Aaron Sloman

Most philosophers appear to have ignored the distinction between the broad concept of Virtual Machine Functionalism (VMF) described in Sloman&Chrisley (2003) and the better known version of functionalism referred to there as Atomic State Functionalism (ASF), which is often given as an explanation of what Functionalism is, e.g. in Block (1995). One of the main differences is that ASF encourages talk of supervenience of states and properties, whereas VMF requires supervenience of machines that are arbitrarily complex networks of causally interacting (virtual, but real) processes, possibly operating on different time-scales, examples of which include many different procesess usually running concurrently on a modern computer performing various tasks concerned with handling interfaces to physical devices, managing the file system, dealing with security, providing tools, entertainments, and games, and possibly processing research data. Another example of VMF would be the kind of functionalism involved in a large collection of possibly changing socio-economic structures and processes interacting in a complex community, and yet another is illustrated by the kind of virtual machinery involved in the many levels of visual processing of information about spatial structures, processes, and relationships (including percepts of moving shadows, reflections, highlights, optical-flow patterns and changing affordances) as you walk through a crowded car-park on a sunny day: generating a whole zoo of interacting qualia. (Forget solitary red patches, or experiences thereof.) Perhaps VMF should be re-labelled "Virtual MachinERY Functionalism" because the word 'machinery' more readily suggests something complex with interacting parts. VMF is concerned with virtual machines that are made up of interacting concurrently active (but not necessarily synchronised) chunks of virtual machinery which not only interact with one another and with their physical substrates (which may be partly shared, and also frequently modified by garbage collection, metabolism, or whatever) but can also concurrently interact with and refer to various things in the immediate and remote environment (via sensory/motor channels, and possible future technologies also). I.e. virtual machinery can include mechanisms that create and manipulate semantic content, not only syntactic structures or bit patterns as digital virtual machines do. Please note: Click on the title above or the link below to read the paper. I prefer to keep all my papers freely accessible on my web site so that I can correct mistakes and add improvements. http://www.cs.bham.ac.uk/research/projects/cogaff/misc/vm-functionalism.html This is now part of the Meta-Morphogenesis project: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html

Updates Open Letter to my MP: Lynne Jones Why large IT development projects are problematic The mathematics of searching for a design Richard Feynman wrote: Getting requirements right from the start is impossible Are problems unique to IT projects? Physical constraints Implications for Government policy What can be done? Some suggested prerequisites: requirements for openness A precedent for this proposal: The internet How the internet grew Implications for government policy Are some projects exceptions? Concluding Comment NOTE: Related comment.

Jablonka & Lamb (J&L) refer only implicitly to aspects of cognitive competence that preceded both evolution of human language and language learning in children. These aspects are important for evolution and development but need to be understood using the design-stance, which the book adopts only for molecular and genetic processes, not for behavioural and symbolic processes. Design-based analyses reveal more routes from genome to behaviour than J&L seem to have considered. This both points to gaps in our understanding of evolution and epigenetic processes and may lead to possible ways of filling the gaps

For over half a century I have been interested in the role of intuitive spatial reasoning in mathematics. My Oxford DPhil Thesis (1962) was an attempt to defend Kant's philosophy of mathematics, especially his claim that mathematical proofs extend our knowledge (so the knowledge is "synthetic", not "analytic") and that the discoveries are not empirical, or contingent, but are in an important sense "a priori" (which does not imply "innate") and also necessarily true. I had made my views clear in courses on philosophy of science and mathematics when teaching at Sussex University (from 1964) which was why one of our former students, Mary Pardoe (then Mary Ensor) who had become a mathematics teacher informed me, while visiting the university, that she had found a new diagrammatic proof of the triangle sum theorem. I reported her proof in some papers and presentations on methods of representation and reasoning, e.g. here, but neither she nor I has encountered anyone else who knew about the proof.

