No help on the hard problem

The hard problem of consciousness is to explain why certain physical states are conscious: why do they feel the way they do, rather than some other way or no way at all? Arthur Reber (2016) claims to solve the hard problem. But he does not: even if we grant that amoebae are conscious, we can ask why such organisms feel the way they do, and Reber’s theory provides no answer. Still, Reber’s theory may be methodologically useful: we do not yet have a satisfactory theory of consciousness, but perhaps the study of simple minds is a way to go about finding one

Lewisian Scorekeeping and the Future

The purpose of this paper is to draw out a little noticed, but (I think) correct and important, consequence of David Lewis’s theory of how the values of contextual parameters are determined. According to Lewis (1979), these values are often determined at least in part by accommodation; to a first approximation, the idea is that contextual parameters tend to take on the values they need to have in order for our utterances to be true. The little-noticed consequence of Lewis’s way of developing these ideas is that what we say is determined in part by the way the conversation unfolds after our utterance. That is, Lewisian accommodation entails a non-standard form of externalism, according to which what we say is determined not only by factors internal to us at the time of our utterance, nor even by truths about our physical or social environment at the time of utterance or by our history, but also by truths about our future—truths about times after the time of our utterance. Seeing this consequence clearly lets us refine and improve upon Lewis’s account of when accommodation can occur

Relativism, metasemantics, and the future

Contemporary relativists often see their view as contributing to a semantic/post-semantic account of linguistic data about disagreement and retraction. I offer an independently motivated metasemantic account of the same data, that also handles a number of cases and empirical results that are problematic for the relativist. The key idea is that the content of assertions and beliefs is determined in part by facts about other times, including times after the assertion is made or the belief is formed. On this temporal externalist view, speaker behaviours such as retraction of previous assertions play a role in making it the case that a past utterance has a given meaning.PostprintPeer reviewe

Metasemantic ethics

The idea that experts (especially scientific experts) play a privileged role in determining the meanings of our words and the contents of our concepts has become commonplace since the work of Hilary Putnam, Tyler Burge, and others in the 1970s. But if experts have the power to determine what our words mean, they can do so responsibly or irresponsibly, from good motivations or bad, justly or unjustly, with good or bad effects. This paper distinguishes three families of metasemantic views based on their attitudes towards bad behaviour by meaning‐fixing experts, and draws a series of distinctions relevant for the normative evaluation of meaning‐determining actions.PostprintPeer reviewe

State-of-the-art and gaps for deep learning on limited training data in remote sensing

Deep learning usually requires big data, with respect to both volume and variety. However, most remote sensing applications only have limited training data, of which a small subset is labeled. Herein, we review three state-of-the-art approaches in deep learning to combat this challenge. The first topic is transfer learning, in which some aspects of one domain, e.g., features, are transferred to another domain. The next is unsupervised learning, e.g., autoencoders, which operate on unlabeled data. The last is generative adversarial networks, which can generate realistic looking data that can fool the likes of both a deep learning network and human. The aim of this article is to raise awareness of this dilemma, to direct the reader to existing work and to highlight current gaps that need solving.Comment: arXiv admin note: text overlap with arXiv:1709.0030

Revisionary analysis without meaning change (or, could women be analytically oppressed?)

This chapter develops a conception of philosophical analysis which makes sense of the idea that a correct analysis can be revisionary (in that it departs from ordinary or expert belief and linguistic usage). The view is superior to the alternatives defended by most proponents of ‘conceptual ethics’ and ‘conceptual engineering’ (according to which revisionary theorizing involves replacing words or concepts) because it better explains the arguments we advance when we engage with proposed revisionary analyses. A key idea is that analytic claims can emerge in the course of debate without change of meaning, so that our acceptance (perhaps late in the debate) of some analyticity can fix the meaning of a word as we used it all along. The discussion focuses on Haslanger’s revisionary analysis of gender.Publisher PD

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Scepticism about moral superiority

Chapman & Huffman suggest that we might change people’s behavior toward animals by resisting an argument that because humans are intellectually superior to animals, they are also morally superior to animals. C & H try to show that the premise is false: Humans are not intellectually superior. Several commentators have resisted this response. We suggest that there are other ways of attacking the argument: The notion of moral superiority on which the argument relies is dubious, and the obvious ways of reformulating the argument are instances of the “naturalistic fallacy.

Conference report : The importance of the gut microbiome and nutrition on health

Open access via CUP agreementPeer reviewedPublisher PD