A closely related (more digestible?) PDF Presentation is available here
Supervenience and Causation in Virtual Machinery
Also available on Slideshare.net (flash format):
This paper is
A messy automatically generated PDF version is
A partial index of discussion notes is in
This is part of the 'Meta-Morphogenesis' project:
One of the key themes in the project is concerned with evolution of construction-kits, which include construction kits for building virtual machinery:
This is one of several papers related to the "Computational Qualia" project
summarised here by Ron Chrisley:
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 include many different processes running concurrently on modern (single-CPU or multi-CPU) computers performing various tasks concerned with handling interfaces to physical devices, managing file systems, handling user-access, dealing with security, providing tools, entertainments, and games, and processing research data.
A less obvious example of virtual machine functionalism is the kind of functionalism involved in a large collection of possibly changing socio-economic structures and processes interacting in a complex community. Yet another example is illustrated by the complex network of mental virtual machines involved in the many levels and types of information about spatial structures, processes, and relationships (including percepts of moving shadows, reflections, highlights, optical-flow patterns and changing affordances) processed in parallel 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.)
Keywords: asynchronous concurrent causation, atomic state functionalism, counterfactual conditionals, definability, information processing, interfaces to environment, operating system, physics, qualia, representation, self monitoring, virtual machine functionalism, virtual machine supervenience,
Until fairly recently (and when I was at school) the word "machine" was defined by physicists in terms of application of forces and transmission or conversion of energy, both of which occur in steam engines, internal combustion machines, windmills, electric motors, and many more. But for decades we have extended the notion to include information processing machines, i.e. computing machines, whose main function is not to transfer forces or energy. However, they can be used to control machines that do convert energy and apply forces, e.g. car engines and machines in factories. So our current notion of virtual machinery is mainly concerned with complex machines that process information, which may be made up of other machines that perform simpler tasks, made of other machines that perform simpler tasks, ... eventually making use of physical components.
"Virtual" does not mean "unreal"! (Added 25 Oct 2014)
The word "virtual" has now become a technical term that is very misleading for people who do not understand it fully, for the virtual machines are all real machines, and they have real causal powers: they do things that we depend on. Sometimes, like badly designed or damaged, physical machines, Virtual machines do the wrong things for unobvious reasons, and their designers and maintainers have to try to find out what's actually going on in them, how it differs from what was intended to be going on in them, and what, if anything can be done to change the components or their behaviours so as to produce the desired effects.
People who have never had the experience of designing and debugging complex virtual machinery may find it hard to believe how much that diagnosis and fixing process (i.e. debugging) has in common with finding out what's going wrong inside a complex mechanical or electrical machine and changing either a part at a time, or a more abstract design feature, in order to produce a new version that does what's required. In both cases, what is learnt in that process can demonstrate the need for a major re-design of the machine (physical or virtual), for example altering its architecture -- the number and variety of major components and how they are connected (i.e. changing the architecture of the VM).
Philosophers who do not understand that are at risk
of wasting effort on arguments purporting to show that events in virtual
machines do not "really" exist, or do not "really" cause other events, or in
some cases attempting to show that they do exist and do have causal powers but
that's because they "really" are not virtual machines, but physical machines (a
type of "identity" thesis, which in philosophy of mind becomes a mind-brain
identity thesis). For more detailed discussion of this point, based on my own
experience of building and maintaining some complex software systems composed
of multiple interacting virtual machines, see this discussion note (partly
overlapping with the current document):
"The Reality of Computation"
The phrase "virtual machine" entered the computing community originally because it referred to a kind of information processing machine that could either be constructed from electronic components or could be constructed from programs running on another computing machine. The latter was then called a virtual machine: it was thought of as a shadowy, but functionally equivalent version of the alternative, physical, machine. For example, when new physical computer designs needed to be tested to ensure that they had the required capabilities it was often cheaper to build a "virtual" instance in a running computer than to build a physical instance. That is still part of the design and testing process of machines of many kinds, e.g. wind-tunnel testing of a proposed new airliner design.
Since at least the early 1970s, and probably earlier, the term "virtual machine" has acquired a different, deeper, significance: it is now applied to more and more complex machines that are not replicas, or stand-ins, for actual or possible physical machines: they are machines created mainly by programs running on other machines, which may also be virtual machines. Most of them could not exist in any other form. That is because while running they create, rearrange, modify, discard and replace many components with a flexibility and speed that would be impossible if all the parts and processes had to be physical parts and processes, and all the connections between parts had to be physical connections. (That's why many virtual machines include a "garbage collector", a mechanism that can work out which portions of the underlying machine's memory are still in use and which can be reused to create new components, because some temporarily constructed components are no longer in use, and the "virtual space" they occupy can be reallocated. It's interesting to ask whether and how human brains do something similar, e.g. reusing the temporary processing space required for understanding each of these paragraphs as you read it.)
Unfortunately computer scientists also sometimes use the term "virtual machine" to refer not to an actual running system, but to a mathematically specified abstraction, defining a class of possible running systems. For example, the Java virtual machine (JVM) specifies a type of computer on which the Java programming language can run, The JVM, in that sense is just an abstract entity which is capable of having instances that do things, though it does nothing. It is possible to prove mathematical theorems about the JVM, just as theorems can be proved about a class of high dimensional geometries that may or may not have instances in the universe. But in the case of a JVM we can also create running instances: and each running instance has many causal powers in virtue of which it changes itself and other things in the same computer, or other things connected by external interfaces or a computer network, such as the internet. I sometimes try to avoid confusion by using RVM (for Running Virtual Machine) to refer to the types of virtual machine that are the topic of this document: the ones that actually do things and interact with other things, including physical things and other RVMS, for instance when sending email to another computer.
Virtual Machine Functionalism (VMF) attempts to account for the nature and causal powers of mental mechanisms and the states and processes they produce, by showing how the powers, states and processes depend on and can be explained by complex running 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.
[*] This semantic content is information in Jane Austen's sense, illustrated with examples from "Pride and Prejudice" here, not Shannon's sense of "information", which is more concerned with mathematical properties of information bearers and information channels.
The importance of this idea has been emphasised before (by several thinkers, especially cyberneticists, including Norbert Wiener, Ludwig von Bertalanffy, Heinz von Foerster, William T. Power, and others). Most of them (perhaps all of them?) assumed an insufficiently wide range of types of control, involving only scalar or vector quantities. They assumed that all information used by or controlled by a system is encoded numerically. For example, the direction of motion of a vehicle or ship can be represented in degrees (the angle from North, or from a target direction), and the controlling influence could be the angle by which a rudder is turned, or the differences in power between engines on left and right, among other possibilities.
So they assumed that all the systems studied could be described in terms of "variables" with a range of possible numerical values (usually a fixed set of variables), and the formal description of the dynamical properties of such systems used various sorts of equations linking those variables, e.g. differential equations where variations are continuous.
However, developments in Logic, Linguistics, Software Engineering and AI have shown the need for far more varieties of control states, control mechanisms, information structures, self monitoring and types of self modification, though I suspect that even when all the different current ideas are put together they still do not account for the richness and variety of biological systems. How can we do that?
Just as we have a sort of chemistry of physical composition, we also need a "chemistry" of composition of information-using control systems, that can be assembled into more and more complex "molecules" that are just as real as chemical molecules, but have very different properties, including kinds of intentionality (as John McCarthy pointed out long ago, using the possibility of "fooling" a thermostat by holding a candle under it, as an example). 19th Century mathematics is not up to that task. The development of programming languages and methodologies during the twentieth century made a huge difference, but it is likely that there are still more varieties of mechanism in organisms that we have neither discovered nor reinvented. I suspect a key to the answer lies in the notion of a "construction kit": the physical world provides a powerful but very low level construction kit (including space, time, energy, chemistry, etc.) and biological evolution produces increasingly complex construction kits of many different kinds, some of which are themselves built out of evolved construction kits rather than directly built from physical components. This is similar to, but more complex than the history of information processing machinery produced by human engineers since the mid 20th Century.
Added Nov 2014
Tentative non-mathematical thoughts on entropy, evolution and construction kits
(What happened to Droguli?)
Added 18 Jan 2015
Construction kits required for biological evolution
(Including evolution of minds and mathematical abilities.)
The scientific explanatory role of construction kits
What sort of construction-kit must the physical universe provide to make it possible for life, mind, ecosystems, cultures, etc. to evolve from a planet formed from a cloud of dust?
