Online prediction for streaming time series data has practical use for many real-world applications where downstream decisions depend on accurate forecasts for the future. Deployment in dynamic environments requires models to adapt quickly to changing data distributions without overfitting. We propose POLA (Predicting Online by Learning rate Adaptation) to automatically regulate the learning rate of recurrent neural network models to adapt to changing time series patterns across time. POLA meta-learns the learning rate of the stochastic gradient descent (SGD) algorithm by assimilating the prequential or interleaved-test-then-train evaluation scheme for online prediction. We evaluate POLA on two real-world datasets across three commonly-used recurrent neural network models. POLA demonstrates overall comparable or better predictive performance over other online prediction methods.

Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games. A cornerstone of this success is the auto-curriculum framework, which shapes the learning process by continually creating new challenging tasks for agents to adapt to, thereby facilitating the acquisition of new skills. In order to extend MARL methods to real-world domains outside of video games, we envision in this blue sky paper that maintaining a diversity-aware auto-curriculum is critical for successful MARL applications. Specifically, we argue that \emph{behavioural diversity} is a pivotal, yet under-explored, component for real-world multiagent learning systems, and that significant work remains in understanding how to design a diversity-aware auto-curriculum. We list four open challenges for auto-curriculum techniques, which we believe deserve more attention from this community. Towards validating our vision, we recommend modelling realistic interactive behaviours in autonomous driving as an important test bed, and recommend the SMARTS/ULTRA benchmark.

It's no surprise that artificial intelligence is a key ingredient in the modern tech space. From machine learning to wearables to robotics, the AI across industries is a growing necessity for businesses looking to remain competitive in the long term. Yet there are a few common reasons why businesses often fall short in their AI strategy implementation. Information for this eWEEK Data Points article was supplied by Dr. Charla Griffy-Brown, Professor of Information Systems and Technology Management, and Associate Dean of Executive and Part-Time Programs at Pepperdine University's Graziadio School of Business. Here she discusses five key reasons AI strategies fail and what businesses can do to avoid these pitfalls.

Title to be confirmed Speaker: Fabio Petroni Organised by: Stanford MLSys Join the email list to find out how to register for each seminar. Title to be confirmed Speaker: Chad Jenkins (University of Michigan) Organised by: Robotics Today Watch the seminar here. Title to be confirmed Speaker: Samory K. Kpotufe Organised by: London School of Economics and Political Science Register here.

In the past few years, we have observed a huge supply-demand gap for autonomous driving engineers. The core problem is that autonomous driving is not one single technology but rather a complex system integrating many technologies, and no one single academic department can provide comprehensive education in this field. We advocate to create a cross-disciplinary program to expose students with technical background in computer science, computer engineering, electrical engineering, as well as mechanical engineering. On top of the cross-disciplinary technical foundation, a capstone project that provides students with hands-on experiences of working with a real autonomous vehicle is required to consolidate the technical foundation.

Existing e-learning environments primarily focus on the aspect of providing intuitive learning contents and to recommend learning units in a personalized fashion. The major focus of the KnowledgeCheckR environment is to take into account forgetting processes which immediately start after a learning unit has been completed. In this context, techniques are needed that are able to predict which learning units are the most relevant ones to be repeated in future learning sessions. In this paper, we provide an overview of the recommendation approaches integrated in KnowledgeCheckR. Examples thereof are utility-based recommendation that helps to identify learning contents to be repeated in the future, collaborative filtering approaches that help to implement session-based recommendation, and content-based recommendation that supports intelligent question answering. In order to show the applicability of the presented techniques, we provide an overview of the results of empirical studies that have been conducted in real-world scenarios.

Artificial Intelligence is the next step in image editing, so find out how you'll edit your portraits of tomorrow with this tutorial on editing portrait images using Luminar AI. AI is set to ramp up image editing software like never before. The autonomous tweaks AI could make saves hours of work by cutting out backgrounds or skies, retouching portraits, and enhancing composition. But you don't have to look into the future for this kind of intelligent image editing, it's here today, and you can use it to improve your photos. Luminar AI by Skylum is editing software that runs entirely on artificial intelligence.

Python AI Machine Learning, OpenCV Start your career path in Python Artificial Intelligence Machine Learning now!! What you'll learn Python required for AI, Machine Learning & Data Science 2021 Ready to explore machine learning and artificial intelligence in python? This python Artificial Intelligence machine learning and OpenCV course (A-Z) contains 5 different series designed to teach you the ins and outs of Machine Learning and Artificial intelligence. It talks about fundamental Machine Learning algorithms, neural networks, Deep Learning, OpenCV and finally developing an Artificial Intelligence that can play the game of Flappy Bird.

Big data Analysis covered with machine learning algorithms. This Course divide in three part. Description This Course will design to understand Data Science using Machine Learning Algorithms with big data concept. Big data Analysis covered with machine learning algorithms. This Course divide in three part.

Academic timetabling is the task of scheduling courses to specific times in such a way that there are no conflicts. Most of the models considered in the literature assume that this conflict information is already known. However in many real life timetabling problems, courses are taught in multiple sections and until a student is assigned to a specific section of a course, the conflict information is not known. M.W. Carter [Car00] sums it up nicely "When courses are offered in multiple sections as they are at Waterloo, it creates a timetabling paradox. Students request a course, but timetabling assigns days and times to course sections. We cannot assign times to sections until we know which students are in each section. But we cannot assign students to sections until we know when the sections are timetabled!"