> The standard cosmological model affords no such explanation
Einstein GR → Friedmann eqns → universe can be expanding or contracting
Rest of this amounts to "what about initial conditions? things become divergent at t→0"
Yes, this is called the Big Bang singularity, GR breaks down there. You are not the first person to notice this. It is considered one of the biggest open problems in the field.
The stuff about accelerating expansion is confused (AI?) slop which eventually gets around to admitting that there's no problem at t>0 and no change to the existing problem at t=0, so I don't know why you didn't just cut it.
This is some real "unsolicited snail mail to physics departments"-tier content, even for HN I think it's embarrassing to have this on the front page.
I guess because every code monkey in Silicon Valley has wet dreams about overthrowing the hidebound physics establishment for some reason
Dunning Krueger is most prevalent in software engineers for some reason. Fortunately most of us grow out of it and begin to appreciate how hard other disciplines are.
What stuff about accelerating expansion are you referring to? The author is a physicist not just a barstool talker. He's right as you point out that there are huge interesting and unexplained phenomena that sometimes have pretty little names that discourage further question asking. It's sad to see such negativity instead of engaging with the material a bit
While this is true, it's also true that out of all of his published work in physics (which you can find at the "Google Scholar" link on the Author page of his log), none of it, as far as I can see, is about the hypothesis described in this article. All his writings about that appear on his blog. Which means that, if he has tried to get any of this published in peer-reviewed journals, he's failed.
This could mean that he is a visionary whose insight is not recognized by the establishment, which is the story he tells. It could also mean that his speculations actually don't lead anywhere useful. My Bayesian prior is heavily on the latter.
I would say the appeal to authority was in the post I responded to: "the author is a physicist". I was simply pointing out that "physicist" is a broad term, and the fact that he has that job title doesn't automatically make him knowledgeable about the specific subject area in question.
As for the actual article, I think it's too vague to even be able to criticize anything specific. It's words strung together, but no actual testable model that I can see. (One key red flag: the page where he says the "mathematical details" are--does not even have a single equation.)
One red flag that jumped out at me was, not a single link to a published paper, or even an arxiv preprint. Just other blog posts. Actual physics isn't done in blog posts. What has actually been published about all this for other physicists to critique? It's easy to make anything sound plausible if no one else but you gets to speak.
If you look on arxiv.org you will see all kinds of speculations by physicists that go outside our best current models. AFAIK a paper doesn't even have to be submitted for publication in a journal to be posted as a preprint there.
Also, there is always the alternative explanation that the reason other physicists have not paid any real attention to the author's claims is that those claims are, as I said in another post upthread, too vague even to criticize. (Wolfgang Pauli used the phrase "not even wrong".)
Physics/cosmology-related HN submissions tend to have poor comment quality, so this being upvoted to the front page isn’t too surprising, unfortunately.
Aren't laymen allowed to wonder about these things and share their thoughts?
You should remember that the groundwork on which all the scientific pursuits stands on was built by curious laymen ...Today it seems that without a Phd (apparently even that is not enough, given the reactions to this post), you are not allowed to ponder about such things...
On the one hand it's much more realistic and detailed than an after-image; if I picture a red apple, and someone asks what kind of apple it was, I can retroactively say "oh, like a Red Delicious, really dark and shiny and with that concave part, not at all like a Macintosh or whatever. But it was a bit rounder than a normal Red Delicious probably because I was averaging towards a kind of typical 'red apple'."
(FWIW I think I'm near the upper end of detailed visualizing.)
But on the other hand it isn't located in my visual field, it's kind of in its own separate visual field. It doesn't have a spatial relationship to the objects in front of me and doesn't occlude them (although when I'm visualizing something carefully I lose a lot of my awareness of what I'm actually looking at because I'm not attending to it, even if my eyes are wide open).
I think the most important question here is whether Huggingface was "in on it" – whether they knew in advance that this was going to happen and agreed to it. Second most important is whether OAI caused this to happen on purpose, with HF as an unwilling target.
Three options:
(1) Both companies are lying through their teeth and the entire thing is bullshit
(2) OpenAI hacked another company's servers and deliberately gambled on felony charges for the sake of PR
(3) OpenAI is telling the truth and a rogue AI agent hacked another company, opening them up to legal liability accidentally
All three of these are a lot more interesting than the "skeptical" story that this is just OAI doing business-as-usual PR hype, nothing to see here, move along.
Current LLMs can absolutely multiply floats without a tool call. In fact, that's a much more rote symbol-manipulation task than doing original math research.
With what accuracy? And with how many intermediate tokens?
We can replace multiplication with any class of problems which should go from 0->100% solution almost immediately if there was actually a concept learned.
There is not. Because they are plain ol' fits. And there are no "emergent" features that pop out without having a sufficient set, where "sufficient" is absolutely gigantic and equivalent to memorizing enough of the space to compress the problem. LLMs are Rain Man.
They interpolate within a known distribution. Search allows places outside of distribution to be explored.
This paper should be required reading [1]. You can explore the curves yourself. You can see exactly what it's doing. And you also have this nagging thing—which you know and I know—that all these models converge and do not diverge upwards. An "emergent" "hyperintelligence"—a characteristic that could be found if something was actually learned and combined with a new concept—would not have this problem.
Exponentials on exponentials added to compute and data and the problem classes still sit at not great places, and require agents, feedback loops, and trial and error to solve. The models are the problem, but more importantly, the people selling things these models could never do are the problem.