TO BE EXTENDED
Note 1 (external implementations):
As P. F. Strawson pointed out in his (1959) some semantic (intentional, referential) relations may be partially implemented in causal connections between things in the environment, including long dead things. And, as every mathematician knows (including Plato), structures, processes and events on paper, blackboards, in sand, and now computing devices outside the thinker, too can also implement some of a thinker's virtual machinery (Sloman 1978 Ch 7 also Ch 6).
Note 2 (Ignorance of some philosophers about computation):
(Slightly revised: 13 Oct 2014)
For many thinkers the notion of a computer is still stuck in the 1930s, when Alan Turing presented the idea of a Turing machine, which was shown to have a kind of universality insofar as other forms of automatic reasoning that had been proposed could all be modelled in a Turing machine (and vice versa in some cases), and a Universal Turing Machine (UTM) could model any other Turing machine.
In that context it is reasonable to think of a computer as having a well defined state at any time, which changes in a controlled way in accordance with a fixed set of rules stored in the machine and alters its internal state (including discrete symbols on a discrete, unbounded tape) in accordance with those rules. The abstract machine implemented in that way is an "Atomic State Virtual Machine", whose mode of operation can be represented by the sort of diagram presented below, which has a collection of states between which it can switch.
However, Since the 1930s the ideas developed and implemented in computer systems engineering have gone far beyond such a simple model of computation, to incorporate: multiple and changing interfaces to the environment, multiple independently changing (unsynchronised) internal subsystems, self-monitoring and self-modification, use of analog components (which change continuously rather than discretely), and construction of networks of such devices on which large abstract virtual machines can run, in parallel with other, possibly related, virtual machines on the same physical system -- though not necessarily in synchrony.
So anyone who discusses computational modelling or functionalist theories of mind in terms of 'The state of the machine', a 'sequence of states', 'the algorithm', and other notions relevant to computation as defined in 1936, is now showing serious ignorance, which is surprisingly common -- surprisingly in view of how many people now make use of examples of a far more general notion of computation in their every day life.
(A possible remedy, proposed at the end of the preface to The Computer Revolution in Philosophy (1978), would be to require up to date computing education -- of the right sort, e.g. not just how to use social apps -- to be a major part of philosophy degrees along with epistemology, logic, philosophy of science, philosophy of mind, philosophy of mathematics, and philosophy of biology. But who would teach the teachers?)
NOTE ON TERMINOLOGY
This document does not use standard terminology for varieties of functionalism discussed, since, as far as I know, the need to distinguish these varieties has not generally been acknowledged, and the importance of the most complex type, Virtual Machine Functionalism has not generally been recognised, though Rickles-IEP comes close, especially in the discussion of causation (Section 4). In particular he writes, at the end of the section:
Likewise the work by Maley and Piccinini mentioned below.
So I have created my own labels for the cases that need to be distinguished. I'll be happy to be informed that the cases have already been described elsewhere and there are alternative labels in use.
This discussion paper is not about virtual machines that are surrogates for physical machines in that sense! Rather the sorts of virtual machine that I discuss include some that could not exist in anything but a virtual machine form (i.e. implemented in some kind of lower level machine) because the machine requires a kind of flexibility of structure that would either be impossible, or too slow or too costly in a physical machine -- for example a planning program that creates, tests and attempts to extend partial plans builds the plans in software. Building them by creating new physical machinery for each plan fragment in order to check its feasibility would be either too costly and slow, or may not even physically possible given the constraints on the system that is to create and use the plans.
One of my conjectures is that natural selection "discovered" the need for, and the power of, virtual machinery long before human engineers did, and long before humans existed. There were probably many intermediate versions of biological virtual machinery of varying complexity and functionality. The development of optical sensors on animals with complex physical architectures moving rapidly through complex physical environments (e.g shrubbery) or engaging in physical battles with other intelligent animals, would have increased the requirements for both complexity of information structures created and used, and the speeds at which structures and relationships change, a set of requirements that could not be met by constantly rewired physical networks for example. Chemical processes can match the required complexity and speed of changing structures, but would not meet the connectivity requirements.
In the most interesting cases, discussed in more detail below, there are VMs whose specification uses concepts that are not definable in the language of physics. So it is not possible to go directly from the specification to a physical design: instead a more complex process is required, of working out how to make something physical that performs the required functions. That sort of engineering design process produces an implementation of the design. In general there will be many possible physical implementations for a machine required for a biological function and there are deep and interesting questions about how we should describe the relationship between such a physical machine and the virtual machine it implements. I'll return to that later.
For the rest of this paper, I shall consider only virtual machines for which there need not be any non-virtual alternative that is practical. These could be described as essentially virtual machines. Biological evolution seems to have "discovered" the need for essentially virtual machinery long before we did. From now on I'll use "virtual machine" to refer to such indispensable information processing mechanisms. I want to consider especially the sub-class of virtual machines that could not be fully specified in the language of physics, even if all the working instances are implemented in physical machinery.
So, a virtual machine (VM), in the sense used here, is a machine that does something by making use of physical mechanisms, but whose states, processes, and functions are not defined using physical concepts. Some aspects of that idea are very old but the idea has been considerably enriched and has developed in new precise forms as a result of problems that had to be solved in designing increasingly complex computing systems. Virtual machines on computers include both limited function VMs, like spelling checkers, web browsers, email systems, anti-virus systems, and platform VMS, which provide new layers of functionality supporting a multitude of new types of VMS, of which the best known examples are operating systems (e.g. Unix/Linux, MacOS, Windows, Solaris, Android and many others). In recent years that distinction has become blurred as more and more systems originally designed as application VMs are extended with platform VM functionality, like web browsers that not only display text and images but support new running programs, designed by different application developers.
The internet is a particularly complex mixture of physical machines and virtual machines made of networks of smaller physical and virtual machines. The World Wide Web, is particularly complex highly distributed, platform virtual machine running on the internet.
I'll explain what I mean by "virtual machine functionalism" (VMF) as a theory about the nature of minds, after introducing simpler, more familiar, variants of functionalism in philosophy of mind, with which it can be contrasted. Almost everything I've read by philosophers about functionalism as a theory of mind has failed to allow for the possibility of VMF, even though the key concepts and design techniques are already familiar to computer systems engineers, having been developed over many decades to support increasingly complex applications of computing technology, and even though most philosophers nowadays make use of a wide variety of interacting virtual machines of different sorts every day on their computers, tablets, mobile phones, etc. I find their apparent lack of curiosity about what's going on in these cases absolutely astounding -- e.g. when they make no mention of what has been learnt by computer systems engineers in the last half century when they write about functionalism, physicalism, dualism, supervenience, and related topics, including the new bizarre fashion for "fictionalism" in philosophy of mind. I am sure Socrates, Plato, Descartes, Hume, Kant, Leibniz and other great philosophers of the past would have been appalled at this lack of curiosity about and studied ignorance of these amazing technological developments.
In current philosophy of mind "functionalism" is a label used in connection with
different theories that attempt to explain how minds, along with mental states,
and mental processes, can exist in a physical universe. There are several
different forms, some of them presented in
A very simple example is to think of hunger as a state in which if the input is information about the possibility of obtaining food by performing one of a set of possible actions, or information from which the machine can infer such a possibility, then in the hunger state that input will trigger an output that causes the machine to consume the food, or, if that's not possible in the current situation, to take an action that brings it closer to consuming the food. For this concept of hunger, it does not matter what the actual mechanisms are inside the machine, as long as they operate in such a way that such a dispositional state exists. More sophisticated variants can allow the current state to include more than one desire or need, each of which can have a tendency to cause particular actions to occur (actions that meet the need), and in that case what actually happens will depend either on the relative strengths of the competing desires/needs, or some rule in the system for taking control when a conflict exists.
I call that "Atomic state functionalism" because each state of the machine is a unit (though it may have components, like a vector of numbers representing coordinates in a space), and the operation of the machine amounts only to successively switching between these states whenever triggered by a new input.
Figure ASVM, below, crudely illustrates Atomic State Functionalism, where each state is labelled by a letter ("a". "b", "c", ... etc.). The diagram represents the functionality of a machine that cycles between reading its input and then switching state and possibly producing some output, in accordance with a fixed set of rules: essentially a Turing Machine, with some input output devices that can copy symbols from the tape to a motor controller or copy symbols from a digitised sensory device to the tape. The diagram deliberately leaves open the mode of implementation of the virtual machine in a physical machine. However, during the last eight or so decades, suitable mechanisms have been designed and built with steadily increasing sophistication (including reducing physical size, cost, power consumption, and increasing the speed and the number of possible states a machine can have. E.g. if a machine has N binary switches in its "memory" then it has 2N possible internal states.
