How it all looks from the ground.
For her vacation, Yuliya decided to show me the beautiful German mountains and took me for a couple of days to Grainau — it's a piece of Bavaria that's almost like Switzerland. At least, it is similar to the pictures of Switzerland that I've seen :-D
In short, it's a lovely place with a measured pace of life. If you need to catch your breath, calm your nerves, and enjoy nature, then this is the place for you. But if you can't live without parties, you'll get bored quickly.
What's there:
This is briefly, and now in detail.
Guess when I switched models.
Recently OpenAI released GPT-4o-mini — a new flagship model for the cheap segment, as it were.
Of course, I immediately started migrating my news reader to this model.
In short, it's a cool replacement for GPT-3.5-turbo. I immediately replaced two LLM agents with one without changing prompts, reducing costs by a factor of 5 without losing quality.
However, then I started tuning the prompt to make it even cooler and began to encounter nuances. Let me tell you about them.
Nate Silver — the author of "The Signal and the Noise" — is widely known for his successful forecasts, such as the US elections. It is not surprising that the book became a bestseller.
As you might guess, the book is about forecasts. More precisely, it is about approaches to forecasting, complexities, errors, misconceptions, and so on.
As usual, I expected a more theoretical approach, in the spirit of Scale [ru], but the author chose a different path and presented his ideas through the analysis of practical cases: one case per chapter. Each chapter describes a significant task, such as weather forecasting, and provides several prisms for looking at building forecasts. This certainly makes the material more accessible, but personally, I would like more systematics and theory.
Because of the case studies approach, it isn't easy to make a brief summary of the book. It is possible, and it would even be interesting to try, but the amount of work is too large — the author did not intend to provide a coherent system or a short set of basic theses.
Therefore, I will review the book as a whole, provide an approximate list of prisms, and list some cool facts.
Cover of the book "Piranesi"
"Piranesi" is both a continuation of the magical stories of Susanna Clarke and an independent book.
The book has no direct connection with the world of English magic [ru] from "Jonathan Strange & Mr. Norrell". If desired, one can find a connection and even say that the worlds are the same, only at different times: the events of "Piranesi" take place in the early 2000s. However, the author did not give any hints on this. Therefore, I consider the worlds to be different for now.
Susanna continues to persistently and effectively dig not even in the direction of animism as the basis of world perception but in the direction of extremely holistic view of the world, in contrast to the currently dominant reductionism.
The latter blows my mind. As an engineer, I'm an intuitive reductionist due to professional deformation. Reading "Jonathan Strange" and "Piranesi", I felt how Clarke, like Peter the Great, cuts a window in my brain to another picture of the world, a different world perception. And it's wonderful.
By the way, don't confuse holism with, say, an engineering view of the world, a-la systems engineering [ru] or even science. The latter is about decomposing reality into isolated parts with clear boundaries and synthesizing "pure" models of the world [ru], while in holism, the parts have no clear boundaries and penetrate each other.
But it is my interpretation, there are interpretations when holism is just an alternative name for a systems thinking/view — it's hard to find literature on this topic now, so it's hard for me to say where the truth is.
So, "Piranesi"
I found a few new concepts for tracking.
There is computational mechanics, which deals with numerical modeling of mechanical processes and there is an article about it on the wiki. This post is not about it.
This post is about computational mechanics, which studies abstractions of complex processes: how emergent behavior arises from the sum of the behavior / statistics of low-level processes. For example, why the Big Red Spot on Jupiter is stable, or why the result of a processor calculations does not depend on the properties of each electron in it.
The concept of a device that can exist in a finite set of states and can predict its future state (or state distribution?) based on the current one.
Computational mechanics allows (or should allow) to represent complex systems as a hierarchy of ε-machines. This creates a formal language for describing complex systems and emergent behavior.
For example, our brain can be represented as an ε-machine. Formally, the state of the brain never repeats (voltages on neurons, positions of neurotransmitter molecules, etc), but there are a huge number of situations when we do the same thing in the same conditions.
Here is a popular science explanation: https://www.quantamagazine.org/the-new-math-of-how-large-scale-order-emerges-20240610/
P.S. I will try to dig into scientific articles. I will tell you if I find something interesting and practical. P.P.S. I have long been thinking in the direction of a similar thing. Unfortunately, the twists of life do not allow me to seriously dig into science and mathematics. I am always happy when I encounter the results of other people's digging.