Can the g Factor Play a Role in Artificial General Intelligence Research?

In recent years, a trend in AI research has started to pursue human-level, general artificial intelli-gence (AGI). Although the AGI framework is characterised by different viewpoints on what intelligence is and how to implement it in artificial systems, it conceptualises intelligence as flexible, general-purposed, and capable of self-adapting to different contexts and tasks. Two important ques-tions remain open: a) should AGI projects simu-late the biological, neural, and cognitive mecha-nisms realising the human intelligent behaviour? and b) what is the relationship, if any, between the concept of general intelligence adopted by AGI and that adopted by psychometricians, i.e., the g factor? In this paper, we address these ques-tions and invite researchers in AI to open a dis-cussion on the theoretical conceptions and practi-cal purposes of the AGI approach

The benefits of prototypes: The case of medical concepts

In the present paper, we shall discuss the notion of prototype and show its benefits. First, we shall argue that the prototypes of common-sense concepts are necessary for making prompt and reliable categorisations and inferences. However, the features constituting the prototype of a particular concept are neither necessary nor sufficient conditions for determining category membership; in this sense, the prototype might lead to conclusions regarded as wrong from a theoretical perspective. That being said, the prototype remains essential to handling most ordinary situations and helps us to perform important cognitive tasks. To exemplify this point, we shall focus on disease concepts. Our analysis concludes that the prototypical conception of disease is needed to make important inferences from a practical and clinical point of view. Moreover, it can still be compatible with a classical definition of disease, given in terms of necessary and sufficient conditions. In the first section, we shall compare the notion of stereotype, as it has been introduced in philosophy of language by Hilary Putnam, with the notion of prototype, as it has been developed in the cognitive sciences. In the second section, we shall discuss the general role of prototypical information in cognition and stress its centrality. In the third section, we shall apply our previous discussion to the specific case of medical concepts, before briefly summarising our conclusions in section four

Spazi multidimensionali per la rappresentazione semantica

Nel campo delle scienze cognitive molti oggi condividono l’ipotesi che siano necessari differenti tipi di rappresentazioni per modellare i sistemi cognitivi sia naturali, sia artificiali. Si considerino le rappresentazioni basate su reti neurali, i formalismi simbolici e rappresentazioni analogiche quali rappresentazioni diagrammatiche o modelli mentali. Tutti questi metodi hanno successo nello spiegare e modellare alcune classi di fenomeni cognitivi, ma nessuno è in grado di rendere conto di tutti gli aspetti della cognizione. A partire da queste considerazioni, riteniamo che sistemi intelligenti e architetture cognitive possano trarre vantaggio dalla combinazione di sistemi di rappresentazione diversi. Si pone allora il problema di fare interagire rappresentazioni di natura differente in maniera cognitivamente e teoricamente fondata. La nostra ipotesi è che gli spazi concettuali possano offrire una sorta di lingua comune, che consentirebbe di integrare e generalizzare molti aspetti delle impostazioni sopra menzionate, superando i limiti delle varie proposte intese singolarmente

Rappresentare i disordini mentali mediante ontologie

Come è emerso dall’analisi filosofica e dalla ricerca nelle scienze cogni- tive, la maggior parte dei concetti, tra cui molti concetti medici, esibisce degli “effetti prototipici” e non riesce ad essere definita nei termini di condizioni necessarie e sufficienti. Questo aspetto rappresenta un problema per la pro- gettazione di ontologie in informatica, poiché i formalismi adottati per la rap- presentazione della conoscenza (a partire da OWL – Web Ontology Langua- ge) non sono in grado di rendere conto dei concetti nei termini dei loro tratti prototipici. Nel presente articolo ci concentriamo sulla classe dei disordini mentali facendo riferimento alle descrizioni che ne vengono date nel DSM-5. L’idea è quella di proporre un approccio ibrido, in cui i formalismi delle ontologie sono combinati a una rappresentazione geometrica della conoscenza basata sugli spazi concettuali

Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation

During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[ 48]) are based on a purely connectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are also available [34]. In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by Peter G¨ardenfors [24] more than fifteen years ago, can play in the current development of the Knowledge Level in Cognitive Systems and Architectures. In particular, we claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by G¨ardenfors [23] for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one. In particular we focus on the advantages offered by Conceptual Spaces (w.r.t. symbolic and sub-symbolic approaches) in dealing with the problem of compositionality of representations based on typicality traits. Additionally, we argue that Conceptual Spaces could offer a unifying framework for interpreting many kinds of diagrammatic and analogical representations. As a consequence, their adoption could also favor the integration of diagrammatical representation and reasoning in CAs

Ontologies, Disorders and Prototypes

As it emerged from philosophical analyses and cognitive research, most concepts exhibit typicality effects, and resist to the efforts of defining them in terms of necessary and sufficient conditions. This holds also in the case of many medical concepts. This is a problem for the design of computer science ontologies, since knowledge representation formalisms commonly adopted in this field (such as, in the first place, the Web Ontology Language - OWL) do not allow for the representation of concepts in terms of typical traits. The need of representing concepts in terms of typical traits concerns almost every domain of real world knowledge, including medical domains. In particular, in this article we take into account the domain of mental disorders, starting from the DSM-5 descriptions of some specific disorders. We favour a hybrid approach to concept representation, in which ontology oriented formalisms are combined to a geometric representation of knowledge based on conceptual space. As a preliminary step to apply our proposal to mental disorder concepts, we started to develop an OWL ontology of the schizophrenia spectrum, which is as close as possible to the DSM-5 descriptions

Computationalism under attack

A partire dalla prima metà degli anni ottanta nella comunità della scienza cognitiva il computazionalismo è stato messo “sotto attacco” da parte di diversi critici di quelle impostazioni della scienza cognitiva che a partire da allora si sono dette “classiche” o “simboliche”. Da allora la comunità degli scienziati cognitivi si è divisa sui diversi “paradigmi” che di volta in volta sono stati opposti al computazionalismo (classico). Nel nostro intervento precisiamo in primo luogo che il computazionalismo non deve essere identificato con ciò che potremmo chiamare il “paradigma del computer”, ossia con la tesi secondo la quale la mente funzionerebbe “come un computer”. Le contrapposizioni sopra menzionate traggono parte della loro forza dall’identificare il computazionalismo con il paradigma del computer. Ossia, la loro plausibilità si basa sull’assumere come obiettivo polemico qualche visione ristretta del computazionalismo. Ciò può essere esemplificato discutendo alcune affermazioni di Tim van Gelder tese ad opporre l’impostazione computazionale a quella dinamicista. Riteniamo che possa essere chiarificatrice al proposito la distinzione di Marr tra spiegazioni al livello della teoria computazionale (livello 1) e al livello delle rappresentazioni e degli algoritmi (livello 2). Sulla base di questa distinzione, alcuni asseriti paradigmi alternativi al computazionalismo possono essere considerati consistenti con una teoria computazionale nel senso del livello 1 di Marr, ma risolversi in scelte differenti per quel che concerne gli algoritmi e le rappresentazioni (livello 2). Ad esempio, l’impostazione dinamicista tende a ritenere che il livello 2 sia prescindibile nelle spiegazioni cognitive

Concepts, Perception and the Dual Process Theories of Mind

In this article we argue that the problem of the relationships between concepts and perception in cognitive science is blurred by the fact that the very notion of concept is rather confused. Since it is not always clear exactly what concepts are, it is not easy to say, for example, whether and in what measure concept possession involves entertaining and manipulating perceptual representations, whether concepts are entirely different from perceptual representations, and so on. As a paradigmatic example of this state of affairs, we will start by taking into consideration the distinction between conceptual and nonconceptual content. The analysis of such a distinction will lead us to the conclusion that concept is a heterogeneous notion. Then we shall take into account the so called dual process theories of mind; this approach also points to concepts being a heterogeneous phenomenon: different aspects of conceptual competence are likely to be ascribed to different types of systems. We conclude that without a clear specification of what concepts are, the problem of the relationships between concepts and perception is somewhat ill-posed