In The Age of Approximation

Please note that this article is one year old…and that in the meantime Microsoft was sued for the way they rolled out their new omniscience machine

Lots of talk about Windows 10; I like this article from Ars Technica about the necessity for (what may seem like) excessive data collection integrated into the platform. But therein, we see the changing face of computing as one leveraging approximation over precision (a la Olfaction).

Olfaction as a model for the future of software development? How about the present.

From the article-

Siri needs to know the names of your contacts to be able to set up calls or send messages. Cortana needs to know when and where your appointments are to tell you when you need to leave the home or office to get to them.

But there's a deeper reason: the software powering these capabilities is fundamentally heuristic, using approximation and guesswork to generate its results. Traditionally this wasn't the case; a hardware keyboard with no autocompletion doesn't need any fancy heuristics, it just needs to directly map key presses to characters. But speech recognition, software keyboards of all kinds, and handwriting recognition don't have this precision. The software driving these things has to construct and evaluate a range of different possible interpretations and then pick a most likely option among those interpretations.

This is the way of olfaction. Within the impossibly complex chemosphere, the nose-brain must approximate in order to make sense. The ultimate need for flexibility is reflected in the design of the receptor patch that receives vaporous molecules - some receptors code for specific odor molecules, some for many, and some for nothing at all. Even at the outset, olfaction is a game of ambiguity.

On Randomness and Certainty

Let’s say you read a headline announcing an advance in the way we generate random numbers. You might think to yourself, "First of all, why do we need random numbers in the first place?" Random numbers are important for many science experiments, and the field of statistics is ultimately based on randomness. Scientists and mathematicians need random numbers as a part of their toolbox in order to do good work. Next, you might ask, how do we get random numbers then? Old school methods involve the flipping of a coin, or the rolling of dice. These methods take a lot of time if you’re trying to produce a huge list of random numbers. Nowadays we use computers. The problem is, even computers produce results that are predictable. Hard to predict, but predictable nonetheless.

What are the chances? You mean to tell me that we can’t actually produce randomness? The paradox is that computers generate random numbers based on an algorithm. Everything they do is based on an algorithm, a set of instructions. Yet, nowhere in a set of instructions can it say, “Generate a random number.” Algorithms cannot think for themselves; technically, we the human programmers do the thinking. (And yet even we can’t generate random numbers.) I’m starting to confuse even myself here, so I should cut to the chase.

In a world where information avails itself to us in an ever-accelerating fashion, we might be led to think that one day we will be sure about everything – a  theory of everything, an omniscience of all future events. But if we remember that science – the thing we use to “be certain” about things – needs randomness to work, and yet we don’t really know how to get absolute randomness, then we can temper our visions of a fully programmed world where all existence is automated. Uncertainty will always be with us.

Notes:

Random number generator 'improved'

May 2016, BBC News