Eric Dietrich

This book surveys and examines the most famous philosophical arguments against building a machine with human-level intelligence. From claims and counter-claims about the ability to implement consciousness, rationality, and meaning, to arguments about cognitive architecture, the book presents a vivid history of the clash between the philosophy and AI. Tellingly, the AI Wars are mostly quiet now. Explaining this crucial fact opens new paths to understanding the current resurgence AI (especially, deep learning AI and robotics), what happens when philosophy meets science, and the role of philosophy in the culture in which it is embedded. Organising the arguments into four core topics - 'Is AI possible', 'Architectures of the Mind', 'Mental Semantics and Mental Symbols' and 'Rationality and Creativity' - this book shows the debate that played out between the philosophers on both sides of the question, and, as well, the debate between philosophers and AI scientists and engineers building AI systems. Up-to-date and forward-looking, the book is packed with fresh insights and supporting material, including: - Accessible introductions to each war, explaining the background behind the main arguments against AI - Each chapter details what happened in the AI wars, the legacy of the attacks, and what new controversies are on the horizon. - Extensive bibliography of key readings

This paper offers an explanation for why some parts of philosophy have made no progress. Philosophy has made no progress because it cannot make progress. And it cannot because of the nature of the phenomena philosophy is tasked with explaining—all of it involves consciousness. Here, it will not be argued directly that consciousness is intractable. Rather, it will be shown that a specific version of the problem of consciousness is unsolvable. This version is the Problem of the Subjective and Objective. Then it is argued that the unsolvability of this latter problem is why there are other unsolvable philosophy problems.

The perennial nature of some of philosophy’s deepest problems is a puzzle. Here, one problem, the realism–anti-realism debate, and one type of explanation for its longevity, are examined. It is argued that realism and anti-realism form a dialetheic pair: While they are in fact each other’s logical opposite, nevertheless, both are true (and both false). First, several reasons why one might think such a thing are presented. These reasons are merely the beginning, however. In the following sections, the dialetheic conclusion is directly argued for by showing how realism and anti-realism satisfy Priest’s “inclosure schema”. In the last section and the conclusion, the conscious mind’s role in creating realism and anti-realism is discussed. This role further supports the conclusion that realism and anti-realism form a dialetheic pair.

This paper examines three cases of the clash between science and philosophy: Zeno’s paradoxes, the Frame Problem, and a recent attempt to experimentally refute skepticism. In all three cases, the relevant science claims to have resolved the purported problem. The sciences, construing the term broadly, are mathematics, artificial intelligence, and psychology. The goal of this paper is to show that none of the three scientific solutions work. The three philosophical problems remain as vibrant as ever in the face of robust scientific attempts to dispel them. The paper concludes by examining some consequences of this persistence.

The open-domain Frame Problem is the problem of determining what features of an open task environment need to be updated following an action. Here we prove that the open-domain Frame Problem is equivalent to the Halting Problem and is therefore undecidable. We discuss two other open-domain problems closely related to the Frame Problem, the system identification problem and the symbol-grounding problem, and show that they are similarly undecidable. We then reformulate the Frame Problem as a quantum decision problem, and show that it is undecidable by any finite quantum computer.

This paper examines three cases of the clash between science and philosophy: Zeno’s paradoxes, the Frame Problem, and a recent attempt to experimentally refute skepticism. In all three cases, the relevant science claims to have resolved the purported problem. The sciences, construing the term broadly, are mathematics, artificial intelligence, and psychology. The goal of this paper is to show that none of the three scientific solutions work. The three philosophical problems remain as vibrant as ever in the face of robust scientific attempts to dispel them. The paper concludes by examining some consequences of this persistence.

