A new trend in deep learning, represented by Mutual Information Neural Estimation (MINE) and Information Noise Contrast Estimation (InfoNCE), is emerging. In this trend, similarity functions and Estimated Mutual Information (EMI) are used as learning and objective functions. Coincidentally, EMI is essentially the same as Semantic Mutual Information (SeMI) proposed by the author 30 years ago. This paper first reviews the evolutionary histories of semantic information measures and learning functions. Then, it briefly introduces the author’s semantic information G theory with (...) the rate-fidelity function R(G) (G denotes SeMI, and R(G) extends R(D)) and its applications to multi-label learning, the maximum Mutual Information (MI) classification, and mixture models. Then it discusses how we should understand the relationship between SeMI and Shannon’s MI, two generalized entropies (fuzzy entropy and coverage entropy), Autoencoders, Gibbs distributions, and partition functions from the perspective of the R(G) function or the G theory. An important conclusion is that mixture models and Restricted Boltzmann Machines converge because SeMI is maximized, and Shannon’s MI is minimized, making information efficiency G/R close to 1. A potential opportunity is to simplify deep learning by using Gaussian channel mixture models for pre-training deep neural networks’ latent layers without considering gradients. It also discusses how the SeMI measure is used as the reward function (reflecting purposiveness) for reinforcement learning. The G theory helps interpret deep learning but is far from enough. Combining semantic information theory and deep learning will accelerate their development. (shrink)

Radical democratic thinking is becoming intrigued by the material situatedness of its political agents and by the role of nonhuman participants in political interaction. At stake here is the displacement of narrow anthropocentrism that currently guides democratic theory and practice, and its repositioning into what we call ‘the nonhuman condition’. This Critical Exchange explores the nonhuman condition. It asks: What are the implications of decentering the human subject via a new materialist reading of radical democracy? Does this reading dilute political (...) agency? Or should this be seen, on the contrary, as an invitation for new voices and demands to enter into democratic assemblages? How might engagement with the more-than-human disrupt or extend theories of radical democracy? Hans Asenbaum and Amanda Machin discuss the human democratic subject both through radical democratic and new materialist lenses. They suggest that a new materialist decentred subject does not lose agency but further gains political responsibility in radical democracy. Jean-Paul Gagnon highlights the possibility of becoming through loss. In losing our anthropocentric arrogance—our understanding of being other and better than animal—we become more connected and discover our place in human-nonhuman assemblages. Melissa Orlie agrees that something can be gained through renouncing dominant human fantasies and presents a ‘radical democratic naturalism’. By renouncing land violence and acknowledging nature’s subjectivity, we can further nonexploitative radical democratic politics. This is where the notion of ‘tidalectic’ processes introduced by James L. Smith is helpful. Focusing on water and the Indigenous knowledges around rivers, he considers how democracy and community can be reimagined as plural, cyclical and attentive to the nonhuman with which the human is inevitably entangled. Diana Leong offers a different approach. Considering the nonhuman from the perspective of Black Studies demands an awareness to how blackness has long been constituted as non- or partially human. (shrink)

There is an existing debate regarding the view that technological instruments, devices, or machines can assert ‎or testify. A standard view in epistemology is that only humans can testify. However, the notion of quasi-‎testimony acknowledges that technological devices can assert or testify under some conditions, without ‎denying that humans and machines are not the same. Indeed, there are four relevant differences between ‎humans and instruments. First, unlike humans, machine assertion is not imaginative or playful. Second, ‎machine assertion is (...) prescripted and context restricted. As such, computers currently cannot easily switch ‎contexts or make meaningful relevant assertions in contexts for which they were not programmed. Third, ‎while both humans and computers make errors, they do so in different ways. Computers are very sensitive to ‎small errors in input, which may cause them to make big errors in output. Moreover, automatic error control ‎is based on finding irregularities in data without trying to establish whether they make sense. Fourth, ‎testimony is produced by a human with moral worth, while quasi-testimony is not. Ultimately, the notion of ‎quasi-testimony can serve as a bridge between different philosophical fields that deal with instruments and ‎testimony as sources of knowledge, allowing them to converse and agree on a shared description of reality, ‎while maintaining their distinct conceptions and ontological commitments about knowledge, humans, and ‎nonhumans.‎. (shrink)

In Philosophy and Technology 36, David Watson discusses the epistemological and metaphysical implications of unsupervised machine learning (ML) algorithms. Watson is sympathetic to the epistemological comparison of unsupervised clustering, abstraction and generative algorithms to human cognition and sceptical about ML’s mechanisms having ontological implications. His epistemological commitments are that we learn to identify “natural kinds through clustering algorithms”, “essential properties via abstraction algorithms”, and “unrealized possibilities via generative models” “or something very much like them.” The same issue contains a (...) commentary on Watson’s paper in which Tom Sterkenburg fiercely opposes the epistemological claim that clustering algorithms can identify natural kinds. Sterkenburg argues there’s nothing about clustering arguments themselves that enables natural kind identification. What’s at stake in the Watson-Sterkenburg debate is whether it is possible for clustering algorithms to identify natural kinds and whether they underly human identification of natural kinds. My contribution to the Watson-Sterkenburg debate is twofold. First, I argue Sterkenburg’s criticism of Watson’s claim is too severe because it denies any clustering algorithm to identify any natural kind. Secondly, I argue Watson’s reply overestimates both clustering algorithms’ and humans’ access to natural kinds and his claim has to be restricted accordingly to ‘We identify kinds via clustering algorithms, or something very much like them’. (shrink)

