30 karma · joined September 9, 2019
The dot product example gave me pause because map2 seems to be the same as zipWith. Does that exist in Ante? Without context I might have thought map2 was going to act as bimap. Take that for what you think it's worth :)
Also I might be having a brain fart -- but isn't the dot product in your example equal to 32?
[1] https://thestrangeloop.com/2018/its-just-matrix-multiplicati...
[1] https://www.lume.ufrgs.br/bitstream/handle/10183/174950/0010...
[1] http://www.picbreeder.org/
[2] https://www.youtube.com/watch?v=qv6UVOQ0F44
[3] https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_t...
Maybe this article should be called "I sell, therefore I am".
The quote from the article, to me, sounds ridiculous. I don't lock myself in a room and do marvelous work for other people, so that they know it exists, or so that it will last longer than I do -- and I suspect many other creators feel similarly.
for reference: https://ai.googleblog.com/2019/06/introducing-tensornetwork-...
I found the irony here rather amusing.
Role: Software Engineer / Research Engineer
Remote: Yes
Willing to relocate: No
Technologies: Python, C, AI, Haskell, OpenCV, Git, PostgeSQL, SQL, AWS, Scala, NumPy, Keras, TensorFlow, Docker, Django, Bash, Linux, autonomous systems
Resume: Upon email request, include company name
Email: charles.kinbote.public@gmail.com
I'm a software engineer with research experience, primarily in stochastic methods like Genetic Algorithms and Monte Carlo Tree Search for non-convex optimization problems like robot exploration and high-performance quantum circuit simulation. I'd be interested in any role where I can branch into other AI domains, including ML. I have interests in neuro-evolution for NLP, for example. Generally, though, I am open to a wide range of opportunities, happy to chat with anyone.
edit: after noticing this other hacker news article (https://news.ycombinator.com/item?id=21107706), I wanted to add that this line of thinking is applicable to understanding programs and proofs written by humans as well. Programs and proofs can be well-understood when their pieces, and the way those pieces compose, are well-understood. When the pieces, e.g. lemmata in a proof, are large or hard to decompose, the proof (i.e. the solution to a problem) is harder to verify and understand.