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Chapter I: A Greater Ape Approaches

- 4 minutes read - 675 words

Sample from the 1st section of Chapter 1 ஃ

Homo sapiens are pretty cool, as far as animals go! Despite the mass extinctions, life itself is doing well. The blue whale is the largest animal known to exist in all of evolutionary history. The biggest animal ever is with us right now, and it’s an intelligent mammal with a large emotion-processing limbic system! That’s cool, but something new is coming. Something, arguably, even cooler than a whale.

The robots are coming … No, wait, they’re here! They’re writing this book?! Yes, AIs I’ve trained wrote large swaths of this or inspired my writing. I solicited help from AIs for the cover art, author bio and book description as well. I trained the robot to write in a style (mine), and then I read hundreds of pages of its printed output to get ideas.

Periodic retraining of it while writing this book served as a sort of developmental editor. Topics generated from training on my writing helped break writer’s block. Any direct writings from the robot are contained in excerpts.

I often use these concepts interchangeably: model, ML, AI, and robot. However, there is a distinction. I think of a model as a specific piece of code responsible for training and predicting. ML (machine learning) is a field of engineering and research. ML encompasses the algorithms (models) that can improve their usefulness through training. AI refers to an ML-based system that provides some real world utility. An AI will involve one or more models, and methods to store, process, and serve up info to a user. Above all, I prefer to just say I’m building robots. A robot incorporates the other terms and has human characteristics to it.

I often try to make connections from computers to biological life. Considering a robot as an organism, its fundamental biochemistry is ML mathematics and theory. Its calculus equations are like a Krebs cycle, studded with must-have formulas. The models it runs are akin to genes and proteins; a model has a function, transcribed and expressed through hyperparameters of different cellular organs and epigenetics. AI is the chromosome, a collection of genes. The complete set of chromosomes (the ploidy) directs the organism. A robot culminates in a set of AIs that determine what its life will be. Evolutionary principles shape these robots through time. The data is its ecosystem, it must adapt to the inputs from the environment.

With this concept in mind, that AI is like life and obeys natural laws, we can draw a lot of interesting conclusions. To begin, the driving force of selection for the best AIs will be our hands. Though our hands were driven by natural selection, and our neuropsychology is full of incredible art and spirituality in addition to rational science. Therefore, AI has a chance to follow suit and become an interesting, emotional organism like the walking apes have. I have much more on this in later chapters.

This is a strange hybridization of topics. You might think I’m reaching here. But hybridization is everything. It is the crossroads of creativity. The reason for the tangled tree of life. Code merges and exhilarating company acquisitions! In primatological terms, when there’s less than a couple million years of species divergence, interbreeding is possible. This explains how Homo sapiens are a smear of relationships with other Homo: denisovans, neanderthalensis, possibly floresiensis and other yet unnamed extinct hominins. In genetic terms, it’s called introgression. In programming, it’s called merging a fork. We’re just like the freaky hybrid monkey offspring of distinct species recently found.

Life mixes it up! Complex life itself started with promiscuity between the ancestors of eukaryotes and bacteria. Even crazier, up to 8% of the human genome is of retroviral origin, including the critical functions of mammalian placenta. Without being a little bit virus, we might still lay eggs.

This book makes an omelette, or a minestrone soup; stirring up biology, art and AI. More on making soup later, which I believe is a great analogy for doing creative data science.


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