A Possible Evolutionary Neural Net Model of Turing's Child Brain

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A Possible Evolutionary Neural Net Model of Turing's Simple Child Brain

In 1950 Turing wrote a paper Compuing Machinery and Intelligence and suggested that one approach to building an intelligent system, that could play the imitation game, might be to start by simulating a child's brain.

“Presumably the child-brain is something like a note-book as one buys it from the stationers. Rather little mechanism, and lots of blank sheets. …  Our hope is that there is so little mechanism in the child-brain that something like it can be easily programmed.”

 While an enormous amount of research had been done relating to the brain and artificial intelligence since you will find nothing in the published literatureto suggest that Turing's hope was justified.

This paper looks at the archives of an early and very unconventional computer language, CODIL, which (like dozens of other experimental languages at the time) never became commercially viable. CODIL was designed to help a human user and a computer clerk work together symbiotically on complex problems where uncertainties made the conventional pre-define algorithm approach impractical. 

But the underlying mechanism supporting CODIL is very simple (like Turing's child brain), and could be a model of how information has passed from parents to their children for thousands of generation - making a very deep learning network. 

The assessment shows that CODIL could be considered to be a language for exchanging information between two neural networks - one the brain of the human user and the other in the computer.  This was achieved by handling information in a way that resembles human short term memory. In addition, because the human is providing the information in "network form" the computer does not need to use time-consuming trial and error learning, avoiding the learning problems of much A.I> research.

Because the work was abandoned in 1988 many of the crucial questions, such as "Could a CODIL-like model supporrt natural language?" were not examind before the project closed. As a result this paper looks ways in which the CODIL research relates to evolution of human intelligence.

Read the paper here

I would be very grateful for any comments you can make, with citations to any similar reseach I may have missed,  In particular I would be interested to know if you think the research should be restarted.

Summary of "An Evolutionary Model of Human Intelligence"

An Evolutionary Model of Human Intelligence

By Chris Reynolds

Draft Summary (full paper to follow)

While there is a vast amount of published scientific research about the structure of the human brain, how it evolved, and what it can do, there is a significant gap in our knowledge about it. Basically we evolved in a complex environment and there is no adequate mathematical model of the “complex biological computer” in our heads. What is missing is an explanation of how the activity of single neurons evolved to support the intellectual performance of the human species.  

This paper lays the foundations for a predictive evolutionary model which suggests why there was a significant spurt in human intelligence, compared with animals, and provides an explanation for human brain information processing strengths, and also its limitations, such as confirmation bias.

The proposed model is based on very general decision making nodes which are mapped onto an infinite recursive neural network. This network can be considered the biological equivalent of the infinite numeric array of numbers which is the logical basis for the well known deterministic stored program computer model. Within the model the decision making nodes exchange messages, and in theory all nodes and messages can be broken down into collections of simpler nodes and messages. Individual nodes may be anything from a single neuron, via complete brains, to a human committee, or even man-made tools such as computers. Because the network is infinite, it is capable of representing every neuron of every animal and human brain that has ever existed – and the messages exchanged between them. Evolution from the simplest animal brains into the far more powerful human brain represent a pathway through the logically simple (but very infinitely extensive) network of nodes over a period of about a billion years.

The critical feature of the model, which makes it a complex model, is that virtually all the information needed to construct the deterministic network has been irretrievably lost or is otherwise inaccessible. For this reason the model allows nodes to morph between being deterministic or complex depending on context, and what is known of their history.

Not all aspects of brain evolution are covered. The model describes the way information stored in the network of logically identical nodes is used to make decisions. It is not directly concerned with the physical form of the nodes.  However it is often useful to consider, for example, how the brain is physically constructed and that neurons occur in brains which have a limited life time. Because significant research has been published on how neural networks can learn to recognise patterns the paper does not consider alternative algorithms for such trial and error learning, but simply assumes that in evolutionary terms it is an expensive process. Instead the paper concentrates on the ways language is used by humans to construct a more effective, and significantly more efficient,  decision making network based on the same basic structure used in animal brains. 

