SideQuark

LUTs don’t magically add color volume, change the possible colors or dynamic range, or add much at all to the physics of a display. They’re usually used to reduce bit depth. They do not add more low or high colors than the screen physics started with.

Your eye also doesn’t have a consistent gamma, nor does the camera, now does any viewing technology. If you’re complaining about the slight linear section of many gamma curves, they are very important for avoiding various artifacts.

> but the pattern holds across more than 70 years of evidence

No, those are also stupidly small samples. Look at the papers I listed.

> That 2014 study you linked is noting that it does happen with other kinds of blindness

Yes, from 1950 till 2014, as more and more kinds of blindness were found with schizophrenia, the type of blindness has been dwindling to smaller and smaller classes, ensuring there is not enough predictive power in the claims. Again, look at the papers I listed. ALL of this is covered.

> Isn't that saying the same thing the article does.

The article says lots of nonsense, like the most likely outcome of data is somehow the best evidence for an unproven claim. It implies the Australia study says a thing it DOES NOT SAY. Is this not enough bad reporting to question the accuracy of the article?

> What am I missing?

Simply look at the papers I posted. They are right there for you to read. The article is click bait trying to claim there is some surprising scientific claim that HAS NO SCIENTIFIC PROOF.

Market cap will price in the debt, as it always does. Empirical evidence (dig through Google scholar) finds that cash assets, debt, profits, settlements, and the like, all are reflected in market cap changes at over 99% accuracy (the 1% is from measurement noise, so it may well be 100%).

Making debt of that form illegal would kill any company that needed money to stay afloat, such as during some emergency, or war, or COVID, or tons of events that companies regularly survive.

And laws can be completely useless when enforced intermittently.

Laws that are enforced and more importantly are enforceable have a much higher rate of making a difference. The same works here.

> The most rigorous evidence comes from a 2018 whole-population study tracking nearly half a million children born in Western Australia between 1980 and 2001. Of those, 1,870 developed schizophrenia, but not one of the 66 children with cortical blindness did.

1870/500,000 * 66 = 0.247

Not a single blind child getting it is the most likely outcome, and this is called "the most rigorous evidence"?

It didn't protect rats in a study https://www.sciencedirect.com/science/article/abs/pii/S09209...

There sure is a lot of reported cases of all sorts of blindness with schizophrenia, constantly shrinking the pool of types of the two, making this conjecture constantly shrinking https://pmc.ncbi.nlm.nih.gov/articles/PMC4246684/

It also seems the Australia study does not quite say what the article claims it does - a follow-up study: https://jmsgr.tamhsc.edu/the-lack-of-comorbidity-between-ear...

Too bad this article simply doesn't mention all this. Of course the article will get less clicks with a less wild title.

M-expressions were never implemented and never used.

Completely worked out at least 20 years ago: https://www.lomont.org/papers/2005/CompareFloat.pdf

> The extra half ulp error makes no difference to the accuracy of calculations

It absolutely does matter. The first, and most important reason, is one needs to know the guarantees of every operation in order to design numerical algorithms that meet some guarantee. Without knowing that the components provide, it's impossible to design algorithms on top with some guarantee. And this is needed in a massive amount of applications, from CAD, simulation, medical and financial items, control items, aerospace, and on and on.

And once one has a guarantee, making the lower components tighter allows higher components to do less work. This is a very low level component, so putting the guarantees there reduces work for tons of downstream work.

All this is precisely what drove IEEE 754 to become a thing and to become the standard in modern hardware.

> the problem is that languages traditionally rely on an OS provided libm leading to cross architecture differences

No, they don't not things like sqrt and atanh and related. They've relied on compiler provided libs since, well, as long as there have been languages. And the higher level libs, like BLAS, are built on specific compilers that provide guarantees by, again, libs the compiler used. I've not seen OS level calls describing the accuracy of the floating point items, but a lot of languages do, including C/C++ which underlies a lot of this code.

All of these are standard fare in abstract algebra classes, and I didn’t care to write it all out. Once you have the “inverse” operations - and reciprocal, the entire structure follows, for a large set of objects, whether N or Q or R or C or finite fields or division rings, and a host of other structures. So I only wrote - and 1/x

Then, subtraction is (x#y)#0 = x-y. Reciprocal is x#0 = 1/x. Addition follows from x+y=x-((x-x)-y). This used the additive identity 0.

Multiplication follows from

x^2= x-1/(1/x + 1/(1-x)), so we can square things. Then -2xy = (x-y)^2 -x^2 - y^2 is constructible. Then we can divide by -2 via x/-2 = 1/((0-1/x)-1/x), and there’s multiplication. In terms of #, this expression only needed the constant 1, which is the multiplicative identity.

Now mult and reciprocal give x * 1/y = x/y, division.

Any nontrivial ring needs additive and multiplicative identities 1!=0, which are the only constants needed above. If you assume this is Q or R or C, it may be possible to derive one from the other, not sure. But if you’re in these fields, you know 0 and 1 exist.

