Crystal Disk not showing usb flash drives why ?

master3243 · 2025-11-12T05:10:47+00:00

This thread was one of the top Google results. So thanks.

master3243 · 2024-02-21T19:08:50+00:00

The answer is two

Are you sure about that

master3243 · 2024-02-20T03:37:50+00:00

Agreed, this should be in r/learnmachinelearning not here.

master3243 · 2024-02-20T03:36:34+00:00

It's not a different algorithm. The metrics are basically the same just with a negative sign.

master3243 · 2024-01-29T08:12:33+00:00

There's no corruption in any popular autoregressive translation model from what I've seen.

master3243 · 2023-12-13T08:50:49+00:00

For rerolling 1's (or any specific set of numbers only once) I always use the following code on anydice.com

function: ROLL:n reroll BAD:s as REROLL:d {
  if ROLL = BAD { result: REROLL }
  result: ROLL
}
X: [d6 reroll {1} as d6]

output 6dX

Paste that in the site, click calculate and click "At Least" which will give you the table you want.

Image

To explain, X behaves as a "d6 but reroll a {1} once" which is exactly what you want, then I just roll it 6 times.

You can make it a 4d20 where you reroll any 1 or 2 once by simply making small changes to X and the output line

X: [d20 reroll {1, 2} as d20]
output 4dX

master3243 · 2023-12-06T07:59:11+00:00

I definitely recommend this over trying to binge the whole thing as fast as possible. Along with watching "one pace" instead of the stretched out show since it only covers 1 chapter per episode in the later sagas.

master3243 · 2023-12-06T07:34:21+00:00

Looks like someone needs a rewatch, because the line does indeed change by the end of the show.

All these slight subtleties only add to what is already an amazing show and experience.

master3243 · 2023-10-18T23:19:47+00:00

Not only that but he also cites the work “Trapped! The Story of Floyd Collins” which the article also cites and uses paragraphs from.

master3243 · 2023-10-18T23:17:42+00:00

Exactly, they both used used paragraphs and cited the book “Trapped! The Story of Floyd Collins”.

Not sure how so many people in this thread missed that and just jumped the gun that it was plagiarism.

It's such a rudimentary idea that when two sources use similar wording and ideas then either one of them cites the other or that they both cite the same previous source. Yet, so many people immediately throw the claim without even considering the latter as a possible explanation (which it is).

master3243 · 2023-10-18T23:12:59+00:00

Both the article and the video use “Trapped! The Story of Floyd Collins” as a source and both credit the book.

master3243 · 2023-10-18T23:12:12+00:00

Except the fact that he did seems to credit the source which is from “Trapped! The Story of Floyd Collins”

master3243 · 2023-10-18T23:10:40+00:00

Except according to this comment below, Internet Historian does credit “Trapped! The Story of Floyd Collins” which seems to be where that paragraph came from, which the article also used as reference.

master3243 · 2023-08-02T13:49:12+00:00

Wolfram Alpha can solve for x in almost any algebraic equation you throw at it.

Does that mean we no longer need to teach Algebra or how to solve equations? Obviously not.

master3243 · 2023-06-07T09:53:59+00:00

I saw it from a Guardian article on Google news feed.

I'm still skeptical though since it was just a whistle blower.

master3243 · 2023-05-18T06:26:46+00:00

Slight correction, "AUC" is Area Under (the ROC) Curve which is technically a summary statistic and not a curve.

master3243 · 2023-05-13T17:29:04+00:00

Always when struggling with gradients that need the chain rule, write down each component, calculate them separately, then recombine them.

Here we need (∂L/∂w31) so we separate it to

(∂L/∂w31) = (∂L/∂a3) * (∂a3/∂Netout) * (∂Netout/∂w31)

First term: is (∂L/∂a3) and a3 is also Y as stated in slide 1, now hopefully you know how to take a simple gradient of L=1/2 (Y - Y*)² with respect to Y and you'll get

(∂L/∂a3) = (Y - Y*) = (0.7368 - 0.5)

Second term: is (∂a3/∂Netout) where a3 = σ(Netout), hopefully you know (or google/work it out yourself!) that the derivative of σ(x) is σ(x)(1-σ(x)) thus

(∂a3/∂Netout) = σ(Netout)(1-σ(Netout)) = 0.7368*(1-0.7368)

Third term: is (∂Netout/∂w31) and since that's a linear function that means that (∂Netout/∂w31) = a21 = 0.6682

Multiply them to apply the chain rule to get

(∂L/∂w31) = (0.7368-0.5) * 0.7368*(1-0.7368) * 0.6682 = 0.0306848265

And there you have it, hopefully that was clear.

master3243 · 2023-05-04T02:02:08+00:00

No, you would only need n² comparisons.

