Over the weekend, I listened to this podcast conversation between the psychologist & philosopher Alison Gopnik and writer Ted Chiang about using children’s learning as a model for developing AI systems. Around the 23-minute mark, Gopnik observes that care relationships (child care, elder care, etc.) are extremely important to people but is nearly invisible in economics. And then Chiang replies:
One of the ways that conventional economics sort of ignores care is that for every employee that you hire, there was an incredible amount of labor that went into that employee. That’s a person! And how do you make a person? Well, for one thing, you need several hundred thousand hours of effort to make a person. And every employee that any company hires is the product of hundreds of thousands of hours of effort. Which, companies… they don’t have to pay for that!
They are reaping the benefits of an incredible amount of labor. And if you imagine, in some weird kind of theoretical sense, if you had to actually pay for the raising of everyone that you would eventually employ, what would that look like?
It’s an interesting conversation throughout — recommended!
Chiang has written some of my favorite things on AI in recent months/years, including this line that’s become one of my guiding principles in thinking about AI: “I tend to think that most fears about A.I. are best understood as fears about capitalism.”
Each year, Edge has asked a group of scientists, philosophers, musicians, writers, and designers a simple but provocative question and collects the answers on their website. Past questions have included:
What do you think about machines that think? (2015)
What have you changed your mind about? Why? (2008)
What do you believe true even though you cannot prove it? (2005)
What is the most important invention in the past two thousand years? (1999)
This year, the question is: What scientific term or concept ought to be more widely known?
Of all the scientific terms or concepts that ought to be more widely known to help to clarify and inspire science-minded thinking in the general culture, none are more important than “science” itself.
Many people, even many scientists, have traditionally had a narrow view of science as controlled, replicated experiments performed in the laboratory-and as consisting quintessentially of physics, chemistry, and molecular biology. The essence of science is conveyed by its Latin etymology: scientia, meaning knowledge. The scientific method is simply that body of practices best suited for obtaining reliable knowledge.
Here are some of the responses. Alison Gopnik chose “Life History”:
“Life history” is the term biologists use to describe how organisms change over time-how long an animal lives, how long a childhood it has, how it nurtures its young, how it grows old. Human life history is weird. We have a much longer childhood than any other primate-twice as long as chimps, and that long childhood is related to our exceptional learning abilities. Fossil teeth suggest that this long childhood evolved in tandem with our big brains-we even had a longer childhood than Neanderthals. We also rapidly developed special adaptations to care for those helpless children-“pair-bonding” and “alloparents.” Fathers and unrelated kin help take care of human children, unlike our closest primate relatives.
And we developed another very unusual life history feature-post-menopausal grandmothers. The killer whale is the only other animal we know that outlives its fertility. The human lifespan was expanded at both ends-longer childhood and a longer old age. In fact, anthropologists have argued that those grandmothers were a key to the evolution of learning and culture. They were crucial for the survival of those helpless children and they also could pass on two generations worth of knowledge.
Jessica Flack chose “Coarse-Graining”:
In physics a fine-grained description of a system is a detailed description of its microscopic behavior. A coarse-grained description is one in which some of this fine detail has been smoothed over.
Coarse-graining is at the core of the second law of thermodynamics, which states that the entropy of the universe is increasing. As entropy, or randomness, increases there is a loss of structure. This simply means that some of the information we originally had about the system has become no longer useful for making predictions about the behavior of a system as a whole. To make this more concrete, think about temperature.
Temperature is the average speed of particles in a system. Temperature is a coarse-grained representation of all of the particles’ behavior — the particles in aggregate. When we know the temperature we can use it to predict the system’s future state better than we could if we actually measured the speed of individual particles. This is why coarse-graining is so important — it is incredibly useful. It gives us what is called an effective theory. An effective theory allows us to model the behavior of a system without specifying all of the underlying causes that lead to system state changes.
And physicist Nigel Goldenfeld chose “The Scientific Method” itself:
There’s a saying that there are no cultural relativists at thirty thousand feet. The laws of aerodynamics work regardless of political or social prejudices, and they are indisputably true. Yes, you can discuss to what extent they are an approximation, what are their limits of validity, do they take into account such niceties as quantum entanglement or unified field theory (of course they don’t). But the most basic scientific concept that is clearly and disturbingly missing from today’s social and political discourse is the concept that some questions have correct and clear answers. Such questions can be called “scientific” and their answers represent truth. Scientific questions are not easy to ask. Their answers can be verified by experiment or observation, and they can be used to improve your life, create jobs and technologies, save the planet. You don’t need pollsters or randomized trials to determine if a parachute works. You need an understanding of the facts of aerodynamics and the methodology to do experiments.
There are 200 more contributions from bold-faced names like Richard Dawkins, Hanna Levin, Brian Eno, Kevin Kelly, and Danny Hillis. Have fun!
Stay Connected