Human-machine interaction is increasingly important in industry, and this trend will only intensify with the rise of Industry 5.0. Human operators have skills that need to be adapted when using machines to achieve the best results. It is crucial to highlight the operator's skills and understand how they use and adapt them [18]. A rigorous description of these skills is necessary to compare performance with and without robot assistance. Predicate logic, used by Vergnaud within Piaget's scheme concept, offers a promising approach. However, this theory doesn't account for cognitive system constraints, such as the timing of actions, the limitation of cognitive resources, the parallelization of tasks, or the activation of automatic gestures contrary to optimal knowledge. Integrating these constraints is essential for representing agent skills understanding skill transfer between biological and mechanical structures. Cognitive architectures models [2] address these needs by describing cognitive structure and can be combined with the scheme for mutual benefit. Welding provides a relevant case study, as it highlights the challenges faced by operators, even highly skilled ones. Welding's complexity stems from the need for constant skill adaptation to variable parameters like part position and process. This adaptation is crucial, as weld quality, a key factor, is only assessed afterward via destructive testing. Thus, the welder is confronted with a complex perception-decision-action cycle, where the evaluation of the impact of his actions is delayed and where errors are definitive. This dynamic underscores the importance of understanding and modeling the skills of operators.

In complex autonomous driving (AD) software systems, the functioning of each system part is crucial for safe operation. By measuring the current functionality or operability of individual components an isolated glimpse into the system is given. Literature provides several of these detached assessments, often in the form of safety or performance measures. But dependencies, redundancies, error propagation and conflicting functionality statements do not allow for easy combination of these measures into a big picture of the functioning of the entire AD stack. Data is processed and exchanged between different components, each of which can fail, making an overall statement challenging. The lack of functionality assessment frameworks that tackle these problems underlines this complexity. This article presents a novel framework for inferring an overall functionality statement for complex component based systems by considering their dependencies, redundancies, error propagation paths and the assessments of individual components. Our framework first incorporates a comprehensive conversion to an assessment representation of the system. The representation is based on Subjective Networks (SNs) that allow for easy identification of faulty system parts. Second, the framework offers a flexible method for computing the system's functionality while dealing with contradicting assessments about the same component and dependencies, as well as redundancies, of the system. We discuss the framework's capabilities on real-life data of our AD stack with assessments of various components.

Generative AI's rapid advancement sparks interest in its cognitive abilities, especially given its capacity for tasks like language understanding and code generation. This study explores how several recent GenAI models perform on the Clock Drawing Test (CDT), a neuropsychological assessment of visuospatial planning and organization. While models create clock-like drawings, they struggle with accurate time representation, showing deficits similar to mild-severe cognitive impairment (Wechsler, 2009). Errors include numerical sequencing issues, incorrect clock times, and irrelevant additions, despite accurate rendering of clock features. Only GPT 4 Turbo and Gemini Pro 1.5 produced the correct time, scoring like healthy individuals (4/4). A follow-up clock-reading test revealed only Sonnet 3.5 succeeded, suggesting drawing deficits stem from difficulty with numerical concepts. These findings may reflect weaknesses in visual-spatial understanding, working memory, or calculation, highlighting strengths in learned knowledge but weaknesses in reasoning. Comparing human and machine performance is crucial for understanding AI's cognitive capabilities and guiding development toward human-like cognitive functions.

Digital Behavior Change Interventions (DBCIs) are supporting development of new health behaviors. Evaluating their effectiveness is crucial for their improvement and understanding of success factors. However, comprehensive guidance for developers, particularly in small-scale studies with ethical constraints, is limited. Building on the CAPABLE project, this study aims to define effective engagement with DBCIs for supporting cancer patients in enhancing their quality of life. We identify metrics for measuring engagement, explore the interest of both patients and clinicians in DBCIs, and propose hypotheses for assessing the impact of DBCIs in such contexts. Our findings suggest that clinician prescriptions significantly increase sustained engagement with mobile DBCIs. In addition, while one weekly engagement with a DBCI is sufficient to maintain well-being, transitioning from extrinsic to intrinsic motivation may require a higher level of engagement.

