As computers became multi-component systems in the 1950s, handling the speed differentials efficiently was identified as a major challenge. The desire for better understanding and control of ‘concurrency’ spread into hardware, software, and formalism. This paper examines the way in which the problem emerged and was handled across various computing cultures from 1955 to 1985. In the machinic culture of the late 1950s, system programs called ‘monitors’ were used for directly managing synchronisation. Attempts to reframe synchronisation in the subsequent algorithmic (...) culture pushed the problem to a higher level of abstraction; Dijkstra’s semaphores were a reaction to the algorithms’ complexity. Towards the end of the 1960s, the culture of ‘structured programming’ created a milieu in which Dijkstra, Hoare, and Brinch Hansen (among others) aimed for a concurrency primitive which embodied the new view of programming. Via conditional critical regions and Dijkstra’s ‘secretaries’, the co-produced ‘monitor’ appeared to provide the desired encapsulation. The construct received embodiment in a few programming languages; this paper ends by considering Modula and Concurrent Pascal. (shrink)

Scientific understanding is a fundamental goal of science. However, there is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of scientific understanding. In this paper, we propose a framework to create a benchmark for scientific understanding, utilizing tools from philosophy of science. We adopt a behavioral conception of understanding, according to which genuine understanding should be recognized (...) as an ability to perform certain tasks. We extend this notion of scientific understanding by considering a set of questions that gauge different levels of scientific understanding, covering information retrieval, the capability to arrange information to produce an explanation, and the ability to infer how things would be different under different circumstances. We suggest building a Scientific Understanding Benchmark (SUB), formed by a set of these tests, allowing for the evaluation and comparison of scientific understanding. Benchmarking plays a crucial role in establishing trust, ensuring quality control, and providing a basis for performance evaluation. By aligning machine and human scientific understanding we can improve their utility, ultimately advancing scientific understanding and helping to discover new insights within machines. (shrink)

Some computational phenomena rely essentially on pragmatic considerations, and seem to undermine the independence of the specification from the implementation. These include software development, deviant uses, esoteric languages and recent data-driven applications. To account for them, the interaction between pragmatics, epistemology and ontology in computational artefacts seems essential, indicating the need to recover the role of the language metaphor. We propose a User Levels (ULs) structure as a pragmatic complement to the Levels of Abstraction (LoAs)-based structure defining the ontology and (...) epistemology of computational artefacts. ULs identify a flexible hierarchy in which users bear their own semantic and normative requirements, possibly competing with the logical specification. We formulate a notion of computational act intended in its pragmatic sense, alongside pragmatic versions of implementation and correctness. (shrink)

Optimization is about finding the best available object with respect to an objective function. Mathematics and quantitative sciences have been highly successful in formulating problems as optimization problems, and constructing clever processes that find optimal objects from sets of objects. As computers have become readily available to most people, optimization and optimized processes play a very broad role in societies. It is not obvious, however, that the optimization processes that work for mathematics and abstract objects should be readily applied to (...) complex and open social systems. In this paper we set forth a framework to understand when optimization is limited, particularly for complex and open social systems. (shrink)

Alan Turing is often portrayed as a materialist in secondary literature. In the present article, I suggest that Turing was instead an idealist, inspired by Cambridge scholars, Arthur Eddington, Ernest Hobson, James Jeans and John McTaggart. I outline Turing’s developing thoughts and his legacy in the USA to date. Specifically, I contrast Turing’s two notions of computability (both from 1936) and distinguish between Turing’s “machine intelligence” in the UK and the more well-known “artificial intelligence” in the USA. According to my (...) proposed historical interpretation, Turing did not view computations in the real world to be exhaustively and deterministically characterized by his automatic machines from 1936. (shrink)

The more we rely on digital assistants, online search engines, and AI systems to revise our system of beliefs and increase our body of knowledge, the less we are able to resort to some independent criterion, unrelated to further digital tools, in order to asses the epistemic reliability of the outputs delivered by them. This raises some important questions to epistemology in general and pressing questions to applied to epistemology in particular. In this paper, we propose an experimental method for (...) the assessment of GPT-3’s capacity to generate consistent and coherent sets of outputs. When several outputs to one and the same input are very repetitive they tend to be consistent with each other, that is they do not contradict each other. But consistency does not make the set of outputs as a whole more informative than the outputs considered individually. We argue that the less informative a set of outputs is, the less coherent it is. We establish a conceptual distinction between consistency and coherence in the light of what some epistemologists refer to as a coherence theories of truth and justification. While much attention has been given to GPT-3’s capacity to produce internally coherent individual outputs, we argue, instead, that more attention should be given to its capacity to produce consistent and coherent outputs generated on a single-input-multiple-outputs basis. (shrink)

The article discusses the process of “conceptual borrowing”, according to which, when a new discipline emerges, it develops its technical vocabulary also by appropriating terms from other neighbouring disciplines. The phenomenon is likened to Carl Schmitt’s observation that modern political concepts have theological roots. The authors argue that, through extensive conceptual borrowing, AI has ended up describing computers anthropomorphically, as computational brains with psychological properties, while brain and cognitive sciences have ended up describing brains and minds computationally and informationally, as (...) biological computers. The crosswiring between the technical languages of these disciplines is not merely metaphorical but can lead to confusion, and damaging assumptions and consequences. The article ends on an optimistic note about the self-adjusting nature of technical meanings in language and the ability to leave misleading conceptual baggage behind when confronted with advancement in understanding and factual knowledge. (shrink)

