Just beautiful #murakami #art (at The Broad)



Just beautiful #murakami #art (at The Broad)

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#art stare (at The Broad)



#art stare (at The Broad)

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Hypnotic #art (at The Broad)

A post shared by Raquel (@raki_rara) on



Hypnotic #art (at The Broad)

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The Tennessee Williams #cheers #dtla (at The Boardroom)



The Tennessee Williams #cheers #dtla (at The Boardroom)

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Took a minute to find it… but ahh needs me a nice…



Took a minute to find it… but ahh needs me a nice cocktail to decompress #dtla (at The Boardroom)

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Yijing, you spoil me with this homemade #dessert made w/ red…



Yijing, you spoil me with this homemade #dessert made w/ red date, peach gum, white fungus, and matrimony vine. #getinmybelly (at Arts District Los Angeles)

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Indistinguishable from prejudice

“Any sufficiently advanced technology is indistinguishable from magic,” said Arthur C. Clarke famously.

It is also the case that any sufficiently advanced technology is indistinguishable from prejudice.

Especially if that technology is machine learning. ML creates algorithms to categorize stuff based upon data sets that we feed it. Say “These million messages are spam, and these million are not,” and ML will take a stab at figuring out what are the distinguishing characteristics of spam and not spam, perhaps assigning particular words particular weights as indicators, or finding relationships between particular IP addresses, times of day, lenghts of messages, etc.

Now complicate the data and the request, run this through an artificial neural network, and you have Deep Learning that will come up with models that may be beyond human understanding. Ask DL why it made a particular move in a game of Go or why it recommended increasing police patrols on the corner of Elm and Maple, and it may not be able to give an answer that human brains can comprehend.

We know from experience that machine learning can re-express human biases built into the data we feed it. Cathy O’Neill’s Weapons of Math Destruction contains plenty of evidence of this. We know it can happen not only inadvertently but subtly. With Deep Learning, we can be left entirely uncertain about whether and how this is happening. We can certainly adjust DL so that it gives fairer results when we can tell that it’s going astray, as when it only recommends white men for jobs or produces a freshman class with 1% African Americans. But when the results aren’t that measurable, we can be using results based on bias and not know it. For example, is anyone running the metrics on how many books by people of color Amazon recommends? And if we use DL to evaluate complex tax law changes, can we tell if it’s based on data that reflects racial prejudices?[1]

So this is not to say that we shouldn’t use machine learning or deep learning. That would remove hugely powerful tools. And of course we should and will do everything we can to keep our own prejudices from seeping into our machines’ algorithms. But it does mean that when we are dealing with literally inexplicable results, we may well not be able to tell if those results are based on biases.

In short: Any sufficiently advanced technology is indistinguishable from prejudice.[2]

[1] We may not care, if the result is a law that achieves the social goals we want, including equal and fair treatment of tax players regardless of race.

[2] Please note that that does not mean that advanced technology is prejudiced. We just may not be able to tell.

The post Indistinguishable from prejudice appeared first on Joho the Blog.

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Sometimes no meat is needed #getinmybelly (at Zinc Los Angeles)



Sometimes no meat is needed #getinmybelly (at Zinc Los Angeles)

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Cousin IT (at DTLA)



Cousin IT (at DTLA)

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LA #foodbowl (at Grand Park)



LA #foodbowl (at Grand Park)

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