Deep Learning? Bah Humbug. But I Guess it could Pay the Bills

Hi X,

I saw your ad on Y for this role. The role interests me, but i will not be available for a full-time commitment for the next six months, as I'm about to start a contract of that duration. My CV is attached, but it is a strange one, so you'll have to tell me if there's any chance you'd like to work with me in the future.

Here's what's not in my CV

I pursued "gradschool," in software development, finance, and mathematics, autodidactically, from 2008 to 2013:


You'll find that I have a fair amount of experience in software development (full-stack) but not a lot in C++ and Python - I've dabbled in those, and never had to use those before, but in the long term that's not a problem as skills will improve with practice.

The lowest-level language I've spent at least a few months working in... is Haskell; the largest project I worked on in Haskell was a web-development framework comprising web-server, MVC architecture, cookies, sessions, and connections to a MongoDB database.

Before that, I studied Erlang/OTP in order to make use of its applications in distributed computing - it turned out to be quite fun to work in for binary/text processing due to a language construct called IO-lists. I did study the Princetonian WordNet project, and practiced Erlang by scripting an extraction of all the data from it (https://github.com/jerng/wordnet-studies).

Limitations of past work in mathematics/statistics

My appreciation for robotics goes as far as this: a good robot will be able to do all the following things better than humans have in the past: invent new hypotheses, critique existing science, rewrite physics and mathematics from arbitrarily defined foundations (these are after all, linguistic representations of facts - facts are fixed, but an infinite set of representations is possible, and standard theory is just one of them).

Accordingly, the majority of my (very limited) work in mathematics post-high-school has been in the area of foundations - how it all works, abstract algebra, category theory, etc. (Coincidentally related to my study of the Haskell programming language.)

My bachelor's degree is in Philosophy (2005).

I was primarily interested in the information architecture of university syllabi because it seemed that the academic organisation itself wasn't very MECE.

I ended up working on the quantification of human experience, and concluded that 80s-00s AI efforts seemed to get the data structures wrong... high-profile AI work of this era puts too much emphasis on finding relationships between morphological constructs, and not enough on performing operations on representations of sensual data. So I think in the long run, "deep learning" will only work when we're representing human thought as models of sensation, and not mere networks of words. I'll keep working on it in my spare time, if I don't develop a career in this field.
Over to you. I'm happy to meet in person or on VOIP for a chat.

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