It is a well-known story that theoretical computer science and biology have been drawing inspiration from each other for decades. While computer science has tried to mimic the functioning of living systems to develop computing models, including automata, artificial neural networks, and evolutionary algorithms, biology has used computing as a metaphor to explain the functioning of living systems.4 For example, biologists have used Boolean logic to conceptualize gene regulation since early 1970s, when Jacques Monod wrote the inspirational statement "… like the workings of computers."40 This article contends that information processing is the link between computer science and molecular biology. In computer science, a model of computation such as finite state machines or Turing machines defines how to generate output from a set of inputs and a set of rules or instructions.

The rapid progression of Artificial Intelligence (AI) systems, facilitated by the advent of Large Language Models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries. This trend has sparked significant discourse centered around the ever-increasing need for LLM-based AI systems to function among humans as part of human society, sharing human values, especially as these systems are deployed in high-stakes settings (e.g., healthcare, autonomous driving, etc.). Towards this end, neurosymbolic AI systems are attractive due to their potential to enable easy-to-understand and interpretable interfaces for facilitating value-based decision-making, by leveraging explicit representations of shared values. In this paper, we introduce substantial extensions to Khaneman's System one/two framework and propose a neurosymbolic computational framework called Value-Inspired AI (VAI). It outlines the crucial components essential for the robust and practical implementation of VAI systems, aiming to represent and integrate various dimensions of human values. Finally, we further offer insights into the current progress made in this direction and outline potential future directions for the field.

Machine learning is employed in healthcare to draw approximate conclusions regarding human diseases and mental health problems. Compared to older traditional methods, it can help to analyze data more efficiently and produce better and more dependable results. Millions of people are affected by schizophrenia, which is a chronic mental disorder that can significantly impact their lives. Many machine learning algorithms have been developed to predict and prevent this disease, and they can potentially be implemented in the diagnosis of individuals who have it. This survey aims to review papers that have focused on the use of deep learning to detect and predict schizophrenia using EEG signals, functional magnetic resonance imaging (fMRI), and diffusion magnetic resonance imaging (dMRI). With our chosen search strategy, we assessed ten publications from 2019 to 2022. All studies achieved successful predictions of more than 80%. This review provides summaries of the studies and compares their notable aspects. In the field of artificial intelligence (AI) and machine learning (ML) for schizophrenia, significant advances have been made due to the availability of ML tools, and we are optimistic that this field will continue to grow.

Originally published on Towards AI. Nowadays, frameworks such as Keras, TensorFlow, or PyTorch provide turnkey access to most deep learning solutions without necessarily having to understand them in depth. But this can get problematic as soon as your model is not working as expected. You may need to tweak it yourself. So, if you are here to understand the concept of Perceptron in deep learning, I think you are on the right track if you want to be able to contribute one day to this ecosystem in any way, it is essential to understand the roots of these systems.

In AI research, so far, the attention paid to the characterization and representation of function and affordance has been sporadic and sparse, even though this aspect features prominently in an intelligent system's functioning. In the sporadic and sparse, though commendable efforts so far devoted to the characterization and understanding of function and affordance, there has also been no general framework that could unify all the different use domains and situations related to the representation and application of functional concepts. This paper develops just such a general framework, with an approach that emphasizes the fact that the representations involved must be explicitly cognitive and conceptual, and they must also contain causal characterizations of the events and processes involved, as well as employ conceptual constructs that are grounded in the referents to which they refer, in order to achieve maximal generality. The basic general framework is described, along with a set of basic guiding principles with regards to the representation of functionality. To properly and adequately characterize and represent functionality, a descriptive representation language is needed. This language is defined and developed, and many examples of its use are described. The general framework is developed based on an extension of the general language meaning representational framework called conceptual dependency. To support the general characterization and representation of functionality, the basic conceptual dependency framework is enhanced with representational devices called structure anchor and conceptual dependency elaboration, together with the definition of a set of ground level concepts. These novel representational constructs are defined, developed, and described. A general framework dealing with functionality would represent a major step toward achieving Artificial General Intelligence.

LaMDA is Google's latest artificial intelligence (AI) chatbot. Blake Lemoine, a Google AI engineer, has claimed it is sentient. He's been put on leave after publishing his conversations with LaMDA. If Lemoine's claims are true, it would be a milestone in the history of humankind and technological development. Google strongly denies LaMDA has any sentient capacity.