headd @ba11ism-blog - Tumblr Blog

trashythingsgohere

swarnpert

trucknuts 2

aquaristlifeforme

smokedout111-deactivated2017040

birdjob

Me by me, London - April 2017

<3

Real Glitchy.

If your friends don’t do this at your funeral, are they really your friends??????

jehovahhthickness

And the sobbing is what KILLS me

bill-jimbersons-funhouse

I gotta have this at my funeral…..I’m so excited to die now!

typeshitdontforget

The family sick

newdarkage

In the past, we assumed that when machines reached near-human performance in tasks like image recognition, it would be thanks to fundamental breakthroughs into the nature of cognition. We would be able to lift the lid on the human mind and see all the little gears turning. What’s happened instead is odd. We found a way to get terrific results by combining fairly simple math with enormous data sets. But this discovery did not advance our understanding. The mathematical techniques used in machine learning don’t have a complex, intelligible internal structure we can reason about. Like our brains, they are a wild, interconnected tangle. Because machine learning tracks human performance so well in some domains (like machine translation or object recognition), there is a temptation to anthropomorphize it. We assume that the machine’s mistakes will be like human mistakes. But this is a dangerous fallacy. As Zeynep Tufekci has argued, the algorithm is irreducibly alien, a creature of linear algebra. We can spot some of the ways it will make mistakes, because we’re attuned to them. But other kinds of mistakes we won’t notice, either because they are subtle, or because they don’t resemble human error patterns at all.

Build a Better Monster: Morality, Machine Learning, and Mass Surveillance (via newdarkage)