Many philosophers have been attracted to a restricted version of the principle of indifference in the case of self-locating belief. Roughly speaking, this principle states that, within any given possible world, one should be indifferent between different hypotheses concerning who one is within that possible world, so long as those hypotheses are compatible with one’s evidence. My first goal is to defend a more precise version of this principle. After responding to several existing criticisms of such a principle, I (...) argue that existing formulations of the principle are crucially ambiguous, and I go on to defend a particular disambiguation of the principle. According to the disambiguation I defend, how we should apply this restricted principle of indifference sensitively depends on our background metaphysical beliefs. My second goal is to apply this disambiguated principle to classical skeptical problems in epistemology. In particular, I argue that Eternalism threatens to lead us to external world skepticism, and Modal Realism threatens to lead us to inductive skepticism. (shrink)

As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments from (...) feminist philosophy of science to machine learning programs to make the case that the resources required to respond to these inductive challenges render critical aspects of their design constitutively value-laden. I demonstrate these points specifically in the case of recidivism algorithms, arguing that contemporary debates concerning fairness in criminal justice risk-assessment programs are best understood as iterations of traditional arguments from inductive risk and demarcation, and thereby establish the value-laden nature of automated decision-making programs. Finally, in light of these points, I address opportunities for relocating the value-free ideal in machine learning and the limitations that accompany them. (shrink)

I propose a pragmatic approach to the kind of reference-shifting occurring in indexicals as used in e.g. written notes and answering machine messages. I proceed in two steps. First, I prepare the ground by showing that the arguments against such a pragmatic approach raised in the recent literature fail. Second, I take a first few steps towards implementing this approach, by sketching a pragmatic theory of reference-shifting, and showing how it can handle cases of the relevant kind. While the (...) immediate scope of the paper is restricted to indexicals and reference-shifting, and the discussion is confined to a specific range of theories and cases, the approach proposed is compatible with a fairly broad range of more or less semantically conservative theories, and many of the conclusions drawn are significant for the evaluation of pragmatic explanations in philosophy more generally. The overall goal is to offer a new perspective on the issues under discussion, and to prompt philosophers to reconsider some of the established methods by which pragmatic explanations are evaluated. (shrink)

On a literal reading of `Computing Machinery and Intelligence'', Alan Turing presented not one, but two, practical tests to replace the question `Can machines think?'' He presented them as equivalent. I show here that the first test described in that much-discussed paper is in fact not equivalent to the second one, which has since become known as `the Turing Test''. The two tests can yield different results; it is the first, neglected test that provides the more appropriate indication of intelligence. (...) This is because the features of intelligence upon which it relies are resourcefulness and a critical attitude to one''s habitual responses; thus the test''s applicablity is not restricted to any particular species, nor does it presume any particular capacities. This is more appropriate because the question under consideration is what would count as machine intelligence. The first test realizes a possibility that philosophers have overlooked: a test that uses a human''s linguistic performance in setting an empirical test of intelligence, but does not make behavioral similarity to that performance the criterion of intelligence. Consequently, the first test is immune to many of the philosophical criticisms on the basis of which the (so-called) `Turing Test'' has been dismissed. (shrink)

Is there a notion of domain specificity which affords genuine insight in the context of the highly modular mind, i.e. a mind which has not only input modules, but also central ‘conceptual’ modules? Our answer to this question is no. The main argument is simple enough: we lay out some constraints that a theoretically useful notion of domain specificity, in the context of the highly modular mind, would need to meet. We then survey a host of accounts of what domain (...) specificity is, based on the intuitive idea that a domain specific mechanism is restricted in the kind of information that it processes, and show that each fails at least one of those constraints. (shrink)

The goal of creating Artificial General Intelligence (AGI) – or in other words of creating Turing machines (modern computers) that can behave in a way that mimics human intelligence – has occupied AI researchers ever since the idea of AI was first proposed. One common theme in these discussions is the thesis that the ability of a machine to conduct convincing dialogues with human beings can serve as at least a sufficient criterion of AGI. We argue that this very (...) ability should be accepted also as a necessary condition of AGI, and we provide a description of the nature of human dialogue in particular and of human language in general against this background. We then argue that it is for mathematical reasons impossible to program a machine in such a way that it could master human dialogue behaviour in its full generality. This is (1) because there are no traditional explicitly designed mathematical models that could be used as a starting point for creating such programs; and (2) because even the sorts of automated models generated by using machine learning, which have been used successfully in areas such as machine translation, cannot be extended to cope with human dialogue. If this is so, then we can conclude that a Turing machine also cannot possess AGI, because it fails to fulfil a necessary condition thereof. At the same time, however, we acknowledge the potential of Turing machines to master dialogue behaviour in highly restricted contexts, where what is called “narrow” AI can still be of considerable utility. (shrink)

