Machine-based Episteme/Epistemology

October 24, 2011 § Leave a comment

It is pretty clear that even if we just think of machines being able to understand this immediately triggers epistemological issues. If such a machine would be able to build previously non-existent representations of the outer world in a non-deterministic manner, a wide range of epistemological implications would be invoked for that machine-being, and these epistemological implications are largely the same as for humans or other cognitive well-developed organic life.

Epistemology investigates the conditions for “knowledge” and the philosophical consequences of knowing. “Knowledge” is notoriously difficult to define, and there are many misunderstandings around, including the “soft stone” of so-called “tacit knowledge”; yet for us it simply denotes a bundle consisting from

  • – a dynamic memory
  • – capacity for associative modeling, i.e. adaptively deriving rules about the world
  • – ability to act upon achieved models and memory
  • – self-oriented activity regarding the available knowledge
  • – already present information is used to extend the capabilities above

Note that we do not demand communication about knowledge. For several reasons and based on Wittgenstein’s theory on meaning we think that knowledge can not be transmitted or communicated. Recently, Linda Zagzebski [1] achieved the same result, starting from a different perspective. She writes, that “[…] knowledge is not an output of an agent, instead it is a feature of the agent.“. In agreement with at least some reasonably justified philosophical positions we thus propose that it is also reasonable to conceive of such a machine as mentioned before as being enabled to knowledge. Accordingly, it is indicated to assign the capability for knowing to the machine. That knowledge being comprised or constituted by the machine is not accessible for us as “creators” of the machine, for the very same reason of the difference of the Lebenswelten.

Yet, the knowledge acquired by the is also not “directly” accessible for the machine itself. In contrast to rationalist positions, knowledge can’t be separated from the whole of a cognitive entity. The only thing that is possible is to translate it into publicly available media like language, to negotiate a common usage of words and their associated link structures, and to debate about the mutually private experiences.

Resorting to the software running on the machine and checking the content of the machine will be not possible either. A software that will enable knowing can’t be decomposable in order to serve an explanation of that knowledge. The only things one will find is a distant analog to our neurons. As little as reductionism works for the human mind it will work for the machine.

Yet, such machine-knowledge is not comparable to human knowledge. The reason for that is not an issue of type, or extent. The reason is given by the fact that the Lebenswelt of the machine, that is the totality of all relations to the outer world and of all transformations of the perceiving and acting entity, the machine, would be completely different from ours. It will not make any sense to try to simulate any kind of human-like knowledge in that machine. It always will be drastically different.

The only possibility to speak about the knowing and the knowledge of the machine is through epistemological concepts. For us it doesn’t seem promising to engage in fields like “Cognitive Informatics,” since informatics (computer science) can not deal with cognition for rather fundamental reasons: Cognition is not Turing-computable.

The bridging bracket between the brains and minds of machine and human being is the theory of knowing. Consequently, we have to apply epistemology to deal with machines that possibly know. The conditions for that knowledge could turn out to be strange; else we should try to develop the theory of machine-based knowledge from the perspective of the machine. It is important to understand that attempts like the Turing-Test [2] are inappropriate, for several reasons so: (i) they follow the behavioristic paradigm, (ii) they do not offer the possibility to derive scales for comparison, (iii) no fruitful questions can be derived.

Additionally, there are some arguments pointing to the implicit instantiation of a theory as soon as something is going to be modeled. In other words, a machine which is able to know already has a—probably implicit—theory about it, and this also means about itself. That theory would originate in the machine (despite the fact that it can’t be a private theory). Hence, we open a branch and call it machine-based epistemology.

Some Historical Traces of ‘Contacts’

(between two strange disciplines)

Regarding the research about and the construction of “intelligent” machines, the relevance of thinking in epistemological terms has been recognized quite early. In 1963, A. Wallace published a paper entitled “Epistemological Foundations of Machine Intelligence”[3] that quite unfortunately is not available except the already remarkable abstract:

Abstract : A conceptual formulation of the Epistemological Foundations of Machine Intelligence is presented which is synthesized from the principles of physical and biological interaction theory on the one hand and the principles of mathematical group theory on the other. This synthesis, representing a fusion of classical ontology and epistemology, is generally called Scientific Epistemology to distinguish it from Classical General Systems theory. The resulting view of knowledge and intelligence is therefore hierarchical, evolutionary, ecological, and structural in character, and consequently exhibits substantial agreement with the latest developments in quantum physics, foundations of mathematics, general systems theory, bio-ecology, psychology, and bionics. The conceptual formulation is implemented by means of a nested sequence of structural Epistemological-Ontological Diagrams which approximate a strong global interaction description. The mathematico-physical structure is generalized from principles of duality and impotence, and the techniques of Lie Algebra and Lie Continuous Group theory.