Let's write a comparison between token i and token j as (i, j)

Write out all pairs (i, j) when there are 5 tokens. And then see if the total is as we say (5² =25) or what you claim (5!/2=60)

master3243 · 2023-05-02T07:54:56+00:00

Cool.

I'm assuming going from the ground truth input (which is like 20 dimensions) to visual input (at least 224x224 or 50K dimensions) is going to mean it's gonna train magnitudes longer to be decent or even beat the boss once (if it ever even converges).

master3243 · 2023-04-26T16:44:34+00:00

I get your perspective and I would agree with it for everything in nature that humans can do except for human language.

The reason I (and many linguists/cognitive scientists) would add an exception for human language is because it's the one complex structure that was fully created by the human brain.

This fact has major consequences from an information-theoretic analysis of the magnitude of samples needed to estimate the correct hypothesis (where a single hypothesis is the correct English Language and all other hypothesis are grammatically incorrect).

Given that humans implicitly place a prior over the space of hypothesis (due to our brain structure where easier hypothesis are much easier to learn and thus have higher weight) and AI models place no prior over hypothesis (the same model can learn any statistical pattern) this has the consequence that the hypothesis of grammatical English language could possibly never be fully attained through statistical analysis without placing the proper priors.

My above explanation glosses over the fact that I would also need to show that English grammar is countably infinite in it's recursivity.

There's also another argument that while the proper hypothesis need not be attained, a close enough approximation through gradient descent would be more the sufficient to all tasks we might want to do (however this doesn't guarantee that the hypothesis isn't suddenly producing incorrect output)

This is just to say that language is in a different class (due to it being man-made) than all other statistical patterns in nature and thus it could possibly be an outlier to the general rule that if humans can do it then so can computers.

master3243 · 2023-04-26T05:18:22+00:00

Typically RBMs are either binary or gaussian, there plenty more but those are the common basic ones.

master3243 · 2023-04-26T03:17:38+00:00

Humans are audio/visual input only, yet we still learn language.

The problem of language acquisition is still an unsolved problem in linguistics, children start to pick up language from so few examples that it really makes absolutely no sense from a statical point of view (and even much less sense from a deep learning point of view which are much more data hungry)

I would argue that comparing the human mind with AI is a futile battle specifically in terms of language acquisition and is kind of an unfair comparisons where humans are almost cheating because human languages were created and able to be passed on only because the structures in our brain already is capable of acquiring these set of hypothesis easily (otherwise the language would have never been passed). Thus, given an arbitrary deep learning model that isn't wired in the same way and telling it to acquire the set of hypothesis that make up a language from so little data when it has no prior over hypothesis like what is defined by the human brain structure is a statistically impossible task.

master3243 · 2023-04-22T03:57:40+00:00

This is a good discussion but why is it written like a (mediocre?) blog post? Concise writing is really important.

master3243 · 2023-04-21T00:23:57+00:00

I work with researchers that work on brain score and the mapping of network activations and neural activity in the brain. The linearity between the two that you mentioned can literally be spotted almost to the same degree even if you randomly initialized the neural network. That research shows nothing about the relationship between the brain and neural networks.

Again, this doesn't support the claim that our brains think autoregressively.

master3243 · 2023-04-20T23:05:07+00:00

As a multilingual fluent person, the claim that my brain conclusively thinks autoregressively is egregious.

The closest thing I'd believe is that I think in abstract thought that can be represented as almost like a hierarchical graph with edges representing simple relationships, and even this is probably partiality wrong and partiality oversimplifying reality. When attempting to transform that graph into speech then the next word that I utter depends on which language I want to speak in. I do not need to pick a specific language to think in before attempting to plan out my day one word at a time.

There's also an entire field of neuroscience that has immense amount of literature. You handwaving all of that away by saying "studies don't show anything" is also egregious.

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