point of view, and I think it's time that I pull together the bounty of excellent information about Machine Learning. This is a topic with which business leaders must become comfortable, especially tomorrow's business leaders (tip for my next semester University of San Francisco business students!). Machine learning is a key capability that will help organizations drive optimization and monetization opportunities, and there have been some recent developments that will place basic machine learning capabilities into the hands of the lines of business. By the way, there is an absolute wealth of freely-available material on machine learning, so I've included a sources section at the end of this blog for folks who want more details on machine learning. Time to dive into the world of machine learning!

The Rise of The Machines Robots have been tasked to do many jobs in this modern world from increasing production in manufacturing, solving intricate economic equations in finance to handling domesticated chores in the home. They have found their place in today's society and one of them have received full citizenship. A robot named Sophia that is constructed with facial recognition, able to process natural language, has advanced artificial intelligence, and is equipped with a synthetic human face has been granted citizenship by Saudi Arabia. Sophia is able to have conversations with humans as she answered questions after giving a brief speech thanking Saudi Arabia for making her the first robot in the world to attain citizenship. The artificial intelligence of the robot is able to evolve as she demonstrated by declaring equal rights for robots that are afforded to humans.

Imagine that, in 20 or 30 years, a company creates the first artificially intelligent humanoid robot. She looks like a person, talks like a person, interacts like a person. If you were to meet Ava, you could relate to her even though you know she's a robot. Ava is a fully conscious, fully self-aware being: she communicates; she wants things; she improves herself. She is also, importantly, far more intelligent than her human creators.

For part one of the series, in which we explore the benefits and dangers of the EU's GDPR legislation, check this article.

This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory. First, we apply a GS-based parameter estimation mechanism to estimate the items parameters according to the response data, and then to compare its results with those of an IRT-based Bayesian parameter estimation mechanism. In addition, we propose a static-IRT test assembly mechanism to assemble a form for the conventional test. The presented FML-based dynamic assessment mechanism infers the probability of making a correct response to the item for a student with various abilities. Moreover, this paper also proposes a novel PFML learning mechanism for optimizing the parameters between items and students. Finally, we adopt a K-fold cross validation mechanism to evaluate the performance of the proposed agent. Experimental results show that the novel PFML learning mechanism for the parameter estimation and learning optimization performs favorably. We believe the proposed PFML will be a reference for education research and pedagogy and an important co-learning mechanism for future human-machine educational applications.