Interesting to realize that the living things that lay the foundation of the biosphere are extremely robust as compared to the humans that are destroying the biosphere.
Seed can remain dry and dormant for ages only to reborn when provided the proper nutrients. In contrast, most animals require a continual living vessel to reconstruct itself from its DNA.
Evolution appears to constantly perform tradeoffs between robust organisms and organisms that are adaptable. Co-evolution makes possible the development of complex species as a consequence of robust organisms.
We exist as a consequence of the multitude of less complex individuals that make up our biosphere. We are in fact symbionts that are in constant cooperation with our microbiome. Absent our individual ecologies leads to death.
We are inseparable from our microbiome, we are inseparable from our societies, and we are inseparable from our biosphere. The biggest lie we are told is that as individuals, we are independent of our world.
It only appears this way because we are aware of our own agency. This is a fiction that our minds have evolved to function in this world. Without this fiction, we become as mindless as computers.
There are many arguments over the decades as to why the brain is not like a computer. They are all very weak arguments in the face of the reality that all reality is indeed computation.
The reason however that we and all living things are different from computers is because we all generate ourselves from within. Computers and all machines, in contrast, are built from outside.
This distinction becomes extremely relevant when we explore brains. How do brains develop from a single seed? In exploring this problem, we discover the abstractions needed to understand intelligence.
More here: gum.co/empathy

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More from @IntuitMachine

13 Oct
There are two kinds of robustness in this universe, the static kind and the dynamic kind. You need both kinds to stand the test of time.
The blind spot of almost every proposed mechanism of neuroscience is the ignorance of one kind over another.
The characteristic of emergence is that there always exists a fast process that is in continual feedback with a slower process. In an analog system, it is the slower process that is emergent. In a digital system, it is the faster process that is emergent.
Read 14 tweets
11 Oct
When I wrote this, it was highly speculative and likely to be very weird. But it turns out that it's actually not that weird at all! medium.com/intuitionmachi…
I am not making the Penrose hypothesis. Rather, I suspect the math in quantum theory is informative to deep learning networks.
Here is @PsychScientists exploring a book on Perception by Turvey. They consider the issue of non-locality. psychsciencenotes.blogspot.com/2020/06/lectur…
Read 7 tweets
10 Oct
Wonderful discussion with Paul Cisek at the Learning Salon. Paul proposes a refactoring of our taxonomy for understanding cognition. He argues that the structure should be driven by studying the history of evolution. crowdcast.io/e/learningsalo…
What I love about the Learning Salon that is hosted by @criticalneuro @neuro_data John Krakauer is that the hosts are all ready to tear apart the arguments of the speaker. Krakauer has an uncanny ability in conjuring up strong cases against the speaker.
I subscribe to Cisek's thesis in that to understand cognition, we should be informed by evolution. Cognition is a consequence of history (or the baggage) that lead us to our present state. Studying this information can lead to explanations of the peculiarities of human thinking.
Read 13 tweets
8 Oct
Damn, this book is thick!! @coecke Image
I've observed that most books have very few diagrams. I really don't understand why authors think that it's easier to explain something without a diagram.
Perhaps there exists a lack of ability to express something in a diagram. This book has an unimaginable number of diagrams. I randomly opened the book and there were 7 diagrams between two pages.
Read 5 tweets
6 Oct
Brian Cantwell Smith lecture on philosophy and the meaning of computation explains why the language of philosophy just uses a different vocabulary from that of computer science.
In this lecture, he argues that 4 common definitions of computation are inadequate: (1) Symbol processing (2) Turing equivalence (3) Information Processing and (4) Digital.
His more abstract definition is that computation is the interplay of meaning and mechanism. It is the mechanization of an agent's intentionality.
Read 13 tweets
6 Oct
Found this by Steve Bynes that I think is worth reading lesswrong.com/posts/diruo47z…
7 guiding principles of the brain: (1) 2 subsystems (2) cortical uniformity (3) blank-slate neocortex (4) A neocortical algo (5) subcortex steers neocortex (6) neocortex is blacbox wrt subcortex (7) unknown subcortex algos.
To summarize, Steve Byrnes argues that the subcortex is underexplored, more complex than the neocortex and is critical to AI safety. I don't disagree.
Read 5 tweets

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