Thursday 12 January 2017

ALGORITHMOPHOBIA

Dick Pountain/Idealog 262/05 May 2016 11:48

The ability to explain algorithms has always been a badge of nerdhood, the sort of trick people would occasionally ask you to perform when conversation flagged at a party. Nowadays however everyone thinks they know what an algorithm is, and many people don't like them much. Algorithms seem to have achieved this new familiarity/notoriety because of their use by social media, especially Google, Facebook and Instagram. To many people an algorithm implies the computer getting a bit too smart, knowing who you are and hence treating you differently from everyone else - which is fair enough as that's mostly what they are supposed to be for in this context. However what kind of distinction we're talking about does matter: is it showing you a different advert for trainers from your dad, or is it selecting you as a target for a Hellfire missile?

Some newspapers are having a ball with algorithm as synonym for the inhumane objectivity of computers, liable to crush our privacy or worse. Here are two sample headlines from the Guardian over the last few weeks: "Do we want our children taught by humans or algorithms?", and "Has a rampaging AI algorithm really killed thousands in Pakistan?" Even the sober New York Times deemed it newsworthy when Instagram adopted an algorithm-based personalized feed in place of its previous reverse-chronological feed (a move made last year by its parent Facebook).

I'm not algorithmophobic myself, for the obvious reason that I've spent years using, analysing, even writing a column for Byte, about the darned things, but this experience grants me a more-than-average awareness of what algorithms can and can't do, where they are appropriate and what the alternatives are. What algorithms can and can't do is the subject of Algorithmic Complexity Theory, and it's only at the most devastatingly boring party that one is likely to be asked to explain that. ACT can tell you about whole classes of problem for which algorithms that run in managable time aren't available. As for alternatives to algorithms, the most important is permitting raw data to train a neural network, which is the way the human brain appears to work: the distinction being that writing an algorithm requires you to understand a phenomenon sufficiently to model it with algebraic functions, whereas a neural net sifts structures from the data stream in an ever-changing fashion, producing no human-understandable theory of how that phenomenon works.  

Some of the more important "algorithms" that are coming to affect our lives are actually more akin to the latter, applying learning networks to big data sets like bank transactions and supermarket purchases to determine your credit rating or your special offers. However those algorithms that worry people most tend not to be of that sort, but are algebraically based, measuring multiple variables and applying multiple weightings to them to achieve ever more appearance of "intelligence". They might even contain a learning component that explicitly alters weightings on the fly, Google's famous PageRank algorithm being an example .

The advantage of such algorithms is that they can be tweaked by human programmers to improve them, though this too can be a source of unpopularity: every time Google modifies PageRank a host of small online businesses catch it in the neck. Another disadvantage of such algorithms is that they can "rot" by decreasing rather than increasing in performance over time, prime examples being Amazon's you-might-also-like and Twitter's people-you-might-want-to-follow. A few years ago I was spooked by the accuracy of Amazon's recommendations, but that spooking ceased after it offered me a Jeffrey Archer novel: likewise when Twitter thought I might wish to follow Jimmy Carr, Fearne Cotton, Jeremy Clarkson and Chris Moyles.

Flickr too employs a secret algorithm to measure the "Interestingness" of my photographs: number of views is one small component, as is the status of people who favourited it (not unlike PageRank's incoming links) but many more variables remain a source of speculation in the forums. I recently viewed my Top 200 pictures by Interestingness for the first time in ages and was pleasantly surprised to find the algorithm much improved. My Top 200 now contains more manipulated than straight-from-camera, pictures; three of my top twenty are from recent months and most from the last year; all 200 are pix I'd have chosen myself; their order is quite different from "Top 200 ranked by Views", that is, what other users prefer. As someone who takes snapshots mostly as raw material for manipulation, the algorithm is now suggesting that I'm improving rather than stagnating, and closely approximates my own taste, which I find both remarkable and encouraging. The lesson? Good algorithms in appropriate contexts are good, bad algorithms in inappropriate contexts are bad. But you already knew that didn't you...  

1 comment:

  1. Can't see you as being a great Clarkson fan, somehow.

    This is a good and timely article because what I would think of as being an algorithm is very different to the popular perception. But then I trained as a scientist, and my idea of a 'theory' is a lot more rigorous than the popular use of the term as a non-scientific dismissal of inconvenient facts.

    I do a fair amount of chemical information work in my spare time, and certain algorithms, such as the Figueras Ring Perception and Ullman Substructure algorithms, are critical to our processes. The main difference between that kind of algorithm and the kind the media fixate on is that I could describe how they function using a whiteboard and marker.

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