The use of technical media in Austrian mathematics lessons is largely limited to the GeoGebra medium. GeoGebra [5] proved to be a great tool to visualize and analyze classroom problems, but certain tasks like proving geometric facts rigorously by using a visual explanation is not supported by GeoGebra. As an alternative approach, we focus on introducing JGEX [6] as opposed to GeoGebra, specifically in the area of competition tasks. Geometric proofs are no longer an important part of secondary school curriculum in Austria and many other countries. Formerly, however, Euclidean plane geometry and proving more complicated facts was a part of the expected knowledge of secondary level.

ChatGPT may be as good as or better than students at assessments in around a quarter of university courses. However, this generally only applies to questions with a clear answer that require memory recall, rather than critical analysis. Yasir Zaki and his team at New York University Abu Dhabi in the United Arab Emirates contacted colleagues in other departments asking them to provide assessment questions from courses taught at the university, including computer science, psychology, political science and business. These colleagues also provided real student answers to the questions. The questions were then run through the artificial intelligence chatbot ChatGPT, which supplied its own responses.

Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is difficult, which is why annotation tasks are often outsourced to paid crowdworkers. Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP. To investigate whether and how well Citizen Science can be applied in this setting, we conduct an exploratory study into engaging different groups of volunteers in Citizen Science for NLP by re-annotating parts of a pre-existing crowdsourced dataset. Our results show that this can yield high-quality annotations and attract motivated volunteers, but also requires considering factors such as scalability, participation over time, and legal and ethical issues. We summarize lessons learned in the form of guidelines and provide our code and data to aid future work on Citizen Science.

When I was studying journalism at university, we had an assignment called News Day, designed to replicate a day in the life of a reporter. You arrived at school in the morning and were assigned a story to be filed by the end of the day. I've forgotten what my specific story assignment was -- it was 12 years ago -- only that it had something to do with climate change. What I do remember, with painful lucidity, is an interview with an academic who'd agreed to help me. After about 10 minutes, he correctly intuited from my questions that I didn't understand the issue, whatever it was.

As Innovation Lead for Precision Medicine at Innovate UK I am sometimes asked about the best STEM subjects to study, usually by parents wanting to help their children select the best university courses. Something they're really interested in, I have tended to say, but now add that something involving AI (Artificial Intelligence) might be a very wise choice. AI's nothing new, but now seems on the verge of making a big impact in clinical settings, reflected in our competition applications in the area of precision medicine. There are many ways AI can play a role in the medical arena, where being able to find patterns and associations in large data sets is fundamental to developing new technologies and services. These large data sets include disparate patient information, such as the increasing levels of genetic information we will have about patients, and linking it to phenotypic information (observable physical properties e.g.

Artificial intelligence and its sub-domains look set to be the next major growth area for software developers, programmers, hackers and just about anyone who has anything to do with software. There doesn't appear to be an area of life that it doesn't touch – self-driving cars, tagging porn stars on Pornhub, healthcare, security and so on. The sad truth, though, is that most of us don't understand the field. For the most part, it's not like "normal" programming where an algorithm is developed, tested and released as a product. Machine learning, for example, relies on selecting a model, developing it, training the model, testing and then releasing.

Linear algebra is a field of mathematics and an important pillar of the field of machine learning. It can be a challenging topic for beginners, or for practitioners who have not looked at the topic in decades. In this post, you will discover how to get help with linear algebra for machine learning. Top Resources for Learning Linear Algebra for Machine Learning Photos by mickey, some rights reserved. Take my free 7-day email crash course now (with sample code).