"What's so great about classical music?" A philosophical perspective

"The aim and final end of all music should be none other than the glory of God and the refreshment of the soul." - Bach
Classical music’s ability to engage and enliven our inner experience is a primary reason why it holds so much philosophical interest. What is it about classical music as an art form that enables it to connect so strongly with our inner life? Part of the answer would seem to lie in the fact that it is an auditory art. The perception of aesthetic content through hearing differs in fundamental ways from the perception of aesthetic content through vision, especially in the case of visual arts that make use of representation. One of the greatest differences between the two modes of artistic perception is that unless we are given rather clear guidelines, we do not interpret musical sounds as representations of objects. The preexisting ability to interpret and assign meanings to visual images does not automatically come into play when we hear musical sounds. It appears that music has the capacity to engage our aesthetic sensibility without also engaging the cognition of objects. This sensibility is linked in complex ways to inner experience, feelings, moods, and emotions.

If correlation doesn’t imply causation, then what does?

Credit to Randall Munroe.
It’s all very well to piously state that correlation doesn’t imply causation, it does leave us with a conundrum: under what conditions, exactly, can we use experimental data to deduce a causal relationship between two or more variables?

What is art?

Thomas Cole, Ruins in the Campagna di Roma, Morning, 1842.
This post is introduces definition of what art is. I'll introduce different theories art and consider their respective merits and pitfalls. To start we will need to have a clear idea on what we hope to achieve with a definition of art and what sort of thing that definition would need to be.

The Humanities offer what Medicine needs

You will search medical textbooks in vain for the differential diagnosis distinguishing modern illness from postmodern illness. According to physician and literary scholar Rita Charon, if this dichotomy and the evolution of the former condition into the latter were established, the project of narrative ethics would follow logically. According to David Morris, a contributor to Rita Charon's book Stories Matter, the modern perspective is "biomedical": we are our genes, our organs, our laboratory measurements. The postmodern perspective is "biocultural": we are made of stories -- cultural, familial, interpersonal, psychological, emotional, and biologic narratives.

The role of neural networks in machine learning

Neural networks (NN) hold a tremendous amount of potential for deep learning, part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. NN and deep learning are now computationally feasible due to GPUs, it shows unbeatable power on complex prediction problems that have very high dimensionality and millions-billions of samples.

An insight into philosopher Paul Feyerabend, an imaginative maverick

    "...And it is of course not true that we have to follow the truth. Human life is guided by many ideas. Truth is one of them. Freedom and mental independence are others. If Truth, as conceived by some ideologists, conflicts with freedom, then we have a choice. We may abandon freedom. But we may also abandon Truth." - "How to Defend Society Against Science", Paul Feyerabend

    Paul Feyerabend (1924-1994), having studied science at the University of Vienna, moved into philosophy for his doctoral thesis, made a name for himself both as an expositor and (later) as a critic of Karl Popper's “critical rationalism”, and went on to become one of the twentieth century's most famous philosophers of science. An imaginative maverick, he became a critic of philosophy of science itself, particularly of “rationalist” attempts to lay down or discover rules of scientific method.

    Meditative thoughts on symmetry in relation to the nature of beauty

    Tunga, Untitled, 2011, ink on paper, 29 7⁄8 × 20". From the series “La voie humide,” 2011–16.
    Symmetry is evidently a kind of unity in variety, where a whole is determined by the rhythmic repetition of similars. We have seen that it has a value where it is an aid to unification. Unity would thus appear to be the virtue of forms; but a moment's reflection will show us that unity cannot be absolute and be a form; a form is an aggregation, it must have elements, and the manner in which the elements are combined constitutes the character of the form. A perfectly simple perception, in which there was no consciousness of the distinction and relation of parts, would not be a perception of form; it would be a sensation.

    The story of how I won

    Winged Victory of Samothrace
    This is the story of how I won. This is the story of how I spoke out against wrongdoing that sought to hurt me fundamentally as a human being. I overcame these struggles with the fearlessness that has been given to me. The world is full of moral ambiguities and existential horrors. Yet I made the right decisions at the right time in such a way that I found success and happiness.

    An overview of the neuroscience of consciousness

    Alchemical Illustration from the Emerald Tablet of Hermes.
    The Tablet had such an impact on the minds of histories greatest philosophers, esotericists and mystical thinkers, that it became the esoteric industry standard for every medieval and later renaissance system of alchemy.
    Conscious experience in humans depends on brain activity, so neuroscience will contribute to explaining consciousness. What would it be for neuroscience to explain consciousness? How much progress has neuroscience made in doing so? What challenges does it face? How can it meet those challenges? What is the philosophical significance of its findings? This blogpost addresses these and related questions.

    The role of deep learning in neuroscience

    "Hidden Treasure" by Joan Belmar
    The brain is truly one of the final frontiers of human exploration. Understanding how the brain works has vast consequences for human health and for computation. Imagine how computers might change if we actually understood thinking and even consciousness. Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning (ML), however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. 

    How to become who you are, according to Nietzsche


    In "Hiking with Nietzsche: On Becoming Who You Are," American professor of philosophy John Kaag shows how important and salient philosophy's role in everyday life is. By hiking through mountains and experiencing what the Swiss Alps have to offer, Kaag illustrates a view of Nietzsche's life that provides an intimate understanding of the challenges Nietzsche for which the German philosopher sought answers. Comparing himself to Zarathustra and Dionysus, Nietzsche actualizes his true potential in a way that other philosophers may face struggles with. He's able to overcome the limits and disadvantages of discourse and rumination and, instead, write about the urgency of addressing issues of his time - many of which persist in the present day.

    As I finish reading this book, it's time for me to become who I am.

    The (3M) model-mechanistic-mapping criteria of explanation

    The 3M (model-mechanistic-mapping) model says that a model of a target phenomena explains that phenomena to the extent that a. the variables in the model correspond to identifiable components, activities and feature and organizational features that produces maintains or underlie the phenomena b. the mathematical dependencies that are posited among the these perhaps mathematical variables within that model correspond to causal relations among the components of that mechanism.

    Reinforcement Learning: Super Mario, AlphaGo and beyond

    Most of the literature we find on machine learning talks about two types of learning techniques – supervised and unsupervised. Supervised learning is where we have a labeled dataset. This means we already have data from which to develop models using algorithms such as Linear Regression, Logistic Regression, and others. With this model, we can make further predictions like given data on housing prices, what will the cost of a house with a given set of features be. Unsupervised learning, on the other hand, doesn’t have a labeled dataset, but still, we do have abundant data. The model we create in this setting just needs to derive a pattern amongst the data available. We do this with algorithms such as K Means Clustering, K Nearest Neighbors, etc. to solve problems like grouping a set of users according to their behavior in an online shopping portal. But what if we don’t have so much data? What if we are dealing with a dynamic environment and the model needs to gather data and learn in real time? Enter reinforcement learning. In this post, I'll take a look at the basics of what reinforcement learning is, how it works and some of its practical applications.