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Lisp was way more popular for classic AI for many many years, and possibly still is. For machine learning, you want very fast matrix operations for the actual
by traviscj 10y ago
Lisp was way more popular for classic AI for many many years, and possibly still is.
For machine learning, you want very fast matrix operations for the actual training/evaluation parts and very good data munging for the "get the data from this file/API/database to your ML library". The Python ecosystem is strong at both, which sets up a good feedback loop for even better libraries to be built on top.
That's my take, anyway.
- eschaton 10y agoBack then there weren't really ML libraries to speak of, you had to write your own. Though Symbolics did have a neural network tool and framework they sold. And you could certainly do things like shuffle lots of data easily for more hardcore processing. For example, Symbolics didn't just have a Weitek floating point accelerator option for their systems, they had vector processing boards for them too that you could use from Lisp. (And even a GPU, the Framethrower, that did full-frame HD with 2D and 3D acceleration! S-Graphics is amazing for the time.) Also, a lot of AI work back then was focused on symbolic processing. That's largely been eclipsed by the more math-heavy approaches these days but symbolic processing is still around, for example in tools like Cyc.