unblough

Said another way, LIGO can't work because the ruler itself is squashing as space squashes, so you can't measure if space is compressing.

I think the chief confusion here is that you may be thinking the light arrives at the detector in the same amount of time regardless of spacetime curvature. That is, the the ruler is itself squishing.

But what needs to be considered is the constant speed of light.

This implies that what happens is, in the presence of additional curvature, and constant speed of light, the additional distance traveled will have appeared to slow the light.

In the laser interferometer this registers as interference.

https://m.youtube.com/watch?v=ajZojAwfEbs

It is also worth noting that any claims of detection are thoroughly investigated and confirmed with other detectors.

It is difficult for a single LIGO detector to confirm a gravitational wave signal on its own. The initial discovery of gravitational waves required that the signal be seen in both detectors (Hanford and Livingston).

https://www.ligo.caltech.edu/page/what-is-ligo

This Connection is Untrusted Go Back The owner of costantini.pw has configured their website improperly. To protect your information from being stolen, Firefox has not connected to this website.

I tried to see what it built for you, and this was the only url in your bio.

From your linked post:

What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I’ve in fact reached the opposite conclusion). Fast forward about a year: I’m training RNNs all the time and I’ve witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me.

This reads more like humanizing the language of the post then any legitimate surprise from the author.

The rest of the post then goes into great detail showing that “we DO really know what happened” to paraphrase the definition the op provides for their use of “surprise”.

Conclusion We’ve learned about RNNs, how they work, why they have become a big deal, we’ve trained an RNN character-level language model on several fun datasets, and we’ve seen where RNNs are going.

I am pushing back on people conflating the innate complexity of a high dimensional polynomial with a misplaced reverence of incomprehensibility.

In fact, it is known that RNNs are Turing-Complete in the sense that they can to simulate arbitrary programs (with proper weights).

Mathematically proven to be able to do something is about as far from surprise as one can get.

I think this is an uncharitable reading of this thread.

I’m arguing against the breathless use of “surprising”.

My gp explains what I think you overlooked in this dismissive response.

to analyze its decisions computationally necessitates similar levels of computation for each decision being made as what was used to compute the weights.

Explainable but intractable is still far from surprising for me.

It's surprising because it wasn't the intent of LLMs. LLMs are just predictive models that guess the most likely next word. Having the results make sense was never a priority.

If you took the same amount of data for the GPT3+ but scrambled it's tokenization before training THEN I would agree with you that its current behaviour is surprising, but the model was fed data that has large swaths that are literal question and answer constructions. It's over fitting behavior is largely why it's parent company is facing so much legal backlash.

Even more mind boggling is the fact that randomness is part of its algorithm

The randomness is for token choice rather than any training time tunable so fails to support the "i.e. we don’t really know what happened" sentiment. We do know, we told it to flip a coin, and it did.

i.e. temperature, and that without it the output is kind of meh.

Both without it and with it. You can turn up the temperature and get bad results as well as you can turn it down and get bad results.

If adding a single additional dimension to the polynomial of the solution space turned a nondeterministic problem into a deterministic one, then yes, I would agree with you, that would be surprising.

Or by "async" do you just mean concurrent code? I'm reading "async" to mean lightweight coroutines or similar.

Yeah, my bad, I was utilizing a colloquial definition of a term that has a technical definition in a technical conversation. A lamentation lo the lossyness of language.

I guess I assumed we were talking about something other than in terms of red/blue because I'd argue red/blue's "hard"ness transcends myth to mathematical fact.

Is parallel programming hard? Without any further details or specifics, yes it is. It is far harder to conceptualize code instructions executing simultaneously, than one-at-a-time in a sequential order.

If I program (map inc [0 1 2 3]) is it really any more difficult to conceptualize the (inc ) function performing on each element sequentially than in parallel?

I think the difficulty of parallel programming is less innate and more two fold:

1) languages often default to sequential so to do async requires introducing additional primitives to the programmer

2) knowing when to effectively use parallel programming

When I have a list or stream that I know has independent elements that require wholly independent calculations then parallel programming is straightforward

Where people get hung up is trying to shoe horn async where it is either unnecessary (performance is equal or worse than sequential) or introduces breaking behavior (the computations are in fact interdependent).

Weird thing is it was designed to model language. It’s surprising that it returns sound answers as often as it does.

Is this surprising? Can you point to researchers in the field being “surprised” by LLMs returning sound answers?

“surprising”, i.e. we don’t really know what happened.

This ie reads like a sort of popsci conclusion.

We know exactly what happened. We programmed it to perform these calculations. It’s actually rather straightforward elementary mathematics.

But, what happens is so many interdependent calculations grow the complexity of the problem until we are unable to hold it in it our minds, and to analyze its decisions computationally necessitates similar levels of computation for each decision being made as what was used to compute the weights.

As for its effectiveness, familiarity with the field of computational complexity points to high dimensional polynomial optimization problems being broadly universal solvers.

This comment from "50 days ago" absolutely rocked me then and continues to provide a sobering and depressing real life metric to the climate crisis. I've showed anyone who will listen this chart.

https://news.ycombinator.com/item?id=37257643

"Infinitesimal" is just an idea, as far as I know. Nothing real is infinitesimal.

The unreal (re: abstracted) aspect is what places it outside the confines of “language” for me.

Are black holes real? Do they have singularities? If yes, that can be an example of your “real” infinitesimal.

My opinion is that infinitesimals are more than real they are essential. They are the building blocks of all that is “real”.