By drawing on the philosophy of Bernard Stiegler, the phenomena of mechanical (a.k.a. artificial, digital, or electronic) intelligence is explored in terms of its real significance as an ever-repeating threat of the reemergence of stupidity (as cowardice), which can be transformed into knowledge (pharmacological analysis of poisons and remedies) by practices of care, through the outlook of what researchers describe equivocally as “artificial stupidity”, which has been identified as a new direction in the future of computer science and machine (...) problem solving as well as a new difficulty to be overcome. I weave together of web of “artificial stupidity”, which denotes the mechanic (1), the human (2), or the global (3). With regards to machine intelligence, artificial stupidity refers to: 1a) Weak A.I. or a rhetorical inversion of designating contemporary practices of narrow task-based procedures by algorithms in opposition to “True A.I.”; 1b) the restriction or employment of constraints that weaken the effectiveness of A.I., which is to say a “dumbing-down” of A.I. by intentionally introducing mistakes by programmers for safety concerns and human interaction purposes; 1c) the failure of machines to perform designated tasks; 1d) a lack of a noetic capacity, which is a lack of moral and ethical discretion; 1e) a lack of causal reasoning (true intelligence) as opposed to statistical associative “curve fitting”; or 2) the phenomenon of increasing human “stupidity” or drive-based behaviors, which is considered as the degradation of human intelligence and/or “intelligent human behavior” through technics; and finally, 3) the global phenomenon of increasing entropy due to a black-box economy of closed systems and/or industry consolidation. (shrink)

Application of the reliability of repairable systems on solving problems from constructing production systems takes an important place in the process of finding the optimal solution among the suggested system choices. The basic hypothesis when using the reliability of the repairable systems is that every machine is representing a component, a fact that is debatable when talking about technical sciences. However, considering the second assumption of the stationary process, the function of the availability is introduced. It represents the measure (...) between the reliability and the benefit of maintaining the components and the system as a whole one. An additional and the last reason for analyzing constructing machine as a whole one has an economic reason. From the aspect of the total cost and the capacity of the system, the function of the availability is catching more and more impact in the process of determinating the total cost in bill of quantities as the most important part of tender documents. Until former works, techno-economic optimisation usually ran over when choosing the system of the construction machines. In the work, there are shown the methodologies for gathering and analyzing the indicators of availabilities per unit of a company's machine park. The time of demission and reparation have been investigated among 34 components in the period of 6 years. Based on the obtained result for different conditions of machine exploitation and facilities, it is suggested the methodology of the future behaviour of the corresponding within the model optimization availability of constructing production system with the aim of eliminating extra criterion. The suggested model takes in consideration the total cost of the components when they are functional, and when they are not. This kind of approach enables the one who take choices decision of the height of profits, in the lenght of work in function of the projection of cash flow and managing with real indicators of functioning the system at the project level. Given the fact that the maintanance policy of machines is based on availability, and not on reliability of components, with the aim of maximizing the availability of different budget scenarios, a logical approach to the selection of the objective function in the terms of management of construction production systems is to minimize costs for a given availability of system constructing machinery is based on the use of historical data base with the values of the operational availability of components, conditions in which they function, in the process of forecasting the project availability for projects that follow. Ranking variant solutions is the result of the adopted function of the aim as a consequence due to enlargement methods of availability for serial parallel systems with the associated restrictions. For optimal system availability is taken a value that belongs to minimal total cost. However, it is predicted that after the end of the financial year, the real and projected indicators of system functions get compared to each other. Informational support to the proposed model is informational company system, which was built in the PHP programming language on the Linux software platform, and with data based in My SQL. The suggested model is tested on the system for the production and embedding bitumen bound materials on the specific project. Obtained results confirm the assumptions used in work. (shrink)

This paper introduces Exclusion Logic - a simple modal logic without negation or disjunction. We show that this logic has an efficient decision procedure. We describe how Exclusion Logic can be used as a deontic logic. We compare this deontic logic with Standard Deontic Logic and with more syntactically restricted logics.

The author has extensive law enforcement experience and the paper is intended to provoke thought on the use of technology as it pertains to information sharing between the police and the private sector. -/- As the world edges closer and closer to the convergence of man and machine, the human capacity to retrieve information is increasing by leaps and bounds. We are on the verge of knowing everything and anything there is to know, and literally in the blink of (...) an eye! -/- This means that police will have the capacity to learn everything about everyone with the only restriction being privacy legislation. But it also means that those involved in immoral, unlawful or illegal activity will have that same capacity and with no such restriction. ‘Bad Brother’ may be far more dangerous than ‘Big Brother!' -/- The global community requires a secure and credible system to retrieve and assess all of the information ‘generally available to the public.' A system that will strive to keep ‘Big Brother’ in check and ‘Bad Brother’ out, all the while providing a means of alerting citizens to genuine risks or to dangerous people. Such as system would help diffuse the systemic inaccurate and harmful profiling that is often based on rumours and innuendo. -/- There is an identified public-private partnership opportunity. A chance to work with privacy advocate groups and background checking private companies to define, design and deliver on something that will be of immense benefit to citizens around the globe. -/- We have an opportunity to create something that will work to ensure that only the best information gets used and in a moralistic, lawful and legal way! Technology continues to move forward at incomprehensible speeds – failure to act could have serious consequences. (shrink)

