10 Misconceptions about Neural Networks by Stuart Reid.
From the post:
Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and credit risk modelling. They have also been used to construct stochastic process models and price derivatives. Despite their usefulness neural networks tend to have a bad reputation because their performance is “temperamental”. In my opinion this can be attributed to poor network design owing to misconceptions regarding how neural networks work. This article discusses some of those misconceptions.
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The car numberplate game was a game where passengers in a car, usually children, would compete to find license plates from different states (in the US). That was prior to children being entombed in intellectual isolation bubbles with iPads, Gameboys, DVD players and wireless access, while riding.
Hard to believe but some people used to look outside the vehicle in which they were riding. Now of course what little attention they have is captured by cellphones and not other occupants of the same vehicle.
Rather than rail against that trend, may I suggest we update the car numberplate game to “mistakes about neural networks?”
Using Stuart’s post as a baseline, send a text message to each passenger pointing to Stuart’s post and requesting a count of the number of “mistakes about neural networks” they can find in an hour.
Personally I would put popular media off limits for post-high school players to keep the scores under four digits.
When discussing the scores, after sharing browsing histories, each player has to analyze the claimed error and match it to one on Stuart’s list.
I realize that will require full bandwidth communication with others in your physical presence but with practice, that won’t seem so terribly odd.
I first saw this in a tweet by Kirk Borne.