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James Taylor

Interesting post. Certainly many of the uses of business rules, predictive analytics and other kinds of decisioning technology are to add "smarts" to otherwise dumb processes or to replace time consuming and costly manual interruptions to processes.
Clearly the kind of semantic coherence you discuss can add enormous value to the kind of decisioning I discuss but I also think that business rules, as a form of metadata, might be part of how one solves the problems you discuss directly.


Hi Dave,
Anyone who really understands autism is unlikely to attack you over this - I'm writing a paper for a library studies course on the Semantic Web, and have a 21 year old son with some autistic features. When he does make a spontaneous inference of his own, (he is possibly just as dyspraxic as he is autistic), it is a cause for great rejoicing.
It would appear the SW crowd realise that decentralising context is very important to this work. It is the cataloguing approach of developing agreement on terms, instead of providing lots of instances, that characterises a view of information retrieval as a bookshelf and not a web. I think they are on to something very powerful, and hope it will not fall by the wayside simply for lack of support from the commercial sector.

Dave McComb

Nic, I think I share both your pessimism about AI coupled with a resignation that the human mind is the best model we have. However, I'm reminded that progress in flight accellerated a lot when we quit gluing feathers on our arms and started studying aerodynamics. In a similar vein I'm not looking for AI to immitate human thought as much as to find and implement some aspects of it. In this regard I find inference, as implemented in description logics, to be a very useful next step, in terms of helping us deal with the vast amounts on information engulfing us.

Nic Lewis

Upon reading my ramblings I realise a summary point is missing.

'Complexity' inasmuch as data sets will remain. We have to have the core data.

'Inference' will take evolution - however in Moore's world that can be years not many, many millenia.

We need to design, encourage and instigate manufactured systems that self develop. I refrain from the term 'Artificial Intelligence' because in my opinion it is an oxymoron.

Self development will be fun, scary, revealing and educational, but who's going to press that 'on' button?

Nic Lewis


I stumbled across your writings (the way in which this happened may, of course, be grist to the mill of information handling techniques) and felt compelled to comment.

We (higher primates) solve problems using mental 'analytical' processes and apply the 'rules' we perceive from that process to mechanics to reduce the labour required to solve similar problems in the future. I hope that tallies with your view.

It would seem, at present, that we have have no other proven model for that problem solving than the human mental ability. It may be short sighted but I feel we need to exclude divine intervention, in whatever shape, from this discussion. I also exclude work in IT as it does not yet, as far as I know, meet the complex (and subtle inconsistency) of the brain.

Given the way in which we currently solve problems, and assuming we are not considering extremely unusual solutions, it would seem that we could apply some of our (limited) knowledge of the human mental process to this dilemma.

If we did that in a simplistic way though, we would have to accept a margin of error (as humans naturally introduce) in order to acheive more 'intuitive' systems.

That margin of error may, of course, lead to unexpected outcomes; that is the realm of Messers Clarke, Heinlein, Bradbury et al and whilst of entertaining interest is likely to be non-productive.

So what is the resolution? I have some ideas about this, not yet tried or tested but I hope worthy of further investigation. I beleive there are ways to compromise the morass of datasets, arrays, categories etc., that our systems deal with to reach 'intuitive' or 'semantically related' decisions whilst retaining integrity of data. You mention 'difference' as a defining term with regard to 'similarity' - I had to chuckle, think back to Charles Babbage to whom a lot of us owe our livelihood!

I work in secure data transmission where '5 nines' are not good enough. Zero errors are a 'must, and designing systems to ensure that the inevitable errors are *corrected* is the most importan element of the architecture. It is in doing this that my mind turned to the more variable world and it is of great interest to me.

I hope this hasn't bored you to tears and also hope some dialogue persues.

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