This document explains, from the viewpoint of a philosopher/scientist atheist, why intelligent design should be taught alongside standard evolutionary theory. I have been very disappointed by things I have read by scientists recommending suppression of this topic, and even in one case arguing that the worst arguments in favour of ID should be collected together and refuted, which is a prescription for scientific dishonesty. An honest attack would present the best arguments, as cogently as possible, before exposing their flaws.

The aim of the thesis is to show that there are some synthetic necessary truths, or that synthetic apriori knowledge is possible. This is really a pretext for an investigation into the general connection between meaning and truth, or between understanding and knowing, which, as pointed out in the preface, is really the first stage in a more general enquiry concerning meaning. After the preliminaries, in which the problem is stated and some methodological remarks made, the investigation proceeds in two stages. First there is a detailed inquiry into the manner in which the meanings or functions of words occurring in a statement help to determine the conditions in which that statement would be true. This prepares the way for the second stage, which is an inquiry concerning the connection between meaning and necessary truth. The first stage occupies Part Two of the thesis, the second stage Part Three. In all this, only a restricted class of statements is discussed, namely those which contain nothing but logical words and descriptive words, such as "Not all round tables are scarlet" and "Every three-sided figure is three-angled".

This position paper presents the beginnings of a general theory of representations starting from the notion that an intelligent agent is essentially a control system with multiple control states, many of which contain information (both factual and non-factual), albeit not necessarily in a propositional form. The paper attempts to give a general characterisation of the notion of the syntax of an information store, in terms of types of variation the relevant mechanisms can cope with. Similarly concepts of semantics pragmatics and inference are generalised to apply to information-bearing sub-states in control systems. A number of common but incorrect notions about representation are criticised (such as that pictures are in some way isomorphic with what they represent).

This is a contribution to construction of a research roadmap for future cognitive systems, including intelligent robots, in the context of the euCognition network, and UKCRC Grand Challenge 5: Architecture of Brain and Mind. A meeting on the euCognition roadmap project was held at Munich Airport on 11th Jan 2007. This document was in part a response to discussions at that meeting. An explanation of why specifying requirements is a hard problem, and why it needs to be done, along with some suggestions for making progress, can be found in this presentation: http://www.cs.bham.ac.uk/research/projects/cosy/papers/#pr0701 "What's a Research Roadmap For? Why do we need one? How can we produce one?" Working on that presentation made me realise that certain deceptively familiar words and phrases frequently used in this context (e.g. "robust". "flexible", "autonomous") appear not to need explanation because everyone understands them, whereas in fact they have obscure semantics that needs to be elucidated. Only then can we understand what the implications are for research targets. In particular, they need explanation and analysis if they are to be used to specify requirements and research goals, especially for publicly funded projects. First draft analyses are presented here. In the long term I would like to expand and clarify those analyses, and to provide many different examples to illustrate the points made. This will probably have to be a collaborative research activity.

How can a virtual machine X be implemented in a physical machine Y? We know the answer as far as compilers, editors, theorem-provers, operating systems are concerned, at least insofar as we know how to produce these implemented virtual machines, and no mysteries are involved. This paper is about extrapolating from that knowledge to the implementation of minds in brains. By linking the philosopher's concept of supervenience to the engineer's concept of implementation, we can illuminate both. In particular, by showing how virtual machines can be implemented in causally complete physical machines, and still have causal powers, we remove some philosophical problems about how mental processes can be real and can have real effects in the world even if the underlying physical implementation has no causal gaps. This requires a theory of ontological levels.

