What Gary Couldn’t Imagine

2019 ◽  
Vol 44 ◽  
pp. 293-311
Author(s):  
Tufan Kiymaz ◽  

In this paper, I propose and defend an antiphysicalist argument, namely, the imagination argument, which draws inspiration from Frank Jackson’s knowledge argument, or rather its misinterpretation by Daniel Dennett and Paul Churchland. They interpret the knowledge argument to be about the ability to imagine a novel experience, which Jackson explicitly denies. The imagination argument is the following. Let Q be a visual phenomenal quality that is imaginable based on one’s phenomenal experience. (1) It is not possible to imagine Q solely based on complete physical knowledge. (2) If it is not possible to imagine Q solely based on complete physical knowledge, then physicalism is false. (3) Therefore, physicalism is false. Even though objections have been raised to this argument in the literature, there is, as far as I know, no explicit defense of it. I argue that the imagination argument is more plausible than the knowledge argument in some respects and less plausible in others. All things considered, it is at least as interesting and serious a challenge to physicalism as the knowledge argument is.

Author(s):  
Maria Ayelen Sanchez

En 1982, Frank Jackson propuso un experimento mental con el objetivo de refutar al fisicalismo y a su teoría de la identidad entre estados mentales y cerebrales. Este experimento, conocido como “el argumento del conocimiento” (Knowledge argument), tuvo una enorme repercusión en el ámbito de la filosofía de la mente, a tal punto que actualmente sigue generando debates en torno a su aceptación. En el presente trabajo expondremos la objeción realizada al mismo por Daniel Dennett, la cual pretende desacreditar la conclusión de Jackson. Finalmente, utilizaremos elconcepto de “pregunta desviada”, tal como lo emplea Hilary Putnam en su análisis del problema mente-cuerpo, para dejar planteada una crítica a la objeción presentada por Dennett.


2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.


Author(s):  
Joseph Levine

This paper presents a sketch of a theory of phenomenal consciousness, one that builds on the notion of a “way of appearing,” and draws out various consequences and problems for the view. I unabashedly endorse a version of the Cartesian Theater, while assessing the prospects for making such a view work. As I treat phenomenal consciousness as a relation between a subject and what it is she is conscious of, I face a difficulty in making sense of hallucination, since the object of awareness is missing. I distinguish my position from direct realists who endorse disjunctivism, and end on a somewhat speculative note.


It would be impossible in an obituary of ordinary length to convey any idea of the many-sided activity by which Lord Kelvin was continually transforming physical knowledge, through more than two generations, more especially in the earlier period before practical engineering engrossed much of his attention in importunate problems which only he could solve. It is not until one tries to arrange his scattered work into the different years and periods, that the intensity of his creative force is fully realised, and some otion is acquired of what a happy strenuous career his must have been in early days, with new discoveries and new aspects of knowledge crowding in upon him faster than be could express them to the world. The general impression left on one's mind by a connected survey of his work is overwhelming. The instinct of his own country and of the civilised world, in assigning to him a unique place among the intellectual forces of the ast century, was not mistaken. Other men have been as great in some special department of physical science: no one since Newton—hardly even Faraday, whose limitation was in a sense his strength—has exerted such a masterful influence over its whole domain. He might have been a more learned mathematician or an expert chemist; but he would then probably have been less activity, the immediate grasp of connecting principles and relations; each subject that he tackled was transformed by direct hints and analogies brought to bear from profound contemplation of the related domains of knowledge. In the first half of his life, fundamental results arrived in such volume as often to leave behind all chance of effective development. In the nidst of such accumulations he became a bad expositor; it is only by tracing his activity up and down through its fragmentary published records, and thus obtaining a consecutive view of his occupation, that a just idea of the vistas continually opening upon him may be reached. Nowhere is the supremacy of intellect more impressively illustrated. One is at times almost tempted o wish that the electric cabling of the Atlantic, his popularly best known achievement, as it was one of the most strenuous, had never been undertaken by him; nor even, perhaps, the practical settlement of electric units and instruments and methods to which it led on, thus leaving the ground largely prepared for the modern refined electric transformation of general engineering. In the absence of such pressing and absorbing distractions, what might the world not have received during the years of his prime in new discoveries and explorations among the inner processes of nature.


Dialogue ◽  
2012 ◽  
Vol 51 (2) ◽  
pp. 313-326 ◽  
Author(s):  
Huiming Ren

ABSTRACT: I argue that the Ability Hypothesis cannot really accommodate the knowledge intuition that drives the knowledge argument and therefore fails to defend physicalism. When the thought experiment is run with, instead of Mary, an advanced robot Rosemary, for whom there presumably is no distinction between knowledge-how and knowledge-that, proponents of the Ability Hypothesis would have to give a far-fetched and counterintuitive explanation of why Rosemary wouldn’t learn anything new upon release.


2021 ◽  
Author(s):  
Sascha Flaig ◽  
Timothy Praditia ◽  
Alexander Kissinger ◽  
Ulrich Lang ◽  
Sergey Oladyshkin ◽  
...  

<p>In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a “predictive control” scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the „Long short-term memory“ architecture.</p><p>To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.</p><p>Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.</p><p>As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.</p><p>To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.</p><p>In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.</p>


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