Despite the ongoing tragedy of the pandemic, we continue to live in a period of remarkable technical advance. Fortunately our society has advanced to the point where even when confined to home, we can continue to innovate. If we let our minds run a little bit, I wonder what we can come up with.
Any innovation these days is likely to involve both collaboration, and the most modern arrays of hardware that can be assembled. A certain proverb says, "Many hands make light the work". This is true for both processors and people. Both get viruses, but I digress.
For the sake of argument, a euphemism I use for stimulating discussion, let's assume someone has plunked down in front of us the fastest airplane money can buy. We immediately ask ourselves, "Where could we go, and more importantly, where should we go with it?"
John F. Kennedy said, “For of those to whom much is given much is required”, echoing the writings of Luke the Physician who said “For unto whomsoever much is given, of [them] shall be much required.”
The question is, "How can we bring the most benefit into the world, from the gifts we have been given?". Among these gifts is our ability to reason and communicate nearly instantly worldwide.
Three Observations Motivated by Personal History
1) Time Sharing is Back
I began my computing career with the help of Dr. Carl Sneed, an associate professor at the University of Missouri, one of five I attended over the years. I had signed up for an introductory computing course which was taught on the IBM 360 TSO mainframe in 1975.
|IBM 360 with peripherals|
Dr. Sneed was kind enough to walk me through the following process:
Dr. Carl Sneed, University of Missouri
a) Write one's Fortran IV program on 80-character paper.
b) Transfer each line on the paper to a punched card, using a punched card machine that announced each character with a kerchunk, like a sewing machine that has placed a stitch.
|The IBM 029 Card Punch|
c) Place the deck of cards on the card reader.
IBM System/360 Model 20 Card Reader
d) Press the button which made a Las Vegas card fanning sound as the deck was read.
|Card Reader Panel Buttons|
e) In those days, the size of one's deck was very much the status symbol, but I digress.
f) I specifically remember two programming assignments I had to get running:
- The 3,4,5 triangle problem
- The parabola problem
The parabola problem was the most important to me personally, having grown up in a family where such figures were important. The assignment did not ask for it, but I was compelled, even obsessed by the unassigned task of DRAWING the parabola whose roots were computed by the program. This drawing took place on a Calcomp plotter.
Despite multiple attempts I never succeeded in accomplishing (on the IBM 360) the completion of this task, but the drive to do it never left me. It became a central focus of all future computing, and led me from aerospace engineering to computer science to computer graphics at the University of Utah.
It eventually would result in this, which you can click on if you like animations.
g) After the card deck was read, the next activity I clearly remember was the WAIT.
One had to wait to collect the printout that resulted from the execution of your program to find out if it had functioned correctly, or even at all.
Wide form computer printout
Like the Tom Petty song, waiting was the hardest part. This would range from 5 minutes on a good day, to 30 minutes or even, "Pick it up tomorrow" on a busy day.
h) In those days, the priority with which one's jobs ran was very much a status symbol, but I digress.
i) On obtaining the tree-consuming fan-folded printout of nearly poster size proportion, one would deduce, usually in seconds, any shortcoming the program had, which would lead to a repetition of the steps above.
Now why do I present, in such excruciating detail, the above series of steps? Because if we skip over the personal computing revolution to the current state of machine learning we find we have arrived at the same place again.
Enter Machine Learning
Fast forward 45 years. Besides all the mish-mash of algorithm design and coding, machine learning (ML) consists of three principal steps:
1) Training the neural network from data
2) Testing the neural network on data
3) Deploying the resulting inference engine for general use
The most time-consuming step by far is training the network. The Waiting problem has reappeared. since for most problems of current interest, training networks cannot be realistically done on a user's personal computer in a reasonable amount of time. So it has to be farmed out to a CPU, TPU, GPU, or APU in the cloud via Microsoft Azure, IBM Cloud, Google Cloud, Amazon Web Services and the like. The machines that execute the jobs sit in racks and those racks sit in server farms.
They process our jobs and we wait and we pay. An example of a massively parallel job is GTP-3, a language inference engine that has 175 billion weights in its neural network and cost an estimated $12 million dollars to train.
So to follow Dr. Sneed's kind example, how do we make machine learning as easy as possible to learn and execute? How can we minimize the number of steps, the administrative and emotional overhead necessary to appropriate ML into our computational lives? ML is already available on demand using services like Google Home Assistant, Microsoft Cortana, Apple Siri, and Amazon Echo. These enable positively useful C3PO-like conversations with machines, whose only lack is a robotic delivery mechanism.
C3PO - Ready to Answer Questions
Transforming the current generation of personal assistants into more robotically enabled ones would seem to be a natural direction for growth and development. At this writing, one can already purchase a robotic canine from Boston Dynamics for $75,000 USD. A Google Assistant to use for a head is three orders of magnitude less expensive, $29 USD at this writing. So there is one idea.
So that would be one interesting project, although I personally would prefer a more anthropomorphic version since hands come in handy for robotic assistants.