This Will Make You Smarter - John Brockman Flashcards
“The richest or busiest or most connected participants in a system will account for much, much more wealth or activity or connectedness than average” - C. Shirky
“You see the pattern everywhere: The top 1 percent of the population controls 35 percent of the wealth. On Twitter, the top 2 percent of users sends 60 percent of the messages. In the health-care system, the treatment of the most expensive fifth of patients creates four-fifths of the overall cost”
“The richest or busiest or most connected participants in a system will account for much, much more wealth or activity or connectedness than average”
“Furthermore, this pattern is recursive. Within the top 20 percent of a system that exhibits a Pareto distribution, the top 20 percent of that slice will also account for disproportionately more of whatever is being measured, and so on”
“This pattern was so common that Pareto called it a “predictable imbalance”; despite this bit of century-old optimism, however, we are still failing to predict it, even though it is everywhere”
“Part of our failure to expect the expected is that we have been taught that the paradigmatic distribution of large systems is the Gaussian distribution, commonly known as the bell curve. In a bell-curve distribution - the average and the median (the middle point in the system) are the same”
“Pareto distributions are nothing like that: The recursive 80/20 weighting means that the average is far from the middle”
“We should stop thinking that average family income have anything to do with one another, or that enthusiastic and normal users of communications tools are doing similar things, or that extroverts should be only moderately more connected than normal people.
We should stop thinking that the largest future earthquake or market panic will be as large as the largest historical one; the longer a system persists, the likelier it is that an event twice as large as all previous ones is coming”
Clay Shirky - Adjunct Professor - NYU
This Will Make You Smarter - Page 198 - 200
“It is possible that in a certain class of information-processing systems, the robust phenomenology of being a self will inevitably appear - although these systems never were, or had, anything like a self.
It is empirically plausible that we might just be such systems” - T. Metzinger
“A self-model is the inner representation that some information-processing systems have of themselves as a whole.
A representation is phenomenally transparent if it (a) is conscious and (b) cannot be experienced as a representation.
Therefore, transparent representations create the phenomenology of naive realism—the robust and irrevocable sense that you are directly and immediately perceiving something that must be real.
Now apply the second concept to the first: A transparent self-model necessarily creates the realistic conscious experience of selfhood—of being directly and immediately in touch with oneself as a whole.
This concept is important, because it shows how, in a certain class of information-processing systems, the robust phenomenology of being a self would inevitably appear—although these systems never were, or had, anything like a self.
It is empirically plausible that we might just be such systems”
Thomas Metzinger - Philosopher
This Will Make You Smarter - Page. 214
“The essence of causation as a concept rests on our tendency to have information about earlier events before we have information about later events” - D. Dalrymple
“The essence of causation as a concept rests on our tendency to have information about earlier events before we have information about later events and as such is better understood as the flow of information between two connected events, from the earlier event to the later one
If information about all events always came in the order in which the events occurred, then correlation would indeed imply causation.
But in the real world, not only are we limited to observing events in the past but also we may discover information about those events out of order. Thus, correlations we observe could be reverse causes (information about A allows us to update our estimate of B, although B happened first and thus was the cause of A) or even more complex situations (e.g., information about A allows us to update our estimate of B but is also giving us information about C, which happened before either A or B and caused both)”
David Dalrymple - Researcher, MIT Media Lab
This Will Make You Smarter - Page 218 - 220
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