[An Atomic-State Virtual Machine running on a Physical Machine.]
For example, if an organism is very hungry and slightly thirsty and believes there is food in front of it, that may trigger a transition to a state in which the hunger is decreased (by eating) the thirst is increased (e.g. because the food was salty) and there is no longer a belief that food is available. A different input might have led to a transition in which thirst was decreased and hunger left unchanged.
What atomic state functionalism and molecular state functionalism have in common is the notion that the transitions that occur form a single (linear) sequence of simple or compound states and the performance of an action is synchronised with a state transition. So Figure ASVM could be modified to represent a molecular state machine simply by replacing some of the letters labelling states, with groups of letters labelling sub-states, and allowing some letters to occur in several different groups.
There are several further subdivisions between varieties of functionalism that have been
made in the philosophical and cognitive science literature, some of them presented in this
More detailed accounts are given in the Stanford Encyclopaedia of Philosophy:
Additional references are included below.
The focus shifts from changing states of the individual, or properties of the individual, to multiple internal machines (or "sub-machines"), where each machine is a collection of enduring entities and relationships including causal relationships in which processes of various kinds can occur, and which can have causal interactions with other machines with which they are in some way connected, or with which they may overlap (like two people sharing an appointments book, or a note-pad for example). Moreover, the number of such internal machines need not be fixed: some of the actions of a component machine or group of machines may include construction of a new internal machine, that will then run in parallel with the old ones. Other actions could destroy one or more of the internal machines.
There can be many, even changing numbers of, sub-machines constituting such a virtual machine, and many of the sub-machines may themselves be made up of (sub-)sub-machines, some of them switching between discrete states, others changing continuously. Some of the sub-machines may be partly within the individual, partly external, providing sensory or motor interfaces to the environment, or sensory-motor interfaces, such as hands that have to acquire some information by manipulating objects, and eyes that constantly change direction of gaze. One rather abstract depiction of such a multi-component virtual machine is in Fig MultiVM, below.
In atomic state virtual machines, and simpler multi-component virtual machines, sensory input that is available is not recorded internally until that sensory channel is ready. But Molecular State Functionalism allows input channels where information streams in continuously and asynchronously (i.e. not waiting for the next 'read input' instruction) through many channels, triggering responses in internal concurrently active virtual machines to which the sensors concerned are linked. Additional streams of information can fan out to various other machines (which may or may not ignore some of what they receive). In computers such parallel input streams may make use of "interrupt" mechanisms, and "interrupt handling" subsystems, which normally "sleep", i.e. do nothing, but are forced to "wake up" and take decisions when new inputs arrive.
The contents of information streams need not be restricted to scalar values (like currents, voltages, or utility measures) or to bit patterns, but could include, for example, logical expressions, instructions, descriptions, image fragments, diagrams, and in some cases complex structures such as molecules or information encoded in molecules (as in genes).
In addition, some of the sub-machines may create local information stores whose contents are not immediately transmitted anywhere else, but can be accessed by other sub-systems as required. The information stores may be of many different kinds varying according to whether they include factual information or control information, general specifications or instance information, detected or inferred regularities or records of contents of particular space-time regions (sometimes called "episodic memory"), explanatory theories, predictions awaiting testing, "compiled" or learned procedures for rapid performances, grammars for internal languages, and many more.
A system composed of multiple concurrently active, interacting virtual machines with multiple external interfaces, also concurrently active, is depicted crudely in Fig MultiVM. A different view is provided in Fig RobotVM, below, emphasising that perceptual and action subsystems can include information-processing sub-systems and not just physical devices.
In the system depicted by the above figure (MultiVM), the set of virtual (sub-)machines need not be fixed. The red polygons are intended to represent states of sub-machines in which they can spawn new virtual machines (also setting up communication between them and between the new machines and old machines, or sensory motor mechanisms). A system in which existing machines can spawn new machines as required can vary its complexity as required to deal with new problems, or new sub-problems of current problems.
In computers this is now commonplace, insofar as running procedures can invoke other procedures by creating new activation records for them, and that process can recurse. So the number of currently active procedures is unbounded in principle, though physical resource limits (or the size of the universe) can impose a limit in practice. (Note that computers, including domestic desk-top PCs increasingly make use of multi-core CPUs in addition to parallel external interfaces and device controllers. Since the component CPUs can be turned on or off as needed, this extends the possibilities for changing the number of running processes as needed).
It is also possible in principle for some of the enduring or temporary VMs to have continuous state changes e.g. changing measures of compression, temperature, velocity, etc., instead of only discrete steps as in a Turing machine or the CPU of a typical mono-processor computer.
As James Gibson and others have pointed out, many of the sensory devices are not passive receptors but active explorers, e.g. hands exploring surfaces of objects, eyes using saccades and other movements, to select from the optic array. This is crudely depicted in "Fig RobotVM", where all the components indicated will have their own internal state-transitions, and possibly internal architectures composed of yet more VMs.
(A Multi-component Virtual Machine
Linked to specific sensorimotor morphology.)
Different machines can use the information acquired in different ways, e.g. triggering a motor response (in the case of reflexes) but also triggering goals to be achieved, questions to be answered, processes of analysis and interpretation to be initiated, modifications of ongoing processes, 'waking up' dormant machines, decomposing and analysing new information (e.g. parsing), checking new information against previously constructed hypotheses, or questions, or goals, and many more.
The scientific/philosophical task of discovering the variety of types of concurrent interacting virtual machinery can be informed by introspection, reading good novels, doing experiments, studying psychology, studying brain mechanisms, trying to build working models to explain observed animal behaviours (e.g. "Betty" the hook-making New Caledonian crow who made headlines in 2002) and other things. [REF Elske van der Vaart, 2013]
But trying to unravel such a complex multiply linked, highly, but not entirely, integrated, mixture of different sorts of machinery is usually intractable, as shown in more detail in a separate discussion of what can and cannot be learnt from "black-box" tests. In particular, for a system that has potentially infinitely many possible histories, as Turing machines potentially do, understanding what it is doing internally may be impossible, without having privileged access to some features of its internal operations.
In such cases, progress may depend on combining approaches of different disciplines, and extending evidence-based information with powerful, testable conjectures about mechanisms.
Moreover, we can try to understand what processes of evolution (supplemented by learning, development, and social processes) could have led to a current design, by going back to earlier stages and finding out what transitions occurred: what new mechanisms were added and what problems they solved, what benefits they produced, what side effects (possibly dysfunctional) they had etc. I.e. the design history of current machines can help us understand what's in current machines and why. (The failure to think that way often leads people to ask the wrong questions: e.g. they ask why changes are not noticed in the 'change blindness' experiments instead of asking about how changes can be detected, or asking questions about tool-use or 'theory of mind' in young children or other animals, instead of asking about matter-manipulation and meta-semantic competences.)
I have some online discussions of difficulties in using current computing ideas to implement some types of biological virtual machinery, e.g. mechanisms used in geometrical reasoning and some motivational mechanisms. [REFS]
I don't claim the difficulties are insurmountable, though it is worth noting that biological information-processing makes heavy use of chemical computations that are very different from manipulations of bit patterns.
However, we have learnt a huge amount in the last half century by encountering problems that required invention/creation of new forms of virtual machinery, and that's still going on. That includes creation of VMs whose functions and internal operations are not definable in the language of physics, even though the machines are implementable in physical machines.
An example is a chess VM in which it is possible for a threat to be detected and an attempt made to find a defence against that threat by searching the space of possible subsequent moves (the game tree). The notions of "detecting", "threat", "defence", "move", and "searching" as understood by the VM designer, are not definable in terms of notions of physical particles, mass, velocity, electric charges, currents, voltages, etc., or even definable in terms of operations of bit patterns in a computer, since designers can come up with new working implementations of the old strategies, implying that they are not using an implementation specification, to define the concepts used. If they tried to produce such a definitional specification using known physical mechanisms (e.g. by constructing a disjunction of all possible descriptions of currently known types of physical implementation, using only the language of physics) that could not include future Chess VMs that use new kinds of computer whose physical states are very different from those of previously used computers. Moreover, because of the fine-grained multiple realisability of VMs, any adequate disjunction would probably be too large to be expressible in this universe, in any physically implemented language. So, since we do not know how physics will advance, or how new technology based on current physics will produce new future implementations, the concepts we use now cannot be equivalent to any disjunction of physical descriptions of implementations. More discussion of the indefinability claim is needed. Compare Block's "Functional Reduction" paper.