Please contact me at [email protected] if you are interested in reading a particular chapter or being sent the entire manuscript for private use. -/- The thesis offers a comprehensive argument in favor of a regulationist approach to autonomous weapon systems (AWS). AWS, defined as all military robots capable of selecting or engaging targets without direct human involvement, are an emerging and potentially deeply transformative military technology subject to very substantial ethical controversy. AWS have both their enthusiasts and their detractors, prominently (...) advocating for a global preemptive ban on AWS development and use. Rejecting both positions, the author outlines a middle-of-the-road regulationist approach that is neither overly restrictive nor overly permissive. The disqualifying flaws of the rival prohibitionist approach are demonstrated in the process. After defining the core term of autonomy in weapon systems, the practical difficulties involved in applying an arms control regime to AWS are analyzed. The analysis shows that AWS are an extremely regulation-resistant technology. This feature when combined with their assumed high military utility makes a ban framework extremely costly to impose and enforce. As such it is ultimately very likely to fail at the benefit of the most unscrupulous international actors and at a very substantial risk to those abiding with international law. Consequently, to be ethically viable, a prohibitionist framework would need to offer substantial moral benefits impossible to attain through the rival regulationist approach. The remainder of the thesis undertakes to demonstrate that this is not the case. Comparing the considerations of military and strategic necessity to humanitarian concerns most commonly voiced by prohibitionists requires finding a common denominator for all values being referred to. Consequently, the thesis proceeds to show that both kinds of concerns are ultimately reducible to respect for basic human rights of all stakeholders, and so that the prohibitionist and regulationist approach may ultimately be compared in terms of consequences their adoption would generate for basic human rights realization. The author then evaluates both the potential humanitarian benefits, and the potential humanitarian hazards of AWS introduction. The benefits of leaving frontline combat to machines are outlined, with the unique kinds of suffering that would be abolished by such a development being described in detail. The arguments against AWS adoption are then divided into three classes: arguments related to alleged impossibility of compliance with The Laws of Armed Conflict, non-consequentialist and broad consequentialist arguments. This analysis, which comprises the greater part of the entire thesis, shows that the concerns behind compliance arguments are indeed substantial and have to be accommodated via a complex framework of best practices, regulations and localized restrictions on some kinds of AWS or AWS use in particular environments. They do not, however, justify a universal ban on using all the diverse forms of AWS in all environments. Non-consequentialist objections are found either reducible to other classes of arguments or thoroughly unconvincing, sometimes to the point of being actually vacuous. Broad consequentialist concerns are likewise found to be accommodable by regulation, empirically unfounded or causally disconnected from the actions of legitimate actors acquiring AWS, and therefore irrelevant to the moral permissibility of such actions. The author concludes that the proponents of prohibitionism are unable to point to moral benefits substantial enough to justify the costs and risks inherent in the approach. A global ban is, in fact, likely to have a worse humanitarian impact that well-regulated AWS adoption even if the strategic risks are disregarded. On the other hand, the analysis shows that there indeed exists an urgent need to regulate AWS through a variety of technological, procedural and legal solutions. These include, but are not limited to, a temporary moratorium on anti-personnel AWS use, development of internationally verified compliance software and eventual legal requirement of its employment, a temporary moratorium on AWS proliferation to state actors and a ban on their proliferation to non-state agents. (shrink)

Some modern cosmological models predict the appearance of Boltzmann Brains: observers who randomly fluctuate out of a thermal bath rather than naturally evolving from a low-entropy Big Bang. A theory in which most observers are of the Boltzmann Brain type is generally thought to be unacceptable, although opinions differ. I argue that such theories are indeed unacceptable: the real problem is with fluctuations into observers who are locally identical to ordinary observers, and their existence cannot be swept under (...) the rug by a choice of probability distributions over observers. The issue is not that the existence of such observers is ruled out by data, but that the theories that predict them are cognitively unstable: they cannot simultaneously be true and justifiably believed. (shrink)

Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...) they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

ABSTRACT. Two arguments have recently been advanced that Maxwell-Boltzmann particles are indistinguishable just like Bose-Einstein and Fermi-Dirac particles. Bringing modal metaphysics to bear on these arguments shows that ontological indistinguishability for classical (MB) particles does not follow. The first argument, resting on symmetry in the occupation representation for all three cases, fails since peculiar correlations exist in the quantum (BE and FD) context as harbingers of ontic indistinguishability, while the indistinguishability of classical particles remains purely epistemic. The second argument, (...) deriving from the classical limits of quantum statistical partition functions, embodies a conceptual confusion. After clarifying the doctrine of haecceitism, a third argument is considered that attempts to deflate metaphysical concerns altogether by showing that the phase-space and distribution-space representations of MB-statistics have contrary haecceitistic import. Careful analysis shows this argument to fail as well, leaving de re modality unproblematically grounding particle identity in the classical context while genuine puzzlement about the underlying ontology remains for quantum statistics. (shrink)

