We study online learning for optimal allocation when the resource to be allocated is time. Examples of possible applications include a driver filling a day with rides, a landlord renting an estate, etc. Following our initial motivation, a driver receives ride proposals sequentially according to a Poisson process and can either accept or reject a proposed ride. If she accepts the proposal, she is busy for the duration of the ride and obtains a reward that depends on the ride duration. If she rejects it, she remains on hold until a new ride proposal arrives. We study the regret incurred by the driver first when she knows her reward function but does not know the distribution of the ride duration, and then when she does not know her reward function, either. Faster rates are finally obtained by adding structural assumptions on the distribution of rides or on the reward function. This natural setting bears similarities with contextual (one-armed) bandits, but with the crucial difference that the normalized reward associated to a context depends on the whole distribution of contexts.
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal. This concept emerged several years ago and is being adopted by a rapidly-growing number of educational institutions around the globe. In recent years, the boost of artificial intelligence (AI) and machine learning (ML), together with the advances in big data analysis, has unfolded novel perspectives to enhance personalized education in numerous dimensions. By taking advantage of AI/ML methods, the educational platform precisely acquires the student's characteristics. This is done, in part, by observing the past experiences as well as analyzing the available big data through exploring the learners' features and similarities. It can, for example, recommend the most appropriate content among numerous accessible ones, advise a well-designed long-term curriculum, connect appropriate learners by suggestion, accurate performance evaluation, and the like. Still, several aspects of AI-based personalized education remain unexplored. These include, among others, compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing diversity, removing the biases induced by the data and algorithms, and the like. In this paper, while providing a brief review of state-of-the-art research, we investigate the challenges of AI/ML-based personalized education and discuss potential solutions.
Policy Optimization (PO) is a widely used approach to address continuous control tasks. In this paper, we introduce the notion of mediator feedback that frames PO as an online learning problem over the policy space. The additional available information, compared to the standard bandit feedback, allows reusing samples generated by one policy to estimate the performance of other policies. Based on this observation, we propose an algorithm, RANDomized-exploration policy Optimization via Multiple Importance Sampling with Truncation (RANDOMIST), for regret minimization in PO, that employs a randomized exploration strategy, differently from the existing optimistic approaches. When the policy space is finite, we show that under certain circumstances, it is possible to achieve constant regret, while always enjoying logarithmic regret. We also derive problem-dependent regret lower bounds. Then, we extend RANDOMIST to compact policy spaces. Finally, we provide numerical simulations on finite and compact policy spaces, in comparison with PO and bandit baselines.
Artificial Intelligence (AI) is evolving at an exponential rate. Today, it has expanded beyond tech and geographical constraints and is slowly bringing massive changes worldwide. In recent times, AI influencers are driving conversations about AI news and trends across social media and beyond while also offering advice to numerous enterprises. Plus, they also help us keep updated with the recent innovations and information about AI. Analytics Insight brings 10 LinkedIn influencers who share the latest trends in the AI domain through insightful articles on their LinkedIn blogs.
Data is now considered to be one of the fastest-growing, multibillion-dollar industries. As a result, corporations and organizations are trying to make the most out of the data they already have and determine what data they still need to capture and store. In addition, there continues to be an incredible need for data scientists to make sense of the numbers and uncover hidden solutions to messy business problems. A recent study using the LinkedIn job search tool shows that a majority of top tech jobs in the year 2020 are jobs that require skills in data science. With all the exciting opportunities in data science, educating yourself about data science is a great way to gain the skills and experience needed to stand out in this competitive field and give your employer an edge over the competition.
In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.
With the emergence of e-learning and personalised education, the production and distribution of digital educational resources have boomed. Video lectures have now become one of the primary modalities to impart knowledge to masses in the current digital age. The rapid creation of video lecture content challenges the currently established human-centred moderation and quality assurance pipeline, demanding for more efficient, scalable and automatic solutions for managing learning resources. Although a few datasets related to engagement with educational videos exist, there is still an important need for data and research aimed at understanding learner engagement with scientific video lectures. This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement. We introduce several novel tasks related to predicting and understanding context-agnostic engagement in video lectures, providing preliminary baselines. This is the largest and most diverse publicly available dataset to our knowledge that deals with such tasks. The extraction of Wikipedia topic-based features also allows associating more sophisticated Wikipedia based features to the dataset to improve the performance in these tasks. The dataset, helper tools and example code snippets are available publicly at https://github.com/sahanbull/context-agnostic-engagement
In display advertising, a small group of sellers and bidders face each other in up to 10 12 auctions a day. In this context, revenue maximisation via monopoly price learning is a high-value problem for sellers. By nature, these auctions are online and produce a very high frequency stream of data. This results in a computational strain that requires algorithms be real-time. Unfortunately, existing methods inherited from the batch setting suffer O($\sqrt t$) time/memory complexity at each update, prohibiting their use. In this paper, we provide the first algorithm for online learning of monopoly prices in online auctions whose update is constant in time and memory.
TL;DR: The 2020 Master Microsoft Excel & Power BI Certification Bundle is on sale for £27.19 as of Oct. 2, saving you 97% on list price. "Data science" seems to be all the rage these days. The buzzword sounds super fancy, but when you actually break it down, it's just the ability to wrangle big data, break it down, and use it to make decisions. From self-driving cars in the automotive industries, to risk management in insurance, to recommending what Netflix series to binge-watch next, data science is behind it all. And you can use a tool that's been around since the '80s to familiarise yourself with the data-driven world: good ol' Microsoft Excel.
Business intelligence (BI) brings a varied collection of strategies that uncover the hidden insights beneath the data sources and convert raw data into intelligent information for business decision making. To stay competitive, businesses must rediscover and use the data they have generated, this makes BI so important. Business intelligence, lets organisations to extract insights from a pool of accessible data to deliver exact, significant, and nearly real-time inputs for decision making. This specialization is offered in collaboration with Tableau, and is aimed for newcomers to data visualization with no prior experience using Tableau. In this course, you will view examples from real world business cases and journalistic examples from leading media companies.