A familiar example is the concept of an English sentence, such as "The cat sat on the ancient mat" written in ink or paint or a collection of thumb-tacks on a white painted wall. It is not possible to specify in general using only the language of physics what constitutes an instance of that sentence. That would require use of concepts like "word", "noun", "verb", "subject", "indirect object", "tense", which (I claim, though I'll not argue here) are not definable in the language of physics.
Nevertheless every written or printed instance of that sentence will have a physical description which could be used by a machine to produce a copy, even if the machine has no understanding of what words, sentences, cats or mats are. That sentence is a static structure. In a virtual machine there are not only static structures but also operations on those structures, including constructing them, modifying them, interpreting them, correcting mistakes in them, and those VM processes are not describable in the language of physics, even if their physical instantiations, e.g. changing patterns on a screen, are.
Nevertheless the concepts can be related to concepts of physics, or concepts of digital electronics, or concepts of computation in a particular programming language, since system designers can implement special cases of English sentences and processes that produce, modify or interpret those sentences, The process of implementation is often very difficult to automate and sometimes new things that are learnt in the process of generating and fixing bugs can transform the concepts, e.g. by subdividing cases, or revealing new abstractions. People with no experience of software design, testing, debugging, and development may find this hard to understand.)
(Note: Some of Dennett's ideas in his "Real Patterns" are discussed below.)
(Note: I need to compare this with Block's argument that Functionalism and Physicalism are incompatible, in his "Functional Reduction".)
One of the problems about this debate is that it is not clear what counts as physics, since physics has been extended in very surprising ways (e.g. providing mechanisms for trans-continental conversations that would probably have been unimaginable to most of Newton's contemporaries.) That's why I've given physics different possible lower levels in the following figure illustrating some of the variety of virtual machinery, natural and human-made.
How many levels are there in physics?
Is there a "bottom level"?
This argument that we can build physically implemented VMs whose description requires use of concepts that are not definable in the language of physics, really requires a much more elaborate, extended discussion. Descriptions of chess playing are a rather simple example. There are many more examples related to the design not only of games but also many kinds of software functionality that people now take for granted, e.g. word processors, spelling checkers, theorem provers, email systems, tutorial systems, internet mechanisms, protection against malware, other security and privacy functions, provision of banking services, remote buying and selling, social networks, and many more. [Papers on this are on my web site. Use google. But there's still work to be done.]
The task of clarification also includes showing how causation can go upwards, downwards and sideways in virtual machinery, and how some VMs can have self-monitoring (introspective) capabilities -- a type of competence that engineers have put into some of their products, but evolution seems to have provided for organisms very much earlier, and in more complex forms that are still not understood.
Unfortunately, all that is not yet a standard part of philosophy curricula, so I keep meeting philosophers who haven't a clue what I am talking about, or assume it must be the kind of thing they have already studied (e.g. layering of turing machines) and so jump to inappropriate conclusions. Unfortunately when typing on their word-processors or using email, or web browsers, or chess programs, they don't ask 'How is this possible?' Or if they do, they assume wrong, drastically over-simplified answers.
One of the features of virtual-machine functionalism is that it allows some VMs processing 'low level' sensory input to incrementally and collaboratively construct enduring internal changing interpretations of the information, including references to states and processes outside the whole system. Other VMs can interrogate, challenge, use, or modify some of those interpretations. Many of the phenomena that lead to theories about qualia, sense-data, phenomenal consciousness, can be seen as pointers to such mechanisms.
The parallel flow of information structures, some of which is control information, specifying what something should do next, can produce some routes and stores that are transient and constantly being over-written, along with others that are preserved for various lengths of time, some available for use in social interaction or interaction with the individual's 'future self' i.e. being available later as 'memories' or 'unfulfilled goals', or unanswered questions or untested conjectures, etc.
Insofar as the entities described by such a private language are contents of the internally detected and recorded virtual machine states, e.g. results of grouping of features produced by self-organising networks (e.g. Kohonen nets) they would appear to be concepts of types of qualia, or sense-data, or phenomenal experience. If that's right, we have found a way of justifying much philosophical talk that is often taken to be woolly, anti-scientific metaphysics. Instead it turns out to be part of biology, suitably extended to deal with recent products of evolution.
The case of internal classifications of intermediate states of perceptual virtual machinery developed by some kind of self-organising classifier mechanism is different from another case, namely where the internal perceptual states have a very complex structure that varies systematically with changing relationships between the perceiver and a complex environment. For example if you walk through a full car park on a sunny day, whether you notice or not, your visual experiences will change in very complex ways that are systematically related to your changing relationships to a multitude of surfaces of parts of cars, parts of the ground on which they are parked, the direction of ambient sunlight, the shadows cast by static and moving occupants of the car park, etc. In particular, there will be surfaces that become more or less occluded as you walk, there will be multiple reflections and highlights visible in curved surfaces on car-bodies, all changing in systematic but complex ways.
The details of the changing optic array are much more complex than I have described, and most of the details are not noticed by most people. But it seems that human visual mechanisms make good use of them to acquire rich and fairly precise information about the occupants of the car park (i.e. the many parts and visible surface fragments of cars, lamp posts, fences, side-walks, etc.) Some of the information will be used consciously, e.g. in taking care that your trolley does not scratch a car, while much of it will be used unconsciously to compute various properties, including location, orientation, and curvature of surfaces, the materials of which they are made, and your relationships to them. Some of the patterns of optic flow may be used unconsciously in posture control, as shown by David Lee's moving wall experiment several decades ago.
It also works on a toddler.
D.N. Lee and J.R. Lishman, 1975. Visual proprioceptive control of stance, Journal of Human Movement Studies, 1, pp. 87--95,
A lengthy (and growing) discussion
of the functions of biological vision, including the mathematical functions that
led to the development of Euclidean geometry can be found in
An artist trying to depict a scene faithfully, by replicating much of its appearance (not its actual structure, which cannot be replicated on 2-D paper), has to learn to attend to features of his/her qualia that most people ignore, which correspond to intermediate information structures organised in a 2-D fashion in registration with the retinal information, and whose retinal location can change as the direction of gaze changes. Those internal information structures in the visual virtual machinery really exist and have different kinds of causal roles depending on what else is going on. Nadia, the autistic child described by Lorna Selfe in her 1977 book, was exceptionally good at attending to and depicting on paper some of the contents of her visual experience. (Discussed further here.)
Not all visual qualia need correspond to external reflective surfaces. If you stare at a coloured patch for a while and the patch is removed you may experience an after-image of a different colour. Does the after-image exist? Yes: intermediate visual information structures can be produced in many different ways, and the normal production by an external stimulus is not the only one (as is shown clearly by dreams, hallucinations and after-images). Moreover, you can make your after-image move by tapping your eyeball. Is there something that moves? Yes. Does it move in physical space? No: it moves in a 2-D information space in which it changes its relationships. Some of the contents of that space are produced by external surfaces, e.g. qualia corresponding to a perceived car bonnet. Those qualia will also move when you tap your eyeball, even though the actual car bonnet does not. In some cases left-eye qualia and right-eye qualia that are normally stereoscopically fused come apart during such tapping and there is only motion of the qualia derived from the tapped eye.
http://www.youtube.com/watch?v=AaCedh4Dfs4though I agree with him on many other topics.
"A Phenomenal Confusion About Access and Consciousness",
Dennett on VMF (4 Jul 2014)
In Dennett (1993) "Virtual Machine Functionalism" is suggested as a label, but the concept is not explained in any detail. What Dennett called "the intentional stance" in his 1987 book of that name (a) seems to imply a non-realist view of mental states and processes even though referring to them is not only useful, but even indispensable in our everyday interactions and (b) presupposes that the entities to which states of mind are attributed are rational.
Neither (a) nor (b) is consistent with VMF. Virtual machinery includes states and processes that exist, interact causally (often very obscurely, as any software engineer trying to debug a complex virtual machine she herself has designed knows) and need not be part of a rational agent. The battles between running viral software and running anti-viral software on a computer or a computer network can be real and have effects (including slowing down other processes, or corrupting user information, or transmitting private information to remote machines), without either being simply a physical mechanism, and without either being rational or irrational, since they don't have goals, intentions, etc., even if their designers do. (Similar criticisms can be made of Allen Newell's 1982 paper "The Knowledge Level").
Added 23 Oct 2014: Dennett on Real Patterns
Various things in Dennett's Real Patterns, 1991), come close to the position defended here, but he keeps back-tracking from the only position that is consistent with the kinds of reality that engineers designing, debugging, extending, and checking the powers of running virtual machines have to deal with, e.g. when he writes at the end of the paper:
If the "trumping" Dennett had in mind was predicting details of observable behaviour of a complex system, two comments are in order.