Though artificial intelligence scientists frequently use words such as "belief" and "desire" when describing the computational capacities of their programs and computers, they have completely ignored the philosophical and psychological theories of belief and desire. Hence, their explanations of computational capacities which use these terms are frequently little better than folk-psychological explanations. Conversely, though philosophers and psychologists attempt to couch their theories of belief and desire in computational terms, they have consistently misunderstood the notions of computation and computational semantics. Hence, their theories of such attitudes are frequently inadequate. ;A computational theory of propositional attitudes is presented here. It is argued that the theory of propositional attitudes put forth by philosophers and psychologists entails that propositional attitudes are a kind of abstract data type. This refined computational view of propositional attitudes bridges the gap between artificial intelligence, philosophy and psychology. ;Lastly, it is argued that this theory of propositional attitudes has consequences for meta-processing and consciousness in computers.

The naturalistic fallacy is mentionedfrequently by evolutionary psychologists as anerroneous way of thinking about the ethicalimplications of evolved behaviors. However,evolutionary psychologists are themselvesconfused about the naturalistic fallacy and useit inappropriately to forestall legitimateethical discussion. We briefly review what thenaturalistic fallacy is and why it is misusedby evolutionary psychologists. Then we attemptto show how the ethical implications of evolvedbehaviors can be discussed constructivelywithout impeding evolutionary psychologicalresearch. A key is to show how ethicalbehaviors, in addition to unethical behaviors,can evolve by natural selection.

The sciences occasionally generate discoveries that undermine their own assumptions. Two such discoveries are characterized here: the discovery of apophenia by cognitive psychology and the discovery that physical systems cannot be locally bounded within quantum theory. It is shown that such discoveries have a common structure and that this common structure is an instance of Priest’s well-known Inclosure Schema. This demonstrates that science itself is dialetheic: it generates limit paradoxes. How science proceeds despite this fact is briefly discussed, as is the connection between our results and the realism-antirealism debate. We conclude by suggesting a position of epistemic modesty.

Years ago, when I was an undergraduate math major at the University of Wyoming, I came across an interesting book in our library. It was a book of counterexamples t o propositions in real analysis (the mathematics of the real numbers). Mathematicians work more or less like the rest of us. They consider propositions. If one seems to them to be plausibly true, then they set about to prove it, to establish the proposition as a theorem. Instead o f setting out to prove propositions, the psychologists, neuroscientists, and other empirical types among us, set out to show that a proposition is supported by the data, and that it is the best such proposition so supported. The philosophers among us, when they are not causing trouble by arguing that AI is a dead end or that cognitive science can get along without representations, work pretty much like the mathematicians: we set out to prove certain propositions true on the basis of logic, first principles, plausible assumptions, and others' data. But, back to the book of real analysis counterexamples. If some mathematician happened t o think that some proposition about continuity, say, was plausibly true, he or she would then set out to prove it. If the proposition was in fact not a theorem, then a lot of precious time would be wasted trying to prove it. Wouldn't it be great to have a book that listed plausibly true propositions that were in fact not true, and listed with each such proposition a counterexample to it? Of course it would.

  The naturalistic fallacy is mentionedfrequently by evolutionary psychologists as anerroneous way of thinking about the ethicalimplications of evolved behaviors. However,evolutionary psychologists are themselvesconfused about the naturalistic fallacy and useit inappropriately to forestall legitimateethical discussion. We briefly review what thenaturalistic fallacy is and why it is misusedby evolutionary psychologists. Then we attemptto show how the ethical implications of evolvedbehaviors can be discussed constructivelywithout impeding evolutionary psychologicalresearch. A key is to show how ethicalbehaviors, in addition to unethical behaviors,can evolve by natural selection

Except for a patina of twenty-first century modernity, in the form of logic and language, philosophy is exactly the same now as it ever was; it has made no progress whatsoever. We philosophers wrestle with the exact same problems the Pre-Socratics wrestled with. Even more outrageous than this claim, though, is the blatant denial of its obvious truth by many practicing philosophers. The No-Progress view is explored and argued for here. Its denial is diagnosed as a form of anosognosia, a mental condition where the affected person denies there is any problem. The theories of two eminent philosophers supporting the No-Progress view are also examined. The final section offers an explanation for philosophy 's inability to solve any philosophical problem, ever. The paper closes with some reflections on philosophy 's future