The comparison made by Ada Lovelace in 1843 between the Analytical Engine and the Jacquard loom is one of the well-known analogies between looms and computation machines. Given the fact that weaving – and textile production in general – is one of the oldest cultural techniques in human history, the question arises whether this was the first time that such a parallel was drawn. As this paper will show, centuries before Lovelace’s analogy, such a comparison was made by Gottfried Wilhelm (...) Leibniz. During the 17th century, Leibniz compared his calculating machines with another textile machine, the stocking frame, a machine which mechanized knitting and which was invented in 1589. During the following centuries, this machine was considered as a technological wonder and as a creation of God, and, during the last decades of the 17th century, Leibniz emphasized the need to consider it and other textile machines mathematically. What, then, were the reasons for the parallel drawn between this machine and Leibniz’s automatic computation machines? And what were the consequences of this analogy concerning the artisanal knowledge embedded in manual textile practices? (shrink)

This paper traces a relatively linear sequence of early research approaches to the formal verification of concurrent programs. It does so forwards and then backwards in time. After briefly outlining the context, the key insights from three distinct approaches from the 1970s are identified (Ashcroft/Manna, Ashcroft (solo) and Owicki). The main technical material in the paper focuses on a specific program taken from the last published of the three pieces of research (Susan Owicki’s): her own verification of her _Findpos_ example (...) is outlined followed by attempts at verifying the same example using the earlier approaches. Reconsidering the prior approaches on the basis of Owicki’s useful example illuminates similarities and differences between the proposals. Along the way, observations about interactions between researchers (and some “blind spots”) are noted. (shrink)

Our goal in this paper is to establish a set of criteria for understanding the meaning and sources of attributing (un)fairness to AI algorithms. To do so, we first establish that (un)fairness, like other normative notions, can be understood in a proper primary sense and in secondary senses derived by analogy. We argue that AI algorithms cannot be said to be (un)fair in the proper sense due to a set of criteria related to normativity and agency. However, we demonstrate how (...) and why AI algorithms can be qualified as (un)fair by analogy and explore the sources of this (un)fairness and the associated problems of responsibility assignment. We conclude that more user-driven AI approaches could alleviate some of these difficulties. (shrink)

Abstract“Gamification” refers to adding game-like elements to non-game activities so as to encourage participation. Gamification is used in various contexts: apps on phones motivating people to exercise, employers trying to encourage their employees to work harder, social media companies trying to stimulate user engagement, and so on and so forth. Here, I focus on gamification with this property: the game-designer (a company or other organization) creates a “game” in order to encourage the players (the users) to bring about certain outcomes (...) as a side effect of playing the game. The side effect might be good for the user (e.g., improving her health) and/or good for the company or organization behind the game (e.g., advertising their products, increasing their profits, etc.). The “players” of the game may or may not be aware of creating these side effects; and they may or may not approve of/endorse the creation of those side effects. The organizations behind the games, in contrast, are typically directly aiming to create games that have the side effects in question. These aspects of gamification are puzzling and interesting from the point of view of philosophical analyses of agency and responsibility for outcomes. In this paper, I relate these just-mentioned aspects of gamification to some philosophical discussions of responsibility gaps, the ethics of side effects (including the Knobe effect and the doctrine of double effect), and ideas about the relations among different parties’ agency. (shrink)

This article argues that large language models (LLMs) should be interpreted as a form of gods. In a theological sense, a god is an immortal being that exists beyond time and space. This is clearly nothing like LLMs. In an anthropological sense, however, a god is rather defined as the personified authority of a group through time—a conceptual tool that molds a collective of ancestors into a unified agent or voice. This is exactly what LLMs are. They are products of (...) vast volumes of data, literally traces of past human (speech) acts, synthesized into a single agency that is (falsely) experienced by users as extra-human. This reconceptualization, I argue, opens up new avenues of critique of LLMs by allowing the mobilization of theoretical resources from centuries of religious critique. For illustration, I draw on the Marxian religious philosophy of Martin Hägglund. From this perspective, the danger of LLMs emerge not only as bias or unpredictability, but as a temptation to abdicate our spiritual and ultimately democratic freedom in favor of what I call a tyranny of the past. (shrink)

Emotion recognition technology uses artificial intelligence to make inferences about a person’s emotions, on the basis of their facial expressions, body language, tone of voice, or other types of input. Underlying such technology are a variety of assumptions about the manifestation, nature, and value of emotions. To assure the quality and desirability of emotion recognition technology, it is important to critically assess the assumptions embedded in the technology. Within philosophy, there is a long tradition of epistemological, ontological, phenomenological, and ethical (...) reflection on the manifestation, nature, and value of emotions. This article draws from this tradition of philosophy of emotions, in order to challenge the assumptions underlying current emotion recognition technology and to promote a more critical engagement with the concept of emotions in the tech-industry. (shrink)

To explain why, in scientific problem solving, a diagram can be “worth ten thousand words,” Jill Larkin and Herbert Simon (1987) relied on a computer model: two representations can be “informationally” equivalent but differ “computationally,” just as the same data can be encoded in a computer in multiple ways, more or less suited to different kinds of processing. The roots of this proposal lay in cognitive psychology, more precisely in the “imagery debate” of the 1970s on whether there are image-like (...) mental representations. Simon (1972, 1978) hoped to solve this debate by thoroughly reducing the differences between forms of mental representations (e.g., between images and sentences) to differences in computational efficiency; to carry out this reduction, he borrowed from computer science the concepts of data type and of data structure. I argue that, in the end, his account amounted to nothing more than characterizing representations by the fast operations on them. This analysis then allows me to assess what Simon’s approach actually achieves when transported from psychology to the study of scientific representations, as in Larkin and Simon (1987): it allows comparing, not representations in and of themselves, but rather the computational roles they play in particular problem-solving processes—that is, representations together with a particular way of using them. (shrink)