We propose a novel approach to interactive theorem-proving (ITP) using deep reinforcement learning. Unlike previous work, our framework is able to prove theorems both end-to-end and from scratch (i.e., without relying on example proofs from human experts). We formulate the process of ITP as a Markov decision process (MDP) in which each state represents a set of potential derivation paths. The agent learns to select promising derivations as well as appropriate tactics within each derivation using deep policy gradients. This structure allows us to introduce a novel backtracking mechanism which enables the agent to efficiently discard (predicted) dead-end derivations and restart the derivation from promising alternatives. Experimental results show that the framework provides comparable performance to that of the approaches that use human experts, and that it is also capable of proving theorems that it has never seen during training. We further elaborate the role of each component of the framework using ablation studies.

Applications for self-regulated teaching are very popular (e.g., with Duolingo estimates of 100M downloads from Google Play at the time of writing). One of the central challenges for research on intelligent user interfaces is to identify algorithmic principles that can pick the best interventions for reliably improving human learning toward stated objectives in light of realistically obtainable data on the user. The computational problem we study is how, when given some learning materials, we can organize them into lessons and reviews such that, over time, human learning is maximized with respect to a set learning objective. Predicting the effects of teaching interventions on human learning is challenging, however. Firstly, the state of user memory is both latent (that is, not directly observable) and non-stationary (that is, evolving over time, on account of such effects as loss of activation and interference), and an intervention that is ideal for one user may be a poor choice for another user -- there are large individual-to-individual differences in forgetting and recall.

The more dependent we become on apps, the more demand there'll be for skilled programmers. It just so happens that learning how to code is easier than ever in 2021. In fact, we've rounded up 12 amazing deals on courses and training programs that will teach you the skills you need to start creating your own software, and they're on sale for a limited time! Go, or GoLang, is Google's open-source programming language that's designed to simplify many programming tasks. This course is perfect for beginners, as Go is one of the fastest-growing languages in the industry thanks to its ease of use and familiar syntax.

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.