Another thought about Turing and Brooks

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? Although this sounds flippant, there are many instances where we forget that another person is a real person: soldiers must do this in order to carry out their job; terrorists must de-humanize their enemy in order to justify their atrocities.

I think that we only really recognize another person as human when we can relate personally to them. In most cases, what that means, is recognizing the other person’s behavior as symptomatic of something that we ourselves have experienced. In effect, the model building process consists largely of seeing someone’s reaction to an event and relating it to something that we ourselves have experienced. (An aside: how often, when told of some event or situation as it affects someone we know, do we react by quoting something from our own past/situation that somehow is analogous?)

At the heart of this phenomenon is something curious: conventionally, the Turing test is phrased in such a way as to decide whether the other entity is human or not. However, it may be more accurate to say that what we do everyday is try to decide if we ourselves could somehow be that other person (or entity) we are interacting with? Furthermore, it may be, that this emphasizing is only possible because fundamentally, we are all clones to 99.99%: we are all running the same operating system in our mind as it were. We can predict the other person’s responses because they could be our responses also.

What does this mean? Well, perhaps we need a new formulation of Turing’s test: an entity can be considered human if we believed that we would react the way that the entity reacts had we been that entity. Another consequence may be that machines may be smart and intelligent etc. but not human simply because the code that they run is not our code. A cultural difference between people and machines if you will.

Turning Turing upside down

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 from the perspective of a binary TUM, you would be hard put to see a significant difference between a browser and a regular TUM. But that would be missing an essential difference.

The browser understands a different world than the TUM because the concepts that underlie its state machine are concepts from the world of the web, not the world of ones and zeros. Its semantic engagement with the world is different; arguably higher level than the binary TUM. The browser stands on the shoulders of the binary TUM, but nevertheless reaches higher.

What, one may ask, would the level above the browser’s level look like? And is there an infinite stack of levels waiting for our discovery?

Ontologies for matching

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 be able to put the right provider in touch with the right consumer; in a sense that is the measure of the quality of the match maker.

The more that a match maker knows about the domain, the range of things being offered and consumed, the easier it is for the match maker to do a good job.

Enter the Ontology: in a modern social networking application, the domain knowledge of the match maker can be encoded and used as the underlying basis for performing matches.

This is, in my view, a killer application for semantic web technology.