True contradictions are taken increasingly seriously by philosophers and logicians. Yet, the belief that contradictions are always false remains deeply intuitive. This paper confronts this belief head-on by explaining in detail how one specific contradiction is true. The contradiction in question derives from Priest's reworking of Berkeley's argument for idealism. However, technical aspects of the explanation offered here differ considerably from Priest's derivation. The explanation uses novel formal and epistemological tools to guide the reader through a valid argument with, not just true, but eminently acceptable premises, to an admittedly unusual conclusion: a true contradiction. The novel formal and epistemological tools concern points of view and changes in points of view. The result is an understanding of why the contradiction is true.

Good sciences have good metaphors. Indeed, good sciences are good because they have good metaphors. AI could use more good metaphors. In this editorial, I would like to propose a new metaphor to help us understand intelligence. Of course, whether the metaphor is any good or not depends on whether it actually does help us. (What I am going to propose is not something opposed to computationalism -- the hypothesis that cognition is computation. Noncomputational metaphors are in vogue these days, and to date they have all been equally plausible and equally successful. And, just to be explicit, I do not mean “IQ” by “intelligence.” I am using “intelligence” in the way AI uses it: as a semi-techical term referring to a general property of all intelligent systems, animal (including humans), or machine, alike.).

There is a prevalent notion among cognitive scientists and philosophers of mind that computers are merely formal symbol manipulators, performing the actions they do solely on the basis of the syntactic properties of the symbols they manipulate. This view of computers has allowed some philosophers to divorce semantics from computational explanations. Semantic content, then, becomes something one adds to computational explanations to get psychological explanations. Other philosophers, such as Stephen Stich, have taken a stronger view, advocating doing away with semantics entirely. This paper argues that a correct account of computation requires us to attribute content to computational processes in order to explain which functions are being computed. This entails that computational psychology must countenance mental representations. Since anti-semantic positions are incompatible with computational psychology thus construed, they ought to be rejected. Lastly, I argue that in an important sense, computers are not formal symbol manipulators.

Concern over the nature of AI is, for the tastes many AI scientists, probably overdone. In this they are like all other scientists. Working scientists worry about experiments, data, and theories, not foundational issues such as what their work is really about or whether their discipline is methodologically healthy. However, most scientists aren’t in a field that is approximately fifty years old. Even relatively new fields such as nonlinear dynamics or branches of biochemistry are in fact advances in older established sciences and are therefore much more settled. Of course, by stretching things, AI can be said to have a history reaching back t o Charles Babbage, and possibly back beyond that to Leibnitz. However, all of that is best viewed as prelude. AI’s history is punctuated with the invention of the computer (and, if one wants t o stretch our history back to the 1930s, the development of the notion of computation by Turing, Church, and others). Hence, AI really began (or began in earnest) sometime in the late 1940s or early 1950s (some mark the conference a t Dartmouth in the summer of 1957 as the moment of our birth). And since those years we simply have not had time to settle into a routine science attacking reasonably well understood questions (for example, many of the questions some of us regard as supreme are regarded by others as inconsequential or mere excursions).

The naturalistic fallacy is mentioned frequently by evolutionary psychologists as an erroneous way of thinking about the ethical implications of evolved behaviors. However, evolutionary psychologists are themselves confused about the naturalistic fallacy and use it inappropriately to forestall legitimate ethical discussion. We briefly review what the naturalistic fallacy is and why it is misused by evolutionary psychologists. Then we attempt to show how the ethical implications of evolved behaviors can be discussed constructively without impeding evolutionary psychological research. A key is to show how ethical behaviors, in addition to unethical behaviors, can evolve by natural selection.