|Your Questions Answered|
Monday, 24 September 2018
JS asked; "Can you model for the impact of language, writing, print, radio, TV and the internet?"
I am basically interested in modelling the flow of information within brains and, using a language, between human brains. It is important to note that the model is not concerned with the physical form of the decision making nodes or the messages. An eye is a decision making node which receives photons and converts them into messages to neurons in the brain. In the same way books, TVs and computer systems can be considered to be integral part of the overall network model.
One of billions of possible complex information flow examples: The node “William Shakespeare” generated a message “Macbeth” which was sent to by the book to actor nodes living 400 years later – and the resulting performance ended up in your brain via a TV and your eye.
My model is (at present) only an initial qualitative model – but the model shows how improved information tools can have an effect on the knowledge stored in the brains of modern humans. It is an open question whether the model could be developed to measure impact by, for instance, saying what percentage of a given individual’s knowledge came from the use of the internet.
Saturday, 22 September 2018
J Nievergelt wrote:
“We are pushing against the limits of complexity that we know how to handle. ... However, we also run into problems that are commputationally light but also so complex that we do not know how to design, document, and debug them. This kind of problem is relatively new. – this complexity barrier may well be the most important limitation that the computer field will be subject to in the immediate future.”
Wesley Davis wrote:
“The ultimate success or failure of computer systems at present is overdependent on the problem definition and system design stage, The desire to simplify systems leads to a conflict between the needs of people and the computer specialist's interest in containing the project within present methodology. ... for its [computer system] purpose is to make a company responsive, flexible, and aware of its commercial situation. It is unlikely to do this without these characteristics being implicit in its design.
M A Jackson wrote:
“We should be positively looking for and developing programming methods that do a1low inconsistency, redundancy, ambiguity and incompleteness; we should recognise that these seem to be vices only because the error-prone techniques of procedural programming make them so.”
Seymour Papert wrote:
“Machines can't think,” said he, “because stupid humans don't know how to teach them to think. We try and teach them as we think machines should be taught, and it doesn't work.”
Computers have changed enormously since 1968, and we nearlly all have, in our pockets or bags, a mobile phone which is thousands of times more powerful than the computer shown in the above picture. Computers now provide a very wide range of usebuf services, Dispite all the advances, have they really solved the problems posed by complex human systems?
What do you think?
See a transcript of the original 1968 suvey
|Picture from HITXP blog|
My “biological computer” model of the evolution of human intelligence involves information flowing between “decision making nodes” in a infinite recursive network. Every node can receive, process, and transmit messages and may consist of clusters of simpler nodes. The simplest nodes represent individual neurons, with groups of interlinked neurons up to complete brains, individual animals, and extending to social groups of animals or humans exchanging information (including large organizations) and tools made by humans – such as the Internet. “Animal” nodes have a limited lifespan and (if the brain makes good decisions) can pass genetic information to new, initially ignorant, “animal” nodes.
The driving mechanism is based on CODIL, a computer language that mimics human working memory. It suggests how information can be used to make decisions within brains, and how it can be passed between brains so that it is not lost on death. It identifies limits on animal intelligence, predicts failings of the human mind, and identifies key tipping points in the development of tools and language, and brain size changes. Human intelligence results from recycling cultural information through the network over many thousands of generations.
Of course such a short text only scratches the surface, and further information on my research, and supporting details will be appearing on this blog, if it is not already here.
If you can't find what you want to know why not
Sunday, 16 September 2018
|If only this skull |
could still talk
I am always happy to answer questions about how my research relates to a particular research paper and in a discussion on the FutureLearn course "A Question of Time" Paul asked about "On the antiquity of language: the reinterpretation of Neandertal linguistic capacities and its consequences" by Dan Dediu and Stephen C. Levinson. This paper concludes that Neanderthals and Denisovans may have had very similar language capacities to us - they too had complex tool making technologies. Paul asked:
When did the conditions arise to allow more rapid evolution of information (technical, such as for tools but also hunting & food gathering & processing techniques, cultural info etc.) ? After all, we possess much the same genes as our ancestors did 60,000 years ago, but our world is significantly different due to evolution of information.Dediu and Levinson's paper looks in detail at the relevant literature about the discover of human fossils, evidence for tool making , etc. while my approach starts in a very different way by looking at how a network of neurons might evolve into an intelligent brain. The immediately relevant parts of my model are as follows:
Sunday, 9 September 2018
Because of my long term interest in mental health matters I recently decided to do a FutureLearn course on Psychology and Mental Health and the reading material included a paper by Peter Kinderman entitled A Psychological Model of Mental Disorder. This made me think that I should be looking at my evolutionary model of human intelligence to see whether it might be able to model some mental health problems. The model already predicts some weaknesses in how the human mind works in areas such as confirmation bias - so could it suggest possible causes for mental illness.
