Iunnrais

There is no such thing as a weapon that is only capable of being used for defense. It’s a logical contradiction on the order of a true paradox such as “this statement is false”. It is theoretically possible to have some sort of defensive shield with no way to move it, but even such a thing could be deployed offensively as an area denial tactic, ask any rts player. If you can stop force, it can be used as force.

Another way to think about it is the old question of which would win, the proverbial unstoppable force, or the immovable object? Einstein’s theory of relativity gave us a framework to say that both terms refer to exactly the same thing. An immovable object, by definition, is actually an unstoppable force. And an unstoppable force, by definition, is an unmovable object.

So it goes with weapons. A weapon is a weapon by its nature. It is only the wielder who decides if it is defensive or not. If you can’t trust the wielder, then you can’t trust the wielder, and that’s basically it, and the root of all problems.

https://xkcd.com/2030/

In some contexts this is true. Other contexts reverse this. Ukraine is showing a context in which it is easier to defend than attack, for instance.

Others have answered the question regarding other languages being mistaken for each other by non-speakers (of course this happens). Just wanted to add that Google has had a problem discerning Japanese and Chinese for the longest time and it drives me nuts. This is something a computer should easily be able to distinguish— we’re not talking about human recognition, we’re using an entirely different block of Unicode!

The most infuriating was when Google Maps’s text-to-speech insisted on using the mandarin pronunciation for kanji when navigating IN JAPAN. I’m glad it no longer does that, but at the expense of still not using Japanese… if you have your phone set to English, now it’ll use only the English that appears in road signs, and pronounce the words according to English phonics rules. Not as bad, but still… why? Why not just allow Japanese pronunciation of place names while in Japan? Why must my desire to hear “turn right” also come with having Kinkakuji pronounced “kihnkeighkuhji”?

I hold that If their claims were true, any sketchy marketing would be forgiven very, very quickly.

Technically, they have a very short distance to climb if they wanted to: just give the battery (or preferably multiple batteries) to an independent 3rd party to test to their hearts content. If they aren’t full of shit, that’d clear things up in an instant. If someone can show their claims are true, that’s all they need.

If they are completely full of shit, then there’s no no way out of the hole. I think this is unlikely given the carefully selected tests they’ve already released— they have… something. It likely isn’t an entirely non-existent product appearing only on paper.

If they are only partially full of shit, in that they have a battery that is decent or better than current batteries but not totally fulfilling their claims, then it’s a moderate hole to climb out of. Some egg on the face, but survivable.

If they are substantially full of shit, with batteries that are equivalent or worse than current batteries, then they’re gonna be laughed out of the market real fast. Probably mentioned in the same breath as Peter Molyneux, for similar reasons.

Yeah, the intro is a bit long, no denying that. I personally find the build up worth it though in terms of narrative payoff. But I do always dread the slow start when I replay it. Fair.

It’s not? OoT has a nice pc port too.

Not to mention that Twilight Princess is a good game, and beloved by many.

To be clear, it seems you object to the concept of sequels and movie serieses (how do you pluralize “series”?) more than compilations?

The following wall of text is a simplification that I hope will help you understand.  The simplification of the simplification (tldr) is: for as long as it has context window available, it figures out the meaning of every word in the entire conversation based on its position relative to every other word in the conversation.

The longer explanation (but still a simplification) is as follows:

An LLM does math not just on every word you send it, but on how every word you send it relates to every other word you send it.  You can think of every “token” in an LLM’s context window as being a discrete slot that can take a word (or part of a word, or punctuation, whatever… that’s why we always say “token” not “word”), and that slot has very very complicated wiring that connects to every other slot in the context window.  And the output of each of those connections is itself connected to more wiring, and the output of that to more wiring, and so on.  Each of these layers seems to help with grammar, understanding, and linking of concepts… it also turns out that a lot of the connections aren’t even used, but having them all wired in allows the system to find the most optimal arrangement by itself.  The way it figured out how to wire all the “slots” together is based on terabytes of training data.

Part of this wiring passes through a “dictionary” of sorts (not what it’s called, but we’ll run with it for this simplification), which encodes every token as a long LONG series of numbers.  Each number in that series corresponds to a “semantic concept”.  For example, one of the numbers in the series might determine how “plural” a word might be.  Another number might determine how masculine or feminine the word is.  Another number might encode how rude the word is.  Another might be how “cat-related” a word is.  I keep saying “might” because we didn’t write the “dictionary” ourselves, we got another machine to make it for us by analyzing literal terabytes of human written texts and checking for word co-locations (what words appear in the vicinity of other words).  Academic Linguists have been having a golden age recently by studying the math of how the machines mapped words, and have slowly been piecing together what the various numbers mean-- it’s really quite fascinating.

Anyway, the context window is not an arbitrary array, and increasing a context window by even a single token basically requires rewiring the whole thing, which is why an LLM’s context window is inherently limited.  And if there isn’t a slot to put a token in, then it simply can’t think about it.

So, an LLM does “think”… in a sense.  It does “reason”… but only as to what the *words* mean, not about logical consistency or adherence to the real world or facts.  You may have heard the Symphony of Science song “A Glorious Dawn” (https://www.youtube.com/​watch?v=zSgiXGELjbc&list=​RDEMft98UQ9nSZoCk8V-gaQ7zQ&​start_radio=1) where Carl Sagan says:

"But the brain does much more than just recollect It inter-compares, it synthesizes, it analyzes  it generates abstractions

The simplest thought like the concept of the number one  Has an elaborate logical underpinning\l The brain has its own language For testing the structure and consistency of the world"

An LLM does SOME of this.  It inter-compares, but only between definitions of words in its dictionary.  It analyzes… but only between definitions of words in its dictionary.  It has its own elaborate logical underpinning, but these logical connections apply to WORDS and COMBINATIONS of words, not to ideas like our brain does.