Genes are often described by biologists using metaphors derived from computa- tional science: they are thought of as carriers of information, as being the equivalent of ‘‘blueprints’’ for the construction of organisms. Likewise, cells are often characterized as ‘‘factories’’ and organisms themselves become analogous to machines. Accordingly, when the human genome project was initially announced, the promise was that we would soon know how a human being is made, just as we know how to make airplanes and buildings. Impor- tantly, (...) modern proponents of Intelligent Design, the latest version of creationism, have exploited biologists’ use of the language of information and blueprints to make their spurious case, based on pseudoscientific concepts such as ‘‘irreducible complexity’’ and on flawed analogies between living cells and mechanical factories. However, the living organ- ism = machine analogy was criticized already by David Hume in his Dialogues Concerning Natural Religion. In line with Hume’s criticism, over the past several years a more nuanced and accurate understanding of what genes are and how they operate has emerged, ironically in part from the work of computational scientists who take biology, and in particular developmental biology, more seriously than some biologists seem to do. In this article we connect Hume’s original criticism of the living organism = machine analogy with the modern ID movement, and illustrate how the use of misleading and outdated metaphors in science can play into the hands of pseudoscientists. Thus, we argue that dropping the blueprint and similar metaphors will improve both the science of biology and its understanding by the general public. (shrink)

In order to determine whether current (or future) machines have a welfare that we as agents ought to take into account in our moral deliberations, we must determine which capacities give rise to interests and whether current machines have those capacities. After developing an account of moral patiency, I argue that current machines should be treated as mere machines. That is, current machines should be treated as if they lack those capacities that would give rise to psychological interests. Therefore, they (...) are moral patients only if they have non-psychological interests. I then provide an account of what I call teleo interests that constitute the most plausible type of non-psychological interest that a being might have. I then argue that even if current machines have teleo interests, they are such that agents need not concern themselves with these interests. Therefore, for all intents and purposes, current machines are not moral patients. (shrink)

A number of findings in the field of machine learning have given rise to questions about what it means for automated scoring- or decisionmaking systems to be fair. One center of gravity in this discussion is whether such systems ought to satisfy classification parity (which requires parity in accuracy across groups, defined by protected attributes) or calibration (which requires similar predictions to have similar meanings across groups, defined by protected attributes). Central to this discussion are impossibility results, owed to (...) Kleinberg et al. (2016), Chouldechova (2017), and Corbett-Davies et al. (2017), which show that classification parity and calibration are often incompatible. This paper aims to argue that classification parity, calibration, and a newer, interesting measure called counterfactual fairness are unsatisfactory measures of fairness, offer a general diagnosis of the failure of these measures, and sketch an alternative approach to understanding fairness in machine learning. (shrink)

Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets of modern life. Ethical concerns about this widespread practice have given rise to the field of fair machine learning and a number of fairness measures, mathematically precise definitions of fairness that purport to determine whether a given prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in this context to be a placeholder for a variety of normative egalitarian (...) considerations. We explore a few fairness measures to suss out their egalitarian roots and evaluate them, both as formalizations of egalitarian ideas and as assertions of what fairness demands of predictive systems. We pay special attention to a recent and popular fairness measure, counterfactual fairness, which holds that a prediction about an individual is fair if it is the same in the actual world and any counterfactual world where the individual belongs to a different demographic group (cf. Kusner et al. (2018)). (shrink)

The fact that real-world decisions made by artificial intelligences (AI) are often ethically loaded has led a number of authorities to advocate the development of “moral machines”. I argue that the project of building “ethics” “into” machines presupposes a flawed understanding of the nature of ethics. Drawing on the work of the Australian philosopher, Raimond Gaita, I argue that ethical dilemmas are problems for particular people and not (just) problems for everyone who faces a similar situation. Moreover, the force of (...) an ethical claim depends in part on the life history of the person who is making it. For both these reasons, machines could at best be engineered to provide a shallow simulacrum of ethics, which would have limited utility in confronting the ethical and policy dilemmas associated with AI. (shrink)

When do objects at different times compose a further object? This is the question of diachronic composition. The universalist answers, ‘under any conditions whatsoever’. Others argue for restrictions on diachronic composition: composition occurs only when certain conditions are met. Recently, some philosophers have argued that restrictions on diachronic compositions are motivated by our best physical theories. In Persistence and Spacetime and elsewhere, Yuri Balashov argues that diachronic compositions are restricted in terms of causal connections between object stages. In a (...) recent article, Nikk Effingham argues that the standard objections to views that endorse restrictions on composition do not apply to a view that restricts composition according to compliance with the laws of nature. On the face of it, such restrictions on diachronic composition preserve our common-sense ontology while eliminating from it scientifically revisionary objects that travel faster than the speed of light. I argue that these attempts to restrict diachronic composition by appealing to either causal or nomological constraints face insurmountable difficulties within the context of special relativity. I discuss how the universalist should best respond to Hudson’s argument for superluminal objects, and in doing so, I present and defend a new sufficient condition for motion that does not entail that such objects are in superluminal motion. 1 Introduction2 Diachronic Composition3 Diachronic Composition and Superluminal Objects4 Restricting Diachronic Composition5 Causal and Nomological Restrictions on Composition in a Relativistic Context6 Superluminal Objects and Motion7 Conclusion. (shrink)

I present a new thermodynamic argument for the existence of God. Naturalistic physics provides evidence for the failure of induction, because it provides evidence that the past is not at all what you think it is, and your existence is just a momentary fluctuation. The fact that you are not a momentary fluctuation thus provides evidence for the existence of God – God would ensure that the past is roughly what we think it is, and you have been in existence (...) for roughly the amount of time you think you have. I don’t have a definitive way for the atheist to refute this argument, but I give one suggestion that relies on physics-based simplicity considerations. I close with an epistemological discussion of self-undermining arguments. (shrink)

