A children's book written in 1954 inspired by Wiener's Cybernetics that explains some common day analog systems:

Analog computation originally meant the construction of a simulation of another system by means of a mechanical or electrical system that represented the same dynamics as the physical system. That is an analog of what's being simulated.
Nobert Wiener generalized this idea in Cybernetics to describe control systems that through negative feedback promoted stability. https://t.co/15doR3gxUk
W.Ross Ashby argued that negative feedback was insufficient for maintaining the stability (homeostasis) of complex system. Ashby proposed the Law of Requisite Variety. https://t.co/T4KbmZNLpL
These machines that 'seem to think' are examples of what was known as analog computation. Cybernetics, control theory, biology are examples of analog computation. But what does this mean other than being the opposite of discrete/digital computation?
Computation in an abstract sense is the interplay of intention and mechanism. That intention may be homeostasis, perception or action. Analog systems use mechanisms of this world to implement the mechanisms required to fulfill intentions.
This is in contrast with discrete digital systems that operate in a virtual reality distinct from their underlying implementation. The only virtual analog systems we have are the kinds that are simulated using digital systems.
The other distinction of an analog system is that perception and action can be continuous. Said differently, continuity has meaning in an analog system. In fact, I would argue that the basis of meaning is dependent on continuity.
A digital system (language as an example) has meaning as a consequence of standardization. That is, agreed-upon norms of interpretation. Meaning in digital systems is an emergent feature of collective behavior.
Collectives are composed of individuals that create meaning through the observation of continuity in this world. What Peirce identifies as icons and indexes are signs that express continuity. That of a similarity relationship or a causal relationship.
Prior to the emergence of brains, organisms interacted with this world through hardwired mechanisms. This mirrors how analog systems interact with the world through the physical mechanisms of this world.
Brains however introduce an entirely new behavioral repertoire that is divorce from actual mechanics. Brains predict their analog realities through mechanisms that are yet to be understood.
Brains are able to virtualize analog realities in a manner very different from how digital computers virtualize analog reality. (Left to reader why this is obvious)
We have two options of how to brains could be implemented. Brains could be either (1) analog machines that virtualize analog realities or (2) digital machines that virtualize analog realities. The spiking behavior empirical observed appears to indicate that it is of the 2nd kind.
However, Deep Learning appears to allude to an analog machine virtualizing an analog reality. This is difficult to understand, but that is what Deep Learning appears to show.
However, there is a 3rd option. That is, the brain is both digital and analog. Biology employs the strategy of code duality across multiple scales. My expectation is that this is also the brain's strategy.
@threadreaderapp unroll

More from Carlos E. Perez

Nice to discover Judea Pearl ask a fundamental question. What's an 'inductive bias'?


I crucial step on the road towards AGI is a richer vocabulary for reasoning about inductive biases.

explores the apparent impedance mismatch between inductive biases and causal reasoning. But isn't the logical thinking required for good causal reasoning also not an inductive bias?

An inductive bias is what C.S. Peirce would call a habit. It is a habit of reasoning. Logical thinking is like a Platonic solid of the many kinds of heuristics that are discovered.

The kind of black and white logic that is found in digital computers is critical to the emergence of today's information economy. This of course is not the same logic that drives the general intelligence that lives in the same economy.

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It has been exactly 3 years to "how fund managers .." was released. The book took a lot of time to write. Here is a short thread about how it happened ..


2/n the idea came from @kan_writersside who got me in touch with Dibakar Ghosh at @Rupa_Books .. we discussed the idea that it has been 2 decades to the fund management industry and it deserves a book. A lot was written about about Bharat Shah, Prashant Jain and S.Arora..

3/n but there was not much information about investment philosophies and the overall environment of the mid 90s and later on. Kanishk and Dibakar wanted a broader book for everyone and not just the stock market reader. We went to work

4/n we decided to write about the dotcom boom and bust where it all started. The start fund managers came from there. In Feb 2000 IT index had a pe multiple of 420 and the market cap of the sector was 34% of the market. Banks were 5% and some analysts were still bullish

5/n prashant Jain was one of the few fund managers who was out of the sector in November itself and was quietly watching the index go up. There were others but the legend of Jain was at the top of the mind because it is believed he refused to meet the CFO of a big IT company ..

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This is NONSENSE. The people who take photos with their books on instagram are known to be voracious readers who graciously take time to review books and recommend them to their followers. Part of their medium is to take elaborate, beautiful photos of books. Die mad, Guardian.


THEY DO READ THEM, YOU JUDGY, RACOON-PICKED TRASH BIN


If you come for Bookstagram, i will fight you.

In appreciation, here are some of my favourite bookstagrams of my books: (photos by lit_nerd37, mybookacademy, bookswrotemystory, and scorpio_books)