Emre Sevinç

“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

“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

“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

“Our brain is therefore not simply passively subjected to sensory inputs. From the get-go, it already possesses a set of abstract hypotheses, an accumulated wisdom that emerged through the sift of Darwinian evolution and which it now projects onto the outside world. Not all scientists agree with this idea, but I consider it a central point: the naive empiricist philosophy underlying many of today's artificial neural networks is wrong. It is simply not true that we are born with completely disorganized circuits devoid of any knowledge, which later receive the imprint of their environment. Learning, in man and machine, always starts from a set of a priori hypotheses, which are projected onto the incoming data, and from which the system selects those that are best suited to the current environment. As Jean-Pierre Changeux stated in his best-selling book Neuronal Man (1985), “To learn is to eliminate.”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now