In light of recent breakneck pace in machine learning, questions about whether near-future artificial systems might be conscious and possess moral status are increasingly pressing. This paper argues that as matters stand these debates lack any clear criteria for resolution via the science of consciousness. Instead, insofar as they are settled at all, it is likely to be via shifts in public attitudes brought about by the increasingly close relationships between humans and AI users. Section 1 of the paper (...) I briefly lays out the current state of the science of consciousness and its limitations insofar as these pertain to machine consciousness, and claims that there are no obvious consensus frameworks to inform public opinion on AI consciousness. Section 2 examines the rise of conversational chatbots or Social AI, and argues that in many cases, these elicit strong and sincere attributions of consciousness, mentality, and moral status from users, a trend likely to become more widespread. Section 3 presents an inconsistent triad for theories that attempt to link consciousness, behaviour, and moral status, noting that the trends in Social AI systems will likely make the inconsistency of these three premises more pressing. Finally, Section 4 presents some limited suggestions for how consciousness and AI research communities should respond to the gap between expert opinion and folk judgment. (shrink)

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to (...) enhance model generalizability. (shrink)

The machine conception of the organism (MCO) is one of the most pervasive notions in modern biology. However, it has not yet received much attention by philosophers of biology. The MCO has its origins in Cartesian natural philosophy, and it is based on the metaphorical redescription of the organism as a machine. In this paper I argue that although organisms and machines resemble each other in some basic respects, they are actually very different kinds of systems. I submit (...) that the most significant difference between organisms and machines is that the former are intrinsically purposive whereas the latter are extrinsically purposive. Using this distinction as a starting point, I discuss a wide range of dissimilarities between organisms and machines that collectively lay bare the inadequacy of the MCO as a general theory of living systems. To account for the MCO’s prevalence in biology, I distinguish between its theoretical, heuristic, and rhetorical functions. I explain why the MCO is valuable when it is employed heuristically but not theoretically, and finally I illustrate the serious problems that arise from the rhetorical appeal to the MCO. (shrink)

We use imperatives to refute a naïve analysis of update potentials (force-operators attaching to sentences), arguing for a dynamic analysis of imperative force as restrictable, directed, and embeddable. We propose a dynamic, non-modal analysis of conditional imperatives, as a counterpoint to static, modal analyses. Our analysis retains Kratzer's analysis of if-clauses as restrictors of some operator, but avoids typing it as a generalized quantifier over worlds (against her), instead as a dynamic force operator. Arguments for a restrictor treatment (but against (...) a quantificational treatment) are mustered, and we propose a novel analysis of update on conditional imperatives (and an independently motivated revision of the standard ordering-semantics for root modals that makes use of it). Finally, we argue that imperative force is embeddable under an operation much like dynamic conjunction. (shrink)

We construct a machine that knows its own code, at the price of not knowing its own factivity.

Leading cosmological theories engender a controversial puzzle which has prompted philosophers to propose competing epistemological solutions and physicists to propose methodological changes to cosmology. The puzzle arises from the prediction that every brain on Earth will eventually be vastly outnumbered by physical duplicates formed by random collisions of particles in outer space. Supposing that this prediction is correct, shouldn't you believe that your brain is probably one of these vastly more typical extraterrestrial brains, since you cannot infer your brain's origin (...) from your experiential state? But supposing that your brain is one of these extraterrestrial brains, why be confident in the cosmological theories, since you never actually received testimony supporting these theories? Proposals in the literature either deny the rationality of believing theories that make the prediction or deny the typicality of your brain among its duplicates. This paper argues that these proposals are not entirely satisfactory. Instead, one should be confident in theories making the prediction on the supposition that your brain is one of the extraterrestrial brains. The upshots include that it may be rational to believe that your brain is probably an extraterrestrial brain and that cosmologists should not alter their methodology in response to the puzzle. (shrink)

Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also (...) discuss how work in embodied and situated cognition could provide a valu- able perspective on future research. (shrink)

According to the most popular theories of intentionality, a family of theories we will refer to as “functional intentionality,” a machine can have genuine intentional states so long as it has functionally characterizable mental states that are causally hooked up to the world in the right way. This paper considers a detailed description of a robot that seems to meet the conditions of functional intentionality, but which falls victim to what I call “the composition problem.” One obvious way to (...) escape the problem (arguably, the only way) is if the robot can be shown to be a moral patient – to deserve a particular moral status. If so, it isn’t clear how functional intentionality could remain plausible (something like “phenomenal intentionality” would be required). Finally, while it would have seemed that a reasonable strategy for establishing the moral status of intelligent machines would be to demonstrate that the machine possessed genuine intentionality, the composition argument suggests that the order of precedence is reversed: The machine must first be shown to possess a particular moral status before it is a candidate for having genuine intentionality. (shrink)

