In a new podcast series, Everyday AI, host Jon Whittle (CSIRO) explores the AI that is already shaping our lives. With the help of expert guests, he explores how AI is used in creative industries, health, conservation, sports and space. Episode 4: AI and citizen science – AI in ecology This episode features Jessie Barry from Cornell University's Macaulay Library and Merlin Bird ID, ichthyologist Mark McGrouther, and Google's Megha Malpani. Episode 6: The final frontier – AI in space This episode features Astrophysicist Kirsten Banks, NASA researcher Dr Raymond Francis, and Research Astronomer Dr Ivy Wong.

ChatGPT has multiple uses, from writing or fixing a code to getting suggestions, getting explanations, writing non-plagiarised essays, creating summaries of long write-ups, getting solutions to problems etc. The possibilities are still open to explorations as the beta version is available for users to try out. It especially gained popularity when students started realising that they can use ChatGPT to get through their homework assignments and projects by just letting this "assistant" do it for them. Students around the world have been using it to complete their work and teachers have been reporting about how they are doubting the credibility of the work being submitted to them. These developments are catching a lot of traction on the internet.

Many solutions tailored for intuitive visualization or teleoperation of virtual, augmented and mixed (VAM) reality systems are not robust to robot failures, such as the inability to detect and recognize objects in the environment or planning unsafe trajectories. In this paper, we present a novel virtual reality (VR) framework where users can (i) recognize when the robot has failed to detect a real-world object, (ii) correct the error in VR, (iii) modify proposed object trajectories and, (iv) implement behaviors on a real-world robot. Finally, we propose a user study aimed at testing the efficacy of our framework. Project materials can be found in the OSF repository.

Automatic measurement of student engagement provides helpful information for instructors to meet learning program objectives and individualize program delivery. Students' behavioral and emotional states need to be analyzed at fine-grained time scales in order to measure their level of engagement. Many existing approaches have developed sequential and spatiotemporal models, such as recurrent neural networks, temporal convolutional networks, and three-dimensional convolutional neural networks, for measuring student engagement from videos. These models are trained to incorporate the order of behavioral and emotional states of students into video analysis and output their level of engagement. In this paper, backed by educational psychology, we question the necessity of modeling the order of behavioral and emotional states of students in measuring their engagement. We develop bag-of-words-based models in which only the occurrence of behavioral and emotional states of students is modeled and analyzed and not the order in which they occur. Behavioral and affective features are extracted from videos and analyzed by the proposed models to determine the level of engagement in an ordinal-output classification setting. Compared to the existing sequential and spatiotemporal approaches for engagement measurement, the proposed non-sequential approach improves the state-of-the-art results. According to experimental results, our method significantly improved engagement level classification accuracy on the IIITB Online SE dataset by 26% compared to sequential models and achieved engagement level classification accuracy as high as 66.58% on the DAiSEE student engagement dataset.

Vocal fatigue refers to the feeling of tiredness and weakness of voice due to extended utilization. This paper investigates the effectiveness of neural embeddings for the detection of vocal fatigue. We compare x-vectors, ECAPA-TDNN, and wav2vec 2.0 embeddings on a corpus of academic spoken English. Low-dimensional mappings of the data reveal that neural embeddings capture information about the change in vocal characteristics of a speaker during prolonged voice usage. We show that vocal fatigue can be reliably predicted using all three kinds of neural embeddings after only 50 minutes of continuous speaking when temporal smoothing and normalization are applied to the extracted embeddings. We employ support vector machines for classification and achieve accuracy scores of 81% using x-vectors, 85% using ECAPA-TDNN embeddings, and 82% using wav2vec 2.0 embeddings as input features. We obtain an accuracy score of 76%, when the trained system is applied to a different speaker and recording environment without any adaptation.

An artificial intelligence (AI) course is a type of academic program that focuses on teaching students about the principles and techniques of AI. These courses typically cover machine learning, natural language processing, robotics, and computer vision and may also include elements of mathematics, computer science, and engineering. Students who take an artificial intelligence course may learn how to design, implement, and evaluate AI systems and may also have the opportunity to work on projects that involve building or programming AI systems. The specific content of an AI course will vary depending on the course level (e.g., introductory, intermediate, or advanced) and the institution offering the course. Artificial intelligence (AI) is a rapidly growing field that is transforming many aspects of our society and economy.

It offers sufficient background material on linear algebra, probability, optimization, conditional random fields, L1 regularization, deep learning and more. In the introductory chapter the book lays out different kinds of problems that can be solved by machine learning and describes the types of methods that can be used to solve them. The book progresses on to discuss these and related issues in the chapters ahead. The book uses the language of graphical models to specify models in a concise and intuitive way. Overviews of real-world applications of various techniques are provided. MATLAB and GNU octave code which implements the algorithms provided in the book can be feely downloaded from the book's website. It is not an easy read but an authoritative book intended to be used as a text book.

Academic Earth More than 1,500 video lectures by professors from Harvard, Yale, broken down into single classes on topics like art, architecture, and astronomy.

Machine learning enables the development of new, supplemental, and empowering tools that can either expand existing technologies or invent new ones. In education, space exists for a tool that supports generic student course review formats to organize and recapitulate students' views on the pedagogical practices to which they are exposed. Often, student opinions are gathered with a general comment section that solicits their feelings towards their courses without polling specifics about course contents. Herein, we show a novel approach to summarizing and organizing students' opinions via analyzing their sentiment towards a course as a function of the language/vocabulary used to convey their opinions about a class and its contents. This analysis is derived from their responses to a general comment section encountered at the end of post-course review surveys. This analysis, accomplished with Python, LaTeX, and Google's Natural Language API, allows for the conversion of unstructured text data into both general and topic-specific sub-reports that convey students' views in a unique, novel way.

Time is our most valuable resource. Every one of us wakes up with 24 hours and, if you're anything like me, you've read more books and listened to more podcasts than you can shake a stick at to learn how to maximise those 24 hours. Well, in December last year, I discovered an Artificial Intelligence (AI) tool that sent me down a path of huge discovery and time-saving and today, I want to share this tool with you which is, best of all, free to use. As a small business owner in the PC industry, time is of the essence. If you're like me, you're constantly juggling multiple tasks and trying to find ways to streamline your operations.