The HBP and the future of intelligent machines Sample Clauses

The HBP and the future of intelligent machines. Our overview of the HBP’s activities that hold promise for the future of ICT, and the previous discussion of the SpiNNaker chip in particular, have shown that the Project most certainly has potential to help bring about a very diverse range of variously intelligent machines, and not just in the long term. Yet, when asked if the research done in the Project has some relation to Artificial Intelligence, many collaborators answer that it does not. Some believe that the ultimate goal of the Project could be Artificial General Intelligence or ‘Strong AI’, i.e. producing general-purpose systems which intelligence would be at least equivalent to that of the human mind, but the feasibility of such an outcome is perceived as being far into the future.26 What counts as Artificial Intelligence is not straightforward, and the lack of consensus on defining the term allows for much flexibility in the answers given. 27 There is a general agreement on the distinction between classic AI (broadly speaking, this encompasses symbolic AI and experts systems), Artificial Neural Networks (ANNs are based on highly simplified neuron models that display very little biological realism; different kinds of ANNs are used in machine learning – e.g. for deep learning, supervised and unsupervised learning, reinforcement learning) and Biological Neural Networks (aiming for biological accuracy, they are the models used in computational neuroscience).28 These days, the vast majority of courses and textbooks on Artificial Intelligence encompass Artificial Neural Networks alongside more traditional methods. 29 Those who deny that the HBP is an Artificial Intelligence project draw a firm line between machine learning and computational neuroscience: the former uses the highly simplified Artificial Neural Networks (ANNs), while the latter, such as what is done in the Simulation Subproject of the HBP, is based upon entirely different kinds of biologically realistic models. Still, we have already seen that although biological and artificial neural networks may involve different kinds of models, the SpiNNaker architecture developed in the Neuromorphic Computing Subproject (SP9) has the flexibility to support models in both categories. This is one example showing that, although the HBP may not be strictly speaking an Artificial Intelligence project, innovation in Artificial Intelligence could still be an important, if indirect, outcome of the Project. But further, the extent to which it is possible t...
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