That's like saying book A is in Spanish and book B is also in Spanish, so why did you like book B better? Python is just a language, what you do with it is what matters. Sounds like they built something completely distinct from Watson and it did the job.
And that’s fair. I’m always curious with how people are using the same or similar tools that my company uses. It’s good to hear common struggles or differences in workflows.
I suspect it's not that the included Watson Python didn't or wouldn't work but rather that the Watson-Python costs money (via paying for Watson) and Python-Python does not.
Given your perspective, a more important question might be what underlying algorithms are being used? I’m only a rookie data scientist but knowing that (for example) a random forest outperformed a neural net in this case, or even just a set of heuristics, is solid information. These can be built in Python, R, even C depending on the application, developer experience and a bunch of other stuff.
I'm looking at it from a couple of levels to support my data scientists. They're primarily python-based but we are adding newer data scientists who have experience with R. Algorithmically, we built a neural network for phenotyping.
We can't explain the implementation details, but a human system can literally explain the logic she used to reach a decision. For example, for applications in the justice system that AI has been recommended for, this is a highly important quality.
Eeeeeeh... what we do is more like parallel construction. We can give a series of plausible steps to explain where we ended up, but sometimes we can't really explain why we did some of the steps.
Show me a human that can win a Starcraft championship after only playing ten games. If you find any, they learned the mechanics and strategy somewhere else. That’s transfer learning, appears to be in its infancy in the ML community but making progress.
The scale matters here. I think a better metric for your parent comment would be the delta in skill per game played.
A human is significantly better on game 11 than game 1 (I recently got into Starcraft). Current ML systems are not. It's up for discussion how to take the human's previous experience into account, but the total amount of experience is significantly less that the computer's.
Also “How to Build a Brain” by Chris Eliasmith. Interesting theories on how neurons represent functions and then build into a greater whole. I’m not a neuroscientist but my intuition says he might be getting closer to a solid mid-level (above the level of ion channels) understanding of brain function.
I haven't, though it's been on my list for a while.
I'm collaborating with a woman who has also been working with him to model some of our data with NENGO, so I should really get around to that sooner rather than later.
I hope so hah. It would be nice if Apple made it so, instead of Siri saying "sorry i cant help you with that" she said "i dont know how to do that, can you teach me". And even if it were not really AI but macro-based, it would be better than nothing.
