For this to be true everyone must be logically on the same page. They must share the same axioms. Everyone must be operating off the same data and must not make mistakes or have bias evaluating it. Otherwise inevitably sometimes people will arrive at conflicting truths.

In reality it’s messy and not possible with 100% certainty to discern falsehoods and truthoods. Our scientific method does a pretty good job. But it’s not perfect.

You can’t retcon reality and say “well retrospectively we know what happened and one side was just wrong”. That’s called history. It’s not useful or practical working definition of truth when trying to evaluate your possible actions (individually, communally, socially, etc) and make a decision in the moment.

I don’t think it’s accurate to say that we want to redefine truth. I think more accurately truth has inconvenient limitations and it’s arguably really nice most of the time to ignore them.

As a digression, the definition of knowledge as justified true belief runs into the Gettier problems:

    > Smith [...] has a justified belief that "Jones owns a Ford". Smith 
    > therefore (justifiably) concludes [...] that "Jones owns a Ford, or Brown 
    > is in Barcelona", even though Smith has no information whatsoever about 
    > the location of Brown. In fact, Jones does not own a Ford, but by sheer 
    > coincidence, Brown really is in Barcelona. Again, Smith had a belief that
    > was true and justified, but not knowledge.

Or from 8th century Indian philosopher Dharmottara:

   > Imagine that we are seeking water on a hot day. We suddenly see water, or so we 
   > think. In fact, we are not seeing water but a mirage, but when we reach the 
   > spot, we are lucky and find water right there under a rock. Can we say that we 
   > had genuine knowledge of water? The answer seems to be negative, for we were 
   > just lucky. 

More to the point, the definition of knowledge as linguistic agreement is convincingly supported by much of what has historically been common knowledge, such as the meddling of deities in human affairs, or that the people of Springfield are eating the cats.

I don’t think it’s so clear cut… Even the most adamant “facts are immutable” person can agree that we’ve had trouble “fact checking” social media objectively. Fluoride is healthy, meta analysis of the facts reveals fluoride may be unhealthy. The truth of the matter is by and large what’s socially cohesive for doctors’ and dentists’ narrative, that “fluoride is fine any argument to the contrary—even the published meta-analysis—is politically motivated nonsense”.

> Sounds very human

well yes, of course it does, that article goes out of its way to anthropomorphize LLMs, while providing very little substance

Isn't the point of computers to have machines that improve on default human weaknesses, not just reproduce them at scale?

The other very human thing to do is invent disciplines of thought so that we don't just constantly spew bullshit all the time. For example you could have a discipline about "pursuit of facts" which means that before you say something you mentally check yourself and make sure it's actually factually correct. This is how large portions of the populace avoid walking around spewing made up facts and bullshit. In our rush to anthropomorphize ML systems we often forget that there are a lot of disciplines that humans are painstakingly taught from birth and those disciplines often give rise to behaviors that the ML-based system is incapable of like saying "I don't know the answer to that" or "I think that might be an unanswerable question."

In a way, the main problem with LLMs isn't that they are wrong sometimes. We humans are used to that. We encounter people who are professionally wrong all the time. Politicians, con-men, scammers, even people who are just honestly wrong. We have evaluation metrics for those things. Those metrics are flawed because there are humans on the other end intelligently gaming those too, but generally speaking we're all at least trying.

LLMs don't fit those signals properly. They always sound like an intelligent person who knows what they are talking about, even when spewing absolute garbage. Even very intelligent people, even very intelligent people in the field of AI research are routinely bamboozled by the sheer swaggering confidence these models convey in their own results.

My personal opinion is that any AI researcher who was shocked by the paper lynguist mentioned ought to be ashamed of themselves and their credulity. That was all obvious to me; I couldn't have told you the exact mechanism the arithmetic was being performed (though what is was doing was well in the realm of what I would have expected from a linguistic AI trying to do math), but the fact that its chain of reasoning bore no particular resemblance to how it drew its conclusions was always obvious. A neural net has no introspection on itself. It doesn't have any idea "why" it is doing what it is doing. It can't. There's no mechanism for that to even exist. We humans are not directly introspecting our own neural nets, we're building models of our own behavior and then consulting the models, and anyone with any practice doing that should be well aware of how those models can still completely fail to predict reality!

Does that mean the chain of reasoning is "false"? How do we account for it improving performance on certain tasks then? No. It means that it is occurring at a higher level and a different level. It is quite like humans imputing reasons to their gut impulses. With training, combining gut impulses with careful reasoning is actually a very, very potent way to solve problems. The reasoning system needs training or it flies around like an unconstrained fire hose uncontrollably spraying everything around, but brought under control it is the most powerful system we know. But the models should always have been read as providing a rationalization rather than an explanation of something they couldn't possibly have been explaining. I'm also not convinced the models have that "training" either, nor is it obvious to me how to give it to them.

(You can't just prompt it into a human, it's going to be more complicated than just telling a model to "be carefully rational". Intensive and careful RHLF is a bare minimum, but finding humans who can get it right will itself be a challenge, and it's possible that what we're looking for simply doesn't exist in the bias-set of the LLM technology, which is my base case at this point.)

I think it works until it doesn't. The nature of technical debt of this kind means you can sort of coast on things until the complexity of the system reaches such a level that it's effectively painted into a corner, and nothing but a massive teardown will do as a fix.

Starting with "On" is cooler in philosophical tradition, though, starting in classical and medieval times, e.g. On Interpretation, On the Heavens, etc by Aristotle, De Veritate, De Malo, etc. by Aquinas. Capital is actually "Das Kapital", too