First, that's only one use of a theory of how something works: other uses of such a theory include debugging, extending, re-implementing using different technology, making more resilient by adding new control mechanisms, proving properties, explaining how to use a system, and all the other things that go on in work on computer systems engineering. Predicting is just one among several uses of theories in science and engineering.
Second, if, as is the case in many biological control systems and many artificial systems, the physical levels of control use non-linear feedback loops, then chaos (in the mathematical sense) is very likely: i.e. the behaviour can vary widely over time, in ways that cannot be predicted on the basis of measurements available to scientists whose measuring instruments do not have infinite precision. Even if the behavioural laws are known with perfect precision (e.g. expressed in differential equations), the details of the predicted behaviours can be very sensitive both to errors or noise in initial observations, and will also differ according to the mathematical precision used in the computations (e.g. representing values using 32 bits, 64 bits, 128 bits, etc.). [Add Computer Science Ref]. The "three body problem" is a famous example, as I understand it.
Despite those predictive limitations based on physical specifications, systems can be designed that implement complex control strategies that keep the dynamics within useful bounds, or keep the vast majority of instances useful, in the vast majority of circumstances. Without that we could not rely on computers for so many tasks. It seems that biological evolution also produced a huge variety of examples. Understanding how they work is an important part of science.
Understanding some of the relatively high level design principles can both give us a far better grip on the workings of some naturally occurring control systems, and also support more principled abilities to construct new, varied, types of useful control systems, than trying to describe and design using only the language of physics (or the physical sciences).
Any claim that despite this the physical level of description is superior or the only true description seems to involve a recommendation to abandon multi-level science as part of the search for understanding how our universe works, i.e. as part of the search for truth.
There are good reasons why, from its earliest times, science has been intimately
connected with engineering, including providing new foundations for novel
engineering solutions, designing new ways of probing nature, and providing new
ways of understanding naturally occurring phenomena. A well known case is the
enormous variety of biological control mechanisms using feedback (the simplest
cases being homeostatic control mechanisms). See this BBC Biology web page
The daily lives of system designers are full of examples like this. Any suggestion that what they are talking about does not really exist, or has a lesser form of existence than the physical components of the computers they use is as wrong headed as the claim that when chemists talk about molecules and autocatalytic networks they are really referring to processes involving sub-atomic mechanisms (particles, waves, or whatever). I suspect Dennett knows all this now, though he may not have known it when he first developed his ideas about the intentional stance and the design stance. Like John McCarthy I prefer to refer to the "designer stance" when talking not just about explaining and predicting systems, but also creating them, debugging them, extending them, etc. This could be described as a switch from the stance of a scientist (or philosopher) to the stance of an engineer, though the stances have always interacted fruitfully.
A really good teacher can also produce spectacular changes in the abilities of some pupils, mainly by helping them to change their virtual machinery. In such a context, there is no more doubt about the reality of the operations in the virtual machine than there is about the reality of the behaviour of electrons in transistors.
Can we use these ideas in a science of mind? (Added: 26 Oct 2014)
I have tried to show how claims about reality, or truth, as opposed to convenient fictions, can be supported in theories about virtual machinery on the basis of what we have learnt over several decades about processes of design, implementation, testing, debugging, use of debugging tools, modifications of virtual machinery, discovery and modification of causal influences -- all essentially involving investigations at the level of virtual machinery.
Can this be transferred to a science of mind, given that we cannot perform the same range of exploratory investigations and modifications of virtual machinery in humans or animals. Are we not restricted in those cases merely to studies of input-output correlations, and investigations of the construction and workings of the underlying physical machinery: brain mechanisms, investigated or tinkered with using sophisticated scanning mechanisms, surgery, or in some cases drugs?
Doesn't that mean that we don't have the same kind of access to virtual machinery in minds as we have to virtual machinery in man-made machines, so that there's no evidence to support the use of comparable arguments for realism in VM theories of how minds work?
A full answer will require another paper, or even a large multi-disciplinary tome, recording the many ways in which humans and other animals have developed and used mechanisms for manipulating mental contents and mechanisms -- some of the mechanisms based on use of explicit theories about minds and how they work (good theories and bad theories, but all usually partial theories), some implicit in cultural and educational practices that have evolved over long time periods without anyone noticing, and some based on social, educational, and therapeutic practices developed in attempts to improve or merely change either minds of individuals (therapy, counselling) or minds of groups (e.g. in educational practices, various kinds of propaganda, advertising, etc.).
The main point is that the testing and deployment of theories about what's going
on in the virtual machinery in animal minds, including human minds, will be
seriously restricted both by the intricacy, complexity, vulnerability, and lack
of "debugging and reprogramming interfaces" in products of biological evolution.
So the testing of theories will always have to be more indirect and more
conjectural. However we may be able to gain some important insights into how the
virtual machines and their effects have changed over time if we can identify a
much richer variety of intermediate types of information processing system in
biological evolution since the very earliest stages in the development of life.
Discovering more intermediate stages may provide clues, and indirect evidence,
regarding unobvious intermediate layers that are currently in use but cannot be
directly observed, in humans and other intelligent animals. That is the top
level goal of the Meta-Morphogenesis project, a very difficult,
multi-disciplinary, multi-strand, long term project, potentially related to but
larger in scope than the Human Genome Project. I have provided a high
level overview and many illustrative examples, here:
Collaborators are very welcome.
Note on Ned Block (Apr 2013)
I don't yet know how closely what I've written corresponds to the views of Ned Block on "Phenomenal consciousness", which Dennett finds so puzzling. What I also find puzzling is Block's claim (e.g. in his 1995 paper) that contents of phenomenal consciousness (as opposed to the contents of access consciousness) are allegedly not suited to having cognitive functions. The only sense I can make of this is that for many cognitive functions, other than merely being attended to, they normally need further processing, e.g. to produce perceptual information about visible surfaces in the environment. If that's what Block means, I have no quarrel with him, since it would be an example of the general phenomenon in software engineering that information in its original form cannot be processed in a certain way, though information derived from it via intermediate mechanisms can be. For example, it is impossible to find English syntactic structure in an auditory information stream, though, after several layers of processing to "extract" a sequence of words, the parsing process can be applied.
Sometimes there has to be two-way cooperation between high level and low level processing. (A similar point is made regarding multi-level processing of visual information in the Popeye program mentioned above.) I used to think Block was saying something much more obscure, and probably wrong. My current best guess is that when he made that claim he was making an over-simplifying assumption about the information processing architecture of an animal mind (or robot mind) as many philosophers have done, sometimes following AI researchers and cognitive modellers whose designs don't cope with some of the functionality of human minds and possibly other animal minds. (Such over-simplifications of what needs to be explained often result in proposed architectures that over-emphasise sensory-motor loops and theories of "embodied", or "enactive" cognition that have become fashionable in the last couple of decades, partly because they ignore the biological complexities that led to the CogAff architecture schema and the H-CogAff architecture proposal based on a version of that schema.
In his "Functional Reduction" paper Block discusses varieties of functionalism and their compatibility with varieties of physicalism, in a very interesting way. However I think a consideration of examples of virtual machine functionalism where the virtual machinery includes several concurrently (but not necessarily synchronously) active sub-systems with their own causal relationships, both within the VM and also across its boundaries (e.g. to internal physical memory, to physical interfaces and even to things referred to in the environment) would show that there is no version of physicalism as normally defined that survives, even though all the virtual machinery is ultimately implemented in physical processes. Part of the reason for this is that patterns that can exist in physical structures and processes need not all be definable in the language of physics. For example, what makes something an expression of the English sentence "Today is Fred's birthday" depends not only on how current users of English read text but also who Fred is, which calendar is in use and other complex social facts: whose complete specification would not be expressible in the language of the physical sciences. (A claim that some will find obvious, others not. I'll try later to produce a detailed defence, summarising points made in other papers, including explaining why a description of the facts that make the state of a virtual machine a case of someone wondering what Fred thinks of him could not be translated into a physical description, but I shall not argue that here. (It is partly related to the point about causal indexicality above.)
a well designed robot, with a visual system whose functionality is similar to ours, in a cognitive architecture similar to ours, will, at any time, include collections of intermediate structures that have many of the properties often derided in the notion of a 'Cartesian Theatre'. Maybe it's time to stop deriding it and finding out how to make one that works. (Murray Shanahan has made an interesting attempt in his 2010 book 'Embodiment and the inner life', but I don't think he has considered enough of the required functionality.) I have tried to present some aspects of human mathematical consciousness that most researchers ignore, here.)
