On the philosophical relevance of Gödel's incompleteness theorems

A survey of more philosophical applications of Gödel's incompleteness results

Radiographers' knowledge, attitudes and expectations of artificial intelligence in medical imaging

Introduction: Artificial intelligence (AI) is increasingly utilised in medical imaging systems and processes, and radiographers must embrace this advancement. This study aimed to investigate perceptions, knowledge, and expectations towards integrating AI into medical imaging amongst a sample of radiographers and determine the current state of AI education within the community. Methods: A cross-sectional online quantitative study targeting radiographers based in Europe was conducted over ten weeks. Captured data included demographical information, participants’ perceptions and understanding of AI, expectations of AI and AI-related educational backgrounds. Both descriptive and inferential statistical techniques were used to analyse the obtained data. Results: A total of 96 valid responses were collected. Of these, 64% correctly identified the true definition of AI from a range of options, but fewer (37%) fully understood the difference between AI, machine learning and deep learning. The majority of participants (83%) agreed they were excited about the advancement of AI, though a level of apprehensiveness remained amongst 29%. A severe lack of education on AI was noted, with only 8% of participants having received AI teachings in their pre-registration qualification. Conclusion: Overall positive attitudes towards AI implementation were observed. The slight apprehension may stem from the lack of technical understanding of AI technologies and AI training within the community. Greater educational programs focusing on AI principles are required to help increase European radiography workforce engagement and involvement in AI technologies. Implications for practice: This study offers insight into the current perspectives of European based radiographers on AI in radiography to help facilitate the embracement of AI technology and convey the need for AI-focused education within the profession

Descriptive Complexity, Computational Tractability, and the Logical and Cognitive Foundations of Mathematics

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive science and philosophy, this tractability result has been used to argue that only functions in P can feasibly work as computational models of human cognitive capacities. One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. In descriptive complexity theory, it is established that only first-order (classical) systems are connected to P, or one of its subclasses. Consequently, second-order systems of logic are considered to be computationally intractable, and may therefore seem to be unfit to model human cognitive capacities. This would be problematic when we think of the role of logic as the foundations of mathematics. In order to express many important mathematical concepts and systematically prove theorems involving them, we need to have a system of logic stronger than classical first-order logic. But if such a system is considered to be intractable, it means that the logical foundation of mathematics can be prohibitively complex for human cognition. In this paper I will argue, however, that this problem is the result of an unjustified direct use of computational complexity classes in cognitive modelling. Placing my account in the recent literature on the topic, I argue that the problem can be solved by considering computational complexity for humanly relevant problem solving algorithms and input sizes.Peer reviewe

Computing Mechanisms

This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing justice to the practices of computer scientists and computability theorists. It is also an application of recent literature on mechanisms, because it assimilates computational explanation to mechanistic explanation. The account can be used to individuate computing mechanisms and the functions they compute and to taxonomize computing mechanisms based on their computing power

The Mind as Neural Software? Understanding Functionalism, Computationalism, and Computational Functionalism

Defending or attacking either functionalism or computationalism requires clarity on what they amount to and what evidence counts for or against them. My goal here is not to evaluate their plausibility. My goal is to formulate them and their relationship clearly enough that we can determine which type of evidence is relevant to them. I aim to dispel some sources of confusion that surround functionalism and computationalism, recruit recent philosophical work on mechanisms and computation to shed light on them, and clarify how functionalism and computationalism may or may not legitimately come together.\u

Thinking Like a Machine

As part of ongoing research bridging ethnomethodology and computer science, in this article we offer an alternate reading of Alan Turing’s 1936 paper, “On Computable Numbers”. Following through Turing’s machinic respecification of computation, we hope to contribute to a deflationary position on AI by showing that the activities attributed to AIs are achieved in the course of methodic hands-on work with computational systems and not in isolation by them. Turing’s major innovation was a demonstration that mathematical and logical operations could be broken down into elementary, mechanically executable operations, devoid of intellectual content. Drawing out lessons from a re-enactment of Turing’s methods as a means of reflecting on contemporary artificial intelligence (AI), including the way those methods disappear into the technology, we will suggest the interesting question raised in “On Computable Numbers” is less about the possibilities of designing machines that “can think” (cf. Turing, 1950), but the practical work we do, and which is made possible, when we ourselves set out to think like machines.</jats:p

Utilização de técnicas de Machine Learning na previsão de movimentos incorretos de trabalhadores em contexto industrial