The paper is divided into the following sections:

The Evolution of Speech - and the Information Flow model of Human Intelligence

In considering the evolution of human intelligence I will obviously need to address the relationship between speech and language and a recent article "Why Human Speech is Special" by Philip Lieberman helpfully reviews what is known about the evolution of speech, and includes some useful references.

I will be discussing the role of language and speech in some detail when I complete the relevant section of my report on the last 5 million years of human evolution. However it would be helpful at this stage to use the subject to highlight the difference in approach my model takes.

My approach is to identify the mathematical logic used by the brain to store the information it collects and to make decisions, and it is not directly concerned with the physical form of the messages or the organs which detect/generate the message. In effect it assumes that all neurons use the same messaging logic so that, in theory at least, any part of the brain, at any level, can exchange messages with any other part. Of course some will be assigned special functions - such as processing sight, or controlling movement - and have different inbuilt connections at the generic level. As with any other organ of the body, the relative size will depend on the evolutionary demands on the species and there will be accompanying genetic changes. If evolutionary pressures favour changes to the vocal organs one can expect changes in the parts of the brain most intimately involved - but my model predicts that the internal communication language that the brain uses will remain unchanged.

Of course no-one can deny that the fact that language plays an important part of the evolution of human intelligence. However in information flow terms there are many different ways that animals might communicate if the evolutionary pressures are right. Once an effective communication route has opened the language would evolve at a rate appropriate to the evolution of culture - which is far faster that genetic evolution. This raises the question which came first? Evolution often develops new features by modifying existing features, and the important thing is to understand the evolutionary pressures that brought on the changes. 

 In the case of speech there appear to be two alternative for the origins of :an effective communications language

1. A long-drawn-out process where a simple proto-language was proving to have survival value but there was no really effective way of improving communication - apart from evolving new facilities over a genetic timescale.
2. A much more rapid process where a previously evolved vocal system evolved (for example) to aid in hunting by imitating animal calls turns out to be the basis for a new form of communication.

On the evidence I have so far the information flow model would suggest that the later is the more likely. If you know of any evidence which points either way please feel free to comment below.

Why Sex is an Important Factor in the Evolution of Human Intelligence

A paper in Nature about the DNA in the above bone fragment, found in the Denisova Cave, Russia, has been widely reported in the scientific press over the last few days. The fragment is about 50,000 years old and comes from a girl about 13 years old. The girl's mother was a Neanderthal while the father was a Denisovan with some Neanderthal ancestry. It has been known for some years that there was some interbreeding between Homo sapiens, Neanderthals and Denisovans - and that possibly other species of hominins have been involved - but the finding of a bone from a first generation child is a surprise because early hominin remains are so rare.

The discovery emphasises the importance of sex in the development of human intelligence. Let me explain:

Modelling how the Human Brain Works

On the FutureLearn course "Psychology and Mental Health" I am currently following I have just reached a section which starts:

The New Scientist has a very good, user-friendly, account of the human brain and how it works.

I would be the first to agree that the New Scientist is good at explaining science in easily readable terms – having been a fan since I brought a copy of the very first issues when I was a student. Its web site has good descriptions of the brain, and its components, and other aspects of modern brain research. However there is a glaring omission – there is no overall description of “how it works” which explains how the neurons in the brain work together to support intelligent human mental activities.

 

The New Scientist is not alone. The Scientific American has just published a special issue “Humans: Why we’re unlike any other species on the planet” which fails to answer the same question. In fact you can read book after book about the brain, and also many research papers, and while there are a number of purely descriptive surmises, the best answer you can get is that there is currently no adequate predictive model of how the brain’s biological computer actually works to convert activity at individual cell level into a system capable of doing what the human brain can obviously do.

I am currently working on "An EvolutionaryModel of Human Intelligence “ which attempts to show how activity at the neuron level supports the high level mental activities which characterize the human species and it is perhaps appropriate to add my ideas on the subject with outline details of my research.