Then any element of Q is a finite set of ops. R can be constructed in whatever way you want: Dedekind cuts, Cauchy sequences, whatever usual constructions. Or assume R exists, and compute in it via the f(x,y).

This also works over finite fields (eml does not), division rings, even infinite fields of positive characteristic, function fields (think elements are ratio of polynomials), basically any algebraic object with the 4 ops.

It generates the same class of functions. Read the comments and links in this thread.

> If you take a real analysis class, the elementary functions will be defined exactly as the author of the EML paper does.

I just looked through many of the best known real analysis texts, and not a single one defines them this way. This list included the texts by

Royden, Terence Tao, Rudin, Spivak, Bartle & Sherbert, Pugh, and a few others....

Can you cite a single text book that has this definition you claim is in every real analysis course? I find all evidence points to the opposite.

Yep, I’ve written numerical methods papers, and am very well aware of the field.

A limit process is a definition. Try computing with it. You’ll end up with an infinite sequence, or an approximation.

An iterative process is an infinite series. They’re equivalent.

Newtons method is the same. Completely equivalent to an infinite series as you increase precision.

And both require constants, infinitely precise. So you’re still not doing anything the 1/(x-y) operation cannot do, and to do those series you’ll compute using things amenable to being done via ops easy to do by hand or machine via the 1/(x-y) op.

His paper misses infinitely many such functions.

The world has had many types of logic before and after Boolean logic was created, many used in computing. Boolean logic isn’t a constraint; it’s used where it’s useful, and others are used where they’re useful.

No, it approximates exp poorly over an infinitesimally small interval compared to exp. Resistors and capacitors are no where ideal components, which is why they have spec sheets to show how quickly they diverge.

If we’re making sloppy approximations to a tiny range of exp, then I too can do it with a few terms.

It’s only finite by putting the infinite series into an operation.

And the basic monomial basis is not a single binary operation capable of reproducing the set of basic arithmetic ops. If you want trivial and basic, pick Peano postulates. But that’s not what this thread was about.

Any transcendental function can be produced by arithmetic, since its complete for R.

Go ahead and show how to compute exp or ln without an infinite series without circular reasoning. You can’t, since they’re transcendental.

There are infinitely many ways to make these binary operators. Picking extremely high compute cost ones really doesn’t make a good basis for computation.

Show me a way to physically compute exp or ln that is less gates than add. More gates means costlier in $, more energy in compute, and for these functions, higher latency.

You don’t get to make up free ops, claim there is no cost in reality, and hand wave away reality.

There are infinitely many ways to do what the paper did. There’s no gain other than it’s pretty. It loses on every practical front to simply using current ops and architectures.

Ooh, that 2nd link has a nice construction by Terry Tao giving a clear way to show infinitely many such functions exist for pretty much any set of operations.

Do you claim all things you don’t understand are LLMs? This is what I mean by these and many of your other comments being extremely poor quality to the point of deliberate ignorance.

The paper above was published in 2012 [1], so that’s quite a feat for an LLM. This takes about zero effort to check.

Put some thought or effort into your claims; they’ll look less silly.

[1] https://orcid.org/0000-0002-0438-633X

Yes it can, by using the same infinite series that exp and ln use to compute. This one just costs less in money, hardware, energy, and is faster for basically every basic op.

The exp and ln are infinite series. Exp is roughly the infinite series for cos AND the infinite series for sin. Hiding that every op is an infinite series behind a name doesn’t make things free. It just makes even trivial ops like 1+2 vastly more work.

And those come from the infinite series needed to compute exp and ln. They’re just as much work either way. The exp and ln way are vastly costlier for every op, including simply adding 1 and 2.

Computing exp or ln is an infinite series, and vastly more compute. Hiding series behind a name doesn’t make them free to compute.

It's math. You can check it yourself instead of this (and many other) thoughtless posts.

This isn't unique, or even the least compute way to do this. For example, let f(x,y) = 1/(x-y). This too is universal. I think there's a theorem stating for any finite set of binary operators there is a single one replacing it.

write x#y for 1/(x-y).

x#0 = 1/(x-0) = 1/x, so you get reciprocals. Then (x#y)#0 = 1/((1/(x-y)) - 0) = x-y, so subtraction.

it's common problem to show in any (insert various algebraic structure here ) inverse and subtraction gives all 4 elementary ops.

I haven't checked this carefully, but this note seems to give a short proof (modulo knowing some other items...) https://dmg.tuwien.ac.at/goldstern/www/papers/notes/singlebi...

The compute, energy, and physical cost of this versus a simple x+y is easily an order of magnitude. It will not replace anything in computing, except maybe fringe experiments.

This is not a 17000 qubit general computer. Read the paper.

They did not make a 17000 qubit computer. The qubits were not controllable or general in any way. The paper is linked, look at it.

This title is misleading.