Cognitive Computation sans Representation

The Computational Theory of Mind (CTM) holds that cognitive processes are essentially computational, and hence computation provides the scientific key to explaining mentality. The Representational Theory of Mind (RTM) holds that representational content is the key feature in distinguishing mental from non-mental systems. I argue that there is a deep incompatibility between these two theoretical frameworks, and that the acceptance of CTM provides strong grounds for rejecting RTM. The focal point of the incompatibility is the fact that representational content is extrinsic to formal procedures as such, and the intended interpretation of syntax makes no difference to the execution of an algorithm. So the unique 'content' postulated by RTM is superfluous to the formal procedures of CTM. And once these procedures are implemented in a physical mechanism, it is exclusively the causal properties of the physical mechanism that are responsible for all aspects of the system's behaviour. So once again, postulated content is rendered superfluous. To the extent that semantic content may appear to play a role in behaviour, it must be syntactically encoded within the system, and just as in a standard computational artefact, so too with the human mind/brain - it's pure syntax all the way down to the level of physical implementation. Hence 'content' is at most a convenient meta-level gloss, projected from the outside by human theorists, which itself can play no role in cognitive processing

Resolving Two Tensions in 4E Cognition Using Wide Computationalism

Recently, some authors have begun to raise questions about the potential unity of 4E (enactive, embedded, embodied, extended) cognition as a distinct research programme within cognitive science. Two tensions, in particular, have been raised:(i) that the body-centric claims embodied cognition militate against the distributed tendencies of extended cognition and (ii) that the body/environment distinction emphasized by enactivism stands in tension with the world-spanning claims of extended cognition. The goal of this paper is to resolve tensions (i) and (ii). The proposal is that a form of ‘wide computationalism’can be used to reconcile the two tensions and, in so doing, articulate a common theoretical core for 4E cognition

Outcomes based education? Rethinking the provision of compulsory education in Western Australia

Outcomes based education (OBE), which emphasises a radical reinterpretation of the enterprise of education, is a phenomenon enveloping the Australian compulsory education sector. This paper examines the theoretical tenets of OBE as articulated by its chief exponent, William Spady. It then explores the effects that OBE implementation is having on the Western Australian educational fraternity, touching upon current tensions and emerging consequences. Implementation exigencies in one area of the WA curriculum (Mathematics) are then considered; and finally, possible future ‘outcomes’ are suggested should the identified concerns fail to receive due attention

Towards A Theory-Of-Mind-Inspired Generic Decision-Making Framework

Simulation is widely used to make model-based predictions, but few approaches have attempted this technique in dynamic physical environments of medium to high complexity or in general contexts. After an introduction to the cognitive science concepts from which this work is inspired and the current development in the use of simulation as a decision-making technique, we propose a generic framework based on theory of mind, which allows an agent to reason and perform actions using multiple simulations of automatically created or externally inputted models of the perceived environment. A description of a partial implementation is given, which aims to solve a popular game within the IJCAI2013 AIBirds contest. Results of our approach are presented, in comparison with the competition benchmark. Finally, future developments regarding the framework are discussed.Comment: 7 pages, 5 figures, IJCAI 2013 Symposium on AI in Angry Bird

Improving the Odds: Seven Principles for Investing in Early Care and Education

Recent research and advocacy efforts have led funders, politicians, and the business community to agree that the first years of a child's life can determine the rest of their development. Across ideological divides, there is consensus that investing early makes sense—it helps children prepare for successful futures and creates a high return on investment of public dollars.We have created this short guide, featuring examples and how-to's based on our work with more than a dozen foundations working to make progress in the early care and education space. The guide highlights 7 principles to help funders understand and anticipate the challenges and opportunities of supporting early care and education, including practical advice on how to:Inclusively identify and constructively connect the many actors that provide quality care and education to children and their families.Navigate challenges that arise from a sector filled with different approaches and business models.Balance long-term strategies and outcome measures with short-term wins and progress markers.

