Chaos and the Anti-Thread

[Dreamtimer]

That's a funny name. "Genetically Modified Skeptic".

The dog at the end is precious.

[Dreamtimer]

Did you know...

The DMC color Christmas Red is number 666?

[Emil El Zapato]

I knew IT!

[Aragorn]

I knew IT!

Really, you knew It?

[Emil El Zapato]

lol ... IT .... brrrr.....

[Emil El Zapato]

I get these in my email...don't ask me why ...


Hi everyone,

We've just released a film with someone we believe is going to be a significant new voice. For the last few weeks, seemingly everyone has been talking about Tyson Yunkaporta and his new book 'Sand Talk'. In it, Tyson flips things on their head to look back at our civilization from an indigenous standpoint - the result is a fresh perspective on a lot of the topics we've explored on the channel.

In conversation with Rebel Wisdom's Alexander Beiner, he discusses how an indigenous perspective can open up a new way to look at the economy, the government, complexity theory, the culture wars and more. With a unique blend of humour, theory and indigenous knowledge, he's an exciting new voice in the memetic landscape.

Source: https://www.youtube.com/watch?v=S-3ESBzGg4Q


Our new Rebel Wisdom Digital Campfire 🔥 is off to a roaring start, with multiple conversations, practice sessions and community events happening every week. Next week we're welcoming Peter Limberg (host of The Stoa) who's delivering a session called 'The Psychodynamic is Political: The Art of Memetic Mediation' on 27 August.
In this interactive session, Peter will introduce the concept of memetic mediation, followed by various practices that will help us sniff out our "philosophical allergies", in service to learning how to be responsive rather than reactive in the culture war.

Find out more and get involved by becoming a member here. Thank you for your support and interest - we hope to see you soon.

Thanks,

The Rebel Wisdom Team


WELL THAT's NOT COOL! ...


Honestly, I don't think I'm related to this guy:

Source: https://www.youtube.com/watch?v=mZbmywzGAVs

[Emil El Zapato]

When I watched the above video, I figured it must be 20 years old b-b-but it wasn't.

Source: https://www.youtube.com/watch?v=3UwuUuq5LA4

[Wind]

This is America.

[Emil El Zapato]

that is america and we have people from NZ pulling hard for it ...

[Emil El Zapato]

What is the whiskey that evaporates from its barrel called?

If you know don't post immediately ... let it ferment a little ...

[Elen]

The Scots give it to the Angels.

[Aianawa]

Source: https://www.youtube.com/watch?v=ovJcsL7vyrk


Random n choas

[Dreamtimer]

There's the angel's share and the devil's share when it comes to whiskey.

[Emil El Zapato]

Essentially, it is a variation of this:

he didn't mention radioactive decay and microbial reproduction. they all fit into this family of algorithms.

In computer science, the time complexity is the computational complexity that describes the amount of time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to differ by at most a constant factor.

Since an algorithm's running time may vary among different inputs of the same size, one commonly considers the worst-case time complexity, which is the maximum amount of time required for inputs of a given size. Less common, and usually specified explicitly, is the average-case complexity, which is the average of the time taken on inputs of a given size (this makes sense because there are only a finite number of possible inputs of a given size). In both cases, the time complexity is generally expressed as a function of the size of the input.[1]:226 Since this function is generally difficult to compute exactly, and the running time for small inputs is usually not consequential, one commonly focuses on the behavior of the complexity when the input size increases—that is, the asymptotic behavior of the complexity. Therefore, the time complexity is commonly expressed using big O notation, typically {\displaystyle O(n),}O(n), {\displaystyle O(n\log n),}{\displaystyle O(n\log n),} {\displaystyle O(n^{\alpha }),}{\displaystyle O(n^{\alpha }),} {\displaystyle O(2^{n}),}{\displaystyle O(2^{n}),} etc., where n is the input size in units of bits needed to represent the input.

Algorithmic complexities are classified according to the type of function appearing in the big O notation. For example, an algorithm with time complexity {\displaystyle O(n)}O(n) is a linear time algorithm and an algorithm with time complexity {\displaystyle O(n^{\alpha })}{\displaystyle O(n^{\alpha })} for some constant {\displaystyle \alpha >1}\alpha >1 is a polynomial time algorithm.

This is closer:
Mathematical induction is a method of mathematical proof typically used to establish a given statement for all natural numbers. It is done in two steps. The first step, known as the base case, is to prove the given statement for the first natural number. The second step, known as the inductive step, is to prove that the given statement for any one natural number implies the given statement for the next natural number. From these two steps, mathematical induction is the rule from which we infer that the given statement is established for all natural numbers.

But this is really interesting because he is touching on the NP-Completeness issue ...

NP-complete problems are in NP, the set of all decision problems whose solutions can be verified in polynomial time; NP may be equivalently defined as the set of decision problems that can be solved in polynomial time on a non-deterministic Turing machine. A problem p in NP is NP-complete if every other problem in NP can be transformed (or reduced) into p in polynomial time.

It is not known whether every problem in NP can be quickly solved—this is called the P versus NP problem. But if any NP-complete problem can be solved quickly, then every problem in NP can, because the definition of an NP-complete problem states that every problem in NP must be quickly reducible to every NP-complete problem (that is, it can be reduced in polynomial time). Because of this, it is often said that NP-complete problems are harder or more difficult than NP problems in general.

what he's describing is really a big deal ...

[Emil El Zapato]

4.669 that is the key to universe...Why I never... I think that is the number of the evil one. it's the 4, if it was 3 then it would be heavenly ...