| Something that would massively improve language models ability to reason is whiteboarding. Being trained to make, review, improve, and add to notes. While maintaining a consistent goal. I am unaware of anyone who can reason to any serious depth without a paper, computational, or actual version of a whiteboard. This doesn’t seem like a particularly challenging thing to add to current shallow (but now quite wide) reasoning models. Imagine how fast you could think if you had a mentally stable whiteboard that you could perceive as clearly as you can see, and update as fast as you can think the changes. Our brains have probably been tragically speed limited by our slow vocal & finger speeds for some time. That will take AI’s to a wide AND deep reasoning level far beyond us very quickly. Now add mental file cabinets and an AI could trivially keep track of many goals and it’s progress on them. Again, not likely to be a huge challenge to add. Now, given all that long term reasoning ability, let the AI manage instances of itself working across all the problems with speed adjusted for priority & opportunity. Finally, have the model record every difficult problem it solved, so it’s fast wide (non-whiteboard) abilities can be tuned, moving up level after level. Occasionally do a complete retraining on all data and problem-solution pairs. Again, straightforward scaling. Every new dimension they scale quickly surpasses us & keeps improving. At this point, IMHO, anyone pessimistic about AI has expectations far behind the exponential curve we are in. Our minds constantly try to linearize our experiences. This is the worst time in history to be doing that. |
Thinking about what I am going to draw or write on the whiteboard takes the bulk of time, not the act of drawing or writing. The "update as fast as you can think" part will likely be achieved soon with neural interface, yet it's hard to imagine that this will lead to "superintelligence" of some sort. Same for "mental file cabinets": real or digital files allow to trivially store information, and search systems allow to retrieve it pretty quickly, yet somehow Google didn't make everyone who can use it super smart.
Same goes for vocal speed: coming up with the words to describe the idea and coming up with the idea itself are different things, second being much more hard.
> At this point, IMHO, anyone pessimistic about AI has expectations far behind the exponential curve we are in.
The problem is that the crucial aspect of reasoning is missing in the state of the art models right now. We can make LLMs write to and read from files, but as long as there is a chance that any of its output will be incoherent (and there's a good of this chance now) and there is no mechanism to actually check for errors logically, the whole whiteboard architecture will be a huge demonstration of "garbage in, garbage out".