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?
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.
Digital computers do not have the inductive biases to navigate the complex and messy world of life. Look around, have you seen a machine with the autonomy of a honey bee? They simply have no grounding in life.
General intelligence emerges out of things that are alive. To construct a synthetic general intelligence requires the kind of algorithm that organically grows itself from the inside out.
Which requires the kind of inductive biases that are organic in nature. medium.com/intuitionmachi…
So what this reveals is that an inductive bias is an emergent feature of a complex process. Just like the wetness of water. It is interesting to identify these biases, but they aren't the process themselves. They are how we describe the process.
Things that our brains can identify are things that exhibit recurring patterns. We feel the wetness of water because that wetness repeats itself every time we feel water. This causation invariance in nature forges the causality reasoning necessary for navigating a messy world.
An inductive bias is the identifiable recurring thought process (i.e. habits) the brain has developed in the process of its own constructive development while growing into this world.
This said one should never mistake the map from the actual territory. A cognitive bias developed by too many is to confuse the two.

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More from @IntuitMachine

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.
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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
5 Feb
Thanks to Twitter, I've stumbled upon two must-watch videos that reveal a very important aspect of human minds that we often ignored. The brain's purpose is homeostasis, but what happens when we lose this purpose?
In the first video, Jill Bolte Taylor describes her experience when a stroke damaged the left hemisphere of her brain.
It is important to watch that video before proceeding. In the next video, the author describes his experience with a syndrome described as 'depersonalization'. aeon.co/videos/the-dar…
Read 17 tweets
4 Feb
Nobody really has a good theory of how brains work. Yet we keep hearing people saying that artificial neural networks are not biologically plausible. Who anointed these folks to be the thought police of what is plausible?
A mistake that too many make about artificial neural networks is that they are implementation models (see: Marr's level of explanation). They are not! They are algorithmic models. When you realize this, the question of biological plausibility should be thrown out.
I think Pylyshyn's mapping of the semantic, syntactic and physical to Marr's computational, algorithmic and implementation is just wrong.
Read 12 tweets
3 Feb
Do you think fractals (i.e. iterative and self-similarity) are weird? Well, it isn't as weird as biological iterative processes. medium.com/intuitionmachi…
What's even weirder is that humans have an intuition that something appears organic. What does it actually mean to have an organic design?
Christopher Alexander, an architect, who wrote 'A Pattern Language' that has immensely influenced software development, wrote four books exploring this idea (see: Nature of Order).
Read 21 tweets
3 Feb
Have you ever realized that the big ideas in Deep Learning are just formulations of very old ideas. Generative Adversarial Networks (GANs) @goodfellow_ian is just Hegel's dialectic (i.e. thesis and antithesis hence synthesis).
That @DeepMind @demishassabis Alpha* self-play is just the Socratic method. en.wikipedia.org/wiki/Socratic_…
That skip connections are just a formulation of small-world networks.
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