It should be clear that my position on virtual machinery is different from Dan
Dennett's, discussed briefly
even though we
share a great deal. My position is based on deep experience of designing,
de-bugging, maintaining, and extending software systems used by others, in
The position is also based on experience using (and sometimes finding bugs in) even more complex software produced by companies, research projects, etc.
See also these notes on the reality of computation. (Oct 2014)
The experiment on unconscious seeing, available here, was designed in part to probe some of those mechanisms.
The information processed by sophisticated machinery is not just patterns in physical or virtual machines (like the bit-patterns used in computers). The patterns may be conveyors or encoders of information, but are not the same things as the information they express or encode (although in special cases one information conveyor can refer to another, or even to itself).
I think there is a deep and complex notion of being able to acquire, manipulate, derive, evaluate, analyse, communicate, combine, and use information, but much of its complexity cannot simply be tamed by introspecting what we think we mean by "information" as many people attempt to do. Some of the unobvious complexities are summarised in Sloman 1993. Moreover the concept of information being used here is not Shannon's (syntactic) concept but the much older notion of (semantic) information used by Jane Austen long before Shannon.
The project of tracing the developments of information processing in biological evolution, including identifying intermediate stages that nobody has thought of looking for, will give us far deeper understanding of the problems in the long run.
It seems clear that the very earliest use of information was for control, e.g. a microbe with mechanisms (using chemotaxis) for deciding things roughly like "should this stuff be let in or not?", a decision that may initially be binary but could later be refined e.g. "how much of this stuff should be let in" or "under what conditions is it useful to let this stuff in?", or later "would this stuff be better to ingest than that stuff in my current state?" etc. This doesn't imply that the machines construct English sentences or sentences in any language that could be translated into English. Rather they have mechanisms that perform functions that we can approximately describe in sentences, though in some cases we may need new terminology -- e.g. the servo-control functions of some brain mechanisms.
As evolution progressed and organisms, and their environments, became more and more complex and varied, some of them acquired information processing capabilities that became more complex and varied, including abilities to learn about, detect, and make use of what J.J.Gibson called "affordances" (positive and negative, e.g. opportunities and obstacles) in the environment. We are still in the process of developing good theories about what organisms and machines can do with information, what forms the information can take, how it can be represented or encoded, where and how it can be obtained, how it can be manipulated, what other information can be derived from it, how it can be used, etc. etc.
Human intentionality sits near the peak of a mountain of biological resources for dealing with a mountain of biological needs in many mountains of different situations.
We'll understand those resources best when we know how to replicate their functionality, but there's still a very long way to go. However it's already clear that we know how to make relatively simple machines that have types of intentionality that many non-human animals do not (yet) have. For example, given the current state of software technology, we need have no hesitation in describing a chess virtual machine running on my computer by saying things like:
It has detected the new threat created by my last move and is now looking for a suitable defence against the threat. In doing that it thinks about various possible moves open to it and their consequences. It has noticed that there are three different moves open to it that postpone the threat, insofar as I'll have to make one or more moves to reinstate the threat. But it notices that one of its moves would produce a situation in which it looks at first as if my threat has been completely blocked and the only way to see that it isn't blocked is to notice the opportunity I have of creating a diversion on the left flank that will require the computer to reallocate resources that will allow me then to return to my original threat and force a mate. So it chooses that last way of blocking the threat.This is not a way of speaking in metaphors. This sort of thing may be an accurate description of what is going on inside a sophisticated game playing program.
I am not an expert on computer chess, but I know enough about it to believe that there are chess playing programs for which that kind of description could be true at a certain point in a game, and in principle the sorts of software tools I've developed with colleagues and students would make it possible to design machines with such capabilities, as would many other tools.
However, that would not justify me in replacing the last sentence with:
So it chooses that last way of blocking the threat in the hope that its opponent will not notice the possibility of reinstating the threat.That change would require the intentional competences of the chess virtual machine to be extended so that in addition to being able to represent and reason about actual and possible moves in the chess game, and their consequences, it can also represent and reason about states of mind of its opponent (e.g. it will need to be able to know or work out that some thinking tasks will be more difficult for the opponent than others, and are therefore less likely to be successfully performed by most possible opponents).
Giving the machine that meta-intentional kind of intentionality will require its information processing architecture to be extended to include something like a model or representation of the opponent as a thinking machine.
There are simple versions of such things in existing AI systems, and no doubt there will be more sophisticated versions later on.
One of the assumptions of the Meta-Morphogenesis project is that long before philosophers got interested in these matters, and long before AI researchers started trying to give machines these capabilities, biological evolution 'discovered' the advantages of organisms being able not only to perceive and think about certain subsets of what actually exists, but also to perceive and think about some of the possible ways in which things (e.g. spatial configurations) can change -- and also acquired the ability to evaluate the possibilities in order to select a subset as worth attempting to realise. (This is a long-winded summary of Gibson's claim that animals can perceive and make use of affordances, but generalises Gibson's concept of affordance as explained in http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#gibson.)
There are lots of examples of animal behaviour that are impossible to make sense of without assuming that they have such information-processing capabilities, even if they lack the meta-competences required to detect that they have the capabilities and are using them. Something similar can be said about very young children, before they are capable of going through anything remotely like philosophical reflection on their thought processes, but can think about which box to open to retrieve a toy that is out of sight.
I don't expect many philosophers to agree with me. But if and when we have made more progress in designing such machines most of the philosophers who interact with them will feel compelled to think of them as perceiving, thinking, intending, preferring, etc. I.e. they will treat them as having intentionality. Moreover, they will be justified in doing so because of how the machines work.
That will not be as weak as what Dennett suggests we do to other human beings, namely treat them 'as if' they have intentionality -- as a useful way of making predictions (i.e. adopting the intentional stance). It's no more a stance than believing unsupported apples near the surface of the earth fall is adopting a gravitational stance.
The strategy I am proposing, namely doing philosophical analysis by designing, building testing and explaining working systems, will show that intentionality is not just one thing: there are different levels of complexity/sophistication that require different sorts of underpinning information-processing machinery. This will be one of the important results of the Meta-Morphogenesis project.
The same can be said about being conscious of something. Both consciousness and intentionality, like efficiency, reliability, accuracy, ease of use, are polymorphic concepts: they don't refer directly to properties of things, but to higher-order relationships between properties and relations. The higher-order relationships may be the same even if what they relate varies: For example, the efficiency of a lawnmower in serving its purpose is very different from the efficiency of a proof in serving its purpose. But the relation of economy of means is similar.
States and processes involving intentionality, like the states and processes in the chess machine, often require the existence of virtual machinery, because physical machinery is incapable of performing the right functions, even though the virtual machinery is implemented in physical machinery. Typically the implementations require constantly changing mappings between virtual and physical processes.
"Having tested all versions of functionalism, we recommend that you get yourself basic functionalism plus mechanisms plus neural representations and computations plus naturalistic semantics based on information and control plus properties that are powerful qualities. You'll have a complete account of the mind. Mental states are representational functional states within the appropriate kind of computational mechanistic system. Some mental states even have a qualitative feel." (Quoted with permission.)Their ideas were apparently developed quite independently of the work reported here. One difference is that I stress the fact that accurate descriptions of behaviours and functions of parts and components of virtual machinery may require concepts that are not definable in the language of physics, as explained above, using the example of describing interactions within a Chess virtual machine.
Other points stressed here, apparently not covered in that paper are
Maley and Piccinini appear to share the explanation of the existence of qualia presented here, namely that some virtual machines have components that are capable of inspecting/monitoring some of the intermediate information structures in layered perceptual systems. Since the existence of the intermediate structures does not depend on their being monitored, the implication is that there can be unnoticed qualia, some unnoticed because some subsystem did not switch attention, some unnoticed because there is no subsystem capable of monitoring them, even if there could be. In humans, some of those self-monitoring subsystems seem to develop a long time after birth. (Of course, philosophers who define 'qualia' in terms of being objects of attention, or something like that, will regard the notion of 'unnoticed qualia' as self-contradictory. I suggest that should be compared with claiming that whales cannot be mammals because allowing mammals to have fins is self-contradictory.) A demonstration of unnoticed qualia/unconscious seeing (which does not work for everyone) is in: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/unconscious-seeing.html
I'll be interested to learn whether the idea of Virtual Machine Functionalism is included by any other philosophers who discuss functionalism, or virtual machinery. They may use the idea but refer to it by some other label than "virtual machine functionalism".
