To love mercy sometimes means to give up efficiency. It could mean losing a few points of model accuracy by refusing to take into account features that invade privacy or are proxies for race, leading to discriminatory model behavior. But that’s OK. The merciful are willing to give up some of their rights and advantages so they can help others.
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It’s tempting to assume that a villain lurks behind such a scene when the exact opposite is the problem: A system dominated by machines is all calculations, not thoughts, intentions, or choices. If the input is wrong, so is the output. Read More ›
When we think, we think about reality, not about the neurological processes by which we connect to reality. It is by keeping this understanding clearly in mind that we escape the solipsism that bedevils modern neuroscience. Read More ›
The worst trap that people who are pursuing automation fall into is the desire to automate everything. That’s usually a road to disaster. Automation is supposed to save time and money, but it can wind up costing you both if you don't carefully consider what you automate. Read More ›
Enormous data sets compiled by Big Data methods have a higher probability of meaningless correlations than smaller ones compiled by traditional methods. More than ever, common sense is needed. And common sense only comes from programmers writing their own common sense into the software. Read More ›
We need to decide: Is Twitter the telephone company (a communications platform), the newspaper (a publisher), or interconnected private gossip klatsches where anyone can say whatever they want, whatever ensues? Read More ›
We've all seen this sort of argument before in many other guises. It is commonly called “reductionism.” The reductionist claims that, because an object can be construed as made up of parts, the object is just the parts. It is like saying that because an article like this one is constructed from letters of the alphabet, the article is only rows of letters. Read More ›