ColinWright

> "The idea is that at individual scales the additional load is ignorable, ..."

Three minutes, one pixel of progress bar, 2 CPUs at 100%, load average 4.3 ...

The site is not protected by Anubis, it's blocked by it.

Closed.

It will be interesting to see if Tao's writings are as clear, though possibly he is targetting a different audience.

While I had a LinkedIn account I was not receiving spam.

When I deleted my account, the spam started, and continues to this day.(+)

(+) Which is not a surprise ... once an email address has been leaked it gets onto lists and the spam will never end.

I ended up deciding that I was getting no value from the account, and I heard unpleasant things about the company, so I deleted the account.

Within hours I started to get spam to that unique email address.

It would be interesting to run a semi-controlled experiment to test whether this was a fluke, or if they leaked, sold, or otherwise lost control of my data. But absolutely I will not trust them with anything I want to keep private.

I do not trust LinkedIn to keep my data secure ... I believe they sold it.

Now she's supposed to open her email while keeping the web page open. It took 5 minutes to do that, find the email, copy down the code, close the email ...

Web site has timed out.

Just one of many examples.

(pause)

Ah, now on the second page ...

Thanks for letting me know.

In Gauss's time mathematicians would solve problems, publish the solutions in an encrypted form, and then challenge their contemporaries to solve the problems.

Here the authors of a paper on the arXiv say:

"To assess the ability of current AI systems to correctly answer research-level mathematics questions, we share a set of ten math questions which have arisen naturally in the research process of the authors. The questions had not been shared publicly until now; the answers are known to the authors of the questions but will remain encrypted for a short time."

Tao says:

"... the challenge is to see whether 10 research-level problems (that arose in the course of the authors research) are amenable to modern AI tools within a fixed time period (until Feb 13).

"The problems appear to be out of reach of current "one-shot" AI prompts, but were solved by human domain experts, and would presumably a fair fraction would also be solvable by other domain experts equipped with AI tools. They are technical enough that a non-domain-expert would struggle to verify any AI-generated output on these problems, so it seems quite challenging to me to have such a non-expert solve any of these problems, but one could always be surprised."

Fastest and best feedback for whether the batting team is ahead of the rate.

Far more insidious, however, was something else we discovered:

More than two-thirds of these articles failed verification.

That means the article contained a plausible-sounding sentence, cited to a real, relevant-sounding source. But when you read the source it’s cited to, the information on Wikipedia does not exist in that specific source. When a claim fails verification, it’s impossible to tell whether the information is true or not. For most of the articles Pangram flagged as written by GenAI, nearly every cited sentence in the article failed verification.

This was a long time ago, so we didn't have GPUs or fancy rendering h/ware. We addressed every pixel individually.

So a radar image was painted to the screen, and then the next update was painted on top of that. But that just gives the live radar image ... we wanted moving objects to leave "snail trails".

So what you do for each update is:

* Decrement the existing pixel;

* Update the pixel with the max of the incoming value and the decremented value.

This then leaves stationary targets in place, and anything that's moving leaves a trail behind it so when you look at the screen it's instantly obvious where everything is, and how fast they're moving.

Ideally you'd want to decrement every pixel by one every tenth of a second or so, but that wasn't possible with the h/ware speed we had. So instead we decremented every Nth pixel by D and cycled through the pixels.

But that created stripes, so we needed to access the pixels in a pseudo-random fashion without leaving stripes. The area we were painting was 1024x1024, so what we did was start at the zeroth pixel and step by a prime number size, wrapping around. But what prime number?

We chose a prime close to (2^20)/phi. (Actually we didn't, but that was the starting point for a more complex calculation)

Since phi has no good rational approximation, this didn't leave stripes. It created an evenly spread speckle pattern. The rate of fade was controlled by changing D, and it was very effective.

Worked a treat on our limited hardware (ARM7 on a RiscPC) and easy enough to program directly in ARM assembler.

Yeah, maybe I'll hack out a sketch tomorrow, show it to a few people, and get them to tell me what's missing so I can flesh it out.

I owe Chalkdust an article anyway.

I only have one video in my channel ... never really got going, but I keep promising myself I'll start doing more. My channel is here:

https://www.youtube.com/@ColinTheMathmo

I don't have that talk on video, but I can probably sketch the content for you if you're interested, and then give pointers to pages with the details.

How to contact me is in my profile ... I'm happy to write a new thing.

-- Napoleon.

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I also talk about the Continued Fraction algorithm for factorising integers, which is still one of the fastest methods for numbers in a certain range.

Continued Fractions also give what is, to me, one of the nicest proofs that sqrt(2) is irrational.

It's probably no longer "Commercial In Confidence" ... I should probably write it up sometime.

"Also, is it weird that I still remember the specs of my first computer, 22 years later?"

My first computer was a TRS-80 Model 1, 1.78 Mz Z80 with 16 KB RAM.

That was 48 years ago. Is it weird that I remember that?

Without the attracting masses on either side you can set it swinging and measure the period, which lets you compute the restoring force in the wire.

"In recent weeks there have been a number of examples of Erdos problems that were solved more or less autonomously by an AI tool, only to find out that the problem had already been solved years ago in the literature"

[0] https://mathstodon.xyz/@tao/115788262274999408

https://news.ycombinator.com/item?id=46482345

That's a direct link to the raw graph, not to the comment that gives relevance, but nevertheless, that's where the discussion is.

[0] 837 comments as of 18:12 GMT

What you quote from the FAQ tells you what it "[Dead]" means, it doesn't tell you how to find posts marked as "Flagged".

The FAQ tells you that you can have "Dead" posts displayed by turning on "showdead", but the question is asking how "Flagged" posts can be found.

The FAQ does not answer that question.

The poster here is asking how they can find posts, possibly by anyone, that other users have flagged. What is there in the Guidelines and FAQ will answer that question?

https://mathstodon.xyz/@robinhouston/115821134807127928