Jose A. Hernandez

AI systems increasingly incorporate continual learning mechanisms allowing their behaviour to adapt after deployment, from (1) in-context learning and (2) memory features already in wide use to (3) post-deployment weight modification under research. We argue that, by treating AI systems as frozen artefacts whose performance and safety are assessed at release, current evaluation practices structurally ignore the behavioural trajectory of a system that continues to learn from experience. Our position is that evaluation of continual learning systems should be centred on behavioural trajectories, with the complementary goals of characterising the landscape of possible behaviours and forecasting how behaviour will evolve from a given set of experiences. This can be operationalised through trajectory elicitation sandboxes and predictive monitors that forecast behavioural evolution, but may face fundamental obstacles analogous to those seen in dynamical systems. These are best addressed by (1) applying trajectory-centred evaluation to today's continual learning systems and (2) relying on the resulting evidence to design systems amenable to it, yielding a virtuous cycle in which systems and their evaluations co-evolve.

As AI is integrated into the workplace, organisations increasingly face allocation decisions between human and machine workers. These decisions are increasingly made or assisted by algorithms, creating a Reverse Turing Test dynamic wherein the machine is now the judge. In addition, human and machine workers may ``compete'' for a given task, reproducing aspects of adversarial games. This raises new methodological questions about assessing task suitability between humans and machines. The criteria often used to assess people (e.g., education, experience, references) cannot feasibly scale to AI systems; conversely, AI evaluation methods (benchmarks, red teaming, leaderboards) cannot be easily applied to human workers or yield comparable metrics. In this position paper, we argue that suitability evaluations for task-assignment should be profile-driven -- that is, based on assessments that infer latent constructs such as capabilities and propensities from observed performance. This approach places humans and AI systems on shared scales, supporting comparisons that are predictive of novel-task performance, explanatory of why agents succeed or fail, and auditable. We outline the core features of this approach, discuss its practical implications, and compare it with alternative frameworks for human-machine workplace allocation.

Artificial Intelligence is making rapid and remarkable progress in the development of more sophisticated and powerful systems. However, the acknowledgement of several problems with modern machine learning approaches has prompted a shift in AI benchmarking away from task-oriented testing towards ability-oriented testing, in which AI systems are tested on their capacity to solve certain kinds of novel problems. The Animal-AI Environment is one such benchmark which aims to apply the ability-oriented testing used in comparative psychology to AI systems. Here, we present the first direct human-AI comparison in the Animal-AI Environment, using children aged 6–10. We found that children of all ages were significantly better than a sample of 30 AIs across most of the tests we examined, as well as performing significantly better than the two top-scoring AIs, “ironbar” and “Trrrrr,” from the Animal-AI Olympics Competition 2019. While children and AIs performed similarly on basic navigational tasks, AIs performed significantly worse in more complex cognitive tests, including detour tasks, spatial elimination tasks, and object permanence tasks, indicating that AIs lack several cognitive abilities that children aged 6–10 possess. Both children and AIs performed poorly on tool-use tasks, suggesting that these tests are challenging for both biological and non-biological machines.

There is convergence among researchers of the ‘Big Five’ personality traits taxonomy, that the dimension of conscientiousness best explains differences in work performance. This research is a literature review on the interrelationship between certain traits of the conscientiousness dimension and human virtues, or character traits. It also analyzes whether or not it is rational to argue that the continuous improvement culture enhances the exercise of these character traits. The personal effort to develop one's conscientiousness enriches one's character or way of being, and this development is a consequence of character moulding through an intellectual and willful effort enabling one not only to develop personal habits but also to improve task performance. Continuous improvement culture, on the one hand, should provide a work environment where employees can practice virtues, including those contained in conscientiousness. On the other hand, the requirement is that continuous improvement culture should be developed in an environment that respects the freedom and purpose of the individual.

New types of artificial intelligence (AI), from cognitive assistants to social robots, are challenging meaningful comparison with other kinds of intelligence. How can such intelligent systems be catalogued, evaluated, and contrasted, with representations and projections that offer meaningful insights? To catalyse the research in AI and the future of cognition, we present the motivation, requirements and possibilities for an atlas of intelligence: an integrated framework and collaborative open repository for collecting and exhibiting information of all kinds of intelligence, including humans, non-human animals, AI systems, hybrids and collectives thereof. After presenting this initiative, we review related efforts and present the requirements of such a framework. We survey existing visualisations and representations, and discuss which criteria of inclusion should be used to configure an atlas of intelligence.

Machine learning (ML) models make decisions for governments, companies, and individuals. Accordingly, there is the increasing concern of not having a rich explanatory and predictive account of the behaviour of these ML models relative to the users’ interests (goals) and (pre-)conceptions (ontologies). We argue that the recent research trends in finding better characterisations of what a ML model does are leading to the view of ML models as complex behavioural systems. A good explanation for a model should depend on how well it describes the behaviour of the model in simpler, more comprehensible, or more understandable terms according to a given context. Consequently, we claim that a more contextual abstraction is necessary (as is done in system theory and psychology), which is very much like building a subjective mind modelling problem. We bring some research evidence of how this partial and subjective modelling of machine learning models can take place, suggesting that more machine learning is the answer.

