Science Feed

"Another Nail in the Global Warming Coffin"

Seems pretty important to me.

[T]he results imply that the effect of man-made CO2 emissions does not appear to be sufficiently strong to cause systematic changes in the pattern of the temperature fluctuations. In other words, our analysis indicates that with the current level of knowledge, it seems impossible to determine how much of the temperature increase is due to emissions of CO2.

"Confusing Correlation with Causation"

Gary N. Smith, Fletcher Jones Professor of Economics, Pomona College, addresses something I've wondered about but haven't yet taken the time to try to figure out: how do these machine learning algorithms establish causality?

Artificial intelligence (AI) algorithms are terrific at discovering statistical correlations but terrible at distinguishing between correlation and causation. A computer algorithm might find a correlation between how often a person has been in an automobile accident and the words they post on Facebook, being a good software engineer and visiting certain websites, and making loan payments on time and keeping one’s phone fully charged. However, computer algorithms do not know what any of these things are and consequently have no way of determining whether these are causal relationships (and therefore useful predictors) or fleeting coincidences (that are useless predictors).

And here he proposes the "Smith Test" to determine if computer algorithms can make reliable recommendations.