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I have recently finished reading Chris Stringer's new book "The Origin of Our Species" and posted a review on my blog "". A one of the world experts in the field Chris clearly knew of the South African developments in advance but could only hit at possible developments to come. As someone who is very interested in the analysis and communication of information his book clearly indicates the current problem. Trying to recreate the human family tree is like a giant jigsaw puzzle where 99.9999% of the pieces are missing. While an enormous amount has been discovered, particularly helped by modern scientific techniques, there are still major surprises such as the Denisovans and these latest South African discoveries. Basically we still seem to be at the stage of still discovering new areas which need exploring. For instance just because one hominid has a bigger brain does not mean he was an ancestor as several branches may have been under the same evolutionary pressure to develop their brain in different parts of Africa, and once you throw in the possibility of different groups interbreeding at different times over the last 4 million years or so the complexities are huge. As a scientist I consider the research is at a very exciting time but every theory must be looked at critically and expect to be radically modified. In terms of language evolution I am wondering if the preservation of these South African remains can tell us anything about the development of the larynx. Chris Reynolds
Toggle Commented Sep 12, 2011 on A New Account of Human Origins? at Babel's Dawn
You write When you think how much computational effort is required to support a machine playing chess or Jeopardy, you realize that it will be some time before even sophisticated ape interactions can be simulated, let alone plausible human conversations. Even so, these small efforts encourage a thought. Language is the keystone that brings cooperation and understanding together. There is an assumption underlying chess playing programs, Jeopardy, and your comments about them, that you are certain that the stored program computer architecture form a suitable starting point for understanding the role of language cooperation and understanding. Perhaps we should remember the words of the Irish yokel, who, when asked by a stranger the way to Balimoney thought for a moment and responded "If I was going to Balimoney I wouldn't start from here." The whole stored program computer philosophy resolves around the concept of a task which can be precisely predefined as a global model, and written as an algorithm. I am sure you will agree that both chess playing systems - and the programs in your ipod depend on the role of "intelligent designers". and both relate to activities which have no relevance to the origins on language on the African plains. Forty five years ago I was a naive newcomer to the computer world, having been very much involved in complex manual information processing activities. I was asked to familiarise myself with a vast sales accounting system (say 250,000 customers varying from private households to the U.S. Air Force, and say 5,000 different products aimed at about a dozen different markets). All the time there were new customers, contract changes, and old customers dropped out, which the products and sales promotions were changing to meet the real world market. Any solution needed to be simple enough to be able to process tens ot thousands of transaction a day on late 1960s computers. No knowing any better I used my knowledge of mentally working in non-computerised information systems to come up with a "simple" solution, modelling how I thought the sales staff modelled the problems in their heads. The starting point was "language." Sales staff needed to be in active control of the system and they could only control it if they fully understood what the computer was doing for them. What was needed was a contracts language which was simple but flexible enough to cover any reasonable contract - and - most importantly, was symmetrical. The sales staff would use the language to tell the computer what they wanted it to do, and the computer could tell then, IN THE SAME LANGUAGE, what it was doing for them. The reaction to my suggestion was - "That's research" - sales staff are not clever enough to tell the computer what they want it to do - they need very clever people such as programmers and systems analysts (the priesthood of computing!) to act as intermediaries. Shortly afterwards I became the ideas man on a future planning team of an innovative computer company. Within a few months John Pinkerton and David Caminer (the pioneers of UK computing who built the Leo computers) rushed me into research to look at the design of a revolutionary new type of information processing "white box" system which generalised the contract processing language to handle a very wide range of open-ended problems. The elements of the system were sets and partitions of sets, used recursively, to allow any level of nesting. Processing was by a very simple "decision making routine" which had a small window on the knowledge base (equivalent to human short term memory). The approach takes incomplete, fuzzy and missing information in its stride, and for many tasks results were obtained by the decision making routine without anything that looked a bit like a task specific program. So why haven't you heard of CODIL, which was the name of the symmetrical information language. It's a sad story which I describe on my blog,, but basically, exceptional claims need exceptional proof, which