John Pollock came close, in What Am I? Virtual machines and the mind/body problem, Philosophy and Phenomenological Research., 76, 2, pp. 237--309, 2008, But I am not sure he noticed the need for multi-process virtual machinery with multiple concurrently active causal mechanisms as illustrated in the architecture diagrams and the more complex supervenience diagram below.
Dennett is even closer, as discussed above,
I was introduced to the idea by R.M. Hare when I was one of his 'moral tutees' in Balliol College Oxford, around 1959. By then he had started talking about whether ethical truths supervened on non-ethical truths -- though he was a prescriptivist, not an objectivist about ethics.
Later, the idea of supervenience was extended (by Donald Davidson) to the relationship between mental and physical phenomena, and much discussion since then has been concerned with whether such supervenience exists and if so what sort of supervenience. See http://plato.stanford.edu/entries/supervenience/
Questions discussed are whether properties of one kind (e.g. mental properties, such as being hungry) can supervene on properties of another kind (e.g. being in some complex physiological state), or whether one kind of state or process can supervene on another kind of state or process (though processes are rarely discussed in detail). Similar questions had arisen, using different terminology, in the Social Sciences, e.g. questions about whether social or economic facts were reducible to or in some non-reducible way supervened on non-social facts, including psychological facts and individual human behaviours. "Wholism" and "Individualism" were among labels used for alternative answers to such questions.
Our discussion of Virtual Machine Functionalism makes the claim that not just states, properties, and processes, but complex working virtual machines, with their internal causal interactions, can supervene on physical machinery -- e.g. operations in the working chess machine supervene on aspects of the physical machinery in the computer on which it runs. A key feature of the claim being made here is that the running virtual machinery, and some of its parts, can have causal powers that affect not only parts of the virtual machine, but also parts of the underlying machine and sometimes also its physical environment, e.g. when the virtual machine is a chemical plant control system.
This kind of "downward" causation (causation of physical processes by VM processes) may seem to be very mysterious: but learning how it is done in computers can help to remove the mystery, and help us consider what to look for in how minds work. One of the implications is that the same event can be caused in different ways. (This is closely related to Elizabeth Anscombe's example of the same process being describable in multiple ways in her 1957 book Intention.)
Figure Complex (below) is a better depiction of the supervenience of a working multi-component virtual machine on a physical system, showing more of the required structure in the supervenience relation, with lots of bi-directional causation (the causal arrows go sideways as well as up and down). This diagram doesn't correspond to a virtual machine linked to an independently existing environment via sensors and motors.
It's not only causation that reaches out from the contents of a running virtual machine to external entities: semantics can also. For example, software on my computer can use an email address referring to someone in another country. The remote provision of many products and services on the internet would be impossible but for the use of complex forms of reference to things outside the referring machine, including clients, things bought and sold, obligations, legal constraints, and many more. Work on the so-called "Semantic Web" attempts to formalise such capabilities, but it seems to me so far to be trapped in syntactic manipulations because the practitioners have not yet understood the requirements for semantic competences. (A task in which good philosophers could help. But I don't work on the semantic web, and my information, based on attending workshops and informal discussions as an 'outsider', may be out of date, if there has been progress recently.)
Notes: It is possible to distinguish more kinds of supervenience than are normally considered in philosophical discussions, including the following (with somewhat arbitrary labels):
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Various horizontal, vertical and diagonal lines, and various rectangular, triangular and other shapes all supervene on an array of dots with appropriate regularities. A hexagonal pattern can supervene on an array of packed equilateral triangles. If the dots or lines move in certain ways, that can cause larger scale process patterns to supervene on their motion, for example waves or pressure pulses moving up or down the grid or along the grid.
There are very many kinds of mechanism supervenience studied in mechanics, including cases where forces are amplified, speeds or directions of motion are controlled, energy is transferred, etc. These are normally all thought of as cases of physical causation, but the important point is that different sorts of physical causation occur on different scales. The situation is even more complex in chemical causation.
Much of the power and beauty in Newtonian mechanics derived from the use of mathematics (integral and differential calculus, for example) to demonstrate that various global physical processes necessarily supervene on micro-processes with certain structures. We don't yet have that for biology.
NOTE: Interacting/conflicting/causes in VMs:
There are problems about how some of the kinds of causation in virtual machines work that still require investigation. I have begun to discuss some of them in this online slide presentation:
Talk 86: Supervenience and Causation in Virtual Machinery
The Abstract includes:.... Some of the problems are concerned with concurrent interacting subsystems within a virtual machine, including co-operation, conflict, self-monitoring, and self-modulation. The patterns of causation involving interacting information are not well understood. Existing computer models seem to be far too simple to model things like conflicting tastes, principles, hopes, fears, ...
In particular physical opposing forces and other well understood interacting physical mechanisms are very different from these interactions in mental machinery, even though they are fully implemented in physical machinery. This is likely to be "work in progress for some time to come." .... Two notions of real existence are proposed (a) being able to cause or be caused by other things (existence in our world) and (b) being an abstraction that is part of a system of constraints and implications (mathematical existence). Some truths about causal connections between things with the first kind of existence can be closely related to mathematical connections between things of the second kind. (I think that's roughly Immanuel Kant's view of causation, in opposition to Hume.)
Thanks to Hugh Noble for reminding me of the importance of this.
Some of the evolved new functionality may have been implemented in new physical mechanisms dedicated to that functionality, as happens when new sensors interacting with light, or sound, or chemicals, or contact with nearby surfaces evolve, and are accompanied by new neural circuits closely connected with the sensors. In those cases pre-existing physical mechanisms can be copied and used with new functionality arising out of their new physical context. That would be analogous to the development of new physical interfaces to computing systems in the last half century, requiring new interfacing hardware and low level circuitry for processing sensor signals.
But other novel types of functionality may have required new kinds of virtual machinery to be implemented in old forms of hardware, for example, virtual machinery supporting rapidly constructed, rapidly rearranged, and rapidly interpreted complex configurations of information, such as the changing visual information as an animal swims, runs, or flies through complex, richly structured and highly varied physical environments. Human engineers discovered the need for increasingly complex and diverse forms of virtual machinery to meet such requirements during the decades following the 1950s. My claim is that evolution encountered even more complex and varied examples of similar challenges millions of years ago and (in its usual blind way) created powerful solutions to the problems also using virtual machinery -- whose functions we still mostly do not recognize, partly because most neuroscientists, psychologists, biologists and philosophers lack the education required to think in these terms (even if they are highly competent at programming in systems like Matlab, which are suitable primarily for numerical computations, which are not sufficient for modelling mental processes!).
There's an introduction to the CogAff ideas here:
The layers of virtual machinery described there are sub-divided both in terms of differences of function (including differences in semantic content) and differences of evolutionary age. But it is very likely that the Meta-Morphogenesis project will show that the three layers proposed ('Reactive' (oldest, most common), 'Deliberative' (newer and rarer) and 'Meta-Management' (newest and rarest) perhaps better called Meta-semantic?) layers all have internal subdivisions and there may be more intermediate layers to be added. (Compare Minsky's proposed six layers in The Emotion Machine, which I think divide up the same general ideas in a slightly more detailed way.) The differences between perception, central processing and action also need to be more blurred than some of our diagrams suggest. Here's a recent attempt to combine the horizontal layers and the vertical divisions of function in a diagram Figure CogArch, acknowledging that perception and action sometimes overlap (as pointed out by Gibson and others).
(With thanks to Dean Petters.)
Compare the BICA (Biologically Inspired Cognitive Architecture) web site:
For more details on the CogAff architecture schema, and constraints on
architectures for evolved intelligent agents see:
(Partial overview of the CogAff project)
(The mind as a control system)
Eva Hudlicka 2013 has a useful recent survey of designs for architectures for biologically inspired information processing architectures.
Arrows representing perceptual inputs at different levels reflect the fact that there is information of different sorts in the environment requiring different kinds of perceptual processing, illustrated by the different information levels involved in seeing marks on paper, seeing familiar letters, seeing familiar words, reading phrases, reading and understanding sentences, understanding stories or arguments, etc. New perceptual mechanisms are required, building on old ones, in order to access the more abstract kinds of information. Similar remarks can be made about actions with different levels of sophistication e.g. twitching, chasing an insect away, signalling irritation to another person, etc. (Compare Elizabeth Anscombe's 1957 book Intention.)
