The Future of Learning

Ever since large language models arrived on the scene, I have been forced to rethink what the future of learning could or should look like. Two questions are key, IMHO:

  • What is worth learning in the age of AI? What skills or combination of skills will humans find value and fulfillment in?

  • How best we might learn? When copilots and AI assistants are able to generate dynamic personal UI on the fly and can seamlessly incorporate natural language knowledge, what might be the best experience for effective learning?

What is worth learning in the age of AI?

I don't have answers for this yet. But here are some beliefs that I have updated recently:

  • A lot of cognitive intelligence has become available widely and cheaply. Much of knowledge work can and probably will be automated now. This is also evident from my work at Snowmountain AI.

  • This disruption is happening in the world of bits (0s and 1s) much faster than in the world of atoms (eg: metals and cells). As a (debatable) example, NVIDIA CEO Jensen Huang says that coding might be dead in the water as a career option for the next generation. Instead, he highlighted the youth are better off exploring opportunities in other areas, including education, manufacturing, or farming.

  • I worry that AI may not value human sense of beauty and aesthetics. Why invent new tools that are more elegant, when AI happily works better with old ugly tools because those are in its dataset?

How might we learn best?

I have felt that historically, there have been large gaps between being taught, learning and doing.

This talk by Andy Matuschak not only captures these gaps beautifully, but even has a few ideas on how to bridge them.

A small but concrete step here might to build a more robust spaced-repetition system with AI than what was possible earlier.

  • Instead of manually creating flashcards, why not just select text and images from your browsing/reading, and let AI extract Q & A pairs out of them?

  • When practicing cards, incorporate the user's current projects and goals to make the flashcards relevant?

  • Use language models to vary the question phrasing to emphasize conceptual learning, rather than pattern matching of words?

Why I decided to shut down LearnAwesome

  • Vector embeddings based similarity search seems better than one that is based on manual human tagging.

  • As AI-generated content proliferates, human curation will become even more valuable. But this actually worsens the LearnAwesome model where a few curators do all the work and everyone else leeches off without providing much for support.

  • It now seems possible to automatically build interactivity with personalization into existing curated static content.

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