The purpose of the brain is homeostasis. More specifically, a particular variant referred to by a lesser-known word "allostasis". Accepting this reveals all that is wrong with machine language approaches in modeling brains. Permit me to explain...
Allostasis proposes that efficient regulation depends on the anticipation of needs and preparation for their satisfaction. This is a more complex form of homeostasis, which is typically defined as maintaining a system within a narrow operating range.
The problem with machine learning approaches is that the formulation of the domain of stability is performed by a researcher who explicitly defines an objective function or in the RL paradigm a reward function.
It is a great leap of faith that the research can formulate a
'god seeing eye'.
Reality is unfortunately horrifically complex and it is the nature of brains to livewire itself to the environment through interaction. Brains anticipate and prepare by learning about this world.
Machine learning algorithms do very well in learning narrow tasks. That is because they "direct fit to nature" of the objective or reward function. They fit themselves to the very narrow goals of the environment that they are forged in.
Objective functions are analogous to equations that describe the energy of a system. Direct to fit algorithms are like physical systems that achieve their stability in the state of minimal energy configuration.
But is the stability conditions as described by physics the same stability conditions we find in homeostasis in biology? It could be a good metaphor, but it is entirely wrong.
It's as wrong as the saying that thoughts are like 'data structures'. We have a habit of using metaphors that we are familiar with to describe things that we don't understanding. It's all too easy to fall into the trap of using a bad metaphor.
This history of science is littered with paths that were driven by metaphors that were simply wrong. The concept of Aether and GOFAI are examples of these wrong-headed metaphors.
But of all the metaphors that human civilization has invented, the one that is most insidious in a meta-metaphor. This metaphor originates in philosophy and mathematics. It was aggressively promoted in the early 1900s by Russell and Whitehead.
Although Godel (apologies, I don't know the keystrokes for the umlaut) revealed the error in Russell and Whitehead's formalization of mathematics. Ludwig Wittgenstein attempted also to solve philosophy but only to discover its flaws.
Wittgenstein had thought he had solved philosophy when he wrote his Tractatus Logico. He promptly retired from philosophy after and pursued home building and elementary school teaching. But only to come back a second time to rework his mistake.
Wittgenstein realized that to understand words, we have to understand the context in which these words are expressed. More specifically, he called this context a 'language game'.
In our everyday social interactions with each other, we play many kinds of language games without explicitly being conscious of it. For someone to understand what we say, that someone unconsciously understands the language game from wherein we speak.
Misunderstanding in human communication is a consequence of an impedance mismatch between the language game played by the speak and the game assumed by the listener.
Biosemiotics is an approach to understanding biology by taking a 'language-turn' in its description of the horrific complexity of biology. The molecular machinery of biology achieves coordination via the propagation of signs.
Signs are not just composed of symbols. As C.S. Peirce has formulated, there is a richer milieu of signs. medium.com/intuitionmachi…
I believe the correct metaphor for uncovering the mysteries of general intelligence is through the study of signs.
Because hidden underneath signs are the indexical references to energy. Allostasis in its most abstract description is the anticipation of loss of energy and the preparation for the retrieval of energy. But how do we know what is energy if not for a sign?

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More from @IntuitMachine

8 Mar
The brain's consensus algorithm demonstrated: Image
Permit me to explain why your brain is flipping its interpretation of the image. For starters, human vision acts very similarly to touch. medium.com/intuitionmachi…
When your eyes looks at an image, it is actually rapidly moving around and 'feeling' the image. The part of the eye that can see color and high resolution is just a small fraction of what you see in front of you.
Read 10 tweets
17 Feb
Thrilled today to have anticipated a @DeepMind position paper several years before it was pre-published. This is a hint that I may in fact be at the bleeding edge of understanding general intelligence. Here's the said paper: arxiv.org/abs/2102.03406
The key points of this paper are what the authors describe as symbolic fluency: receptive, constructive, embedded, malleable, separable, meaningful, and graded. Let me explore this in more detail to mine newer insights.
I don't have a need to regurgitate the motivations of the approach other than to say that it derives inspiration from Peirce's formulation of semiotics. medium.com/intuitionmachi…
Read 13 tweets
17 Feb
The folks at DeepMind have discovered C.S.Peirce and thus semiotics. Now I begin to worry. arxiv.org/abs/2102.03406 @santoroAI @DeepMind @AndrewLampinen @dnraposo
Quoted from the paper "Our definition of a symbol draws on the work of the philosopher Charles Sanders Peirce. Peirce outlined three categories of relation—icons, indices, and symbols—whose definitions illuminate the role of convention in establishing meaning."
Perhaps the authors got inspired by my blog post written in 2018. I do hope they continually get inspired by other blog posts on the same topic. medium.com/intuitionmachi…
Read 6 tweets
15 Feb
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.
@yudapearl 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?
Read 13 tweets
6 Feb
The two main areas of metaphysics involve ontology (i.e. what is reality) and epistemology (i.e. how do we know what we know). These two areas are unified under the same tent of Turing computation.
The idea of the universe being a computer is an old one. I believe Konrad Zuse was the earliest person to propose this idea. (Let me know if I am wrong here!). I don't think one can make a distinction between causation and computation other than that the latter is more general.
Epistemology, how we know what we know is bounded by Turing's theory. It is known as the halting problem. That is, there are limits to what one universal Turing machine can predict from observing another Turing machine.
Read 12 tweets
6 Feb
Descartes' logic (i.e. "cogito, ergo sum") is I think, therefore I am. Modern Western culture and civilization is based on this bias. That is, consciousness is the governor of cognition.
Formulations of free will and the hard problem of consciousness are manifestations of this logic. The inversion of this logic, that intuition (i.e. what's below consciousness) is what drives cognition is not as well known or accepted by society.
It's a commonly held belief that the unconscious is an unruly and untamed mind. It is the mind of beasts. The feral mind where if it were not for the governance of the conscious that all hell will break loose. One loses his mind when the governor fails.
Read 15 tweets

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