Speaking My Mind

Rodney Brooks once wrote that robots would be human when treating them as though they were human was the most efficient way of interacting with them. (Not a precise quote.) This is an interesting variation on the Turing test. It assumes that we decide the smartness of machines in the context of frequent interactions with them. It also builds on an interesting idea: that in order to deal with another entity, be it human, animal or mineral, we naturally build an internal model of the entity: how it behaves, what it can do, how it is likely to react to stimuli etc. That model exists for all entities that we interact with; a rock is not likely to kick you back, your word processor will likely crash before you can save the document etc. When the most effective way to predict the behavior of a machine is to assume that it has similar internal structure to ourselves, then it will, for all intents and purposes, be human. So, here is another thought: how do we know that another human is human?

I am probably not alone in visualizing Turing's Universal Machine as a little animacule walking over a linear landscape of ones and zeros: The great innovation of thinkers such as Turing and others was to reduce the complex world of algorithms and functions into something simple and elemental: all computable functions can be thought of as state machines operating over a large collection of ones and zeros, presence and absence. There are arguably many differences between a Turing Universal Machine and a modern browser (quite apart from the fact that, being a Javascript interpreter makes a browser a TUM). But for me, one of the most striking differences is that where a TUM is an animacule in a universe of one and zeroes, the browser is an animacule in a universe of HTML , CSS , HTTP and so on. The browser understands a different world than Turing's computer. Were we to draw a browser as an animacule, it should look like: There are similarities, and if you were to look at it fro

I have previously wondered out loud what ontologies are good for. I now believe that one of the most powerful use cases for semantic technology lies in social networking applications; and matching in general. By social networking I mean "putting people in touch with each other"; especially in situations that are inherently asymmetric. For example, putting potential volunteers in touch with people who could use their services; putting buyers in touch with sellers, and so on. The reason is simple: the language spoken by the two sides is inherently different: a seller or volunteer knows a lot (or maybe not) about what he or she can do or would like to do. But a consumer often does not know to translate his or her problem into a solution that the provider can offer. Put more graphically, providers speak features, and consumers speak problems. This is even if they can find each other. In the middle, there is an opportunity for someone to put the two together. A match maker has to