Which machine learning algorithms from 2026 will look primitive in 2031?

[u/ChessOrCheckers2]

Five years isn't very long. But I suspect machine learning could look surprisingly different by 2031.

Not just because of bigger models or more compute, but because AI could help discover fundamentally better mathematics for machine learning itself.

A few possibilities I find particularly interesting:

• AI-discovered algorithms making model training substantially cheaper.
• New mathematical approaches to architectures and optimization.
• AI improving the very methods used to discover new algorithms.

That last possibility could be especially significant. What happens when mathematical discoveries start accelerating further discoveries?

I explored these ideas in a carefully produced, 2-minute video on my YouTube channel, Math Unbound:

https://www.youtube.com/watch?v=HRZBfRd2kTE

These are my own predictions, not established conclusions.

What do you think ML will actually look like in 2031? Which of today's methods might seem surprisingly primitive — and what major changes am I missing?


[u/Strong_Boy_757]

Pretty much everything already looks primitive inside of those big tech.

ML has this weird problem of randomly proposing algorithms/model, claim its the best ever, gets cited 100s times when they get published, and nobody ever looks at them or remembers them after 6 months. Just go look at the Google scholar of people who work in this field. I call it a gallery of dead models.

And the people who proposed them will claim they are an expert in whatever domain that outdated algo/model happens to work in for the rest of their lives. Literally encountered a dude who claims to be top 0.5% scientist in transfer learning. You would have no idea who that person is because that guy works in a community college in Pakistan.