We explore the question of whether machines can infer information about our psychological traits or mental states by observing samples of our behaviour gathered from our online activities. Ongoing technical advances across a range of research communities indicate that machines are now able to access this information, but the extent to which this is possible and the consequent implications have not been well explored. We begin by highlighting the urgency of asking this question, and then explore its conceptual underpinnings, in (...) order to help emphasise the relevant issues. To answer the question, we review a large number of empirical studies, in which samples of behaviour are used to automatically infer a range of psychological constructs, including affect and emotions, aptitudes and skills, attitudes and orientations (e.g. values and sexual orientation), personality, and disorders and conditions (e.g. depression and addiction). We also present a general perspective that can bring these disparate studies together and allow us to think clearly about their philosophical and ethical implications, such as issues related to consent, privacy, and the use of persuasive technologies for controlling human behaviour. (shrink)

Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the allocation (...) outcomes? If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance by k% of employers can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; (4) partial compliance based on local parity measures can induce extreme segregation. Finally, we discuss implications for auditors and insights concerning the design of regulatory frameworks. (shrink)

This article critically examines one of the most prevalent metaphors in modern biology, namely the machine conception of the organism (MCO). Although the fundamental differences between organisms and machines make the MCO an inadequate metaphor for conceptualizing living systems, many biologists and philosophers continue to draw upon the MCO or tacitly accept it as the standard model of the organism. This paper analyses the specific difficulties that arise when the MCO is invoked in the study of development and evolution. (...) In developmental biology the MCO underlies a logically incoherent model of ontogeny, the genetic program, which serves to legitimate three problematic theses about development: genetic animism, neo-preformationism, and developmental computability. In evolutionary biology the MCO is responsible for grounding unwarranted theoretical appeals to the concept of design as well as to the interpretation of natural selection as an engineer, which promote a distorted understanding of the process and products of evolutionary change. Overall, it is argued that, despite its heuristic value, the MCO today is impeding rather than enabling further progress in our comprehension of living systems. (shrink)

Abstract. Boltzmann brains (BBs) are minds which randomly appear as a result of thermodynamic or quantum fluctuations. In this article, the question of if we are BBs, and the observational consequences if so, is explored. To address this problem, a typology of BBs is created, and the evidence is compared with the Simulation Argument. Based on this comparison, we conclude that while the existence of a “normal” BB is either unlikely or irrelevant, BBs with some ordering may have observable (...) consequences. There are two types of such ordered BBs: Boltzmannian typewriters (including Boltzmannian simulations), and chains of observer moments. Notably, the existence or non-existence of BBs may have practical applications for measuring the size of the universe, achieving immortality, or even manipulating the observed probability of events. -/- Disclaimer and trigger warning: some people have emotional breakdowns when thinking about topics described in the article, especially the “flux universe”. However, everything eventually adds up to normality. (shrink)

3rd ed, 2021. A circumscription of the classical theory of computation building up from the Chomsky hierarchy. With the usual topics in formal language and automata theory.

In this article I explore various facets of Nozick’s famous thought experiment involving the experience machine. Nozick’s original target is hedonism—the view that the only intrinsic prudential value is pleasure. But the argument, if successful, undermines any experientialist theory, i.e. any theory that limits intrinsic prudential value to mental states. I first highlight problems arising from the way Nozick sets up the thought experiment. He asks us to imagine choosing whether or not to enter the machine and uses (...) our choice (or rather the choice he assumes most people will have) as evidence against experientialist theories. But for this strategy to succeed it must be possible to distinguish between self-interested and non-self-interested reasons for declining to enter the machine, and there is no obvious way to do this without begging the question against the hedonist. In successive sections I then (a) consider a common misconception of Nozick’s conclusion (that he thinks machine life is the worst life), (b) consider different intuitions about what is important to well-being but missing from machine life, and finally (c) explain what “the experience requirement” is, and describe its relationship to debates about experientialist theories. (shrink)

Historical processes exert an influence on the current state and evolution of situations of language contact, brought to bear from different domains, the economic and the political, the ideological and group identities, geo-demographics, and the habits of inter-group use. Clearly, this kind of phenomenon requires study from a complexical and holistic perspective in order to accommodate the variety of factors that belong to different levels and that interrelate with one another in the evolving dynamic of human languaging. Therefore, there is (...) a need for the restricted and general complexity approaches to come to a meeting of the minds, and take steps toward a mutual integration based on the acceptance of the shortcomings of each approach, achieving progress through a non-contradictory complementarity of perspectives. It must be conceded that the practical and methodological applications of basic complexical ideas need to be developed much farther in order to apply them to specific research. At the same time, the limits of complex adaptive systems as computational strategies must be accepted in the pursuit of a better understanding of the dynamic and evolutionary processes typical of human beings. New tools for the conception, apprehension and treatment of the data will need to be devised to complement existing ones and to enable us to make headway toward practices that better fit complexical perspectives. It seems obvious that human complexics must be seen as multi-methodological, insofar as necessary combining quantitative-computation methodologies and more qualitative methodologies aimed at understanding the historical mental and emotional world of people. (shrink)

Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why did Turing (...) refer to gender imitation in a test for machine intelligence? In this article, I shall address these questions by unveiling social, historical and epistemological roots of the so-called Turing test. I will draw attention to a historical fact that has been only scarcely observed in the secondary literature thus far, namely that Turing’s 1950 test emerged out of a controversy over the cognitive capabilities of digital computers, most notably out of debates with physicist and computer pioneer Douglas Hartree, chemist and philosopher Michael Polanyi, and neurosurgeon Geoffrey Jefferson. Seen in its historical context, Turing’s 1950 paper can be understood as essentially a reply to a series of challenges posed to him by these thinkers arguing against his view that machines can think. Turing did propose gender learning and imitation as one of his various imitation tests for machine intelligence, and I argue here that this was done in response to Jefferson's suggestion that gendered behavior is causally related to the physiology of sex hormones. (shrink)

