| The problem I have with this argument is that you cannot consider "efficiency" in a vacuum. You need to have a metric against which to measure it. Consider these two scenarios - Goal: remember where to look up information when it comes up in $JOB
Metric: how much you remember, how quickly you find the info Goal: discover new hyper-efficient method of training an AI (or insert popular ML topic here).
Metric: percent improvement vs current pubished best practice (deliberately vague)
Required understanding to make progress: "like a Ph.D. from Stanford" Now you can possibly measure something. The idea achievement of "90% understanding" is VERY topic dependent. Simple topic? Sure 100% understanding, I remembered the Latin names of all of the plants in my house. Complicated topic? The information for "100% understanding" might not even be written in the textbook - it probably includes things like seeing the interconnections between the topics and being able to apply them in slightly different contexts. Make sure you read the studies so you know what they're talking about. In this area, I think summaries are frequently misleading. You have to know what the real evidence is that substantiates the claims. (I cannot tell you how many times I have looked at the evidence and just rolled my eyes - obviously not applicable in settings where I wanted it to be.) Edit:
See this comment (not me) - https://news.ycombinator.com/item?id=41275869 |
Bjarne Stroustop, creator of C, famously rates his C knowledge as 7/10