Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...) intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy. (shrink)

A comprehensive introduction to the Language of Though Hypothesis (LOTH) accessible to general audiences. LOTH is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in (...) syntactic operations defined over representations. Most of the arguments for LOTH derive their strength from their ability to explain certain empirical phenomena like productivity, systematicity of thought and thinking. (shrink)

Theoretiker der Künstlichen Intelligenz und deren Wegbegleiter in der Philosophie des Geistes haben auf unterschiedliche Weise auf Kritik am ursprünglichen Theorieziel der KI reagiert. Eine dieser Reaktionen ist die Zurücknahme dieses Theorieziels zugunsten der Verfolgung kleinerformatiger Projekte. Eine andere Reaktion ist die Propagierung konnektionistischer Systeme, die mit ihrer dezentralen Arbeitsweise die neuronalen Netze des menschlichen Gehirns besser simulieren sollen. Eine weitere ist die sogenannte robot reply. Die Roboterantwort besteht aus zwei Elementen. Sie enthält (a) das Zugeständnis, daß das Systemverhalten eines (...) wie auch immer programmierten konventionellen Digitalrechners mit von Neumann-Architektur nicht schon menschenähnliche Intelligenz aufweist, und (b) die Behauptung, daß es für bestimmte Arten von Maschinen doch zur Intelligenz reicht. In die Liga der intelligenten Wesen könnten Maschinen genau dann aufsteigen, wenn sie Roboter sind. Damit ist gemeint: wenn sie über Wahrnehmungskomponenten (Rezeptoren) und Handlungskomponenten (Effektoren) verfügen, mithilfe deren sie aktiv in kausale Interaktionen mit ihrer Umwelt eintreten können. Im Beitrag wird für die These argumentiert, daß der Roboterantwort eine richtige Intuition zugrunde liegt, von der die Roboterfreunde sich aber zu einer kurzschlüssigen Folgerung verleiten lassen. Es ist richtig, mentale Zustände und Handlungskompetenz eng aneinander zu binden. Einem Wesen, dem man Handlungsfähigkeit zuerkennt, kann man mentale Zustände nicht absprechen. Doch Handelnkönnen und Geisthaben sind nicht hinreichend unabhängig voneinander, als daß man das eine als Rechtfertigung für die Zuerkennung des anderen verwenden könnte. Man sollte Robotern beides absprechen. (shrink)

This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI safety; (...) and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing. (shrink)

Humans are cognitive entities. Our ongoing interactions with the environment are threaded with creations and usages of meaningful information. Animal life is also populated with meaningful information related to survival constraints. Information managed by artificial agents can also be considered as having meanings, as derived from the designer. Such perspective brings us to propose an evolutionary approach to cognition based on meaningful information management. We use a systemic tool, the Meaning Generator System (MGS), and apply it consecutively to animals, humans (...) and artificial agents [1, 2]. The MGS receives information from its environment and compares it with its constraint. The generated meaning is the connection existing between the received information and the constraint. It triggers an action aimed at satisfying the constraint. The action modifies the environment and the generated meaning. Meaning generation links agents to their environments. The MGS is a system: a set of elements linked by a set of relations. Any system submitted to a constraint and capable of receiving information can lead to a MGS. Animals, humans and robots are agents containing MGSs dealing with different constraints. Similar MGSs carrying different constraints will generate different meanings. Cognition is system dependent. Contrary to approaches on meaning generation based on psychology or linguistics, the MGS approach is not based on human mind. We want to avoid the circularity of taking human mind as a starting point. Free will and self-consciousness participate to the management of human meanings. They do not exist for animals or robots. Staying alive is a constraint that we share with animals. Robots ignore that constraint. We first use the MGS for animals with “stay alive” and “group life” constraints. The analysis of meaning and cognition in animals is however limited by our un-complete understanding of the nature of life (the question of final causes). Extending the analysis of meaning generation and cognition to humans is complex and has some true limitations as the nature of human mind is a mystery for today science and philosophy. The natures of our feelings, free will or self-consciousness are unknown. Approaches to identify human constraints are however possible, where the MGS can highlight some openings [3, 4]. Modeling meaning management in artificial agents is rather straightforward with the MGS. We, the designers, know the agents and the constraints. The derived nature of constraints, meaning and cognition is however to be highlighted. We define a meaningful representation of an item for an agent as being the networks of meanings relative to the item for the agent, with the action scenarios involving the item. Such meaningful representations embed the agents in their environments and are far from the GOFAI type of representations. Cognition, meanings and representations exist by and for the agents. We finish by summarizing the points presented here and highlight possible continuations . [1] “Information and Meaning” [2] “Introduction to a systemic theory of meaning” [3] “Computation on Information, Meaning and Representations. An Evolutionary Approach” [4] “Proposal for a shared evolutionary nature of language and consciousness”. (shrink)