This paper is an attempt to summarise and justify critical comments I have been making over several decades about research on consciousness by philosophers, scientists and engineers. This includes (a) explaining why the concept of "phenomenal consciousness" (P-C), in the sense defined by Ned Block, is semantically flawed and unsuitable as a target for scientific research or machine modelling, whereas something like the concept of "access consciousness" (A-C) with which it is often contrasted refers to phenomena that can be described and explained within a future scientific theory, and (b) explaining why the "hard problem" is a bogus problem, because of its dependence on the P-C concept. It is compared with another bogus problem, "the 'hard' problem of spatial identity" introduced as part of a tutorial on semantically flawed concepts. Different types of semantic flaw and conceptual confusion not normally studied outside analytical philosophy are distinguished. The semantic flaws of the "zombie" argument, closely allied with the P-C concept are also explained. These topics are related both to the evolution of human and animal minds and brains and to requirements for human-like robots. The diversity of the phenomena related to the concept "consciousness" as ordinarily used makes it a polymorphic concept, partly analogous to concepts like "efficient", "sensitive", and "impediment" all of which need extra information to be provided before they can be applied to anything, and then the criteria of applicability differ. As a result there cannot be one explanation of consciousness, one set of neural associates of consciousness, one explanation for the evolution of consciousness, nor one machine model of consciousness. We need many of each. I present a way of making progress based on what McCarthy called "the designer stance", using facts about running virtual machines, without which current computers obviously could not work. I suggest the same is true of biological minds, because biological evolution long ago "discovered" a need for something like virtual machinery for self-monitoring and self-extending information-processing systems, and produced far more sophisticated versions than human engineers have so far achieved.

What is the relation between intelligence and computation? Although the difficulty of defining `intelligence' is widely recognized, many are unaware that it is hard to give a satisfactory definition of `computational' if computation is supposed to provide a non-circular explanation for intelligent abilities. The only well-defined notion of `computation' is what can be generated by a Turing machine or a formally equivalent mechanism. This is not adequate for the key role in explaining the nature of mental processes, because it is too general, as many computations involve nothing mental, nor even processes: they are simply abstract structures. We need to combine the notion of `computation' with that of `machine'. This may still be too restrictive, if some non-computational mechanisms prove to be useful for intelligence. We need a theory-based taxonomy of {\em architectures} and {\em mechanisms} and corresponding process types. Computational machines my turn out to be a sub-class of the machines available for implementing intelligent agents. The more general analysis starts with the notion of a system with independently variable, causally interacting sub-states that have different causal roles, including both `belief-like' and `desire-like' sub-states, and many others. There are many significantly different such architectures. For certain architectures (including simple computers), some sub-states have a semantic interpretation for the system. The relevant concept of semantics is defined partly in terms of a kind of Tarski-like structural correspondence (not to be confused with isomorphism). This always leaves some semantic indeterminacy, which can be reduced by causal loops involving the environment. But the causal links are complex, can share causal pathways, and always leave mental states to some extent semantically indeterminate.

This paper outlines a design-based methodology for the study of mind as a part of the broad discipline of Artificial Intelligence. Within that framework some architectural requirements for human-like minds are discussed, and some preliminary suggestions made regarding mechanisms underlying motivation, emotions, and personality. A brief description is given of the `Nursemaid' or `Minder' scenario being used at the University of Birmingham as a framework for research on these problems. It may be possible later to combine some of these ideas with work on synthetic agents inhabiting virtual reality environments.

When scientists discuss experimental observations, they often, unfortunately, use language that evolved for informal discourse among people engaged in every day social interaction, like this: What does the infant/child/adult/chimp/crow (etc) perceive/understand/learn/intend (etc)? What is he/she/it conscious of? What does he/she/it experience/enjoy/desire? What is he/she/it attending to? Similar comments can be made about the terminology used in many philosophical discussions about minds, cognition, language, and the relationships between evolution and learning.

At the ceremony Ron Chrisley introduced me and my work with some kind words and ended with a reference to the claim on my website that I tend to upset vice chancellors and other superior beings. After Ron, I had to make a short speech. I had prepared a few bullet points to be projected on the screen to remind me of what I wanted to say, but for some reason they never appeared, so I talked from memory. I remembered all the points except one, about computing education. Since that is a very important point, I thought I would retrospectively write out an expanded version of the acceptance speech here, with the omitted point included, below, as point 3, and other parts expanded and corrected, thanks to reminders from Sussex colleagues who have read and commented on this