Diane Proudfoot

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.

Did Wittgenstein’s 1939 lectures on the foundations of mathematics—the one certain set of interactions between Wittgenstein and Turing—shape Turing’s philosophy of mind? I argue that Turing’s and Wittgenstein’s criteria for thinking are similar in significant respects, and I investigate possible origins of these similarities in the 1939 lectures. The scholarly literature on the lectures focuses on mathematics and mathematical logic, yet in these lectures Wittgenstein presented several of his general philosophical maxims—maxims with which Turing’s criterion for thinking is in agreement. The case for the hypothesis that Wittgenstein influenced Turing’s philosophical approach to the mind is circumstantial but plausible.

In this paper we apply Anscombe’s account of human linguistic practices and of intentional action in a novel way—to the acts, by members of the Ngāi Tahu tribe in colonial-era Aotearoa New Zealand, of writing Letters to the Editor of local and regional newspapers. We identify the salient contexts of those acts and then draw on Anscombe’s work to identify intentional and moral actions that otherwise risk going unnoticed. Our analysis exemplifies Anscombe’s and Wittgenstein’s view that we can read off intentional action from behaviour in context and also yields a rich historical and conceptual account of the letter-writers. We argue that this approach has theoretical advantages for Indigenous and Postcolonial Studies.

Alan Turing anticipated many areas of current research incomputer and cognitive science. This article outlines his contributionsto Artificial Intelligence, connectionism, hypercomputation, andArtificial Life, and also describes Turing's pioneering role in thedevelopment of electronic stored-program digital computers. It locatesthe origins of Artificial Intelligence in postwar Britain. It examinesthe intellectual connections between the work of Turing and ofWittgenstein in respect of their views on cognition, on machineintelligence, and on the relation between provability and truth. Wecriticise widespread and influential misunderstandings of theChurch–Turing thesis and of the halting theorem. We also explore theidea of hypercomputation, outlining a number of notional machines that“compute the uncomputable.”

Given actual autonomous systems with capacities for harm and the public’s apparent willingness to take moral advice from large language models (LLMs), Einar Duenger Bohn’s (2024) renewed discussion of the Moral Turing Test (MTT) is timely. Bohn’s aim is to defend an unequivocally behavioural test. In this paper, I argue against this direction. Interpreted as testing mere behaviour, the Turing test is a poor test of either intelligence or moral agency, and neither Bohn’s version of the test nor Allen, Varner and Zinser’s influential (2000) version avoids these problems. Also, the MTT’s advantages as advertised by Bohn do not hold up: it is an open empirical question whether embracing a merely behavioural MTT will significantly reduce the challenges in building a computer to pass the test. This issue aside, I argue that Turing’s actual test is superior to current AI benchmarks and to Bohn’s version of the test. A test of moral reasoning or agency in machines that is based on Turing’s actual test has advantages over current tests of moral reasoning in AI, including versions of the MTT. Such a test is an intriguing possibility still to be investigated.

Given actual autonomous systems with capacities for harm and the public’s apparent willingness to take moral advice from large language models (LLMs), Einar Duenger Bohn’s (2024) renewed discussion of the Moral Turing Test (MTT) is timely. Bohn’s aim is to defend an unequivocally behavioural test. In this paper, I argue against this direction. Interpreted as testing mere behaviour, the Turing test is a poor test of either intelligence or moral agency, and neither Bohn’s version of the test nor Allen, Varner and Zinser’s influential (2000) version avoids these problems. Also, the MTT’s advantages as advertised by Bohn do not hold up: it is an open empirical question whether embracing a merely behavioural MTT will significantly reduce the challenges in building a computer to pass the test. This issue aside, I argue that Turing’s actual test is superior to current AI benchmarks and to Bohn’s version of the test. A test of moral reasoning or agency in machines that is based on Turing’s actual test has advantages over current tests of moral reasoning in AI, including versions of the MTT. Such a test is an intriguing possibility still to be investigated.

