Diane Proudfoot

In this article the central philosophical issues concerning human-level artificial intelligence (AI) are presented. AI largely changed direction in the 1980s and 1990s, concentrating on building domain-specific systems and on sub-goals such as self-organization, self-repair, and reliability. Computer scientists aimed to construct intelligence amplifiers for human beings, rather than imitation humans. Turing based his test on a computer-imitates-human game, describing three versions of this game in 1948, 1950, and 1952. The famous version appears in a 1950 article in Mind, ‘Computing Machinery and Intelligence’ (Turing 1950). The interpretation of Turing's test is that it provides an operational definition of intelligence (or thinking) in machines, in terms of behavior. ‘Intelligent Machinery’ sets out the thesis that whether an entity is intelligent is determined in part by our responses to the entity's behavior. Wittgenstein frequently employed the idea of a human being acting like a reliable machine. A ‘living reading-machine’ is a human being or other creature that is given written signs, for example Chinese characters, arithmetical symbols, logical symbols, or musical notation, and who produces text spoken aloud, solutions to arithmetical problems, and proofs of logical theorems. Wittgenstein mentions that an entity that manipulates symbols genuinely reads only if he or she has a particular history, involving learning and training, and participates in a social environment that includes normative constraints and further uses of the symbols.

Traditional accounts hold that reference consists in a relation between the mind and an object; the relation is effected by a mental act and mediated by internal mental contents (internal representations). Contemporary theories as diverse as Fodor’s [Fodor, J.A., 1987. Psychosemantics: The Problem of Meaning in the Philosophy of Mind. MIT Press, Cambridge, MA] language of thought hypothesis, Dretske’s [Dretske, F., 1988. Explaining Behaviour: Reasons in a World of Causes. MIT Press, Cambridge, MA] informational semantics and Millikan’s [Millikan, R.G., 1984. Language, Thought and Other Biological Categories: New Foundations for Realism. MIT Press, Cambridge, MA] teleosemantics share this act– content–object picture (which was also held by several early modern philosophers, in particular Locke). The core of the traditional view is the thesis that reference and intentionality are relational (‘thesis RR’). Although deeply problematic, RR is entrenched also in psychology, linguistics, cognitive science and Artificial Intelligence. Using for the most part arguments employed by Wittgenstein, we mount a case against RR and advance a deflationary account of reference and intentionality according to which neither is relational.

This paper explores the relevance of Wittgenstein’s philosophi- cal psychology for the two major contemporary approaches to the relation between language and cognition. As Pinker describes it, on the ‘Standard Social Science Model’ language is ‘an insidious shaper of thought’. According to Pinker’s own widely–shared alternative view, ‘Language is the magnificent faculty that we use to get thoughts from one head to another’. I investigate Wittgenstein’s powerful challenges to the hypothe- sis that language is a device for communicating independently constituted (or individuated) thoughts. I argue that Wittgenstein offers instead a subtle version of the thesis that language determines thought.

The widespread tendency, even within AI, to anthropomorphize machines makes it easier to convince us of their intelligence. How can any putative demonstration of intelligence in machines be trusted if the AI researcher readily succumbs to make-believe? This is (what I shall call) the forensic problem of anthropomorphism. I argue that the Turing test provides a solution. This paper illustrates the phenomenon of misplaced anthropomorphism and presents a new perspective on Turingʼs imitation game. It also examines the role of the Turing test in relation to the current dispute between human-level AI and ‘mindless intelligence’.

Anthropomorphism is a phenomenon that describes the human tendency to see human-like shapes in the environment. It has considerable consequences for people’s choices and beliefs. With the increased presence of robots, it is important to investigate the optimal design for this tech- nology. In this paper we discuss the potential benefits and challenges of building anthropomorphic robots, from both a philosophical perspective and from the viewpoint of empir- ical research in the fields of human–robot interaction and social psychology. We believe that this broad investigation of anthropomorphism will not only help us to understand the phenomenon better, but can also indicate solutions for facil- itating the integration of human-like machines in the real world.

The imitation game, the recent biopic about Alan Turing's efforts to decipher Nazi naval codes, was showered with award nominations. It even won the 2015 Academy Award for Best Adapted Screenplay. One thing it won't win any awards for, though, is its portrayal of the "imitation game" itself-Turing's proposed test of machine thinking, which hinges on whether a computer can convincingly imitate a person. The Turing test, as it is now called, doesn't really feature in the file. (Given that the movie gets so much of the history wrong, perhaps that's a good thing).

This is a detective story. The starting-point is a philosophical discussion in 1949, where Alan Turing mentioned a machine whose program, he said, would in practice be “impossible to find.” Turing used his unbreakable machine example to defeat an argument against the possibility of artificial intelligence. Yet he gave few clues as to how the program worked. What was its structure such that it could defy analysis for (he said) “a thousand years”? Our suggestion is that the program simulated a type of cipher device, and was perhaps connected to Turing’s postwar work for GCHQ (the UK equivalent of the NSA). We also investigate the machine’s implications for current brain simulation projects.

In 1948 Turing claimed that the concept of intelligence is an “emotional concept”. An emotional concept is a response-dependent concept and Turing’s remarks in his 1948 and 1952 papers suggest a response-dependence approach to the concept of intelligence. On this view, whether or not an object is intelligent is determined, as Turing said, “as much by our own state of mind and training as by the properties of the object”. His discussion of free will suggests a similar approach. Turing said, for example, that if a machine’s program “results in its doing something interesting which we had not anticipated I should be inclined to say that the machine had originated something”. This points to a new form of free will compatibilism, which I call response-dependence compatibilism and explore here.

According to Richard Routley, a comprehensive theory of fiction is impossible, since almost anything is in principle imaginable. In my view, Routley is right: for any purported logic of fiction, there will be actual or imaginable fictions that successfully counterexample the logic. Using the example of ‘impossible’ fictions, I test this claim against theories proposed by Routley’s Meinongian contemporaries and also by Routley himself and his 21st century heirs. I argue that the phenomenon of impossible fictions challenges even today’s modal Meinongians.

Ignoring the temporal dimension, an object such as a railway tunnel or a human body is a three-dimensional whole composed of three-dimensional parts. The four-dimensionalist holds that a physical object exhibiting identity across time—Descartes, for example—is a four-dimensional whole composed of 'briefer' four-dimensional objects, its temporal parts. Peter van Inwagen (1990) has argued that four-dimensionalism cannot be sustained, or at best can be sustained only by a counterpart theorist. We argue that different schemes of individuation of temporal parts are available, which undermines van Inwagen's argument.

It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks 'unorganised machines'. By the application of what he described as 'appropriate interference, mimicking education' an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of 'neurons' is sufficient. Turing proposed simulating both the behaviour of the network and the training process by means of a computer program. We outline Turing's connectionist project of 1948.