August 27, 201
Our first lecture, and it was very interesting.
Review of Prescribed Readings
- So the ‘famous’ Rede Lecture of 1959.
The bottom line here is the intuitive understanding that the Rich stay rich and the Poor stay poor – a common theme in the past few US Presidential Elections, in particular by John Edwards in the 2008 election Democrat Primary process. - The End of University
This New York Times 2006 article again looks at the divide between those who can afford academics and those for whom academia is a way to get into serious debt.
What We Gleaned in Discussion
We were 8 students: 4 from Computer Science (2 from Computer Vision and 2 from Machine Learning) and 4 from the ‘Humanities’ (2 from the Digital Humanities Master’s program, a true humanities student from English Literature, and a visiting Social Scientist)
The spectrum of Academic study, having the arts on one extremity and Math on the other was acknowledged.
Then we find that the people in our group are those who seem to want to find commonality between these dichotomies.
An interesting question was asked from the Humanities side to one of the CS (Computer Science) side as to why he was there, and his answer, clear to most CS students surprised the Humanities students. The reply was that without input from non-computer personnel, anything done in Computer Science is done in a vacuum – which quickly loses meaning without application.
There seemed to be a suggestion amongst the humanities people that the scientists were keeping it all to themselves.This surprises the scientists as we (and I include myself amongst the scientists) are aware of the vast quantities of information on what we do readily available to any who wish to read it.
The ‘Gulf’ within the Sciences
It should be worth noting here, that even within the sciences, there is little sharing between disciplines: Chemists may utilize Computer Vision in getting what they need from their work, as to do Biologists; however, they do not concern themselves with what was required to make the tool work, only that they get out of it what they want — often cursing the computer for messing up when things go awry. The Computer Scientist on the other hand is given a problem: how do we separate this from that in an image and then colorize it so that differentiations become clear to the user. A good example of this is when images taken of a section of the cosmos is colorized to accentuate the relevant otems from the trivial.
The Takeaway
A non-verbalized statement made was that there is a gulf between understanding on either side of this spectrum and we are here to see if we can develop channels (perhaps bridges) to span this gulf and – create a better academic world?
A Note on Programming
Programming is not Computer Science!
If anything, programming is that veneer which allows the ‘novice’ to utilize what Computer Science can offer – without needing a PhD in Computer Science!
Unlike other resources, computed power is not naturally found in the environment – even uranium and electro-magnetism are natural by-products, but computing is a totally man-made thing. Algorithms – the ‘clever’ coding – are part of Computer Science – but while we computer scientists hammer away at improvements, the world at large gets the ‘clever’ bits packaged in a way that says do this: e.g. a Sort is an algorithm taught to most junior computer scientists – however, when the programmer wishes to sort something, s/he calls the Sort() function and doesn’t think about O(n⋅log (n)).
