Search that (nearly) passes the Turing test

After spending a fair bit of time over the past few months developing a search facility that was a bit more ‘human’ in its responses than a simple text-based search would be, I was interested to stumble on this discussion on Applying Turing’s Ideas to Search by John Ferrara. In my case, by working in just one niche  (training courses), the problem of creating more intelligent responses that the article discusses became more realistic. Even relatively simple implementations of some of the ideas gave some pretty powerful and less frustrating advantages from a usability perspective.

As a simple example, a search containing the word ‘free’ such as ‘free training’ clearly means to us humans that we want to see a price of zero, so matching ‘free climbing’ and ignoring £0 in the results clearly seems completely mental to the average user. The fix is pretty simple though. By enriching our search index with extra ‘implied’ data (like including the word ‘free’ when the price is zero) and choosing careful weightings for this data, the results quickly become far less ‘dumb’ and even at times appear surprisingly human. Obviously not always 100% perfect, but definitely better.

Automating our understanding of phrases (rather than simply isolated words) fascinates me too. I did a search today on a stock photo library for ‘Mac keyboard and mouse’. The intent of that search is clear enough to us humans but the starkly Turing-failing response was “By ‘Mac’ do you mean Apple Macintosh or Waterproof Clothing; By ‘keyboard’ do you mean computer input device, synthesizer or piano; By ‘mouse’ do you mean rodent or computer mouse?” Er, yes, I want a picture of a small rodent in a raincoat playing piano please.

That challenge for clarification is a reasonable enough question for a machine to ask but us humans know that when ‘keyboard and mouse’ are mentioned together in the same phrase, we’re pretty certain it doesn’t mean a piano-playing rodent. An what’s more, when keyboard and mouse are mentioned together with ‘Mac’, we know we’re not going to be wearing the mac.

What really fascinates me about all this isn’t really the deep, deep theory of it all (I’m a pragmatist at heart), but simply that a recognition and awareness of the weaknesses of ‘pure’ computer logic when we’re designing computer interfaces can quickly lead to huge steps forward in the usability of a system. By putting some thought into covering predictable and reasonable human expectations in a user interface, it’s relatively easy to avoid forcing your users to think like a computer, and even create a surprisingly positive experience.

From a personal perspective, what is often surprising when I start down this line of thinking, is that relatively little changes and increases in effort can bring big usability results. Even attempting (and inevitably failing) to make your user interface pass the Turing test still gives some real advantages to your users.