The rise of computing in the mid- to-late twentieth century have provided a new vocabulary for discussing cognition. Increasingly, the computer has become the dominant metaphor through which people understand thinking. For example, Ray Kurzweil’s pattern recognition theory of mind understands our neocortex as an algorithmic function. This metaphor continues to inform our understanding of how humans process the world.
But human beings are far more complex than machines processing inputs and outputs. There are many ways in which we consume and process information. The computational metaphor is just that: a metaphor; it both conceals and reveals.
The concept of a human-as-machine metaphor may be extended to biology, as well. Nutritional science that assumes a simple input/output model, for instance, is complicated by the microbial environment inside the body, which can have a significant effect on how we process food.
- AI and machine learning obscure our understanding of why things work - AI and ML may produce answers without being able to explain why they work.
- Cultural bias toward scientific models leaves us less equipped to interpret qualitative information. - Over-reliance on computational models makes it more difficult to explain situations that defy quant models.
Brain, Tega. “The Environment Is Not a System.” RESEARCH VALUES 2018 (blog), December 20, 2017. https://researchvalues2018.wordpress.com/2017/12/20/tega-brain-the-environment-is-not-a-system/.
[[Madsbjerg - Sensemaking|Madsbjerg, Christian. Sensemaking: The Power of the Humanities in the Age of the Algorithm. Hachette Books, 2017.]]
Spector, Tim. Spoon-Fed: Why Almost Everything We’ve Been Told about Food Is Wrong. Vintage Digital, 2020.