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Complex Systems, Inexplicable Models, and the Future of Prediction | David Weinberger

Complex Systems, Inexplicable Models, and the Future of Prediction | David Weinberger

Released Monday, 20th May 2019
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Complex Systems, Inexplicable Models, and the Future of Prediction | David Weinberger

Complex Systems, Inexplicable Models, and the Future of Prediction | David Weinberger

Complex Systems, Inexplicable Models, and the Future of Prediction | David Weinberger

Complex Systems, Inexplicable Models, and the Future of Prediction | David Weinberger

Monday, 20th May 2019
Good episode? Give it some love!
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In Episode 87 of Hidden Forces, Demetri Kofinas speaks with philosopher David Weinberger about the science of prediction, its evolution, and its future.

The two begin by exploring classical approaches developed by early philosophers and mathematicians in the ancient world and upon which advancements were later made by enlightenment thinkers and experimental scientists.   

The models developed in this tradition have, until now, provided explanations for phenomena, which are used to make predictions about the future states or trajectories of these and other phenomena that adhere the same laws of action or motion.   

What is new today is the evolution of what are known as “machine learning algorithms,” many of which provide superior predictions to those generated by conceptual or working models, but which often times cannot provide explanations for these predictions. They are, in this sense, block-box oracles.   

This represents a fundamental break with the sort of epistemological approach taken by the ancient Athenian philosophers who demanded that beliefs be justified by reasoned arguments or those of empirical scientists who relied upon falsifiability of testable hypotheses. In other words, whereas traditional approaches to science have necessitated the development of theoretical models of the world that can be tested empirically through the act of making falsifiable predictions, these new approaches are capable of generating predictions without a means by which to understand the causes at play.    

What are the implications of this new science? If predictions provided by highly intelligent machines become consistently more accurate across all domains of study, would we prefer to accept these inexplicable solutions over less accurate ones whose methodology we understand? At the limit, if we were to implement every prediction of every MLA, would we arrive at a fated, perfectly knowable world? If machines become the equivalent of Delphi’s Oracle, what will be the value of doing science? The scientific method, after all, is the means by which we have been able to navigate and understand the material world, in material terms. Does this re-open humanity’s door to the preoccupation with the mystery of conscious experience, which cannot be explained through the scientific method of objective, empirical analysis?  

These are the questions we explore in this week’s episode with David Weinberger and Demetri Kofinas.

Producer & Host: Demetri Kofinas

Editor & Engineer: Stylianos Nicolaou

Subscribe & Support the Show at http://patreon.com/hiddenforces

Join the conversation on Facebook, Instagram, and Twitter at @hiddenforcespod

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