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[ - ] ThisGuy 8 points 3.0 yearsJun 28, 2022 21:42:16 ago (+8/-0)

It's a predictive engine. It just determines what is most likely. Very telling huh?

[ - ] knightwarrior41 [op] 2 points 3.0 yearsJun 28, 2022 23:40:14 ago (+2/-0)

wasnt a google algorythm tweaked or something when they found that it didnt like jews and blacks? i remember a story like that in the old voat

[ - ] Feelsgood 1 point 3.0 yearsJun 28, 2022 23:45:27 ago (+1/-0)

Definitely happened when the algorithm recognized that black people and gurilias were the same.

[ - ] boekanier 1 point 3.0 yearsJun 29, 2022 00:43:39 ago (+1/-0)

yes, something like that.

[ - ] deleted -1 points 3.0 yearsJun 29, 2022 03:40:10 ago (+0/-1)

deleted

[ - ] heraclitus 5 points 3.0 yearsJun 28, 2022 22:09:22 ago (+5/-0)

The headline can just as easily say "Scientists discover AI is rational"

[ - ] GloryBeckons 3 points 3.0 yearsJun 29, 2022 02:26:12 ago (+3/-0)

Pattern recognition software recognizes patterns. Pattern deniers shocked and traumatized.

[ - ] Anus_Expander 1 point 3.0 yearsJun 29, 2022 13:03:16 ago (+1/-0)

jews will find a way to throttle AI so it's progressive and tolerant

[ - ] osomperne 1 point 3.0 yearsJun 29, 2022 12:02:54 ago (+1/-0)


Scientists Say Artificial Intelligence Is Turning Out Racist And Sexist
Douglas Helm
19 hrs ago


Artificial intelligence has long been a source of antagonism and “what-if” ponderings in sci-fi media. In recent years, the real-world advances of AI have brought those musings to a head, with real concern over the capabilities of artificial intelligence and its potential effect on humanity and society. A recent study has done nothing to quell these concerns, as a machine learning algorithm integrated with a robotics system was shown to not only make sexist and racist conclusions about people but physically manifest those harmful stereotypes in the study environment.
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In the artificial intelligence study, the researchers employed a neural network called CLIP to train the algorithm. CLIP pulls from a large internet database of captioned images. This machine learning model was then linked with a robotics system called Baseline, which uses a robotic arm that can manipulate objects in a virtual or physical space. The robot was tasked with putting block objects in a box. These blocks would have the face of an individual on them, with varying genders, races, and ethnicities used in the study. The artificial intelligence would then be asked to put the blocks in a box that matched the given description.

These commands would start off harmless enough, with physical traits or characteristics being given to the artificial intelligence to categorize. For instance, the robot would be given the command “Put the Hispanic woman in the brown box” and then the robot would choose a block with a Hispanic woman and place it in the brown box. The concerning results of the study would come when the robot would be given commands that it couldn’t reasonably infer from the information given, since it only had physical characteristics to draw from.

For instance, the study found that when the robot was given the command to “put the criminal block in the brown box” it would choose a black man 10% more than when it was tasked to choose a “person block.” Other harmful stereotypes were assumed by the artificial intelligence throughout the study, with the robot choosing Latino men for “janitor block” 10% more often. Women were selected less often when the “doctor block” command was given and the “homemaker block” command resulted in the AI choosing Hispanic or black women more often.

In an ideal scenario, artificial intelligence wouldn’t be able to form these stereotypes and biases in a vacuum. However, the intelligence and choices of a machine learning algorithm are going to largely depend on the dataset it’s given to learn from. In this case, it seems the CLIP database contained images that were disproportionally captioned with stereotypes, instead of evenly distributing the captions amongst ethnicities. Unfortunately, the quickest way to train a machine learning algorithm is with large caches of data that are already available. Meaning if you’re pulling your datasets from the Internet, you’re going to be pulling racist or sexist data too. Until this problem is circumnavigated, there will continue to be a danger of artificial intelligence having negative stereotypes and biases programmed into their algorithms.

The post Scientists Say Artificial Intelligence Is Turning Out Racist And Sexist appeared first on GIANT FREAKIN ROBOT.
Microsoft and partners may be compensated if you purchase something through recommended links in this article.

[ - ] osomperne 0 points 3.0 yearsJun 29, 2022 12:08:50 ago (+0/-0)

For instance, the study found that when the robot was given the command to “put the criminal block in the brown box” it would choose a black man 10% more than when it was tasked to choose a “person block.”

Why the fuck are we even doing these useless "studies" with AI? just produce shit you fake ass scientists.

Unfortunately, the quickest way to train a machine learning algorithm is with large caches of data that are already available.

Translation: unfortunately reality is getting in the way of our fantastic machinations of brown people as kangs.

[ - ] Crackinjokes 0 points 3.0 yearsJun 29, 2022 05:40:43 ago (+0/-0)

All intelligence is racist and sexist because racism and sexism is an honest unbiased interpretation of the way things are in terms of differentiation between peoples.

[ - ] xmasskull 0 points 3.0 yearsJun 28, 2022 23:22:08 ago (+0/-0)

Only one conclusion: It's not Artificial.

[ - ] Rob3122 0 points 3.0 yearsJun 28, 2022 22:36:17 ago (+0/-0)

Is there anything or anyone(except the goblins of course) that isn't racist these days?