The notions of information, representation and enaction entertain historical and complex relations with cognition. Historical relations because representational structures belong to the central hypothesis of cognitive sciences. Complex relations because cognitive sciences apply the notion of representation to animals, humans and robots, and also because the enactive approach tends to disregard the GOFAI type of representations. In this wide horizon of relations, we propose to look at a systemic approach that could bring up a common denominator for information and (...) representations in the build up of cognition, and also keep a link with the enactive approach. Our purpose is to show that systems submitted to constraints can generate meaningful information to maintain their natures, and consequently build up meaningful representations that have some compatibility with the enactive approach. Such a systemic approach to the notion of meaningful information could then make available a link between enaction and meaningful representations. The first part of the presentation is about reminding that cognition does not exist per se, but is related to the system that builds it. We look at cognition as constituted by dynamic meaningful representations built up by systems that have constraints to satisfy in their environments. Cognition is considered here at the level of the system that builds it and uses it in order to maintain its nature in its environment. Such a systemic approach fits with evolution. Organisms build representations to cope with survival constraints (frogs build representations of moving black dots in order to satisfy food constraints). Humans build representations and cognition to satisfy constraints that are conscious and unconscious. Artificial systems can use representations and cognition to run activities related to constraints implemented by the designers or coming from the environment (a goal to reach being considered as a constraint to satisfy). In the second part of the presentation we define what are a meaningful information and a representation for a system submitted to a constraint in its environment, and we link these to the enactive approach. We define a meaningful information (a meaning) as an information generated by a system submitted to a constraint when it receives an external information that has a connection with the constraint. The meaning is precisely that connection. The meaning belongs to the interactions that link the system to its environment. The function of the meaning is to participate to the determination of an action that will be implemented in order to satisfy the constraint. (Menant, 2003). The satisfaction of the constraint goes with maintaining the nature of the system in its environment. A Meaning Generator System (MGS) is defined correspondingly. It is a building block for higher level systems. We present some characteristics of the MGS (groundings of a meaning, domain of efficiency and transfer of meanings, networking of meanings, evolutionary usage). The MGS approach is close to a simplified version of the Peircean triadic theory of signs (Menant, 2003, 2005 ). We define the representation of an item for a system as being the dynamic set of meaningful information corresponding to the item for the system in its environments (an elementary representation being made of a single meaningful information). These representations link the system to its environment by their meaningful components related to the nature of the system. These representations are different from the GOFAI ones. The possibilities for linking these notions of meaning and representation with the enactive approach come from the structure of the MGS: the need for an action is the cause of the meaning generation by and for the system. The action on the environment is for the system to maintain its nature (its identity). The MGS links together the generation of meaningful representations, the nature of the system, and the interactions with the environment. This can be considered as close to enacting a world by meaning generation (Di Paolo and all 2007), and to the enactive concept of sense making (De Jaegher, Di Paolo 2007). We propose that basing the definition of a representation on the notion of meaningful information generated by a system submitted to a constraint can open a way for making the notion of representation compatible with the enactive approach. In the third part of the presentation, we consider some cases of meaningful information and representations for organisms and for robots. Regarding organisms, the MGS can be used in an evolutionary context by looking at the evolution of the systems and of the constraints. Purpose is to modelize the generation of meanings and of representations in order to make available a tool usable for different levels of evolution, as evolution has a place in cognitive sciences (Proust, 2007). Constraints for basic life are survival constraints (individual and species). Group life constraints are also to be considered. Reaching the level of humans in evolution brings in new constraints that cannot be clearly identified as they have to take into account human consciousness which is today a mystery (the “hard problem”). On an evolutionary standpoint, human constraints come in addition to the ones existing for non human organisms. We can make some hypothesis on the nature of human constraints (Maslow pyramid based constraints, anxiety limitation…). For robots, the MGS is initially based on the design of the robot. The meaning generated within a robot is initially derived from the constraints implemented by the designer and from the environment. But some non calculable or non predictable evolutions of the robot can introduce meanings that look proper to the robot. This last point can be linked to the notion of autonomy in robots. In such examples, the dynamic management of meanings thru the MGSs in their environments keeps the link with the enactive approach. We finish the presentation by summarising the points addressed and by proposing several continuations. (shrink)

Just as AI has moved away from classical AI, human-computer interaction (HCI) must move away from what I call ‘good old fashioned HCI’ to ‘new HCI’ – it must become a part of cognitive systems research where HCI is one case of the interaction of intelligent agents (we now know that interaction is essential for intelligent agents anyway). For such interaction, we cannot just ‘analyze the data’, but we must assume intentions in the other, and I suggest these are largely (...) recognized through resistance to carrying out one’s own intentions. This does not require fully cognitive agents but can start at a very basic level. New HCI integrates into cognitive systems research and designs intentional systems that provide resistance to the human agent. (shrink)

[Müller, Vincent C. (ed.), (2013), Philosophy and theory of artificial intelligence (SAPERE, 5; Berlin: Springer). 429 pp. ] --- Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human intelligence. This (...) consensus has now come under threat and the agenda for the philosophy and theory of AI must be set anew, re-defining the relation between AI and Cognitive Science. We can re-claim the original vision of general AI from the technical AI disciplines; we can reject classical cognitive science and replace it with a new theory (e.g. embodied); or we can try to find new ways to approach AI, for example from neuroscience or from systems theory. To do this, we must go back to the basic questions on computing, cognition and ethics for AI. The 30 papers in this volume provide cutting-edge work from leading researchers that define where we stand and where we should go from here. (shrink)