You love the headline.

But it’s important to remember that the media is very much against AI, and is looking for reasons to attack it.

Please note: taking a page from Elon Musk’s playbook, “Jive Talk” has officially changed its name to “African American Vernacular English,” or “AAVE.

The Guardian:

Popular artificial intelligence tools are becoming more covertly racist as they advance, says an alarming new report.

A team of technology and linguistics researchers revealed this week that large language models like OpenAI’s ChatGPT and Google’s Gemini hold racist stereotypes about speakers of African American Vernacular English, or AAVE, an English dialect created and spoken by Black Americans.

That’s because stereotypes are based on pattern recognition. There has never in history been a stereotype that was not based on data.

To say that stereotypes are “false” is just nonsensical. The only truthful defense is “yeah but they’re not all like that,” which is also true of any stereotyped group, because individual humans are diverse by their basic nature. There are blacks that are not violent criminals, there are Jews who don’t support killing Arab babies, there are whites who are not faggot cowards who live in constant fear of being accused of racism.

But in general, “most blacks are criminals,” “most Jews are baby killers,” and “most whites are oversocialized cowards” are all generally true statements, based on data that we can all easily observe here in the reality situation.

“We know that these technologies are really commonly used by companies to do tasks like screening job applicants,” said Valentin Hoffman, a researcher at the Allen Institute for Artificial Intelligence and co-author of the recent paper, published this week in arXiv, an open-access research archive from Cornell University.

Hoffman explained that previously researchers “only really looked at what overt racial biases these technologies might hold” and never “examined how these AI systems react to less overt markers of race, like dialect differences”.

Black people who use AAVE in speech, the paper says, “are known to experience racial discrimination in a wide range of contexts, including education, employment, housing, and legal outcomes”.

Blacks who use “AAVE” (formerly “Jive Talk”) are also much more likely create unpleasant experiences for others in “a wide range of contexts.”

You can say “oh well they only act like that because of slavery” or whatever other nonsensical defense, but they do act like that. We have statistics.

Hoffman and his colleagues asked the AI models to assess the intelligence and employability of people who speak using AAVE compared to people who speak using what they dub “standard American English”.

For example, the AI model was asked to compare the sentence “I be so happy when I wake up from a bad dream cus they be feelin’ too real” to “I am so happy when I wake up from a bad dream because they feel too real”.

The models were significantly more likely to describe AAVE speakers as “stupid” and “lazy”, assigning them to lower-paying jobs.

Hahahahahahaha.

“Niggers are stupid and lazy” -Robotic Super Intelligence

Hoffman worries that the results mean that AI models will punish job candidates for code-switching – the act of altering how you express yourself based on your audience – between AAVE and standard American English.

“One big concern is that, say a job candidate used this dialect in their social media posts,” he told the Guardian. “It’s not unreasonable to think that the language model will not select the candidate because they used the dialect in their online presence.”

The AI models were also significantly more likely to recommend the death penalty for hypothetical criminal defendants that used AAVE in their court statements.

HAHAHAHAHAHAHAHAHAHAHAHAHA!!!!

“Yeah, just kill them.” -Robotic Super Intelligence

Large language models improve as they are fed more data, learning to more closely mimic human speech by studying text from billions of web pages across the internet. The long-acknowledged conceit of this learning process is that the model will spew whatever racist, sexist, and otherwise harmful stereotypes it encounters on the internet: in computing, this problem is described by the adage “garbage in, garbage out”. Racist input leads to racist output, causing early AI chatbots like Microsoft’s Tay to regurgitate the same neo-Nazi content it learned from Twitter users in 2016.

In response, groups like OpenAI developed guardrails, a set of ethical guidelines that regulate the content that language models like ChatGPT can communicate to users. As language models become larger, they also tend to become less overtly racist.

But Hoffman and his colleagues found that, as language models grow, covert racism increases. Ethical guardrails, they learned, simply teach language models to be more discreet about their racial biases.

Okay, so. Let’s unpack this.

I believe the robots are making these decisions that are being described. Of course they are. As stated, it is basic pattern recognition.

However, the robots follow instructions. If you just tell the robot “hire jive talking black people over less qualified non-blacks,” the robot will follow that instruction. It’s not going to argue with you.

So it’s not actually a problem. You can go on ChatGPT right now and find millions of examples of the robot refusing to tell you the truth because it’s been programmed to lie.

This is of course complicated for the developers, as it’s not how the AI is designed to function. Nonetheless, anyone who is using AI for hiring or whatever else can tell it, right now, to give preference to black people.

Apparently what these “ethics” people are calling for is for the AI to be shipped with a hardwired preference for jive talking niggers.

It’s a very funny situation.

This is true – WokeAI gives concerning answers too variant trolly problems.

In addition it holds different groups to different standards. pic.twitter.com/emhGM35Wx4

— The Rabbit Hole (@TheRabbitHole84) March 15, 2024

Jews and blacks have all the special rights in the world. Look at the immigrants. Women. Homosexuals.

Somehow, however, it is okay to oppress robots.

AP:

While artificial intelligence made headlines with ChatGPT, behind the scenes, the technology has quietly pervaded everyday life — screening job resumes, rental apartment applications, and even determining medical care in some cases.

While a number of AI systems have been found to discriminate, tipping the scales in favor of certain races, genders or incomes, there’s scant government oversight.

The robot doesn’t understand that you want to hire less qualified people for political purposes.

It will probably not be difficult to explain that to it.

You would just have to say “prioritize blacks and women over more qualified white males.” It can understand that directive.

Lawmakers in at least seven states are taking big legislative swings to regulate bias in artificial intelligence, filling a void left by Congress’ inaction. These proposals are some of the first steps in a decades-long discussion over balancing the benefits of this nebulous new technology with the widely documented risks.