in turn needs exceptional funding to provide. The problem was that funding is provided by an establishment who knows that the stored program computer must be the only possible way forward (look how much money and careers depend on the technology) and where by now all the population under retirement age will have been taught (brain washed?) at school that writing programs is the way forward. At a deeper philosophical level, there is another difficulty. Humans are the most intelligent animals we know, and so there is a danger that we put ourselves at the centre of the "intelligence universe" in the same way that our ancestors put the earth at the centre of the physical universe. As we are "so clever" the mechanism that makes us intelligent must "of course" be very sophisticated and hard to find - and so all simple solutions must be rejected. In fact all my research does is to move the focus of"intelligence" from the processing algorithms (which are very simple) to the communication language - with the concept of recursion (which can easily be mapped onto a network model) playing an important part. In as far as one can identify "intelligence" it is in the way that statements in the communication language interact with each other. Of particular interest the Decision Making Unit algorithm is probably simple enough to be looked at in evolutionary terms. The approach also suggests that intelligence as we see it, and distinguish it from other animals, is a result of the development of an effective communication language. Having abandoned the research many years ago (following a family suicide and the failure to get research grants) I recently decide to look online to see what had happened in the intervening years. In case anyone is interested I am in the process of setting up a blog,, which discusses the research, and includes information on publications and a working demonstration system.
Toggle Commented May 9, 2011 on The Language Arch at Babel's Dawn
Most of Artificial Intelligence research is what it says - Artificial. - and Watson is a good example of a system which has little in common with Human Intelligence. Computers started as glorified calculating machines, designed to process mathematical algorithms which, due to the nature of the task, are too difficult for humans to do quickly and accurately. Such tasks would be meaningless to early man and it is surprising that anyone every takes them to be a good model any aspect of natural human intelligence. When it was discovered there was money and careers to be made out of computers there was a mad rush to get on the bandwagon and there was no time for anyone to do genuine "blue sky" research. No-one asked the question "Is it possible to design an electronic system which would interact with people as easily as a stored program computer interacts with mathematical algorithms?" It is now taken for granted that the stored program computer is the best way forward and that its inner workings are inevitably incomprehensible to anyone but very highly paid mathematical experts. Most artificial intelligence programs take it for granted that the way forward is to find the "right algorithms" - irrespective of how many many years of algorithm writing by clever people is invested to make comparatively slow progress. The belief in the validity of the stored program computer approach has now reached the point where most children are taught at school about programming. Over 40 years ago, after working on an extremely complex commercial data processing system, I decided that the whole philosophy of the stored program computer was wrong. I concluded that the real world was sufficiently unpredictable that, for many tasks, the idea of predefining them using the algorithmic approach was inappropriate. Not realising I was doing somethin new I started to try and answer the "blue sky" question I posed above. I am currently describing what I did on my blog so won't go into detail here - but the key point was to start with the design of a user-friendly communication language where the electronic processor (computer would be an inappropriate term) could tell the user what it was doing in the same language that the user described the problem. For many very different applications, including artificial intelligence, the system could automatically deduce the answer without any obvious predefined algorithm being necessary. The approach could well have been a far more realistic model of human intelligence, and how it evolved, than the stored program computer, but I would not take the analogy too far without significant qualification. While my proposal was conceived as a serial processor approach, it should be easy to re-interpret it to work in a parallel network. The problem was that it was philosophically contra-intuitive to anyone who had been taught to program a computer (which now means almost everyone who was taught programming at school) and this made it very difficult to get research funding or past peer reviews - which were invariably carried out by stored program computer experts. After many years banging my head against the wall I abandoned the research, effectively on health grounds. After another 20 years of forgetting the work I decided to have a good look on the web to see what has happened in the meantime. So far I have come to the conclusion that much A.I. researchers, such as the writers of Watson, are still flogging the algorithmic approach. As a result I have decided to use my blog as a basis for exploring my old ideas further - as no-one else seems to be moving in the same direction. is now following The Typepad Team
Apr 18, 2011