Many institutions are currently grappling with teaching artificial intelligence (AI) in the face of growing demand and relevance in our world. The Computing Research Association (CRA) has conducted 32 moderated virtual roundtable discussions of 202 experts committed to improving AI education. These discussions slot into four focus areas: AI Knowledge Areas and Pedagogy, Infrastructure Challenges in AI Education, Strategies to Increase Capacity in AI Education, and AI Education for All. Roundtables were organized around institution type to consider the particular goals and resources of different AI education environments. We identified the following high-level community needs to increase capacity in AI education. A significant digital divide creates major infrastructure hurdles, especially for smaller and under-resourced institutions. These challenges manifest as a shortage of faculty with AI expertise, who also face limited time for reskilling; a lack of computational infrastructure for students and faculty to develop and test AI models; and insufficient institutional technical support. Compounding these issues is the large burden associated with updating curricula and creating new programs. To address the faculty gap, accessible and continuous professional development is crucial for faculty to learn about AI and its ethical dimensions. This support is particularly needed for under-resourced institutions and must extend to faculty both within and outside of computing programs to ensure all students have access to AI education. We have compiled and organized a list of resources that our participant experts mentioned throughout this study. These resources contribute to a frequent request heard during the roundtables: a central repository of AI education resources for institutions to freely use across higher education.

As Artificial Intelligence (AI) changes how businesses work, there is a growing need for people who can work in this sector. This paper investigates how well universities in United Kingdom offering courses in AI, prepare students for jobs in the real world. To gain insight into the differences between university curricula and industry demands we review the contents of taught courses and job advertisement portals. By using custom data scraping tools to gather information from job advertisements and university curricula, and frequency and Naive Bayes classifier analysis, this study will show exactly what skills industry is looking for. In this study we identified 12 skill categories that were used for mapping. The study showed that the university curriculum in the AI domain is well balanced in most technical skills, including Programming and Machine learning subjects, but have a gap in Data Science and Maths and Statistics skill categories.

The second run of a five-week free course for anyone on AI starts on the 15th January 2023. We and AI were delighted to work with The Scottish AI Alliance designing content for their course designed to give the general public of Scotland (and beyond!) an introduction to the world of AI. "Living with AI" is a great and rare commitment to making general level AI literacy attainable to everyone to enable a greater understanding of the uses and questions posed by the range of tools and technologies, and to explore their future potential. Although creating materials was challenging due to the rapid changes and innovation, proliferating hype and platform restrictions, feedback from the first run of the course shows how worthwhile the effort was. Each week has a specific topic and the course is made up of written articles, videos from AI experts across Scotland, audio clips, reflective and activity-based exercises, quizzes and opportunities to collaborate with other learners. All content has been refreshed for 2024 and will be released at once, so learners can go at their own pace in their own time.

Gender bias is rampant in AI systems, causing bad user experience, injustices, and mental harm to women. School curricula fail to educate AI creators on this topic, leaving them unprepared to mitigate gender bias in AI. In this paper, we designed hands-on tutorials to raise AI creators' awareness of gender bias in AI and enhance their knowledge of sources of gender bias and debiasing techniques. The tutorials were evaluated with 18 AI creators, including AI researchers, AI industrial practitioners (i.e., developers and product managers), and students who had learned AI. Their improved awareness and knowledge demonstrated the effectiveness of our tutorials, which have the potential to complement the insufficient AI gender bias education in CS/AI courses. Based on the findings, we synthesize design implications and a rubric to guide future research, education, and design efforts.

This article describes and evaluates a new online AI-creativity course. The course is based around three near-state-of-the-art AI models combined into a pop song generating system. A fine-tuned GPT-2 model writes lyrics, Music-VAE composes musical scores and instrumentation and Diffsinger synthesises a singing voice. We explain the decisions made in designing the course which is based on Piagetian, constructivist 'learning-by-doing'. We present details of the five-week course design with learning objectives, technical concepts, and creative and technical activities. We explain how we overcame technical challenges to build a complete pop song generator system, consisting of Python scripts, pre-trained models, and Javascript code that runs in a dockerised Linux container via a web-based IDE. A quantitative analysis of student activity provides evidence on engagement and a benchmark for future improvements. A qualitative analysis of a workshop with experts validated the overall course design, it suggested the need for a stronger creative brief and ethical and legal content.

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.

British universities are popular globally for producing highly competent AI experts. This is why many aspiring AI specialists enroll themselves in the MS in AI programs of British universities. However, not all British universities are created equal and provide quality AI training to students. So, to help you out today we are sharing with you the top six universities for studying Masters in Artificial Intelligence in UK. Here are some of the best universities in the UK that you can join to study AI.

The description of "artificial intelligence in high school" may conjure up a science fiction novel where robots stand around chatting at their lockers. The reality, at Seckinger High School in Gwinnett County, Ga., looks more like this: A social studies teacher pauses a lesson on the spread of cholera in the 19th century to discuss how data scientists use AI tools today to track diseases. A math class full of English-language learners uses machine learning to identify linear and non-linear shapes. The simplest explanation of this technology is that it trains a machine to do tasks that simulate some of what the human brain can do. That means it can learn to do things like recognize faces and voices (helpful for radiology, security, and more), understand natural language, and even make recommendations.

Hear from top leaders discuss topics surrounding AL/ML technology, conversational AI, IVA, NLP, Edge, and more. Artificial intelligence (AI) and machine learning (ML) are disrupting every industry. Their impacts and integrations will only continue to grow. And ultimately, the future of AI is in distributed computing, Ion Stoica, cofounder, executive chairman and president of Anyscale, told the audience this week at VenureBeat's Transform 2022 conference. Distributed computing allows components of software systems to be shared among multiple computers and run as one system, thus improving efficiency and performance.

Harvard offers AI courses for both beginners and professionals working in the healthcare sector or corporate world. Most of the Harvard AI courses are offered in online mode and are hosted through the edX platform. Some executive programs or workshops are offered on campus. Among the several Harvard AI courses offered, "CS50's Introduction to Artificial Intelligence with Python" is the most popular course as per the student reviews and enrollments. It is a project-based course that explores algorithms behind modern Artificial Intelligence and its' applications in technologies like game-playing engines, handwriting recognition, etc. Check out the top Harvard courses to learn Artificial Intelligence Online in 2022.