In some ways this can be mitigated by encoding more and more information into the “dictionary”, which is how you can get an LLM to pass various exams it’s never seen before. But it’s all based on the meanings of the words as it understands them, not logic.

How DOES it think?  Well, at the LOWEST level, it thinks one word at a time, considering what it should say next based on what has already been said.  If it reads “Two plus two equals what?” it looks up the meaning of those numbers, checks the relation to the plus and equal words, does math on the WORDS (not the number 2!) and sees that, hey, there’s a dimension of words that relates to its position on the number line!  I can adjust along this dimension of meaning, and come up with the answer four!  And as long as two, plus, equals, and four are all SUFFICIENTLY well defined in the dictionary, then it can manipulate those ideas just as well as a human, or better.

What happens when it lacks words for concepts that it can map mathematically (what does cat + dog + not kingly + casual + bridgelike + sounds melancholy + french origin + purple + etc etc etc = ?)?  This happens all the time.  It looks for the closest word.  Even if it has an exact concept mapped (very rare), it’ll still look around its concept space a little bit, according to a metric called “heat”, jiggling around the tokens in its dictionary like molecules jiggle when heated.  This gets pretty good results, but not consistent ones… the result isn’t fully random, we don’t get chaos, but we do get different results for the same input.  That’s not necessarily a bad thing.

However… true contradictions can also arise in its definitions.  The most famous example of this was when ChatGPT was asked about a “seahorse emoji”.  Turns out, in the training data, it was able to find connections between seahorse and emoji pretty easily.  It’s very confident that there is one.  Unfortunately, there isn’t… so it has mathematical connections between the concept of seahorse and the concept of emoji, but when it adds them together, NO actual token of a seahorse emoji emerges (because there isn’t one in unicode).  Using the “find the nearest mathematical token that DOES exist” principle, it’ll spit out another emoji.  Then it will look at the emoji, and see that it clearly doesn’t match… that’s *not* a seahorse.  But it “knows” that a seahorse emoji exists according to its dictionary; it has a link there!  So it tries again, and again can’t find it.  So it gets stuck in an endless loop.

Anyway, how do agents fit into all this?  Well, people started thinking-- if we can’t get an LLM to think in terms of ideas and logic outside of the definitions of words, what if we handed off the logic to another program that can do that?  We can train the LLM to associate and link certain words to computer commands to run a program that can do arithmetic, or calculus, or formal logic, or drawing a picture, or arranging text into a table, or things like that.  These external programs can then return text to the LLM, which can process it as words with definitions, and give you a good answer.

We can also use the “definitions of words” approach to approximate thinking about abstractions and ideas.  Just have the LLM start generating associations, but don’t show them to the user, keep them in the backend as “chain of thoughts”.  When the abstraction has gotten to a useful enough point, we can then use it as part of our context window to analyze it as words and get a good result.

Sometimes there’s a problem with using only one dictionary… sometimes words mean VASTLY different things in different contexts.  That’s where the “Mixture of Experts” approach comes in.  You build different dictionaries for different contexts.  You have one LLM figure out which domain is most likely appropriate, then hand the text off to a different LLM who was trained on that other domain with a different dictionary.

It all comes together, and it works.  Mostly.  Usually.  There are problems sometimes.  And it used to be that we could fix problems just by giving it more training data… make the dictionary better.  And it’s probably true that with an infinitely precise dictionary, there’d be no problems at all, just like it has no problems adding two plus two because it has sufficient definition of all those words; except we’ve literally run out of additional training data to give it.  So workarounds and hacks and specialized training and things have been utilized to patch over the bits of the dictionary we don’t have and possibly can’t ever make.

And that’s a simplified version of how LLMs do what they do.

It’s fundamentally not the same thing as autocomplete. Give autocomplete all the data an LLM has, every gig, every terabyte if it, and it still won’t be an LLM. Autocomplete lacks the semantic meaning layer as well as some other parts. People say it’s nothing but autocomplete from a misunderstanding of what a reward function does in backpropagation training (saying “the reward function is to predict the next word” is not even close to the equivalent of “it’s doing the same thing as autocomplete”)

I’m writing this short reply with hopes that when I have more time in the next two days or so I’ll come back with a more complete explanation, (including why context windows have to be limited).

Some ideologies are, in fact, mutually exclusive and cannot tolerate the others. Fascism cannot be tolerated, for instance. Nor can a belief in chattel slavery as a universal good. Sometimes an opposing ideology is just too fucking evil to be allowed to persist.

Setting the line that must not be crossed is a hard no problem though. And misplacing that line an inch incorrect in either direction can be horrible too.

I think you could rationally explore ghosts in the “radically redefining” them arena. Ghosts could rationally exist as an artifact of your mind, and saying that is not the same thing as saying they don’t exist. Hallucinations exist. They aren’t real, but they exist. Ghosts could rationally exist in the exactly same way, as processes in our own heads. It’s when you start saying they interact with the world in a way outside people’s heads that you can’t really reconcile.