A few weeks ago I decided I should learn something about the popular new gene editing technique called CRISPR/cas9, so I watched about three videos from YouTube on this topic that were pretty clear...
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What is CRISPR?
Bozeman Science
Published on Feb 18, 2016
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How CRISPR lets you edit DNA - Andrea M. Henle
TED-Ed
Published on Jan 24, 2019
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CRISPR Explained
Mayo Clinic
Published on Jul 24, 2018
Upon watching that first video, it hit me: "Hey! This is how nature actually does machine learning!" Admittedly the learning being done is not in a brain, but rather in a bacterial cell, and what is being learned is not anything visual, but rather chemical in the form of DNA strands, but still, since it's likely that natural learning systems started simply like this, it's possible the brain's learning mechanism that arose later in evolution uses the bacterial method as a foundation and just added a few modifications. I think that's a pretty hot idea to investigate if anyone's looking for an interesting research project for an article or a science fair project, especially since the way the brain learns at a higher level of abstraction is unknown, and is one of the most important questions in AI. I don't have time to work on this idea, but if that idea catches anyone's fancy and they decide to work on it, I'll chip in with any advice or ideas I have along the way as they develop it.
Here's the CRISPR learning process as I understand it so far, described chronologically:
1. All input with any DNA pattern is assumed to be hostile. Any input to the system has its pattern matched against other patterns the system has seen, which are stored in its CRISPR.
2a. If a match is detected, the input is destroyed before it can affect anything in the cell.
2b. Else if a match is *not* detected, a new "class" is created, and the new input represents that class, and a copy of that new input DNA is permanently stored in the CRISPR, but in a safe way, for match against future patterns. The input is then destroyed, as before. In either case, the input is destroyed under the assumption that it is hostile.
How might this relate to machine learning in a computer?
() Instead of the input consisting of DNA patterns (which are strings of the 4 symbols A, C, G, T) it might consist of strings of bits (which are the 2 symbols 0, 1).
() The matching algorithm would be almost identical to existing string matching functions used in C and other computer languages.
() Instead of matching a string verbatim, possibly only short samples of the string could be used as a method of generalization or of associative memory. This would be the tricky part, since verbatim string matching isn't particularly interesting, but generalization is, so it would require some ideas for what and how much to store--but on the other hand verbatim matching might still be interesting if the stored string were a single feature of the input rather than the entire input. In the latter case, the interest would be in which and how many features would flag a match.
Anyway, that should be a good start to fire someone's imagination, if this sort of topic interests them. Then maybe we all could see some more active projects going on in this forum.