This is an interesting thought and I have to agree it’s a legitimate issue. I have also considered something similar - that in the near future, if all software is designed with AI, or “generated” rather than explicitly engineered, we would certainly reap some benefits from that but we would also cease understanding how anything actually works. Obviously no one person fully understands the abstractions we have currently engineered when considering the totality.. but at least currently we have them.
I share this opinion as well, I think. I’m looking at any CRUD app I’ve worked on and thinking: this is just a specification over a database. Same with most other software but the web and most mobile apps seem ripe to just become generated patterns.
I think I agree with you on how I felt at the end of the post, but at the beginning and most of the way through, I found myself generally agreeing. But I want to look past the “bitcoin” switch after the bait because I generally agree with the author on most points.
Especially as I have delved more into how LLMs actually function and are trained, a lot of the magic in e.g. ChatGPT has evaporated for me, but in a good way. The performance of larger models like GPT-3.5 or 4 is still consistently impressive, to me, but as I’ve increased my understanding I do believe that censorship poses a greater threat than the models themselves.
Yes - and I would expand “censorship” to “control”. We have been shaped by whomever has built the algos for google, fb, twitter, etc and while that itself doesn’t make them evil they need to be evaluated somehow. I think the LLM fear comes when the creators tell us a full evaluation is impossible, etc.
Perhaps I’m misunderstanding the law or article, but wasn’t the author implying that the act of inference on an LLM model itself is a sort of “statistical experiment”:
> In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times.
And so the mean in this case is “perceived like a real human”?
This is an interesting idea but is it feasible? What does “filter these vectors” mean? In the context of deep models are we talking about embedding specific models, weights, parameters, etc at some point in memory with the hardware? Are we taking about filtering input generally and globally (on a general purpose system)?
Gonna leave my footprint on this one too. Here’s my take since my favorite app on my Meta Quest 2 is actually Immersed (for virtual desktop / multi monitor setups): I’m less interested in either Apple or Meta here because I think Apples Vision Pro ideas just reinforce what we’re being told repeatedly: the industry wants to move away from screens, and the future is not screens. We have two big things on the near horizon now: AR/VR and AI. The industry as a whole will push these technologies until they are the norm. Smartphones, PCs, tablets and laptops (maybe even TVs?) will become legacy technology, but they will of course live on in emulated forms (a “window”). Conversational and ubiquitous computing will be the future, like it or not.
Indeed any AI defense capabilities will necessarily need to include some offensive abilities, which for the public are mostly censored right now (eg try asking for any of the obvious public PoCs which it almost certainly was trained on) I find it to be one of the worst aspects of the current alignment….
I don’t think it’s a serious limitation though, since the open-source LLMs like Alpaca/LLaMA have already “won” the mass adoption among engineers, and are only getting better (already better than GPT4 in some areas).
