In this SURF community, we want to bring experts interested in advanced computing and networking together. This community aims to create a space to share experiences, proof of concepts, possible applications, and impact with topics like Quantum Technologies, Specialized Computing Architecture & Energy Efficiency, and Network innovation for future research & education challenges.
Eind 2024 publiceerde Npuls de visie ‘AI en kansengelijkheid’. De belangrijkste boodschap van de visie: als we niets doen, neemt kansengelijkheid voor studenten door AI eerder af dan toe. Er is dus werk aan de winkel. In deze blogserie nemen we je mee in de manier waarop AI kan bijdragen aan eerlijke kansen in het onderwijs. En ook hoe onderwijsinstellingen AI kunnen gebruiken om studenten beter te begrijpen en te ondersteunen. Met aandacht voor de risico’s en praktische stappen voor instellingen om AI op een zorgvuldige en ethische manier toe te passen. Dit is deel 1 in de serie.
SURF has selected HPE to deliver solutions for the backbone and border network domains, and Salumanus is selected to deliver the coherent transceivers. Together with partner Nomios we will implement the next generation SURF network infrastructure, designed to meet the rapidly growing demand for bandwidth, predictability, and resilience across the Netherlands’ research and education community.
English version
Vanaf februari 2025 bundelen SURF en AlgoSoc hun krachten in een langlopende pilot met Nextcloud, een open-source samenwerkingsomgeving. Deze samenwerking biedt gebruikers volledige controle over hun data, draagt bij aan autonomie, en versterkt een gezonder digitaal ecosysteem. Het initiatief biedt bij succes een additioneel alternatief in een markt die gedomineerd wordt door big tech en stimuleert de digitale keuzevrijheid binnen onderwijs en onderzoek.
Dit bericht is een verdere toelichting op het achtergrondartikel:
"Onderzoekers kiezen voor een alternatieve cloud: ‘Ik wil zelf kunnen kiezen met wie ik mijn bestanden deel" (NRC, 31 december 2024).
Ever since the release of ChatGPT, people have been amazed and have been using it to help them with all sorts of tasks, such as content creation. However, the model has faced criticism: some are raising concerns about plagiarism for example. AI-generated content detectors claim to distinguish between text that was written by a human and text that was written by an AI. How well do these tools really work? According to our findings they are no better than random classifiers when tested on AI-generated content.
There are more concerns than just the performance of these tools however. For one, there is no guarantee of avoiding false positives. Wrongfully accusing someone of plagiarism would be especially harmful. Then, it seems likely that this will turn into a game of cat and mouse with language models and tools promising to detect them continually trying to outdo each other. All in all, detection tools do not seem to offer a very robust or long-term solution. Perhaps it would be better to include the impact of artificial intelligence in the existing discussion about the best way to design exams and assignments to test students.
Deep Learning (DL) is becoming more and more popular. Many of the libraries and toolkits used by deep learning engineers for designing, building and training neural networks are open source, such as PyTorch or Tensorflow. These frameworks have contributed to the popularity of deep learning, and make it available for everyone to use, from hobbyists to academic researchers to private companies.
In late November 2022, OpenAI published a new language model that was ready to cause a stir: ChatGPT. But ChatGPT did not come out of the blue. Where did it start, what development have language models undergone, what are the risks and how will this trend continue