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de InformaçãoHoje em dia, as indústrias de manufatura ainda enfrentam dificuldades para aplicar os métodos tradicionais de avaliação do risco de work-related musculoskeletal disorder (WMSD) em aplicações práticas devido ao esforço que é necessário para a recolha contínua de dados de métodos observacionais. Uma solução interessante passa por adotar Inertial Measurement Units (IMU), que podem ser utilizados para automatizar a recolha e processamento de dados, dando apoio a ergonomistas e profissionais de saúde ocupacional. Neste trabalho, é feita uma comparação entre vários algoritmos de Machine Learning para a previsão do movimento, incluindo: três métodos de Long Short-Term Memory (LSTM) - uma LSTM normal com apenas uma layer, uma Stacked LSTM e um Sequence to Sequence (Seq2Seq); e três métodos de regressão - um Multiple Linear Regression, um Random Forest e um Support Vector Machine. O objetivo é prevenir movimentos problemáticos que podem surgir durante movimentos repetitivos de trabalho. O sistema proposto inclui a aplicação inicial do Madgwick orientation filter para transformar os dados em bruto dos sensores inerciais, numa série temporal de orientação de ângulo único, de forma a monitorizar o ângulo de abdução/adução do braço. O modelo Seq2Seq LSTM alcançou os melhores resultados, sendo posteriormente avaliado considerando 11 conjuntos de dados de seres humanos e dois procedi mentos de avaliação (treino e teste com dados de uma só pessoa e utilizando dados obtidos de múltiplas pessoas). Estas avaliações demonstraram um excelente potencial do modelo preditivo desenvolvido para antecipar movimentos problemáticos. Em trabalho futuro, espera-se integrar tal modelo num protótipo de exoesqueleto capaz de bloquear movimentos perigosos.Nowadays, manufacturing industries still face difficulties applying traditional work-related musculoskeletal disorder risk (WMSD) assessment methods in practical applications due to the high effort for continuous data collection of observational methods. An interesting solution is to adopt inertial motion capture systems, namely Inertial Measurement Units (IMU), which can be used to automate data collection and processing, supporting ergonomists and occupational health professionals. In this work, distinct Machine Learning algorithms were compared for a single angle orientation time series prediction, including: three Long Short-Term Memory (LSTM) methods – a one layer, a stacked layer and a Sequence to Sequence (Seq2Seq) model; and three non-deep learning methods – a Multiple Linear Regression, a Random Forest and a Support Vector Machine. The goal is to prevent problematic movements that can arise during repetitive working movements. The proposed system includes an initial Madgwick filter to merge the raw inertial sensor data into a single angle orientation time series, aiming to monitor the abduction/adduction angle of the arm. As the Seq2Seq LSTM achieved the best results, this model was further evaluated for WMSD prevention by considering 11 human subject datasets and two evaluation procedures (single person and multiple person training and testing). The main goal was to achieve a model with excellent capability to anticipate problematic movements and capable of being integrated into a solution for WMSDs, like exoskeletons.This work is a result of the project STVgoDigital - Digitalization of the T&C sector (POCI-01-0247-FEDER-046086), supported by COMPETE 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)

Utilização de técnicas de Machine Learning na previsão de movimentos incorretos de trabalhadores em contexto industrial

Dissertação de mestrado itegrado em Engenharia e Gestão de Sistemas de InformaçãoHoje em dia, as indústrias de manufatura ainda enfrentam dificuldades para aplicar os métodos tradicionais de avaliação do risco de work-related musculoskeletal disorder (WMSD) em aplicações práticas devido ao esforço que é necessário para a recolha contínua de dados de métodos observacionais. Uma solução interessante passa por adotar Inertial Measurement Units (IMU), que podem ser utilizados para automatizar a recolha e processamento de dados, dando apoio a ergonomistas e profissionais de saúde ocupacional. Neste trabalho, é feita uma comparação entre vários algoritmos de Machine Learning para a previsão do movimento, incluindo: três métodos de Long Short-Term Memory (LSTM) - uma LSTM normal com apenas uma layer, uma Stacked LSTM e um Sequence to Sequence (Seq2Seq); e três métodos de regressão - um Multiple Linear Regression, um Random Forest e um Support Vector Machine. O objetivo é prevenir movimentos problemáticos que podem surgir durante movimentos repetitivos de trabalho. O sistema proposto inclui a aplicação inicial do Madgwick orientation filter para transformar os dados em bruto dos sensores inerciais, numa série temporal de orientação de ângulo único, de forma a monitorizar o ângulo de abdução/adução do braço. O modelo Seq2Seq LSTM alcançou os melhores resultados, sendo posteriormente avaliado considerando 11 conjuntos de dados de seres humanos e dois procedimentos de avaliação (treino e teste com dados de uma só pessoa e utilizando dados obtidos de múltiplas pessoas). Estas avaliações demonstraram um excelente potencial do modelo preditivo desenvolvido para antecipar movimentos problemáticos. Em trabalho futuro, espera-se integrar tal modelo num protótipo de exoesqueleto capaz de bloquear movimentos perigosos.Nowadays, manufacturing industries still face difficulties applying traditional work-related musculoskeletal disorder risk (WMSD) assessment methods in practical applications due to the high effort for continuous data collection of observational methods. An interesting solution is to adopt inertial motion capture systems, namely Inertial Measurement Units (IMU), which can be used to automate data collection and processing, supporting ergonomists and occupational health professionals. In this work, distinct Machine Learning algorithms were compared for a single angle orientation time series prediction, including: three Long Short-Term Memory (LSTM) methods – a one layer, a stacked layer and a Sequence to Sequence (Seq2Seq) model; and three non-deep learning methods – a Multiple Linear Regression, a Random Forest and a Support Vector Machine. The goal is to prevent problematic movements that can arise during repetitive working movements. The proposed system includes an initial Madgwick filter to merge the raw inertial sensor data into a single angle orientation time series, aiming to monitor the abduction/adduction angle of the arm. As the Seq2Seq LSTM achieved the best results, this model was further evaluated for WMSD prevention by considering 11 human subject datasets and two evaluation procedures (single person and multiple person training and testing). The main goal was to achieve a model with excellent capability to anticipate problematic movements and capable of being integrated into a solution for WMSDs, like exoskeletons.This work is a result of the project STVgoDigital - Digitalization of the T&C sector (POCI-01-0247-FEDER- 046086), supported by COMPETE 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF