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Clever Hans was a real comically smart horse!

https://en.wikipedia.org/wiki/Clever_Hans

>Clever Hans (German: der Kluge Hans; c. 1895 – c. 1916) was a horse that was claimed to have performed arithmetic and other intellectual tasks. After a formal investigation in 1907, psychologist Oskar Pfungst demonstrated that the horse was not actually performing these mental tasks, but was watching the reactions of his trainer. He discovered this artifact in the research methodology, wherein the horse was responding directly to involuntary cues in the body language of the human trainer, who was entirely unaware that he was providing such cues. In honour of Pfungst's study, the anomalous artifact has since been referred to as the Clever Hans effect and has continued to be important knowledge in the observer-expectancy effect and later studies in animal cognition. Pfungst was an assistant to German philosopher and psychologist Carl Stumpf, who incorporated the experience with Hans into his further work on animal psychology and his ideas on phenomenology.

NLP's Clever Hans Moment Has Arrived (thegradient.pub)

https://thegradient.pub/nlps-clever-hans-moment-has-arrived/

https://news.ycombinator.com/item?id=20861586

Finding and removing Clever Hans: Using explanation methods to debug and improve deep models:

https://www.sciencedirect.com/science/article/pii/S156625352...

Is your AI a “Clever Hans”?

https://medium.com/high-stakes-design/is-your-ai-a-clever-ha...

The Clever Hans Effect in Machine Learning: an overview by Bhusan Chettri

https://www.issuewire.com/the-clever-hans-effect-in-machine-...

Deep Learning, Meet Clever Hans

https://towardsdatascience.com/deep-learning-meet-clever-han...

Welcome to the cleverhans blog: This is a blog by Ian Goodfellow and Nicolas Papernot about security and privacy in machine learning.

http://www.cleverhans.io/

>This is a blog by Ian Goodfellow and Nicolas Papernot about security and privacy in machine learning. We jointly created cleverhans, an open-source library for benchmarking the vulnerability of machine learning models to adversarial examples. The blog gives us a way to informally share ideas about machine learning security and privacy that are not yet concrete enough for traditional academic publishing, and to share news and updates relevant to the cleverhans library.




While writing OP, I was curious enough about how they could solve Clever Hans that I went and read the original Pfungst book. I recommend it: his thoroughness & cleverness in studying Clever Hans (as well as the history of previous 'talking animals') ought to be better known than Clever Hans himself, IMO. (Brief review: https://gwern.net/review/book#clever-hans-pfungst-2011 )




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