Emre Sevinç
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Emre Sevinç

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Practical Philoso...
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Forecasting: prin...
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The Offer You Can...
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“Note that in the above construction we made a number of choices; here we must beware. Choosing a good categorification – like designing a good algebraic structure such as that of preorders or quantales – is part of the art of mathematics. There is no prescribed way to categorify, and the success of a chosen categorification is rather empirical: its richer structure should allow us more insights into the subject we want to model.”
Brendan Fong, Seven Sketches in Compositionality: An Invitation to Applied Category Theory

Jodi Picoult
“According to Darwin’s Origin of Species, it is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is able best to adapt and adjust to the changing environment in which it finds itself. —Leon C. Megginson”
Jodi Picoult, Wish You Were Here

“To get just an inkling of the fire we're playing with, consider how content-selection algorithms function on social media. They aren't particularly intelligent, but they are in a position to affect the entire world because they directly influence billions of people. Typically, such algorithms are designed to maximize click-through, that is, the probability that the user clicks on presented items. The solution is simply to present items that the user likes to click on, right? Wrong. The solution is to change the user's preferences so that they become more predictable. A more predictable user can be fed items that they are likely to click on, thereby generating more revenue. People with more extreme political views tend to be more predictable in which items they will click on. (Possibly there is a category of articles that die-hard centrists are likely to click on, but it’s not easy to imagine what this category consists of.) Like any rational entity, the algorithm learns how to modify its environment —in this case, the user’s mind—in order to maximize its own reward.”
Stuart Russell, Human Compatible: Artificial Intelligence and the Problem of Control

“So the syntax of the regular-expression language is awful; there are various incompatible forms of the language; and the quotation conventions are baroquen [sic]. While regular expression languages are domain-specific languages, they are bad ones. Part of the value of examining regular expressions is to experience how bad things can be.”
Chris Hanson, Software Design for Flexibility: How to Avoid Programming Yourself into a Corner

Stanislas Dehaene
“Yann LeCun's strategy provides a good example of a much more general notion: the exploitation of innate knowledge. Convolutional neural networks learn better and faster than other types of neural networks because they do not learn everything. They incorporate, in their very architecture, a strong hypothesis: what I learn in one place can be generalized everywhere else.

The main problem with image recognition is invariance: I have to recognize an object, whatever its position and size, even if it moves to the right or left, farther or closer. It is a challenge, but it is also a very strong constraint: I can expect the very same clues to help me recognize a face anywhere in space. By replicating the same algorithm everywhere, convolutional networks effectively exploit this constraint: they integrate it into their very structure. Innately, prior to any learning, the system already “knows” this key property of the visual world. It does not learn invariance, but assumes it a priori and uses it to reduce the learning space-clever indeed!”
Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

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