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A new technique can enable a machine-learning model to quantify how confident it is in its predictions, but does not require vast troves of new data and is much less computationally intensive than other techniques. The work was led by researchers from MIT and the MIT-IBM Watson AI Lab.
For those unfamiliar with Joe Peta’s groundbreaking 2013 book Trading Bases, the author is a successful financial analyst and former Wall Street trader. Seriously injured in a traffic accident, Peta’s long and painful recovery included employing his professional skills to develop a baseball wagering methodology. His book is about more than that though, including observations about the 2008 economic meltdown and sports wagering writ large. Peta’s anecdotes alone make it worth the read — imagine being hit by a NYC ambulance and then being billed by the city for the ride to the hospital...