Summary, page 467: "This book is concerned with the influence of Hume’s metaphysics and moral philosophy in 18th-century Europe and it is divided into two main parts. The first part is focused on the exposition of Hume’s metaphysics and moral philosophy in their historical context, because this topic is still mostly unknown in Croatia. The second part deals with the influence of Hume’s metaphysics and moral philosophy on selected European thinkers of the Age of Enlightenment until the beginning of the (...) French Revolution in 1789.". (shrink)

Review of Frederick Charles Beiser's "The Genesis of Neo-Kantianism, 1796-1880".

How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task [1]. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure. However, the original formulation of the apperception task had one fundamental limitation: it assumed (...) the raw sensory input had already been parsed using a set of discrete categories, so that all the system had to do was receive this already-digested symbolic input, and make sense of it. But what if we don't have access to pre-parsed input? What if our sensory sequence is raw unprocessed information? The central contribution of this paper is a neuro-symbolic framework for distilling interpretable theories out of streams of raw, unprocessed sensory experience. First, we extend the definition of the apperception task to include ambiguous (but still symbolic) input: sequences of sets of disjunctions. Next, we use a neural network to map raw sensory input to disjunctive input. Our binary neural network is encoded as a logic program, so the weights of the network and the rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules). (shrink)