https://spectrum.ieee.org/deep-learning-computational-cost

https://spectrum.ieee.org/rodney-brooks-ai/particle-5

[Google tackles the black box problem with Explainable AI](https://www.bbc.com/news/technology-50506431)
> The main question is to do these things called counterfactuals, where the neural network asks itself, for example, 'Suppose I hadn't been able to look at the shirt colour of the person walking into the store, would that have changed my estimate of how quickly they were walking?' By doing many counterfactuals, it gradually builds up a picture of what it is and isn't paying attention to when it's making a prediction.

[Artificial	intelligence	and	behavioral	economics](https://knowen-production.s3.amazonaws.com/uploads/attachment/file/3745/Camerer.pdf) Colin	F.	Camerer
> The	first	idea is	that	AI	can	be	used	in	the	search	for	new	“behavioral”-type	variables	that	affect	choice.	Two examples	are	given from	experimental	data	on	bargaining and	on	risky	choice.	
> The	second	idea	is	that	some	common	limits	on	human	prediction	might	be	understood	as	the	kinds	of	errors	made	by	poor	implementations	of	machine	learning.	That	is,	people	are	thinking	as	if	they	are	executing	machine	learning algorithms	but	are	doing	a	mediocre job	of	it.	
> The	half	idea— it’s	short-- is	that	it	is	important	to	study	how	AI	technology used	in	firms	and	by	other	institutions	can	bot overcome	and	exploit	human	limits.	The	fullest	understanding	of	this	tech-human	interaction	will	require	new	knowledge	from	behavioral	economics	about	attention	and	perceived	fairness.

[Neglected Open Questions in the Economics of Artificial Intelligence](https://marginalrevolution.com/marginalrevolution/2019/06/neglected-open-questions-in-the-economics-of-artificial-intelligence.html) by  Tyler Cowen

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