It's a bit silly to say they don't reason.

Here's some research where they uncover reasoning circuits in some large transformer models https://github.com/guanzhehong/prop-logic-transformer-circuit ... reasoning has always been just math at th end of the day

And what about alpha go and all that monte carlo tree search stuff, id argue that they can reason far better than humans in these setups

They can't understand something...outside their training data

Idk how easy this is for the the large models, but certainly for classic ML, testing for performance on data outside the training data is 100% part of the development process. (Read up on test/train/validation datasets)

There is also in context learning, which gives rise to zero shot task completion for never trained on tasks.

You are confused with sentinel, sapient is an adjective meaning wise or intelligent.

And the earth couldn't possibly revolve around the sun.

They have completely recovered the wiring of fruit fry brains and trained models on that architecture..is a fruit fly conscious? Is a little device with all th same movement and sense capability using the fruit fly con nectome conscious

Computer memory (main memory anyway) is rewritten every time it's read ...nay even more so it's refreshed even when it's not read

And flash drives, you probably aren't getting a decade out of one of them without bit flips

A computer algorithm can absolutely be non deterministic, pick your language and run random.rand()

There is a whole class of such algorithms https://en.wikipedia.org/wiki/Nondeterministic_Turing_machine

But I don't see how it makes a difference, even deterministic algorithms can produce infinite complexity, eg linear neural networks are proven to be able to compute any computable function, or the Mandelbrot set which is produced from a single recurrent statement but encodes a space of infinite complexity

Maybe your point is to say that a brain is performing a non computable function...but since your brain is computing that function..is that a counter example?

Idk, microtiks are pretty prosumer, not at all plug and play. Configuring one is a complex task already, people that have that level of skill should absolutely know not to go doing silly things...

• 3D Desktop Cube workspace switcher
• Grub boot loading screen customize (to matrix style falling letters loop of course)
• tube tv style terminal skin
• Jurassic park style lock screen reject

I had to look this up because that in no way "ensures a free and fair trial"..the opposite is true

A jury is required to vote based strictly on the evidence. A juror can be removed for voting on a coin flip

So this would work for you?

"She floats, so she's a witch, and all witches are guilty so she's guilty"

Oops, realized I didn't answer your question about actually crawling, dig into common crawl documentation, they provide a bunch of technical data and stats that show you the scale..2-4billion pages per month

And note CC just does a sample of the pages it finds. So the more monthly dumps don't contain all of the data afaik

And the number above are for one of the monthly dumps

https://commoncrawl.github.io/cc-crawl-statistics/plots/crawlermetrics

The index (i.e just the list of urls) of common crawl is 250gb. The actual crawl is 500tb ish. Then pull out just the text you get down to 30tb ish. And then you need that all to be in a fts index.. (something like opensearch/elasticsearch) idk how big the cluster you would need for that is.. maybe 2tb per data node max. So 15 pretty beefy vms (8cpu w/64gb memory) with 2tb nvme for data and a few more as controller nodes

Common crawl numbers pulled from this paper

https://aclanthology.org/anthology-files/anthology-files/pdf/L/L18/L18-1286.pdf

Cluster sizing numbers pulled from tea leaf divination

Are you the llm though...this is a mangled rephrasing of the punchline.. was your prompt something like rephrase a common from a similar thread