Stanley & Lehman – Why Greatness Cannot Be Planned

Fascinating insights by Computer Science / Artificial Intelligence profs …

https://amzn.to/2DlhLnX

some have summarized their insights by writing: “only by doing activities that fulfill our curiosity without any pre-defined objectives, true creativity can be unleashed. They call this the ‘Myth of the Objective’: Objectives are well and good when they are sufficiently modest … In fact, objectives actually become obstacles towards more exciting achievements, like those involving discovery, creativity, invention, or innovation—or even achieving true happiness… the truest path to “blue sky” discovery or to fulfill boundless ambition, is to have no objective at all.”

some of Stanley’s and Lehmans insights:

 

  • “The flash of insight is seeing the bridge to the next stepping stone by building from the old ones. ”

 

  • “[Picbreeder] is just one example of a fascinating class of phenomena that we might call non-objective search processes, or perhaps stepping stone collectors. The prolific creativity of these kinds of processes is difficult to overstate”

 

  • “ measuring success against the objective is likely to lead you on the wrong path in all sorts of situations”

 

  • “You can’t evolve intelligence in a Petri dish based on measuring intelligence. You can’t build a computer simply through determination and intellect—you need the stepping stones. ”

 

  • “ambitious objectives are the interesting ones, and the idea that the best way to achieve them is by ignoring them flies in the face of common intuition and conventional wisdom. More deeply it suggests that something is wrong at the heart of search. ”

 

 

I find their books inspiring and insightful. Reframing questions and providing different lines of attack on AI and Search Optimization to Ambitious Goals …

 

 

 

 

Deep Learning – a kind of magic

One synthesis of   Stuart Russel’s  reflections on Deep Learning:

“ Some deep learning networks get up to one thousand layers or more  …  The deep learning hypothesis suggests that many layers make it easier for the learning algorithm to find a predictor, to set all the connection strengths  in the network so that it does a good job.  … to a large extent, it’s still a kind of magic, because it really didn’t have to happen that way.   why this happens is still anyone’s guess.

in Marty Ford’s  new book:  principal architects of AI.

Russell Stuart is  lead co-author  of  THE must have  AI textbook

Artificial Intelligence: A Modern Approach (3rd Edition)

this textbook is the main AI textbook used in over 1000 Universities and Colleges.  If he says it’s a kind of magic … it is a kind of magic!  So if you want to be one of the contemporary magicians or wizards … get on with the deep learning wands.

BONUS … Queen’s kind of magic