>
> I think first of all that one doesn't need to know all of the relevant 
> parts from Neuroscience, Psychology, Machine Learning, Philosophy and CS 
> because usually research is built through teams and connections. The width 
> and breadth of human knowledge is such that it's almost impossible to get 
> an expect, especially in an area as complex as AGI.
>
 
While I think that is definitely true of research, I'm not sure if that 
also holds of engineering problems. But again, I lack any experience to 
draw a conclusion upon. I think, with the current state of human knowledge 
(of which I only possess a glimpse), I'd like to approach AGI from an 
engineering perspective and then generate the research questions from 
there. So, my worry, then, is does a engineering team require at least one 
person to have all the relevant knowledge? My expectations from such a team 
are rather: 1-2 people each having 2.5 years or equivalent experience in 
each of the different fields, and then 1-2 people each having 5-6 years of 
experience in 2 of these, or a 10-12 experience in a single of these - so, 
basically, a mix of generalists and specialists.

I find that specialists abound, while generalists as general as these are 
far and few - Cognitive Science seems the closest to produce such 
generalists.


I'd very much love to know your ideas on how Academia could be 
> revolutionized.
>

 I'm not sure if revolutionize is the right term. My complaints stem from 

   - reinventing the wheel: Aside from research based courses in the prof's 
   specific areas, universities world wide could simply administer a course 
   from a chosen top 5-6 course variants of that course from the world over. 
   And then, the time of the profs and TAs could be better spent on doubt 
   resolution, and learning feedback, as opposed to course material creation. 
   This also lets the students access the best teachers for the course.
   - artificial localization: Even if courses and classes are going online, 
   we seem to be creating artificial boundaries for discussions - each 
   university seems to employ their own forums for students to discuss any 
   doubts. At this point, we have excellent sites like stackexchange for the 
   whole internet to clarify their doubts, and I'd love if universities 
   embrace these sites more. Reinventing the wheel is again a relevant problem 
   here - it doesn't make much sense to clarify the same doubts year after 
   year than simply maintaining a repository of doubts. stackexchange sites 
   are the closest to such a repository I've known. Probably, one will need 
   better search engines and indexing and some human effort to organize all 
   the relevant doubts and make them searchable, but that seems like a much 
   efficient way to do things. 
   - research forums: I also imagine some sort of a research forum that 
   contains all the research questions humans have ever had, organized in some 
   manner. Each thread would be supplemented by reviews, whether the finding 
   can be reproduced and so on. This, to avoid duplicated research efforts, 
   unless they were aimed at reproducing the results; as well as make it 
   easier to keep track of a field. The organization and tracking part, I 
   guess, requires quite a bit of work in NLP; in particular, canonicalizing 
   the research questions to avoid duplication. Because of my lack of 
   experience, I don't know how relevant or useful these things are, but those 
   are some ideas I have been having.


I'm also trying to get into AGI but sadly there're no research centers 
> where I live. I think you need to get a position in a research center if 
> you hope to focus full time on it and get money to pay the bills. For that 
> a masters may not be enough and you need to aim for a doctorate at the very 
> least in a related research field such as computer science or neurology.


Post the AI winter of the last century, I don't have much hopes of getting 
funded by working on pure AGI. I find CogSci to be the closest and even 
there it doesn't seem easy to get funding. The best I hope for is to 
generalize a bit using CogSci; and then, may be specialize in some relevant 
field - as you said in computer science or neuroscience (I assume you mean 
neuroscience), or perhaps, machine learning or I don't know.

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