EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 5·5 min read
Universities would prefer no AI
TL;DR
Higher education is lagging in the AI race, with over 90% of notable AI models in 2025 coming from industry, highlighting a significant institutional failure. While universities focus on traditional issues, the real competition lies in technological advancements driven by industry.
✦ Why It Matters
Evaluate your institution's approach to AI and advocate for necessary changes in curriculum and research focus.
Key Takeaways
- Assess your institution's AI curriculum for relevance.
- Encourage faculty to integrate AI into their courses.
- Promote partnerships between universities and AI firms.
- Advocate for a focus on real-world AI applications in education.
Full Summary
The article critiques universities for their slow response to the rapid advancements in AI technology, likening their resistance to the Victorian Post Office's dismissal of the telephone. Despite significant AI model development occurring in industry, universities continue to monopolize degree granting and focus on traditional curricula.
A survey revealed that only 17% of students in the U.S. and Canada believe their instructors are well-equipped to guide them in AI use. The author argues that universities should be preparing students for an AI-enabled workforce, yet many institutions are preoccupied with enrollment and outdated assessment methods.
The disparity between what AI can do and what universities teach is widening, leading to a potential loss of relevance for higher education in the AI era.