Uno de los grandes problemas que enfrenta la educación en la actualidad, radica en el hecho de que los profesores de pronto se ven invadidos, y en su caso, obligados a utilizar las computadoras, los dispositivos móviles e Internet en el salón de clase. La falta de programas efectivos de capacitación y formación docente en el uso y aplicación de las TIC en los procesos de enseñanza aprendizaje, se ha convertido en un problema de grandes dimensiones que alcanza prácticamente a todos los niveles educativos. Ante tal escenario, el presente trabajo pretende la adopción por parte de los profesores, de una herramienta que les sea de utilidad para orientar su formación en el campo de las TIC: “El árbol diagnóstico de las competencias digitales”, el cual ofrece un panorama general acerca de los recursos informáticos y de la Web que hemos identificado como relevantes en el ejercicio de la docencia. Este “árbol diagnóstico” es el resultado de un proceso de evaluación y seguimiento llevado a cabo con grupos de profesores que ya utilizan la tecnología en su práctica docente; y tiene la finalidad de orientar y facilitar el aprendizaje autónomo de los profesores que se inician en el uso de las TIC.

We present nine facets for the analysis of the past and future evolution of AI. Each facet has also a set of edges that can summarise different trends and contours in AI. With them, we first conduct a quantitative analysis using the information from two decades of AAAI/IJCAI conferences and around 50 years of documents from AI topics, an official database from the AAAI, illustrated by several plots. We then perform a qualitative analysis using the facets and edges, locating AI systems in the intelligence landscape and the discipline as a whole. This analytical framework provides a more structured and systematic way of looking at the shape and boundaries of AI.

El texto que nos ocupa narra una serie de historias clínicas que hunden sus raíces en tiempos del Renacimiento, comparadas con las historias del periodo romántico alemán conocemos cómo sus redactores, médicos magnetizadores, abordan el fenómeno de la curación de ciertas enfermedades nerviosas por vía psíquica. Daemoniaca reescribe "dolorosas experiencias humanas narradas a menudo por testigos presenciales, y en ocasiones de la manera más pormenorizada". Son testimonios sorprendentes, antologías de vidas complicadas por el fenómeno morboso que conduce al deterioro del cuerpo y de la mente. Un compendio de historias de demonios en el que se pretende mostrar una antropología integral, mucho más que medicina, magnetismo animal, folclore y religión.

In the historical context of the ancient Roman Empire, especially in Southwestern Spain, this study employs fuzzy logic methodology to conduct a comprehensive comparative analysis of topographical measurement instruments. These instruments—specifically the groma, surveyor's square, dioptra, chorobate, and odometer—were instrumental in shaping the region's infrastructure and played a crucial role in ancient engineering projects. Fuzzy logic is strategically utilized to assign fuzzy values ranging between 0 and 1 to five pivotal characteristics: precision, complexity, versatility, durability, and ease of use. These attributes enable a nuanced evaluation of each instrument. For instance, an instrument known for its precision would be assigned a value closer to 1, while one with lower precision would receive a value closer to 0. The outcomes of this research provide a multifaceted perspective on the historical importance of these instruments in Southwestern Spain during the Roman era. By offering detailed assessments of their strengths and weaknesses through fuzzy logic-based comparisons, the study highlights the suitability of each tool for distinct measurement requirements. By illustrating the adaptability of fuzzy logic in evaluating historical instruments, this research introduces a novel approach that can be applied to various contexts. It not only reveals the pivotal role played by these instruments during the Roman Empire but also underscores their relevance to contemporary surveying and topography, providing a valuable bridge between ancient and modern practices.

The Minimum Description Length principle is the modernformalisation of Occam's razor. It has been extensively and successfullyused in machine learning, especially for noisy and long sources ofdata. However, the MDL principle presents some paradoxes andinconveniences. After discussing all these, we address two of the mostrelevant: lack of explanation and lack of creativity. We present newalternatives to address these problems. The first one, intensionalcomplexity, avoids extensional parts in a description, so distributingcompression ratio in a more even way than the MDL principle. The secondone, information gain, forces that the hypothesis is informative wrt. the evidence, so giving a formaldefinition of what is to discover.

There is convergence among researchers of the ‘Big Five’ personality traits taxonomy, that the dimension of conscientiousness best explains differences in work performance. This research is a literature review on the interrelationship between certain traits of the conscientiousness dimension and human virtues, or character traits. It also analyzes whether or not it is rational to argue that the continuous improvement culture enhances the exercise of these character traits. The personal effort to develop one's conscientiousness enriches one's character or way of being, and this development is a consequence of character moulding through an intellectual and willful effort enabling one not only to develop personal habits but also to improve task performance. Continuous improvement culture, on the one hand, should provide a work environment where employees can practice virtues, including those contained in conscientiousness. On the other hand, the requirement is that continuous improvement culture should be developed in an environment that respects the freedom and purpose of the individual

This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws – must be integrated into a coherent whole. On our account, making sense of sensory input is a type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. In fact, it is able to do all three tasks simultaneously. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence.

José María Vigil y Antonio Caso fueron dos defensores de la democracia liberal y de la posibilidad de un limitado, aunque efectivo, manejo consciente de los asuntos públicos y sociales dentro de los contextos adversos de México durante la administración de Porfirio Díaz y en el periodo post-revolucionario. Ambos autores basan sus posiciones en una crítica concerniente al positivismo y al determinismo social, que apuntaría hacia una manera de pensar muy distante del pensamiento subjetivista, aunque muy cercana a un “realismo directo” epistemológico que establece que el mundo no es de hecho una construcción subjetiva o intersubjetiva que debe interpretarse subjetiva o intersubjetivamente. Sin embargo, esto no es algo que pueda saberse a través de una copia mental o representación de sus entidades y procesos. Tal vez el defensor más evocador del realismo directo en la historia ha sido el filósofo escocés del siglo XVIII, Thomas Reid, un gran representante del “sensocomunismo” explícitamente recuperado por Vigil y reivindicado tácitamente por Antonio Caso.