1-on-1 communication as a significant bottleneck.
I think it’s one of the bottlenecks inter alia. What happens with two people with strong opinions / lots of knowledge / different backgrounds is that they fail to converge at all, often even on reference / ontology / communication, let alone actual facts / propositions / principles / plans. Because of this, there’s often no “ultimate recourse”; there’s ~no amount of research and thinking and problem solving that you can do in your head, such that you can then go out and spread the truth by just saying “take me to your leader” and then convincing the leader with facts and logic.
I also think the concept slightly falls apart when the users are not already knowledgeable / quick thinking / good at rigorous communication
Plausible, though I’m genuinely unsure, and would guess not. That is, I’d guess there are several kinds of uses for a hyperphone, and some of them would work across various gaps. For example, it’s plausible to me that speaking through a hyperphone would be my preferred way both of teaching and of learning something.
I’d guess there would be a steep learning curve.
There’d be a very steep learning curve at the beginning of development. It’s plausible that you could work things out so that there’s not that much learning curve after development. E.g. async text conversations are quite confusing if you’re stuck in the framework of normal live speech, and they do add some important kinds of friction, but mostly people get along.
Software wise, I think I’m aiming closer to better debates and debate tools, mainly because these things could be made public-facing and seem like an obvious use case for even present-day LLMs.
Possibly. One issue would be that debates are a bit more tense / higher stakes / constrained on average, I think, compared to 1 on 1 collaborative convos. For that reason, specifically for iterating on software, hyperphone convos might be easier to develop.
If you have any further thoughts regarding implementation of those things, I’d be eager to know. I’ll be trying to make my plans more specific.
If someone gets deep into this, I’d be happy to be one volunteer consultant among others about directions / ideas / considerations.
One strong recommendation I’d make is that, if you’re somewhat serious about it, GET DATA ASAP. E.g. set up some debates. About anything, between anyone who wants to debate. See what happens and see if there’s something truly hopeful there for you / the world, and then try to nourish that hopeworthy something. I wouldn’t begrudge anyone the a priori dreaming, which is more fun and can also be a very important ingredient in making cool stuff, but my guess would be that it wouldn’t go anywhere unless you feel hungry for data. You have to ask Thinking what it needs, not tell Thinking what it needs.
But aren’t a lot of your tasks the sort of thing where
there is in fact a ton of training-available data demonstrating good performance
it’s cheap to experiment
etc., other relevant peculiarities of your use cases
?
I think the claim might be true but I don’t see a super compelling reason to think so at the moment.
“Reasoning” helping with self-driving cars might be a compelling demo, but what it would be compelling about is “you can slap together robotics, big data for a specific domain, and some LLM reasoning stuff to duct tape some more of the decision-making, and get something that’s practically useful”. Generalizing to other robotics could kick off a revolution, but it would be slow-going I think?
There could be a fair amount of science overhang, where you just have to search hard enough to put X and needs-X together. E.g. people curing themselves by searching hard using LLMs. Exciting, but not an industrial revolution? In the grand scheme of science it’s not mostly that. A lot of the coolest stuff is really hard, which means there’s not that many people at the forefront, which means that people at the forefront are already familiar with a lot of what’s relevant.
If you can find domains where iteration can be done pretty automatedly, but it’s expensive enough that decision-making still matters, but decision-making is very cognitively costly, but getting kinda-okay-not-creative decision-making would still be quantitatively better, then you could unlock some sort of new paradigm of invention / discovery. E.g. automated labs running automated experiments designing proteins by gippity-tweaking, or similar. Like PACE. But that would also be hard to get started on.
What are other reasons to think this? Plausible I just haven’t seen the idea, haven’t tried too hard.