Ephemeralization

This WIRED article resonated with the New Media Seminar I’m taking at Virginia Tech.

Big Data: One Thing to Think About When Buying Your Apple Watch | WIRED.

I hadn’t heard of  the term ephemeralization coined by Buckminster Fuller before, which is the promise of technology to do “more and more with less and less until eventually you can do everything with nothing.” Fuller cites Ford’s assembly line as one example of ephemeralization.  Ali Rebaie, the author of the WIRED article, writes that the Big Data movement is another form of it.  Our ability to analyze huge datasets has lead to designing more efficient technology.  All in all, Fuller seems to fit right in with the others we have been reading in the seminar.

Advertisements

The vision of machine learning, from 1950

Reading: “Computing Machinery and Intelligence” by Alan Turing. Mind: A Quarterly Review of Psychology and Philosophy 59(236):433-360. October 1950. (one reprint is here by a quick Google search).

Computer scientist majors will learn about the famous Turing Machine in any introductory Theory of Computation class.  They might get a cursory mention of the “Imitation Game,” the subject of this article (with the recent movie, this may change).  I am intrigued by so many aspects of this article, but I will limit my observations to two items.

Part I: Could this article be published today?

The notion of the “Imitation Game” and an exploration of its feasibility is incredibly forward-thinking for Turing’s time  — so much so that he admits to his audience that he doesn’t have much in the way of proof.

The reader will have anticipated that I have no very convincing arguments of a positive nature to support my views.  If I had I should not have taken such pains to point out the fallacies in contrary views.

The article was published in a philosophy journal, so Turing was able to allow his arguments to take idealistic positions which were not practical at the time (though many of his arguments are closer to reality today).  Yet he does not focus on the arguments that establish the feasibility of such a computer (or the program), but lays out a framework for “teaching” machines to play the Imitation Game.  Through his descriptions I can easily see the foundations of fundamental computer science sub-disciplines such as artificial intelligence and machine learning.  He truly was an innovative thinker for his time.  I wonder if a similar forward-thinking article would be published today, with little evidence for idealistic scenarios.  Perhaps there is a Turing of 2015 trying to convince the scientific community of a potential technological capacity that will only be confirmed fifty years from now.

Part II: Scale

There are many numbers in Turing’s article relating to the amount of storage capacity required for a computer to successfully participate in the Imitation Game.  He didn’t seem to be too worried about storage requirements:

I believe that in about fifty years’ time it will be possible to programme computers with a storage capacity of about 109 to make them play the imitation game so well than an average interrogator will not have more than 70 per cent. chance of making the right identification after five minutes of questioning.

I was interested in seeing how accurate his estimates were.  Keep in mind that 10×102=103; that is, each time the exponent increases by one we are multiplying the quantity by 10. For example, if we look at the capacity of the Encyclopedia Brittanica

  • 2×109: capacity of the Encyclopedia Brittanica, 11th Ed. (Turing, 1950)
  • 8×109: capacity of the Encyclopedia Brittanica, 2010 Ed. (last one to be printed)

We see that the size of the encyclopedia has quadrupled in the past 60 years.  Now, let’s look at Turing’s estimates of the capacities of both a future computer and the human brain.

  • 109: capacity of a computer by 2000 (Turing, 1950)
  • 1010-1015: estimated capacity of the human brain (Turing, 1950)
  • 3×1010: standard memory of a MacBook Pro, 2015 (4Gb memory)
  • 4×1012: standard storage of a MacBook Pro, 2015 (500Gb storage)
  • 8×1012-8×1013 : estimated capacity of the human brain (Thanks Slate, 2012)
  • 2×1013: pretty cheap external hard drive (3Tb)

Our current laptops can hold more bits in memory than Turing believed would be able to be stored!  Pretty amazing.  Consider the speed (in FLOPS = floating point operations per second) of two of the world’s supercomputers:

  • 80×1012:  IBM’s Watson, designed to answer questions on Jeopardy (80 TeraFLOPS)
  • 33.86×1015: Tianhe-2, the world’s fastest supercomputer according to TOP500 (33.86 PetaFLOPS)

In 2011, USC researchers suggested that we could store about 295 exabytes of information, which translates to 2.3×1021 bits.  That’s a number even I cannot comprehend.

Hello, World!

Programming languages are, actually, languages. Computer programmers become proficient not in French, German, or Greek, but rather Java, C++, or Python.    First lessons in a new language include learning how to parrot “hello, my name is ____.”  When learning a new programming language, the first lesson involves getting the computer to print a similar phrase.  Nicknamed the “Hello, World!” program, it is often the first stepping stone towards mastering a programming language.

Suffice it to say, this is my “Hello, World!” to academic blogging.  Bear with me while I navigate the cyber seas.