"Simply Logical -- Intelligent Reasoning by Example" by Peter Flach was first published by John Wiley in 1994. It could be purchased as book-only or with a 3.5 inch diskette containing the SWI-Prolog programmes printed in the book (for various operating systems). In 2007 the copyright reverted back to the author at which point the book and programmes were made freely available online; the print version is no longer distributed through John Wiley publishers. In 2015, as a pilot, we ported most of the original book into an online, interactive website using SWI-Prolog's SWISH platform. Since then, we launched the Simply Logical open source organisation committed to maintaining a suite of freely available interactive online educational resources about Artificial Intelligence and Logic Programming with Prolog. With the advent of new educational technologies we were inspired to rebuild the book from the ground up using the Jupyter Book platform enhanced with a collection of bespoke plugins that implement, among other things, interactive SWI-Prolog code blocks that can be executed directly in a web browser. This new version is more modular, easier to maintain, and can be split into custom teaching modules, in addition to being modern-looking, visually appealing, and compatible with a range of (mobile) devices of varying screen sizes.

YouTube users looking for instructions for a specific task may spend a long time browsing content trying to find the right video that matches their needs. Creating a visual summary (abridged version of a video) provides viewers with a quick overview and massively reduces search time. In this work, we focus on summarizing instructional videos, an under-explored area of video summarization. In comparison to generic videos, instructional videos can be parsed into semantically meaningful segments that correspond to important steps of the demonstrated task. Existing video summarization datasets rely on manual frame-level annotations, making them subjective and limited in size. To overcome this, we first automatically generate pseudo summaries for a corpus of instructional videos by exploiting two key assumptions: (i) relevant steps are likely to appear in multiple videos of the same task (Task Relevance), and (ii) they are more likely to be described by the demonstrator verbally (Cross-Modal Saliency). We propose an instructional video summarization network that combines a context-aware temporal video encoder and a segment scoring transformer. Using pseudo summaries as weak supervision, our network constructs a visual summary for an instructional video given only video and transcribed speech. To evaluate our model, we collect a high-quality test set, WikiHow Summaries, by scraping WikiHow articles that contain video demonstrations and visual depictions of steps allowing us to obtain the ground-truth summaries. We outperform several baselines and a state-of-the-art video summarization model on this new benchmark.

Drori et al. (2022) report that "A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level ... [It] automatically answers 81\% of university-level mathematics problems." The system they describe is indeed impressive; however, the above description is very much overstated. The work of solving the problems is done, not by a neural network, but by the symbolic algebra package Sympy. Problems of various formats are excluded from consideration. The so-called "explanations" are just rewordings of lines of code. Answers are marked as correct that are not in the form specified in the problem. Most seriously, it seems that in many cases the system uses the correct answer given in the test corpus to guide its path to solving the problem.

"AI for Ukraine" is a series of workshops and lectures held by international artificial intelligence experts to support the development of Ukraine's tech community during the war. Montreal), Alex J. Smola (Amazon Web), Sebastian Bubeck (Microsoft), Gaël Varoquaux (INRIA), and many other well-known specialists have joined the initiative. This is a non-commercial educational project by AI HOUSE – a company focused on building the AI/ML community in Ukraine and is part of the Roosh tech ecosystem. All proceeds collected upon registration will be donated to the biggest Ukrainian charity fund "Come Back Alive". It's been five months of a completely new reality for every single Ukrainian, one with sirens, bombings, pain, and war.

Master Robotic Process Automation (RPA) and UiPath – go from beginner to advanced. Welcome to the Complete UiPath RPA Developer Course: Build 7 Robots where you will learn everything you need to know to get started as a Robotic Process Automation (RPA) developer. Learn and master UiPath Studio and then build state-of-the-art software robots from scratch. The best part about this course is that this course is entirely project-based, which means you will be getting hands-on experience and learn the skills you need on how to build real-world software robots in an enterprise setting. So if you're the type of person who'learns by doing', then this course is for you.

Looking at all of the very large convolutional neural networks such as ResNets, VGGs, and the like, it begs the question on how we can make all of these networks smaller with less parameters while still maintaining the same level of accuracy or even improving generalization of the model using a smaller amount of parameters. One approach is depthwise separable convolutions, also known by separable convolutions in TensorFlow and Pytorch (not to be confused with spatially separable convolutions which are also referred to as separable convolutions). Depthwise separable convolutions were introduced by Sifre in "Rigid-motion scattering for image classification" and has been adopted by popular model architectures such as MobileNet and a similar version in Xception. In this tutorial, we'll be looking at what depthwise separable convolutions are and how we can use them to speed up our convolutional neural network image models. Before diving into depthwise and depthwise separable convolutions, it might be helpful to have a quick recap on convolutions.

A 12-week, 24-course curriculum covering: Different approaches to Artificial Intelligence, including the “good old” symbolic approach with Knowledge Representation and reasoning (GOFAI). Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics...

Data Science is a coalescence of sundry fields including Statistics, math, Programming, Machine Learning, and domain Erudition with the goal of extracting insights from the data to enable a data-driven decision process, which is the key to business prosperity. Data Scientists accumulate the pertinent business data from sundry internal and external sources, do experiments, and apply sundry statistical techniques to engender vigorous data substratum analytics. They utilize machine learning alimented by data pipelines to provide predictive analytics with a great level of precision. This avails to better understand the business and customers so that they can be accommodated better with a better decision-making process. Why is a Data Science Vocation most desired?

In the world of analytics,modeling is a general term used to refer to the use of data mining (machine learning) methods to develop predictions. If you want to know what ad a particular user is more likely to click on, or which customers are likely to leave you for a competitor, you develop a predictive model. There are a lot of models to choose from: Regression, Decision Trees, K Nearest Neighbor, Neural Nets, etc. They all will provide you with a prediction, but some will do better than others depending on the data you are working with. While there are certain tricks and tweaks one can do to improve the accuracy of these models, it never hurts to remember the fact that there is wisdom to be found in the masses.

In this episode of AI For Business Growth, Dr Andree Bates is joined by Guy Gadney, the CEO of Charisma. Charisma believes that AI is a new toolkit for creatives and can be used to inspire ideas, accelerate development, and create new forms of entertainment. To achieve their vision they run writers rooms, co-author papers on the future of AI and storytelling, and partner with universities around the world. Guy explains the use cases for Charisma Ai, how it enhances user content experience, and the problems that Charisma.Ai solves for the entertainment industry. During the pandemic their AI was used to help digitise theatre productions in an immersive way.