The modern project of naturalizing intelligence began in the middle of last century, and Alan Turing is one of its most celebrated proponents. The assumption that Turing shared the ontological and methodological commitments of canonical naturalists is based on certain widespread beliefs about Turing—namely, that his test of intelligence is behaviourist and his approach to the mind computationalist. This chapter argues that influential versions of these assumptions are false, and instead that, in his claim that intelligence is an ‘emotional concept’, Turing proposed a response-dependence approach to intelligence. Recent objections, however, take the response-dependence interpretation to commit Turing to anti-realism—a philosophical stance that is typically anathema to naturalists. This chapter argues that those objections fail.

In this paper I argue that Turing proposed a new approach to the concept of thinking, based on his claim that intelligence is an ‘emotional concept’; and that the response-dependence interpretation of Turing’s ‘criterion for “thinking”’ is a better fit with his writings than orthodox interpretations. The aim of this paper is to clarify the response-dependence interpretation, by addressing such questions as: What did Turing mean by the expression ‘emotional’? Is Turing’s criterion subjective? Are ‘emotional’ judgements decided by social consensus? Turing’s take on these issues impacts current philosophical debates on response-dependent concepts and on the nature of artificial intelligence.

In the 70 years since Alan Turing’s ‘Computing Machinery and Intelligence’ appeared in Mind, there have been two widely-accepted interpretations of the Turing test: the canonical behaviourist interpretation and the rival inductive or epistemic interpretation. These readings are based on Turing’s Mind paper; few seem aware that Turing described two other versions of the imitation game. I have argued that both readings are inconsistent with Turing’s 1948 and 1952 statements about intelligence, and fail to explain the design of his game. I argue instead for a response-dependence interpretation. This interpretation has implications for Turing’s view of free will: I argue that Turing’s writings suggest a new form of free will compatibilism, which I call response-dependence compatibilism. The philosophical implications of rethinking Turing’s test go yet further. It is assumed by numerous theorists that Turing anticipated the computational theory of mind. On the contrary, I argue, his remarks on intelligence and free will lead to a new objection to computationalism.

Recent work in social robotics, which is aimed both at creating an artificial intelligence and providing a test-bed for psychological theories of human social development, involves building robots that can learn from ‘face-to-face’ interaction with human beings — as human infants do. The building-blocks of this interaction include the robot’s ‘expressive’ behaviours, for example, facial-expression and head-and-neck gesture. There is here an ideal opportunity to apply Wittgensteinian conceptual analysis to current theoretical and empirical work in the sciences. Wittgenstein’s philosophical psychology is sympathetic to embodied and situated Artificial Intelligence (see Proudfoot, 2002, 2004b), and his discussion of facial-expression is remarkably modern. In this chapter, I explore his approach to facial-expression, using smiling as a representative example, and apply it to the canonical interactive face robot, Cynthia Breazeal’s Kismet (see e.g. Breazeal, 2009, 2002). I assess the claim that Kismet has expressive behaviours, with the aim of generating philosophical insights for AI.

Turing was probably the first person to advocate the pursuit of robotics as a route to Artificial Intelligence and Wittgenstein the first to argue that, without the appropriate history, no machine could be intelligent. Wittgenstein anticipated much recent theorizing about the mind, including aspects of connectionist theo- ries of mind and the situated cognition approach in AI. Turing and Wittgenstein had a wary respect for each other and there is significant overlap in their work, in both the philosophy of mathematics and the philosophy of AI. Both took (what would now be called) an externalist stance with respect to machine intelligence. But whereas Turing was concerned only with behaviour, Wittgenstein emphasized in addition history and environment. I show that Wittgenstein's externalist analysis of psychological capacities entails that most, even all, future "artificially intelligent" computers and robots will not use language, possess concepts, or reason. The ar- gument tells, not against AI, but only against AI's traditional and romantic goal of building an artificial "res cogitans" - as first embraced by Turing and now ex- emplified in the work of Brooks and others on cognitive robotics. This argument supports the stance of the growing number of AI researchers whose aim is to pro- duce, not thinking and understanding machines, but high-performance "advanced information processing systems."