Karl Popper was quite a guy!
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Karl Popper was quite a guy!

Although he remains unknown to most people, Karl Popper was a pretty big deal within the “Philosophy of Science” community. One of his claims to fame was to say that a theory isn’t a scientific theory unless we can falsify it, even if, at the same time, we can never prove conclusively that that theory is absolutely true.

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For instance, Isaac Newton went well beyond producing a falsifiable theory about the “force of gravity”. ?Newton also simultaneously conceived a very compact and elegant mathematical formula to describe how this “force” worked. His ground breaking position held off all challengers for well over 200 years until the advent of Albert Einstein. Einstein published an alternate description of what lay behind the phenomenon that we still call gravity. Later, expeditions coinciding with a solar eclipse in the early 1920s showed that his predictions were superior to Newtons. Newton’s theory was falsified. Yet, even now, his mathematical formula still provides a very good first approximation of the effects of gravity even as we abandon the idea that they stem from a “force”.

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It is quite popular to say that anything unscientific isn’t worth discussing. We tell ourselves that our decisions should always be “data driven”, etc. But, I celebrate Karl Popper because he held many philosophical positions that were obviously unscientific at least considering our current limited ability to judge ideas by their ability to predict things or by their falsifiability. These ideas of Poppers exist in the gray region between Science and the Subjective, an area that has fascinated me for decades now.

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An example of this subjective region? Karl Popper said that as humans, it is our duty to be optimistic. Furthermore he meant optimistic in an active sense, because we take actions in the world that we are in the midst of creating, not as passive consumers of whatever text passes across our field of vision as we clutch our phones and try to keep up with our social media feeds. Popper said that we create our world continuously by actively creating new knowledge instead of more passively waiting for perfect knowledge to uncover itself for us by chance, or others. Adios, Plato!

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The possibilities that lie in the future are infinite. When I say “‘It is our duty to remain optimists’ this includes not only the openness of the future but also that which all of us contribute to it by everything we do: we are all responsible for what the future hold in store. Thus it is our duty, not to prophesy evil but, rather, to fight for a better world”.[1]

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This quote represents Poppers own personal belief, and there appears to be nothing remotely falsifiable about it. That is not to say that this philosophical position will always remain unfalsifiable. No matter how much knowledge we accumulate, we will never be able to create knowledge systems that take into account their own replacements. This is why we have “emergence”.

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How did I get here?

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I have stumbled into this area because of my fascination with measurement practice and the history and trends within Metrology (the science of measurement). Measurement exists in some sense within the gray area that I touched on above. Some aspects of measurements are as rock solid as we can make them, and some will forever be uncertain.

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I first encountered this split while spending a decade gathering process and calibration data out on the production floor of biotech and Big Pharma drug manufacturing facilities. I documented thousands of these data points every year in a format intended for possible inspection by the FDA. This was a full time occupation, and it was only my own curiosity that drew me further into what lay behind my observations.

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Popper said that my observations were “theory laden”. After 30 years, I take Popper to mean that data only exists after we establish a structure to hold it. The clearest description of this fundamental truth comes from Nick Barrowman who says it so clearly that I see no need to paraphrase him. In “Why Data Is Never Raw”[2]. Barrowman shows quite clearly and simply that we can never detach ourselves from our measurement results. Data always contain theories, what is more, they are our Human theories.

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So far, I have presented the positions of two separate writers who emphasize the fact that our human activity is central whenever we create new knowledge. We are responsible for and to ourselves. If this responsibility scares us, as it very often does, we may try to set data and knowledge production off to the side and away from ourselves. Who knows, we may even try to hand the job of producing more off to AI! Since data is indeed never raw, we need to remember that we train AI on human artifacts, content, and images. The training data doesn’t come from Heaven, it came from us, here on Earth. AI can be of great assistance as long as we don’t forget where it all started and where it must return.

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I will burden my very patient readers with no more indirect references to Poppers ideas until my $40 dollar (!) copy of his essays[3] shows up under the Christmas tree. Then I will see how to integrate his ideas with my support for Metrology and where I think that it should go next. WE GET TO DECIDE!

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In the meantime, I appreciate you all sticking with me as I continue to fulfill my pledge to avoid the passive voice whenever I can.

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[1] Quoted from The Beginning of Infinity, by David Deutsch, Penguin Books, New York, 2011, page 196.

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[2] https://www.thenewatlantis.com/publications/why-data-is-never-raw .

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[3] The Myth Of The Framework, Karl Popper, (1994)

Stephen Puryear

"Came to Believe"

11 个月

Joseph, thanks for the "like". They make a difference!

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Lida Moazezi, MPH, CSSGB

Senior Quality Assurance Specialist at bioMérieux

11 个月

Thanks for the great post, I enjoyed it so much, as he mentioned “Since data is indeed never raw, we need to remember that we train AI on human artifacts, content, and images. The training data doesn’t come from Heaven, it came from us, here on Earth. AI can be of great assistance as long as we don’t forget where it all started and where it must return.”

The thing is, there exists no data that is raw and unbiased, even data taken automatically. It is biased by the process, instruments, and the measurement design. When I teach measurement uncertainty, the largest chunk of time is spent on repeatability and reproducibility. Without having those two items locked down, the entire analysis will be flawed, sometimes costing millions. Great read!

Robert N.

Associate Professor at Indiana State University

11 个月

Great book about Popper and Wittgenstein’s tumultuous encounter at Cambridge University. https://en.m.wikipedia.org/wiki/Wittgenstein%27s_Poker

Anthony Hamilton

Principal Quality Engineer at Eli Lilly and Company

11 个月

I like this direction and see how this might be used to make a clearer rational/argument for more calibration labs to conduct more real-time repeatability measurements during calibration testing. Does connecting the low ports of a differential transmitter under test and reference standard reduce the variability at 1 kPa? I don’t know, let’s test 10 times at that pressure with the ports connected and 10 times with them not connected and see.

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