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.
However, even if your eyes are moving, you see a stable image in front of you. Your brain creates this illusion out of the many samples it receives from the fovea.
The brain is a parallel processor. There are many processors that each are attempting to discern meaning for each patch of visual stimuli that it receives.
To maintain coherent thoughts, these many processors must reach a consensus. So when you focus on one plate and see it as flipped up or down, the other processors are forced to reinterpret their original interpretation.
The brain is also strongly influenced by what it perceives most recently. In this example, one is conditioned by the original wording that forces one to see the plates in one way.
Illusions like these are clues as to how our brains work. For more on this see: gum.co/empathy
A final note, human cognitive bias are a consequence of eons of evolution. We are biased to see light from above and rarely ever from below. That is why there is no ambiguity when you rotate this image 90 degrees in any direction.
This example is also presented very ingeniously like a magic trick. The pledge is that all the plates are flipped upside down, the turn is that except one of them which you should find. The prestige is when you find the one (it could be any of them) that every other plate flips!

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More from @IntuitMachine

9 Mar
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.
Read 21 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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