The examples of perceptual contents that have provoked most discussion of qualia, sense-data, or phenomenal consciousness are probably elaborated versions of intermediate perceptual information buffers that originally began to evolve in organisms that had only reactive architectures. As more sophisticated forms or processing evolved the modes of access to the contents of those buffers diversified and became more complex, both as a result of evolutionary changes and also because of the ways in which various kinds of learning can modify or extend the mechanisms provided by evolution. (In humans the development of some of the mechanisms is 'deliberately' delayed to allow information acquired in some parts of the system to be used to influence the growth of other parts, in ways that lead to some of the phenomena Annette Karmiloff-Smith refers to as "Representational Redescription" in her book "Beyond Modularity" discussed in more detail here.)
Good analytic philosophers with a deep scientific background and some experience of non-trivial software design and testing could make a major contribution.
(Not a complete list. To be added: a discussion of inadequacies in current computational implementations of causal powers of desires, preferences, etc.)
In other species, sometimes called "altricial", the opposite is the case: they hatch or are born helpless and lacking most of the competences they will later need (though the sucking competence in new-born mammals is more sophisticated than it may seem). Jackie Chappell and I have a paper presenting some sketchy ideas about a spectrum of patterns of development including patterns where some genetically influenced competences are delayed until other competences have been acquired so that they can provide some of the material needed for the later competences. Likewise genetically partly pre-specified meta-competences, and meta-meta-competences may begin to develop after the individual's architecture has developed the necessary supporting mechanisms and acquired some of the information required to match the new high level competence to features of the environment that can vary. Human language development is the best known example, but there are others presented in the work of Karmiloff-Smith mentioned above.
We argued that most species are a mixture of precocial and altricial features, and the labels "altricial" and "precocial" should be applied to competences or features, not to species or whole individuals. To avoid confusion we use different words for this, and refer to competences as more or less pre-configured or meta-configured.
Our sketchy theory is (crudely) summarised in the diagram below showing alternative routes from genome to behaviours, with increasing amounts, from left to right, of involvement of learning and development based on results of previous interaction with the environment using previously developed competences. The pre-configured competences are on the left, increasingly meta-configured competences to the right.
Interactions between individual development, genome, and environment
[Based on Chappell and Sloman 2007]
[Chris Miall helped with an earlier version of the diagram.]
[Diagram last updated, adding records on left and feedback ring on right 13 Aug 2015]
Another implication is that the later meta-meta...configured virtual machinery has functions that can depend in complex ways on the functions of virtual machinery developed earlier in that individual, which in turn can depend ultimately not only on the functions of the most directly specified virtual machinery but also on various increasingly abstract features of the environment -- for instance that some kinds of material can be used for building shelters, and that some of those materials are more durable than others.
And finally, in the case of humans and possibly other social animals the functions of virtual machines developed in the young can depend not only on the functions of virtual machinery in other individuals in the community, but also on social and cultural products of developments in many earlier generations. Any hope that there is a way of expressing these functions in terms of the language of physics is a pipe dream.
In part, this is an indication that what we mean by "cause" has not been properly analysed. Jackie Chappell and I have argued that animals and intelligent machines need two concepts of causation, one Humean, based on evidence of correlations and one Kantian based on understanding of structural relations. An example of the former (Humean causation) might be a child's notion that pushing a light switch up or down makes a light go on or off. An example of the latter (Kantian causation) is the fact that moving a vertex of a planar triangle further from the opposite side causes the area to increase. Causation in and between physical and virtual machinery can be of both kinds. However, that is a topic for discussion elsewhere. This does not affect the notions of freedom/free-will presented in 1992.
The ideas presented here imply that the same event can have different causal explanations in different explanatory contexts. In the discussion of how a chess VM is implemented, it may be appropriate to answer the question "What caused the machine to detect the threat?" by referring to some changes at an implementation level, e.g. changes in bit patterns in the computer memory. At a higher level of description the answer to the question might refer to a strategy deployed by the chess virtual machine for thinking about unexpected moves made by its opponent.
The possibility of different correct answers to "What caused that?" is closely related to the possibility of different correct descriptions answering "What's happening there?", as discussed by Anscombe.
Although the statement that X caused Y may not be definitionally equivalent to any statement about what would have happened if X had not happened, or if X had happened but in different circumstances, there are connections between causation and counterfactual truths. This is ignored by attempts to argue that any physical object (or process) can be interpreted as performing any computation, by arbitrarily mapping portions of the object (or process) to portions of a VM performing the computation. Such mappings prove nothing if they don't correspond to causal relationships, with implications about what would have happened if.... I.e. the mappings should hold across possible states of affairs in which things outside the system change, e.g. things perceived. This needs to be taken into account in any arguments for or against functionalism or computational theories of mind. (Ignored by Putnam and Searle? Give references.)
(There is a lot more to be said about mental causation, and its connection with the reality of causation in virtual machinery.)
Other gaps include requirements for replicating aesthetic enjoyment, including both experiencing performances by other musicians, painters, dancers, etc., and enjoying being a producer (as opposed to labelling things "good" or "bad" as a result of some training process). What it is to find something funny is also a challenge (though I have not yet looked closely enough at the Hurley, Dennett and Adams theory).
Why did the robot cross the road? It was trying to understand the joke.There's lots more work to be done, and it requires close collaboration between high calibre philosophers, computational cognitive scientists, ethologists, geneticists, neuroscientists, roboticists, mathematicians, computer scientists and others. But each needs to understand the work of all the others in some detail: a challenge to future researchers, and their teachers!
In it Popper presents a collection of ideas that seem to me to be very closely related to virtual machine functionalism (VM-functionalism) as described below, even though he clearly did not know about the development of virtual machinery in computing systems, and could not have guessed how complex, varied, and powerful man-made virtual machines would become in the next few decades. But he argued, in effect, that the processes of natural selection are capable of creating new, powerful virtual machines that constitute the minds of organisms, including, eventually, human minds.
Later I shall add a discussion note linked here, with quotations from Popper's lecture explaining how his ideas overlap with and differ from the ideas about virtual machine functionalism presented below.
In his lecture (in Section 2), Popper also reports that he has changed his mind about the status of Darwin's theory of natural selection. However, his recantation is misplaced because it depends on showing that there are non-tautological aspects of the theory which make it falsifiable. However, if Popper had read Chapter 2 of Sloman (1978), he might have been convinced that his demarcation principle was incorrect: many great scientific advances have not started life as laws or generalisations that are falsifiable, but as claims about certain phenomena being possible, along with draft explanations of what makes them possible.
Those explanations need not provide us with the ability to predict when the possibilities will be realised. The book argues that good AI theories can be of that form, along with theories in linguistics and perhaps also chemistry. Then insofar as Darwin's theory explains how it is possible for new life forms, with new physical properties and behaviours to come into existence, it is a scientific theory, even if it is not able to predict when such things will happen (though Popper shows how weak conditional predictions do follow from the theory in special cases, which I regard as a weak defense of Darwin as a scientist, rather than merely a metaphysician.)
But, like all such theories it can be and should be elaborated beyond what Darwin wrote, and that process is still going on.
"According to the Republic, every human soul has three parts: reason, spirit, and Appetite" which may at times be in conflict with one another. http://plato.stanford.edu/entries/plato-ethics-politics/
Chapter 6 of Sloman (1978), pointed out the need for an intelligent system to have a changing collection of interacting processes running in parallel, though I did not use the label "Virtual Machine Functionalism". Chapter 6 distinguished an "executive loop" which was constantly doing things and "a deliberative loop" which reflected on those activities and their results, but neither the diagram nor the text allowed for parallelism between the two loops, which was a mistake, though sometimes the "deliberative" processes reflected on what had previously happened in the "executive" processes. (I later discovered that psychologists, neuroscientists, and psychiatrists tended to use the label "executive" for components that are closer to what I called "deliberative".) The importance of parallelism was emphasized in Chapter 8 (on learning about numbers), Chapter 9 (on vision) and Chapter 10 (on consciousness).
Very few researchers seem to have noticed the possibility of VMF as a philosophical theory of mind that explains philosophical puzzles as well as providing suggestions for both future empirical research and future engineering work on designing intelligent machines. A few have mentioned the topic since Sloman/Chrisley (2003) was published, for example: Aleksander (2007), the second edition of Susan Blackmore's textbook on consciousness (2013), a few reviews, and some discussion papers.
A biologically totally unrealistic view of consciousness.
(What is the percept for? What, if anything, does it do? How?)
(To be continued....)
Note added 30 Apr 2013:
He has published a book which I have not yet read, but intend to:
Explaining The Computational Mind
By Marcin Milkowski,MIT Press, 2013.
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