The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence

This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing

Alan Turing and the “hard” and “easy” problem of cognition: doing and feeling

The "easy" problem of cognitive science is explaining how and why we can do what we can do. The "hard" problem is explaining how and why we feel. Turing's methodology for cognitive science (the Turing Test) is based on doing: Design a model that can do anything a human can do, indistinguishably from a human, to a human, and you have explained cognition. Searle has shown that the successful model cannot be solely computational. Sensory-motor robotic capacities are necessary to ground some, at least, of the model's words, in what the robot can do with the things in the world that the words are about. But even grounding is not enough to guarantee that -- nor to explain how and why -- the model feels (if it does). That problem is much harder to solve (and perhaps insoluble)

Philosophy of Computer Science: An Introductory Course

There are many branches of philosophy called “the philosophy of X,” where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments

Maturana’s Autopoietic Hermeneutics Versus Turing’s Causal Methodology for Explaining Cognition (Reply to A. Kravchenko (2007) Whence the autonomy? A comment on Harnad and Dror (2006)

Kravchenko (2007) suggests replacing Turing’s suggested method for explaining cognizers’ cognitive capacity through autonomous robotic modelling by ‘autopoeisis, Maturana’s extremely vague metaphor for the relations and interactions among organisms and their environments. I suggest that this would be an exercise in hermeneutics rather than causal explanation

The Turing Deception

This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges -- summarization, and question answering -- prompt ChatGPT to produce original content (98-99%) from a single text entry and also sequential questions originally posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, and overall quality. While Turing's original prose scores at least 14% below the machine-generated output, the question of whether an algorithm displays hints of Turing's truly original thoughts (the "Lovelace 2.0" test) remains unanswered and potentially unanswerable for now

Hybridisation for versatile decision-making in game opponent AI

Hybridisation for versatile decision-making in game opponent A

The Computability-Theoretic Content of Emergence

In dealing with emergent phenomena, a common task is to identify useful descriptions of them in terms of the underlying atomic processes, and to extract enough computational content from these descriptions to enable predictions to be made. Generally, the underlying atomic processes are quite well understood, and (with important exceptions) captured by mathematics from which it is relatively easy to extract algorithmic con- tent. A widespread view is that the difficulty in describing transitions from algorithmic activity to the emergence associated with chaotic situations is a simple case of complexity outstripping computational resources and human ingenuity. Or, on the other hand, that phenomena transcending the standard Turing model of computation, if they exist, must necessarily lie outside the domain of classical computability theory. In this article we suggest that much of the current confusion arises from conceptual gaps and the lack of a suitably fundamental model within which to situate emergence. We examine the potential for placing emer- gent relations in a familiar context based on Turing's 1939 model for interactive computation over structures described in terms of reals. The explanatory power of this model is explored, formalising informal descrip- tions in terms of mathematical definability and invariance, and relating a range of basic scientific puzzles to results and intractable problems in computability theory

The Turing Test and the Zombie Argument

In this paper I shall try to put some implications concerning the Turing's test and the so-called Zombie arguments into the context of philosophy of mind. My intention is not to compose a review of relevant concepts, but to discuss central problems, which originate from the Turing's test - as a paradigm of computational theory of mind - with the arguments, which refute sustainability of this thesis. In the first section (Section I), I expose the basic computationalist presuppositions; by examining the premises of the Turing Test (TT) I argue that the TT, as a functionalist paradigm concept, underlies the computational theory of mind. I treat computationalism as a thesis that defines the human cognitive system as a physical, symbolic and semantic system, in such a manner that the description of its physical states is isomorphic with the description of its symbolic conditions, so that this isomorphism is semantically interpretable. In the second section (Section II), I discuss the Zombie arguments, and the epistemological-modal problems connected with them, which refute sustainability of computationalism. The proponents of the Zombie arguments build their attack on the computationalism on the basis of thought experiments with creatures behaviorally, functionally and physically indistinguishable from human beings, though these creatures do not have phenomenal experiences. According to the consequences of these thought experiments - if zombies are possible, then, the computationalism doesn't offer a satisfying explanation of consciousness. I compare my thesis from Section 1, with recent versions of Zombie arguments, which claim that computationalism fails to explain qualitative phenomenal experience. I conclude that despite the weaknesses of computationalism, which are made obvious by zombie-arguments, these arguments are not the last word when it comes to explanatory force of computationalism

Validation of Expert Systems: Personal Choice Expert -- A Flexible Employee Benefit System

A method for validating expert systems, based on psychological validation literature and Turing\u27s imitation game, is applied to a flexible benefits expert system. Expert system validation entails determining if a difference exists between expert and novice decisions (construct validity), if the system uses the same inputs and processes to make its decisions as experts (content validity), and if the system produces the same results as experts (criterionrelated validity). If these criteria are satisfied, then the system is indistinguishable from experts for its domain and satisfies Turing\u27s imitation game. The methods developed in this paper are applied to a human resource expert system, Personal Choice Expert (PCE), designed to help employees choose a benefits package in a flexible benefits system. Expert and novice recommendations are compared to those generated by PCE. PCE\u27s recommendations do not significantly differ from those given by experts. High inter-expert agreement exists for some benefit recommendations (e.g. Dental Care and Long-Term Disability) but not for others (e.g. Short-Term Disability and Life Insurance). Insights offered by this method are illustrated and examined

Is thinking computable?

Strong artificial intelligence claims that conscious thought can arise in computers containing the right algorithms even though none of the programs or components of those computers understand which is going on. As proof, it asserts that brains are finite webs of neurons, each with a definite function governed by the laws of physics; this web has a set of equations that can be solved (or simulated) by a sufficiently powerful computer. Strong AI claims the Turing test as a criterion of success. A recent debate in Scientific American concludes that the Turing test is not sufficient, but leaves intact the underlying premise that thought is a computable process. The recent book by Roger Penrose, however, offers a sharp challenge, arguing that the laws of quantum physics may govern mental processes and that these laws may not be computable. In every area of mathematics and physics, Penrose finds evidence of nonalgorithmic human activity and concludes that mental processes are inherently more powerful than computational processes