Re:Blender 5.3 released with Gaussian Splat support
by mrandish on Tuesday November 10, 2026 @03:15PM
(#50445703)
> I would argue that we have no idea how the technology will change.
I didn't discuss how I think the tech will change. I pointed out a current gap in crystalizing knowledge acquisition gained on-task because I feel that gap is still under-appreciated. There's a lot of very interesting work trying to identify how to cost effectively narrow that gap (Dynamic Modular Adapters, Background Distillation, Test-Time Training, etc).
They all can be effective to varying degrees in different ways but they also all have their own gaps and trade-offs. As of now, there's no clear path to per-instance knowledge crystalization that's both broadly generalizable and scales cost-effectively. Will both be solved eventually? I think almost certainly. Will both be solved in the next 2-5 years? Maybe... but it smells like the kind of fundamental problem that's 'wicked' hard, so I wouldn't be surprised if the LLM you and I can afford to daily drive in 2 - 5 years is far more capable and 'smart' but isn't that much better at automatic crystalized learning from on-task knowledge acquisition than the best LLMs are today.
To be clear, LLMs are fabulously useful in myriad ways and I think they'll continue getting much more useful in many more ways. That's so much a given, it makes identifying gaps fundamental enough there's a good chance they won't be solved in 2-5 years especially interesting.
I didn't discuss how I think the tech will change. I pointed out a current gap in crystalizing knowledge acquisition gained on-task because I feel that gap is still under-appreciated. There's a lot of very interesting work trying to identify how to cost effectively narrow that gap (Dynamic Modular Adapters, Background Distillation, Test-Time Training, etc).
They all can be effective to varying degrees in different ways but they also all have their own gaps and trade-offs. As of now, there's no clear path to per-instance knowledge crystalization that's both broadly generalizable and scales cost-effectively. Will both be solved eventually? I think almost certainly. Will both be solved in the next 2-5 years? Maybe... but it smells like the kind of fundamental problem that's 'wicked' hard, so I wouldn't be surprised if the LLM you and I can afford to daily drive in 2 - 5 years is far more capable and 'smart' but isn't that much better at automatic crystalized learning from on-task knowledge acquisition than the best LLMs are today.
To be clear, LLMs are fabulously useful in myriad ways and I think they'll continue getting much more useful in many more ways. That's so much a given, it makes identifying gaps fundamental enough there's a good chance they won't be solved in 2-5 years especially interesting.