Information system support in construction industry with semantic web technologies and/or autonomous reasoning agents

Information technology support is hard to find for the early design phases of the architectural design process. Many of the existing issues in such design decision support tools appear to be caused by a mismatch between the ways in which designers think and the ways in which information systems aim to give support. We therefore started an investigation of existing theories of design thinking, compared to the way in which design decision support systems provide information to the designer. We identify two main strategies towards information system support in the early design phase: (1) applications for making design try-outs, and (2) applications as autonomous reasoning agents. We outline preview implementations for both approaches and indicate to what extent these strategies can be used to improve information system support for the architectural designer

Symbol grounding and its implications for artificial intelligence

In response to Searle's well-known Chinese room argument against Strong AI (and more generally, computationalism), Harnad proposed that if the symbols manipulated by a robot were sufficiently grounded in the real world, then the robot could be said to literally understand. In this article, I expand on the notion of symbol groundedness in three ways. Firstly, I show how a robot might select the best set of categories describing the world, given that fundamentally continuous sensory data can be categorised in an almost infinite number of ways. Secondly, I discuss the notion of grounded abstract (as opposed to concrete) concepts. Thirdly, I give an objective criterion for deciding when a robot's symbols become sufficiently grounded for "understanding" to be attributed to it. This deeper analysis of what symbol groundedness actually is weakens Searle's position in significant ways; in particular, whilst Searle may be able to refute Strong AI in the specific context of present-day digital computers, he cannot refute computationalism in general

Apprenticeship training in Italy: a cost-effective model for firms?

In times of rapid technological progress and increasing digitalisation in many areas of work and life, it is more important than ever to provide young people with the best possible skills for their entry into the world of work. It is certainly important to provide them with a solid theoretical knowledge base. However, it is also important to impart practical skills to ensure that they are able to adapt to the needs of the labour market. Post-school education in Italy, while providing good formal skills in this respect, is not sufficiently responsive to the needs of the labour market. With this in mind, dual training models have become politically more attractive in Italy and are already being implemented. But despite political support and the reforms in recent years, the popularity of dual training models has hardly increased. From an international point of view, this development is hardly surprising. On the one hand, interest in dual vocational training is increasing: learning a trade at two locations – in a company and at a part-time vocational school – means that apprentices gain valuable professional experience while they are still training, which enables a smoother transition to the labour market. As a result, there is less youth unemployment and a better supply of skilled labour for industry. On the other hand, reforms of this kind often encounter a major obstacle when it comes to practical implementation: a lack of commitment by the companies, especially in countries where an in-company apprenticeship tradition is absent. First and foremost, companies see training as an operational loss: why pay to train an apprentice when qualified employees can be recruited directly on the labour market? What businesses often fail to see is that in-house training does not merely incur costs, but that it also results in monetary benefits, and sometimes in net profits before training has even been completed. However, the question is: under which conditions? The costs and benefits of training are not invariables, they depend on a wide of variety of parameters such as the level of apprentices’ pay, the industry in question, the duration of training, recruiting costs for qualified skilled workers on the labour market – not to mention the quality of the training course. To examine the situation, this study uses simulations to investigate how these parameters would have to be designed in Italy in order to make dual training more attractive for Italian businesses. The conclusions derived in this report are intended to assist Italian policymakers and employers to make more evidence-based decisions, to ensure that Italy’s labour force investments are more likely to yield positive returns

Faith in the Algorithm, Part 1: Beyond the Turing Test

Since the Turing test was first proposed by Alan Turing in 1950, the primary goal of artificial intelligence has been predicated on the ability for computers to imitate human behavior. However, the majority of uses for the computer can be said to fall outside the domain of human abilities and it is exactly outside of this domain where computers have demonstrated their greatest contribution to intelligence. Another goal for artificial intelligence is one that is not predicated on human mimicry, but instead, on human amplification. This article surveys various systems that contribute to the advancement of human and social intelligence

Talking about worlds

I explore the logic of the conditional, using credence judgments to argue against Duality and in favor of Conditional Excluded Middle. I then explore how to give a theory of the conditional which validates the latter and not the former, developing a variant on Kratzer (1981)'s restrictor theory, as well as a proposal which combines Stalnaker (1968)'s theory of the conditional with the theory of epistemic modals I develop in Mandelkern 2019a. I argue that the latter approach fits naturally with a conception of conditionals as referential devices which allow us to talk about particular worlds