Ultimately, what we’re talking about is a philosophical debate that would require one to step “outside” reality to confirm or deny outright so we are just providing our opinions on an unknowable concept.

What is “real” in this context?

Is pi “real”? Is the plank constant? The former was my path to the essentialism of infinitesimals. The latter my path to the essentialism of discrete counting.

This is just wrong lol.

The needless condescension of your “lol” feels a bit premature.

How can you have self correction without superintelligence?

I am having trouble following your opinion through this thread.

Your argument demonstrates the usefulness of mathematics, but does not demonstrate that it isn't a language.

What is “a language” to you? What is an “isn’t a language” to you?

I can grok you referring to axioms and lemmas as a “mathematical language”, but I see such as just the way we communicate something more essential and wholly independent of any need to have been communicated.

A lot of contemporary research mathematics is layered and wrought of “useful” complexities for its desired domain, but how do you dismiss the essential and seemingly unrealness of its abstraction from our perceived reality?

Counting is an example.

Subjective boundaries illuminate the essentialism of distinctness. 2 apples describe the same abstract phenomena as 2 atoms, or 2 galaxies, or 2 orientations of stereoisomers.

What is the “language” here? The word/symbol 2? The subjective boundary that separates something more continuous into discrete forms?

Transcendentals and irrationals alight my meditation on what the hell all this is that we’re experiencing.

You have a triangle with edges that terminate at each vertex, but if two of those edges have equal length than you can interpret their length as unit 1 where the third edge then has a length of (sqrt 2) which is a number without a finite decimal expansion.

What language can be used to defend an infinitesimal equating to a finite value?

This points at an essentialism to me.

Any amount of “language” is incapable of both explaining this completely or explaining it away.

Similar with pi and its relation to a circle which has a well defined circumference that somehow expresses itself with a number that is itself incapable of being expressed or defined.

As you brought up the incompleteness theorems, they too have a similar “infinite in finite” quality.

I am unsure how you can understand godel but argue against the essentialism of the sur-real abstractions he brings attention to.

Just wanted to question why these elements (namely intelligence and reasoning) strike the nerve that they do.

“Just asking questions” is a meme of the unscrupulous.

I think you are unfairly lumping those who believe in human exceptionalism with those cynical of the economics of such claims.

It’s okay, to me, for people to be ignorant of what llms are. What a dismally bland existence if everyone were just llm experts.

What strikes a nerve with me is the people financially incentivized to do so are leveraging the terror, both the awe and fear interpretations, of those ignorant of the tech.

The anthropomorphism argument is case in point, really. It poses the accusation that the other side is imparting human qualities to a machine, without needing to touch on what makes those qualities human or why that matters in the first place.

This reads as circular reasoning. Those claiming the opposite are also failing to define what those qualities are.

Anthropomorphism is a real thing. I can flinch in pain for the sake of my couch when a friend jumps onto it, but that hardly provides, without me needing to define human pain explicitly, an opportunity for said friend to respond with the absurd claim that human pain is in fact couch based.

I recognize your clarification of “discovery” and conclusion from that research, but I do think there is a strong argument that in terms of the stochastic usage of a nonlinear system the “undefined” state of your nullable boolean is itself a falsey state.

For me one major aspect of this “debate” is that the people who see or espouse what I see as over extensions of the abilities of technologies are the ones most unfamiliar with it.

There is an old bit of unscrupulous advice that if someone over assumes your abilities that you should refrain from correcting them.

That is, it benefits the NSA that people think they are actively recording all of their conversations all the time because it forces compliance without the necessary competence, but the people who hold these opinions are often wholly ignorant of the kind of technology required to achieve that level of surveillance.

Have you built your own minigpt? Have you implemented rudimentary transformers?

Are you projecting your desires onto something wholly unworthy of your devotion?

Because the people behind these things are financially incentivized to nod along as your impart more ability than what they know they put into them.

For clarity, my “religion” is math. I believe existence fundamentally is a mathematical construct and as such so are all of its creations.

The brain is to me a mathematical byproduct, but even still, when I familiarized myself with the math of llms and their abilities I recognized that they fall short of being, simulating, or explaining the former.

Llms are stochastic next token pickers, full stop.

Any perceived “intelligence” is projection and anthropomorphising by the agent using them.

I saw a comment on here in another thread stating that the capacity for coherent use of language falls short of being evidence of “intelligence” as children show signs of human “intelligence” long before they can form coherent sentences.

You are unable to.

“LLMs can’t self-correct in reasoning tasks, DeepMind study finds“

https://news.ycombinator.com/item?id=37823543

Anyone who says otherwise is either ignorant of the underlying function of llms or trying to sell you something.

Only thing missing in that video is some rando walking by on campus in the background yelling “when they come, I hope they come for you first!”

If I am understanding your experience correctly the colloquial wisdom here is to use GIN on static data and GIST on dynamic data.

In choosing which index type to use, GiST or GIN, consider these performance differences:

GIN index lookups are about three times faster than GiST

GIN indexes take about three times longer to build than GiST

GIN indexes are moderately slower to update than GiST indexes, but about 10 times slower if fast-update support was disabled (see Section 54.3.1 for details)

GIN indexes are two-to-three times larger than GiST indexes

As a rule of thumb, GIN indexes are best for static data because lookups are faster. For dynamic data, GiST indexes are faster to update. Specifically, GiST indexes are very good for dynamic data and fast if the number of unique words (lexemes) is under 100,000, while GIN indexes will handle 100,000+ lexemes better but are slower to update.

https://www.postgresql.org/docs/9.1/textsearch-indexes.html