The evolutionary model is based on the idea that at the neuron level our brain works in exactly the same way as any other mammal but we have developed a tool, which we call language, to bridge over some of the weaknesses due to the way the animal brain has evolved. Some of these inherent weaknesses could underlie some mental health problems because the genetically evolved mechanism is being asked to do a lot of extra work.
The following can be considered draft discussion notes looking at some of the areas which my model suggests there issues which could adversely affect mental health.
Thursday, 6 September 2018
I was exploring the blog Beautiful Minds and came across a post "IQ and Society - The deeply interconnected web of IQ and societal outcomes" by Scott Barry Kaufman. This started with a definition of "Intelligence" which was published in the Wall Street Journal in 1994. I realised that I had started this blog without defining what I meant by intelligence - and I feel that the following definition served the purpose:
Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—“catching on,” “making sense” of things, or “figuring out” what to do.
Now I am not claiming that my attempt to model human intelligence demonstrates all the above properties - but I see the definition as a goal, and my model as being a step in the right direction.
The body of Scott's blog was doubly interesting to me because he was discusses the link between IQ ans social and educational factors while I have just been involved wit a FutureLearn course "Psychology and Mental Health" which explores and extends the "Nature and Nurture" debate. In many ways it looks as if IQ and mental illness are both correlated with one's life experiences and fit in with my approach to intelligence - which suggests that it is mainly due to exchange of culture between humans, refined over many generations. On this basis someone with a high IQ will tend to have absorbed better quality cultural information when younger, while poor mental health will often be due to poor or disrupted cultural interactions and events.
Wednesday, 5 September 2018
The Scientific American has just circulated a very readable article entitled "The Cultural Origins of Language" and highlights four main points. The aim of theis post is to relate each of these points to the evolutionary model I am developing.
There is no disagreement on the points made here. The article points out that "Noam Chomsky, the extraordinarily influential linguist at the Massachusetts Institute of Technology, was, for decades, rather famously disinterested in language evolution, and his attitude had a chilling effect on the field." This is very relevant to my research. In the mid 1960s I started to look at how CODIL might support natural language - and took one look at the work of Chomsky and decided that CODIL and Chomsky's theories were incompatible and abandoned research in that direction. It is interesting that at about the same time I considered whether CODIL had anything to do with neural networks - and was discouraged by the views of Minsky.
. distinctive physiological, neurological or genetic traits that could explain the uniqueness of human language
a platform of abilities, some of which are shared with other animals.
The article says: "The problem is that after looking for a considerable amount of time and with a wide range of methodological approaches, we cannot seem to find anything unique in ourselves—either in the human genome or in the human brain—that explains language." and later "Findings from genetics, cognitive science and brain sciences are now converging in a different place. It looks like language is not a brilliant adaptation. Nor is it encoded in the human genome or the inevitable output of our superior human brains. Instead language grows out of a platform of abilities, some of which are very ancient and shared with other animals and only some of which are more modern."
This is completely compatible with my work in that my model for the evolution of human intelligence start with the key assumption that at the biological level animal and human brains work in exactly the same way. The fact that my model predicts a number of weaknesses in our mental armoury which are compatible with an evolutionary approach suggests that this starting assumption has some validity.
may arise from culture: the repeated transmission of speech through many generations.
The article discusses research on animals and concludes "The list of no-longer-completely-unique human traits includes brain mechanisms, too. We are learning that neural circuits can develop multiple uses. One recent study showed that some neural circuits that underlie language learning may also be used for remembering lists or acquiring complicated skills, such as learning how to drive. Sure enough, the animal versions of the same circuits are used to solve similar problems, such as, in rats, navigating a maze."
While I agree with what the article says about the importance of culture being passed, by, language from generation to generation there is a big difference. The article avoids discussing how the neural circuits of the brain drive language - while my model suggests a mechanism - and furuther information will be published on this blog shortly. However the underlying idea can be demonstrated by the following CODIL statement:
MURDERER = Macbeth; VICTIM = Duncan; WEAPON = Dagger.
At the neuron level each word is the symbolic name of a node *which could be very complex when examined in details), while the punctuation characters indicate the way the nodes are connected. A human reader of such a statement could easily translate it into a variety of different natural language sentences, and the CODIL language is powerful enough go most (if not all of) the way to allowing such transformations to be processed. Thus the model has the ability to represent information in a neural net and the potential to generate natural languages style sentences.