Large language models (LLMs) represent the currently most relevant incarnation of artificial intelligence with respect to the future fate of democratic governance. Considering their potential, this paper seeks to answer a pressing question: Could LLMs outperform humans as expert advisors to democratic assemblies? While bearing the promise of enhanced expertise availability and accessibility, they also present challenges of hallucinations, misalignment, or value imposition. Weighing LLMs’ benefits and drawbacks compared to their human counterparts, I argue for their careful integration to augment (...) democracy’s ability to address complex policy issues. The paper posits that time-tested democratic procedures like deliberation and aggregation by voting provide safeguards effective against both human and machine advisor imperfections. Additional protective measures include custom LLM training for the advisory role, boosting representatives’ competencies in query formulation, or implementation of adversarial proceedings in which LLM advisors could debate each other and provide dissenting opinions. These could further mitigate the risks that LLMs present in advisory roles and empower human decision-makers toward increased autonomy and quality of their collective choices. My conceptual exploration offers a roadmap for the co-evolution of AI and democratic institutions, setting the stage for an empirical research agenda to finetune the implementation specifics. (shrink)

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

Most of us believe that partiality applies in a broad range of relationships. One relationship on which there is much disagreement is co-nationality. Some writers argue that co-national partiality is not justified in certain cases, like killing in war, since killing in defense of co-nationals is intuitively impermissible in other contexts. I argue that this approach overlooks an important structural feature of partiality—namely, that its scope is sometimes restricted. In this essay, I show how some relationships that generate reasons (...) of partiality are restricted in scope—that is, they generate reasons within particular contexts or with respect to particular goods. I then argue that co-national partiality is scope restricted. I then show how this fact helps proponents of co-national partiality overcome the aforementioned objection to its application in cases like war. (shrink)

Some physical theories predict that almost all brains in the universe are Boltzmann brains, i.e. short-lived disembodied brains that are accidentally assembled as a result of thermodynamic or quantum fluctuations. Physicists and philosophers of physics widely regard this proliferation as unacceptable, and so take its prediction as a basis for rejecting these theories. But the putatively unacceptable consequences of this prediction follow only given certain philosophical assumptions. This paper develops a strategy for shielding physical theorizing from the threat of (...) Boltzmann brains. The strategy appeals to a form of phenomenal externalism about the physical basis of consciousness. Given that form of phenomenal externalism, the proliferation of Boltzmann brains turns out to be benign. While the strategy faces a psychophysical fine-tuning problem, it both alleviates cosmological fine-tuning concerns that attend physics-based solutions to Boltzmann brain problems and pays explanatory dividends in connection with time’s arrow. (shrink)

A Boltzmann Brain, haphazardly formed through the unlikely but still possible random assembly of physical particles, is a conscious brain having experiences just like an ordinary person. The skeptical possibility of being a Boltzmann Brain is an especially gripping one: scientific evidence suggests our actual universe’s full history may ultimately contain countless short-lived Boltzmann Brains with experiences just like yours or mine. I propose a solution to the skeptical challenge posed by these countless actual Boltzmann Brains. (...) My key idea is roughly this: the skeptical argument that you’re one of the Boltzmann Brains requires you to make a statistical inference, but the Principle of Total Evidence blocks us from making the inference. I discuss how my solution contrasts with a recent suggestion, made by Sean Carroll and David Chalmers, for how to address the skeptical challenge posed by Boltzmann Brains. And I discuss how my solution handles certain relevant concerns about what to do when we have higher-order evidence indicating that our first-order evidence is misleading. (shrink)

Jc Beall has made several contributions to the theory of restricted quantification in relevant logics. This paper examines these contributions and proposes an alternative account of restricted universals. The alternative is not, however, a theory of relevant restricted universals in any real sense. It is, however, a theory of restricted universals phrased in the most plausible general quantificational theory for relevant logics—Kit Fine’s stratified semantics. The motivation both for choosing this semantic framework and for choosing the (...) particular theory of restricted quantification we use is because they are the best way of dealing with these topics from Beall’s theory-building theory picture of logic, establishing a second point of contact with Beall’s work. (shrink)

Let’s begin with a simple example. Consider two quarks: one near the tip of your nose, the other near the center of Alpha Centauri. Here is a question about these two subatomic particles: Is there an object that has these two quarks as its parts and that has no other parts? According to one view of the matter (a view that is surprisingly endorsed by a great many contemporary philosophers), the answer to this question is Yes. But I think it (...) is fair to say that according to common sense, the answer to this question is really No, there is no object that has as its only two parts a quark near the tip of your nose and another quark near the center of Alpha Centauri. (shrink)

Let's suppose you think that there are no uncountable sets. Have you adopted a restrictive position? It is certainly tempting to say yes---you've prohibited the existence of certain kinds of large set. This paper argues that this intuition can be challenged. Instead, I argue that there are some considerations based on a formal notion of restrictiveness which suggest that it is restrictive to hold that there are uncountable sets.