There was an earlier thread about Sutton's bitter lesson (link 
<http://incompleteideas.net/IncIdeas/BitterLesson.html>), which basically 
argues that general machine learning methods are always better than 
specialized methods encoded with human knowledge and optimized, which 
seemed like most people agreed with. There is a response on it called The 
Better Lesson by Rodney Brooks (link 
<https://rodneybrooks.com/a-better-lesson/>), pointing out reasons why 
Sutton is wrong. I really recommend giving it a read. 

It made me think about how using certain concepts that we already know 
about the world could actually be useful, rather than building a completely 
blank environment and have it learn everything from scratch. Why throw away 
all the patterns we've recognized already? Plus we can't rely on the 
increase in compute (link 
<https://venturebeat.com/2020/07/15/mit-researchers-warn-that-deep-learning-is-approaching-computational-limits/>),
 
which is integral to general methods, and playing into the process 
perpetuates the ever-increasing carbon footprint of the machine learning 
industry.

There seems to be a duality between these two methodologies: generality and 
specialization. Which is the right approach? But they could work together. 
By using our human ingenuity and our current understanding of the brain, 
maybe we could build a specialized, but limited version of human 
intelligence, to then use to create a general intelligence. Perhaps a truly 
general method for building human intelligence is a task belonging to a 
post-singularity world. How else could we overcome such a large problem 
space?

What do you think? is there any merit to this,? Or I am just not 
experienced enough? 
Maybe I should stop thinking so much and get coding.

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