One of the major challenges of an educational system is choosing appropriate content considering pupils' age and intellectual potential. In this article the experiment of primary school grades (from 1st to 4th grades) is considered for automatically determining the correspondence of an educational materials recommended for pupils by using the School corpus where it includes the dataset of 25 school textbooks confirmed by the Ministry of preschool and school education of the Republic of Uzbekistan. In this case, TF-IDF scores of the texts are determined, they are converted into a vector representation, and the given educational materials are compared with the corresponding class of the School corpus using the cosine similarity algorithm. Based on the results of the calculation, it is determined whether the given educational material is appropriate or not appropriate for the pupils' educational potential.

Visual odometry is a fundamental task for many applications on mobile devices and robotic platforms. Since such applications are oftentimes not limited to predefined target domains and learning-based vision systems are known to generalize poorly to unseen environments, methods for continual adaptation during inference time are of significant interest. In this work, we introduce CoVIO for online continual learning of visual-inertial odometry. CoVIO effectively adapts to new domains while mitigating catastrophic forgetting by exploiting experience replay. In particular, we propose a novel sampling strategy to maximize image diversity in a fixed-size replay buffer that targets the limited storage capacity of embedded devices. We further provide an asynchronous version that decouples the odometry estimation from the network weight update step enabling continuous inference in real time. We extensively evaluate CoVIO on various real-world datasets demonstrating that it successfully adapts to new domains while outperforming previous methods. The code of our work is publicly available at http://continual-slam.cs.uni-freiburg.de.

One of my professional goals this year was to learn more about artificial intelligence (AI). Over the course of the past year, there have been a lot of stories coming out about how schools are adding the concept of artificial intelligence into their curriculum or trying to weave it into different courses offered. The purpose is to help students better understand its capabilities and how it might impact the future of learning and the future of work. When I did some research earlier this year, I was amazed at some of the different uses of artificial intelligence that we interact with each day, and may not realize. A quick Google search of the term "artificial intelligence" turns up 518 million results in .17

Microsoft has revealed its thoughts on how artificial intelligence (AI) could shape how we work in the years to come -- and how it plans to help guide those changes. The announcement was made by Microsoft's Satya Nadella and Jared Spataro at a company event titled The Future of Work with AI. As the name suggests, the show was focused on how artificial intelligence (AI) could affect how we work, both now and in the future. More specifically, the tech giant discussed how it will add AI smarts into its suite of Office apps. In PowerPoint, for example, you will be able to use an AI-powered Copilot that can create entire presentations for you with just a few text prompts.

Support Vector Machines (SVM) are one of the most powerful machine learning models around, and this topic has been one that students have requested ever since I started making courses. These days, everyone seems to be talking about deep learning, but in fact there was a time when support vector machines were seen as superior to neural networks. One of the things you'll learn about in this course is that a support vector machine actually is a neural network, and they essentially look identical if you were to draw a diagram. The toughest obstacle to overcome when you're learning about support vector machines is that they are very theoretical. This theory very easily scares a lot of people away, and it might feel like learning about support vector machines is beyond your ability.

"Turn Ideas into Python Programs with ChatGPT" is a unique online course that teaches you how to use ChatGPT, a powerful AI language model, to generate Python code without any prior experience in programming. This course is perfect for individuals who want to learn how to create Python automations and apps but have little to no coding experience. Instead of learning to code in Python, you'll learn how to write good ChatGPT queries that generate Python code. You'll then run this code in your computer and get the output which could be some generated files, web app, desktop GUI, a data analysis graph, etc. The course is divided into five sections, each of which focuses on a different aspect of using ChatGPT to build Python automations and apps.

Do you want to learn natural language processing and looking for Best Resources to Learn Natural Language Processing?… If yes, then you are in the right place. In this article, I have listed all the best resources to learn natural language processing including Online Courses, Tutorials, Books, and YouTube Videos. So, give your few minutes and find out the best resources to learn natural language processing. You can bookmark this article so that you can refer to this article later.

We study learning in periodic Markov Decision Process (MDP), a special type of non-stationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting. We formulate the problem as a stationary MDP by augmenting the state space with the period index, and propose a periodic upper confidence bound reinforcement learning-2 (PUCRL2) algorithm. We show that the regret of PUCRL2 varies linearly with the period $N$ and as $\mathcal{O}(\sqrt{Tlog T})$ with the horizon length $T$. Utilizing the information about the sparsity of transition matrix of augmented MDP, we propose another algorithm PUCRLB which enhances upon PUCRL2, both in terms of regret ($O(\sqrt{N})$ dependency on period) and empirical performance. Finally, we propose two other algorithms U-PUCRL2 and U-PUCRLB for extended uncertainty in the environment in which the period is unknown but a set of candidate periods are known. Numerical results demonstrate the efficacy of all the algorithms.

In recent years, there is an increasing interest in high school robotics extracurriculars such as robotics clubs and robotics competitions. The growing demand is a result of more ubiquitous open-source software and affordable off-the-shelf hardware kits, which significantly help lower the barrier for entry-level robotics hobbyists. In this project, we present an open-source, low-cost, and lightweight robotic manipulator designed and developed by a high school researcher under the guidance of a university faculty and a Ph.D. student. We believe the presented project is suitable for high school robotics research and educational activities. Our open-source package consists of mechanical design models, mechatronics specifications, and software program source codes. The mechanical design models include CAD (Computer Aided Design) files that are ready for prototyping (3D printing technology) and serve as an assembly guide accommodated with a complete bill of materials. Electrical wiring diagrams and low-level controllers are documented in detail as part of the open-source software package. The educational objective of this project is to enable high school student teams to replicate and build a robotic manipulator. The engineering experience that high school students acquire in the proposed project is full-stack, including mechanical design, mechatronics, and programming. The project significantly enriches their hands-on engineering experience in a project-based environment. Throughout this project, we discovered that the high school researcher was able to apply multidisciplinary knowledge from K-12 STEM courses to build the robotic manipulator. The researcher was able to go through a system engineering design and development process and obtain skills to use professional engineering tools including SolidWorks and Arduino microcontrollers.

Join Microsoft at NVIDIA GTC, a free online global technology conference (GTC), March 20 to 23 to learn how organizations of any size can power AI innovation with purpose-built cloud infrastructure from Microsoft. Microsoft's Azure AI supercomputing infrastructure is uniquely designed for AI workloads and helps build and train some of the industry's most advanced AI solutions. From data preparation to model and infrastructure performance management, Azure's comprehensive portfolio of powerful and massively scalable GPU-accelerated virtual machines (VMs) and seamless integration with services like Azure Batch and open-source solutions helps streamline management and automation of large AI models and infrastructure. Attend NVIDIA GTC to discover how Azure AI infrastructure optimized for AI performance can deliver speed and scale in the cloud and help you reduce the complexity of building, training, and bringing AI models into production. Don't miss session S52469 featuring Nidhi Chappell, a recipient of the 2023 People to Watch, recognized as a high-performance computing (HPC) luminary by HPCwire.