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Trust—particularly between complete strangers, with no interactions besides relatively anonymous market transactions—is the basis of the modern industrial economy. And the story of the development of the modern economy is in large part the story of the invention and improvement of technologies and institutions for managing that trust. In other words, many things about the way the business world is organized make a lot more sense when you realize that they exist because of the constant drive for countries to become less like Greece and more like Canada.

— Lying for Money by Dan Davies (Page 16)

When the same features of a system keep appearing without anyone designing them, you can usually be pretty sure that the cause is economic.

— Lying for Money by Dan Davies (Page 8)

The novel [Frankenstein] should not necessarily be seen as against science and technology: the main message seems to be that scientists need to take responsibility for their creations.

— AI Ethics by Mark Coeckelbergh (Page 19)

Another concern is that this discussion about the (far-off) future impacts of AI distracts from the real and current risks of actually deployed systems. There seems to be a real risk that in the near future the systems will not be smart enough and that we will insufficiently understand their ethical and societal implications and nevertheless use them widely.

— AI Ethics by Mark Coeckelbergh (Page 15 - 16)

Improvement for whom? The government or the citizens? The police or those who are targeted by the police? The retailer or the customer? The judges or the accused? Questions concerning power come into play, for instance when the technology is shaped by only a few mega corporations (Nemitz 2018). Who shapes the future of AI?

— AI Ethics by Mark Coeckelbergh (Page 7)

Darwin and Freud dethroned our beliefs of exceptionalism, our feelings of superiority, and our fantasies of control; today, artificial intelligence seems to deal yet another blow to humanity’s self-image. If a machine can do this, what is left for us?

— AI Ethics by Mark Coeckelbergh (Page 2)

Machine learning involves the development and evaluation of algorithms that enable a computer to extract (or learn) functions from a dataset (sets of examples).

— Deep Learning by John D. Kelleher (The MIT Press Essential Knowledge Series) (Page 6)

Overall, the cognitive charge of a function should be minimal. No one wants to face a wall of text featuring multiple layers of indentations.

— Learn Go with Pocket-Sized Projects by Aliénor Latour, Donia Chaiehloudj, Pascal Bertrand

Dortmunder had learned patience at great cost. The trial and error of life among human beings had taught him that whenever a bunch of them began to jump up and down and shout at cross-purposes, the only thing a sane man could do was sit back and let them sort it out for themselves. No matter how long it took. The alternative was to try to attract their attention, either with explanations of the misunderstanding or with a return to the original topic of conversation, and to make that attempt meant that sooner or later you too would be jumping up and down and shouting at cross-purposes. Patience, patience; at the very worst, they would finally wear themselves out.

— Bank shot by Donald E. Westlake (Page 89)

He didn’t ask the question, and that was strangely more annoying than if he had. I said, “I lost the faith.” “Like a priest?” “Something like that. Not exactly, because it’s not rare for a cop to lose the faith and go on being a cop. He may never have had it in the first place. What it amounted to was that I found out I didn’t want to be a cop anymore.” Or a husband, or a father. Or a productive member of society. “All the corruption in the department? That sort of thing?” “No, no.” The corruption had never bothered me. I would have found it hard to support a family without it.

— The Sins of the Fathers by Lawrence Block (Matthew Scudder (#1)) (Page 15)

About the only way anybody could get hurt really badly around here was by winning the lottery, which occasionally happened to some poor bastard, who was usually, a year later, either dead or in jail or rehab or exile. McWhitney did not play the lottery.

— Dirty Money by Richard Stark (Page 95)