As far as it is possible to get an impression about the actual but lost full paper, Wallace’s approach is formal and mathematical. Biological interaction theory at that time was a fork from mathematical information theory, at least in the U.S. where this paper originates. Another small weakness could be indicated by the notion of “hierarchical knowledge and intelligence,” pointing to some rest of positivism. Anyway, the proposed approach never was followed upon, unfortunately. Yet we will see in our considerations about modeling that the reference to structures like the Lie Group theory could not have worked out in a satisfying manner.

Another early instance of bringing epistemology into the research about “artificial intelligence” is McCarthy [4,5], who coined the term “Artificial Intelligence.” Yet, his perspective appears as by far too limited. First he starts with the reduction of epistemology to first-order logics:

“We have found first order logic to provide suitable languages for expressing facts about the world for epistemological research.” […]

Philosophers emphasize what is potentially knowable with maximal opportunities to observe and compute, whereas AI must take into account what is knowable with available observational and computational facilities.

Astonishingly, he does not mention any philosophical argument in the rest of the paper  except the last paragraph:

“More generally, we can imagine a metaphilosophy that has the same relation to philosophy that metamathematics has to mathematics. Metaphilosophy would study mathematical systems consisting of an “epistemologist” seeking knowledge in accordance with the epistemology to be tested and interacting with a “world”.” […] AI could benefit from building some very simple systems of this kind, and so might philosophy.”

McCarthy’s stance to philosophy is typical for the whole field. Besides the presumptuous suggestion of a “metaphilosophy” and subsuming it rather nonchalant to mathematics, he misses the point of epistemology, even as he refers to the machine as an “observer”: A theory of knowledge is about the conditions of the possibility for knowledge. McCarthy does not care about the implications of his moves to that possibility, or vice versa.

Important progress about the issue of the sate of machines was contributed not by the machine technologists themselves, but by philosophers, namely Putnam, Fodor, Searle and Dennett in the English speaking world, and also among French philosophers like Serres (in his “Hermes” series) and Guattari. The German systems theorists like von Foerster and Luhmann and their fellows never went beyond cybernetics, so we can omit them here.  In 1998, Wellner [6] provided a proposal for epistemology in the field of “Artificial Life” (what a terrible wording…). Yet, his attempt to contribute to the epistemological discussion turns out to be inspired by Luhmann’s perspective, and the “first step” he proposes is simply to stuff robots with sensory, i.e. finally it’s not really a valuable attempt to deal with epistemology in affairs of epistemic machines.

In 1978, Daniel Dennett [6] reframed the so-called “Frame Problem” of AI, of which already McCarthy and Hayes [3] got aware 10 years earlier. Dennet asks how

“a cognitive creature … with many beliefs about the world” can update those beliefs when it performs an act so that they remain “roughly faithful to the world”? (cited acc. to [8])

Recently, Dreyfus [9] and Wheeler [10], who yet disagrees about the reasoning with Dreyfus about it, called the Frame problem an illusionary pseudo-problem, created by the adherence to Cartesian assumptions. Wheeler described it as:

“The frame problem is the difficulty of explaining how non-magical systems think and act in ways that are adaptively sensitive to context-dependent relevance.”

Wheeler as well as Dreyfus recognize the basic problem(s) in the architecture of mainstream AI, and they identify Cartesianism as the underlying principle of these difficulties, i.e. the claim of analyticity, reducibility and identifiability. Yet, neither of the two so far proposes a stable solution. Heideggerian philosophy with its situationistic appeal does not help to clarify the epistemological affairs,  neither of machines nor of humans.

Our suggestion is the following: Firstly, a general solution should be found, how to conceive the (semi-)empirical relationship between beings that have some kind of empirical coating. Secondly, this general solution should serve as a basis to investigate the differences, if there are any, between machines and humans, regarding their epistemological affairs with the “external” world. This endeavor we label as  “machine-based epistemology.”

Machine-based Epistemology

If a machine, or better, a synthetic body that was established as a machine in the moment of its instantiation, would be able act freely, it would face the same epistemological problems as we humans, starting with basic sensory perception and not ending with linking a multi-modal integration of sensory input to adequate actions. Therefore machine-based epistemology (MBE) is the appropriate label for the research program that is dedicated to learning processes implemented on machines. We avoid invoking the concept of agents here, since this already brings in a lot of assumptions.