Edit: It should be mentioned, if there's some scary neural architecture that's super-de-duper and doing something beyond the very obvious next string prediction that LLMs clearly do, it can't do what absolutely ancient ML models could do; a network to multiply two floating point numbers should pop out somewhere without symbolic computation, no?
It does not. There's no magic other than the run-of-the-mill SV fake it til you make it magic. And that magic has failed.
> With what accuracy? And with how many intermediate tokens?
So you are arguing here that LLMs should just "know" the result of a multiplication when the operands are in context, ie, that a hidden multiplier circuit should emerge in their weights.
Is this how you do multiplication, if I give you two 12 digit numbers, does the 24 digit multiplication result just pop in your head? Don't you have to follow a learned algorithm through a tedious system 2 effort? Don't you need to write down the results on paper because you can't actually hold in your head the dozen partial results, each with a dozen digits? How many visual, tactile and reasoning tokens does this consume, moving the hundreds of muscles that make up your hand to draw each number under visual feedback, then reading all those numbers back and transforming ocular activation data into numeric symbols?
It seems to me your system 2 is just following a symbolic algorithm for multiplication, and it does roughly the same steps as the LLM trace I showed you previously.
So if you think this is the mark of your intelligence, why wouldn't it apply to the machine too? Why is it implausible that, following a similar algorithm learned from some mathematical paper, the LLMs has reasoned a new solution to a problem in another math field? Why couldn't the machine combine and morph these algorithms for symbolic manipulation, to yield entirely new and original results? How would those results differ from results human mathematicians generate, using recipes they learned in university?
The emergent behavior that we talk about isn't that the machine can do multiplication in its "head" after knowing the multiplication algorithm. What emerges is the ability to follow any other algorithm, even algorithms that were not in the training set, even algorithms to create other algorithms, which it then executes. This is the emergent behavior that matters for AGI; once it can do that, it's a trivial exercise to create a non-AI tool to automate and accelerate the mechanical tasks - just like we humans do it.
They don't. "Emergence" is some ridiculous hype that a bunch of kids with money peddled to get valuations.
And yes, if "emergence" and the "there's something else going in there!" ridiculousness was true, there would be some other architecture which can do more than its symbol prediction.
Everyone can see the code. Everyone can see the math. Everyone can see the failures of the claims. And there's now about 5 years of everyone watching the lies come undone one-by-one.
You're saying the same thing. vi uses ‘j’ because the ADM-3A printed ‘↓’ on the ‘J’ key because Control-J is newline because the code is J with a bit flipped because bit-flipping was practical on an electromechanical teletype.
Unless I'm mistaken, this uses "standard deviation" to refer to standard error throughout. They differ by a factor of sqrt(num_samples).
This is actually much more commonly useful than the t distribution, in my experience. You can squint at a histogram (or some summary stats), eyeball the stdev, approximate the stderr in your head, and get a pretty good sense of confidence.
I most often find myself doing this for the Bernoulli distribution, where it's also handy to know that the stdev is sqrt(p(1-p)), or "about 1/2 if p is middling, or sqrt(p) when it's small" (and you can flip the polarity to handle p→1).
The map contains a bunch of references to America, the West Indies, Guiana, and Mexico. (Often with a connotation of "faraway exotic place" or "exciting new international development".)
He may not have written about the British colonies but the New World was clearly at least somewhat present in his mind and his audience's minds.
The most far-flung pins on the map are further away than Shakespeare or his audience likely had in mind.
"America" looks like it's at the centroid of the modern continental USA, but Shakespeare was surely thinking of somewhere in the Caribbean. "Asia" is shown somewhere in Mongolia/Kazakhstan, but the quotes suggest Turkey or the Middle East, and Shakespeare surely would have said "Cathay" or "India" if he meant to go that far. Likewise "Russia" is shown in Siberia, but everyone in Russia lives near the European borders thousands of km west.
That said, the references to Ethiopia, India, and the Indies are very clear and can only be where they are shown on the map.
(Don't take any of this as criticism! The map is very cool, it just shows the limits of what a fully automated approach can do. A human approach would be limited by the human's biases instead.)
> Likewise "Russia" is shown in Siberia, but everyone in Russia lives near the European borders thousands of km west.
Yes, and "Love's Labour's Lost" specifically pairs/ contrasts "Russians" with "Muscovites": the "Russia" of St Petersburg is pretty far west of "Moscow."
> These reactors can be made safer, but they all still have a foundational design flaw which means the ultimate goal should be replacing rather than continually spending money reinforcing.
This was about the Fukushima reactors that were completely destroyed? In response to a discussion of Belgian reactors that are completely different?
Einstein GR → Friedmann eqns → universe can be expanding or contracting
Rest of this amounts to "what about initial conditions? things become divergent at t→0"
Yes, this is called the Big Bang singularity, GR breaks down there. You are not the first person to notice this. It is considered one of the biggest open problems in the field.
The stuff about accelerating expansion is confused (AI?) slop which eventually gets around to admitting that there's no problem at t>0 and no change to the existing problem at t=0, so I don't know why you didn't just cut it.
This is some real "unsolicited snail mail to physics departments"-tier content, even for HN I think it's embarrassing to have this on the front page.
I guess because every code monkey in Silicon Valley has wet dreams about overthrowing the hidebound physics establishment for some reason
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