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Back to the Symbol, Or: How the Symbolic Paradigm Can Be of Great Help to Study Wisdom

By IGI Global on Apr 15, 2011
IGI Global would like to thank Dr. David Casacuberta for contributing this guest editorial post.

In a former entry on Artificial Wisdom, I described how a proper understanding of wisdom (human or artificial) was closely linked to the enactive paradigm, also known as the third generation of cognitive sciences. That seems to rule out any symbol-based approach to Artificial Wisdom. Today I want to argue that the opposite holds.

In the 90s, when there was a big conceptual battle between the symbolic approach and the connectionist one in order to decide which theory was better suited to understand cognition, it was common to see the two theories under an ontological perspective. That is, within our skulls, neurons were either processing symbols following syntactic rules or sub-symbols embedded in groups of neurons and processed in parallel. The two theories were trying to establish the ontological building blocks to understand how cognition is possible. Philosophers like Andy Clark or Tim van Gelder worked hard to show that you needn't symbols and syntactic rules to have models on what cognition is and what their primitive elements are.

Entering the 21st century, a new candidate, enactivism, has gained popularity, and it seems to be another candidate to explain cognition.

However, I don't think this is the case: enactivist explanations, based on the continuous coupling and de-coupling between the cognitive system and the environment, so far has not been not able to generate a comprehensive theory that can be used to make sense of what cognition is and what its building blocks are. Enactivism can give very detailed and promising descriptions of minimal cognition issues – like how bacteria make sense of their surroundings – and it can also give us very detailed differential equations to predict coupling a decoupling of very simple systems, but it doesn't have a working general theory about what cognition is. If we were one day able to analyze a person who shows wise behavior using the current enactivist model, what we would get would be some incredibly complex differential equations generating degrees of complexity, which wouldn't make any sense from a theoretical point of view. Maybe, if the equations are well established and we have enough computational power to run the calculations, we would be able to predict when and how this person would show a wise behavior, but we wouldn't be able to understand what wisdom is at all.

Enactivism has presented enough arguments that show the impossibility of holding the symbolic approach from an ontological point of view. Clearly, on the basis of our thoughts and cognitions there are no symbols, but a very complex process of coupling and decoupling between system and environment. However, that doesn't render the symbolic approach useless. Despite the fact that Newton was proved wrong, first by the theory of relativity and next by quantum mechanics, engineers that design bridges still rely on classical kinematics and dynamics to make them, as they offer a good enough approach to build bridges.

So I view the symbolic approach as just an abstraction of what the mind is and how cognition is generated, that is simple and powerful enough to give us the possibility to understand cognitive processes and generate meaningful theories that can be tested and, more importantly, could in the end help to improve our cognition.

For example, if I use a symbolic approach to understand wisdom I might end up generating a symbolic and heuristics model that could be run as an expert system. Despite the fact that we know that our brains do not act like expert systems, designing one can help to generate heuristic rules which a fellow human can understand, for example a cognitive psychologist, or even a therapist, and they can make sense of that. An enactive model would probably be more precise and ontologically accurate, but also useless outside of the specific research project that gave birth to it.

Let's see another example, from the symbolic versus neural networks debate. Clearly, neural network based models are better at face recognition than symbolic systems. However, if want to create a theory of what faces are, what the basic elements that make a face are, and how they relate to each other, I need a symbolic model with elements like "noses", eyes", "eyebrows", "jaws" and so on. If I just train a neural network to recognize the faces of the FBI's Most Wanted, it will be better at tracking terrorists and serial killers than a symbolic system, but it won't teach me much about faces, only some statistical regularities that make it easy to differentiate Osama Bin Laden from Barack Obama. Moreover I don't even get systematicity; if I train the neural network with the faces of the members of the Spanish Parliament, the type of representations and statistical calculations they will make to differentiate Mariano Rajoy and Jose Luís Rodríguez Zapatero will be very different, as the input to generate the statistic analysis is also different. I won't get any theory of what faces are making a cluster analysis on what each neuron does.

However, if I create an expert system to recognize faces, despite the fact that it can be very bad at recognizing actual people, it will help me to develop a theory on main types of facial expression, to understand the main differences between, let's say, Caucasian and Asian types.

The first generation of computer programs that played chess could be used to learn chess and get some heuristics. Deep Blue plays better than them. Actually, it plays better than most human beings, but Deep Blue is not helpful at all to understand how humans play chess. When AI moved from the symbolic approach to the neural network, machine learning, and other statistical methods, we lost our desire to explain things and just got interested in making things that work. We stopped being scientists and philosophers and became only engineers. There is nothing wrong about being an engineer, of course, and doing good software to recognize faces, simulate the weather or beat humans at chess is something good to have. But it is also good to generate abstractions that lead to theories that help us to understand a domain, and not just simulate it.

Until enactivism is able to produce general theories to analyze higher states of cognition, I believe that the symbolic approach is a much better tool to understand the components, rules and heuristics behind human emotion or wisdom from the point of view of AI.

David Casacuberta is a philosophy of science professor in the Universidad Autònoma de Barcelona (Spain). He has a PhD in Philosophy and a master degree in Cognitive Sciences and Language. His current line of research is artificial emotions, considering both its use in software applications, specially media related, as well as theoretical research. He has published several books, book chapters, and papers on the subject. He works for Transit Projects as project manager and scientific coordinator for the EU Projects. He is also a member of the Spanish think-tank edemocracia (www.edemocracia.com) devoted to the study on how ICT can improve democratic processes such as voting and participation. He is also the secretary of the Spanish chapter of Computer Professionals for Social Responsibility (www.cpsr.org) and the Spanish representative in the International Coalition European Digital Rights (http://www.edri.org).You can find Dr. Casacuberta's latest IGI Global chapter at: www.igi-global.com/Bookstore/Chapter.aspx?TitleId=11637, as part of the " Encyclopedia of Digital Government." For comments and discussion on the post, please use the form below or email Dr. Casacuberta.

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