Note that MBE should not be mixed with so-called “Computer Epistemology”, which is concerned just about the design of so-called man-machine-interfaces [11]. We are not concerned about epistemological issues arising through the usage computers, of course.

It is clear that the term machine learning is missing the point, it is a pure technical term. Machine learning is about algorithms and programmable procedures, not about the reflection of the condition of that. Thus, it does not recognize the context into which learning machines are embedded, and in turn it misses also the consequences. In some way machine learning is not about learning about machines. It remains a pure engineering discipline.

As a consequence, one can find a lot of nonsense in the field of machine learning, especially concerning so-called ontologies and meta-data, but also about the topic of “learning” itself. There is the nonsensical term of “reinforcement learning”… which kind of learning could not be about (differential) reinforcement?

The other label Machine-based Epistemology is competing with is “Artificial Intelligence.” Check out the editorial text “Where is the Limit” for arguments against the label “AI.” The conclusion was that AI is too close to cybernetics and mathematical information theory, that it is infected by romanticism and it is difficult to operationalize, that it does not appropriately account for cultural effects onto the “learning subject.” Since AI is not connected natively to philosophy, there is no adequate treatment of language: AI never took the “Linguistic Turn.” Instead, the so-called philosophy of AI poses silly questions about “mental states.”

MBE is concerned about the theory of machines that possibly start to develop autonomous cognitive activity; you may call this “thinking.” You also may conceive it as a part of a “philosophy of mind.” Both notions, thinking and mind, may work in the pragmatics of everyday social situations, for a more strict investigation I think they are counter-productive: We should pay attention to language in order not to get vexed by it. If there is no “philosophy of unicorns,” then probably there also should not be a “philosophy of mind.” Both labels, thinking and mind, pretend to define a real and identifiable entity, albeit exactly this should be one of the targets for a clarification. Those labels can easily cause the misunderstanding of separable subjects. Instead, we could call it “philosophy of generalized mindfulness”, in order to avoid anthropomorphic chauvinism.

As a theory, MBE is not driven by engineering, as it is the case for AI; just the other way round, MBE itself is driving engineering. It somehow brings philosophical epistemology into the domain of engineering computer  systems that are able to learn. Such it is natively linked in a an already well-established manner to other fields in philosophy. Which, finally, helps to avoid to pose silly questions or to follow silly routes.

  • [1] Linda Zagzebski, contribution to: Jonathan Dancy, Ernest Sosa, Matthias Steup (eds.), “A Companion to Epistemology”, Vol. 4, pp.210; here p.212.
  • [2] Alan Turing (1950), Computing machinery and intelligence. Mind, 59(236): 433-460.
  • [3] Wallace, A. (1963), EPISTEMOLOGICAL FOUNDATIONS OF MACHINE INTELLIGENCE. Information for the defense Community (U.S.A.), Accession Number : AD0681147
  • [4] McCarthy, J. and Hayes, P.J. (1969) Some Philosophical Problems from the Standpoint of Artificial Intelligence. Machine Intelligence 4, pp.463-502 (eds Meltzer, B. and Michie, D.). Edinburgh University Press.
  • [5] McCarthy, J. 1977. Epistemological problems of artificial intelligence. In IJCAI, 1038-1044.
  • [6] Jörg Wellner 1998, Machine Epistemology for Artificial Life In: “Third German Workshop on Artificial Life”, edited by C. Wilke, S. Altmeyer, and T. Martinetz, pp. 225-238, Verlag Harri Deutsch.
  • [7]  Dennett, D. (1978), Brainstorms, MIT Press., p.128.
  • [8] Murray Shanahan (2004, rev.2009), The Frame Problem, Stanford Encyclopedia of Philosophy, available online.
  • [9] H.L. Dreyfus, (2008), “Why Heideggerian AI Failed and How Fixing It Would Require Making It More Heideggerian”, in The Mechanical Mind in History, eds. P.Husbands, O.Holland & M.Wheeler, MIT Press, pp. 331–371.
  • [10] Michael Wheeler (2008), Cognition in Context: Phenomenology, Situated Robotics and the Frame Problem. Int.J.Phil.Stud. 16(3), 323-349.
  • [11] Tibor Vamos, Computer Epistemology: A Treatise in the Feasibility of the Unfeasible or Old Ideas Brewed New. World Scientific Pub, 1991.


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