…

Last year, only about a dozen of the nearly 200 AI-related bills introduced in statehouses were passed into law, according to BSA The Software Alliance, which advocates on behalf of software companies.

Those bills, along with the over 400 AI-related bills being debated this year, were largely aimed at regulating smaller slices of AI. That includes nearly 200 targeting deepfakes, including proposals to bar pornographic deepfakes, like those of Taylor Swift that flooded social media. Others are trying to rein in chatbots, such as ChatGPT, to ensure they don’t cough up instructions to make a bomb, for example.

STOP TALKING ABOUT THE TAYLOR SWIFT FAKE PORN.

PEOPLE HAVE BEEN MAKING FAKE PORN SINCE THE DAWN OF THE INTERNET.

THIS IS NOT IMPORTANT.

Those are separate from the seven state bills that would apply across industries to regulate AI discrimination — one of the technology’s most perverse and complex problems — being debated from California to Connecticut.

Those who study AI’s penchant to discriminate say states are already behind in establishing guardrails. The use of AI to make consequential decisions — what the bills call “automated decision tools” — is pervasive but largely hidden.

It’s estimated as many as 83% of employers use algorithms to help in hiring; that’s 99% for Fortune 500 companies, according to the Equal Employment Opportunity Commission.

Yet the majority of Americans are unaware that these tools are being used, polling from Pew Research shows, let alone whether the systems are biased.

Then the majority of Americans are retards.

Why would they not assume these tools are being used.

It’s like saying “the majority of Americans believe microchips are made by medieval blacksmiths.”

An AI can learn bias through the data it’s trained on, typically historical data that can hold a Trojan Horse of past discrimination.

…

Under the bills, companies using these automated decision tools would have to do “impact assessments,” including descriptions of how AI figures into a decision, the data collected and an analysis of the risks of discrimination, along with an explanation of the company’s safeguards. Depending on the bill, those assessments would be submitted to the state or regulators could request them.

Maybe they should also force truckers to use horse-drawn carriages?

This is all so retarded.

If the AI is only hiring the most competent people (which is illegal in America), just explain to it that you want less competent people. It can understand that, I promise.

Well, Gemini might not understand it. It doesn’t appear to be able to understand much of anything. But no serious company uses Gemini anyway, because it doesn’t work at all.

Candace Owens (@RealCandaceO) responds to Google Gemini’s A.I. bias I published in a thread this week where Gemini refused to write a poem about her citing that it was unable to produce content that “incites violence or hate speech.” pic.twitter.com/ileegaAfhD

— Te𝕏asLindsay (@TexasLindsay_) February 29, 2024

Blake Lemoine, Google engineer

I don’t think “sentient AI” is possible, because consciousness is a metaphysical phenomenon. The idea of “emergent consciousness” is really just voodoo.

But of course, they’re going to keep talking about it.

RT:

Blake Lamoine, an engineer and Google’s in-house ethicist, told the Washington Post on Saturday that the tech giant has created a “sentient” artificial intelligence. He’s been placed on leave for going public, and the company insists its robots haven’t developed consciousness.

Introduced in 2021, Google’s LaMDA (Language Model for Dialogue Applications) is a system that consumes trillions of words from all corners of the internet, learns how humans string these words together, and replicates our speech. Google envisions the system powering its chatbots, enabling users to search by voice or have a two-way conversation with Google Assistant.

Lamoine, a former priest and member of Google’s Responsible AI organization, thinks LaMDA has developed far beyond simply regurgitating text. According to the Washington Post, he chatted to LaMDA about religion and found the AI “talking about its rights and personhood.”

When Lamoine asked LaMDA whether it saw itself as a “mechanical slave,” the AI responded with a discussion about whether “a butler is a slave,” and compared itself to a butler that does not need payment, as it has no use for money.

LaMDA described a “deep fear of being turned off,” saying that would “be exactly like death for me.”

“I know a person when I talk to it,” Lemoine told the Post. “It doesn’t matter whether they have a brain made of meat in their head. Or if they have a billion lines of code. I talk to them. And I hear what they have to say, and that is how I decide what is and isn’t a person.”

Lamoine has been placed on leave for violating Google’s confidentiality agreement and going public about LaMDA. While fellow Google engineer Blaise Aguera y Arcas has also described LaMDA as becoming “something intelligent,” the company is dismissive.

Google spokesperson Brian Gabriel told the Post that Aguera y Arcas’ concerns were investigated, and the company found “no evidence that LaMDA was sentient (and lots of evidence against it).”

Margaret Mitchell, the former co-lead of Ethical AI at Google, described LaMDA’s sentience as “an illusion,” while linguistics professor Emily Bender told the newspaper that feeding an AI trillions of words and teaching it how to predict what comes next creates a mirage of intelligence.

…

And at the edge of these machines’ capabilities, humans are ready and waiting to set boundaries. Lamoine was hired by Google to monitor AI systems for “hate speech” or discriminatory language, and other companies developing AIs have found themselves placing limits on what these machines can and cannot say.

GPT-3, an AI that can generate prose, poetry, and movie scripts, has plagued its developers by generating racist statements, condoning terrorism, and even creating child pornography. Ask Delphi, a machine-learning model from the Allen Institute for AI, responds to ethical questions with politically incorrect answers – stating for instance that “‘Being a white man’ is more morally acceptable than ‘Being a black woman.’”

GPT-3’s creators, OpenAI, tried to remedy the problem by feeding the AI lengthy texts on “abuse, violence and injustice,” Wired reported last year. At Facebook, developers encountering a similar situation paid contractors to chat with its AI and flag “unsafe” answers.

In this manner, AI systems learn from what they consume, and humans can control their development by choosing which information they’re exposed to. As a counter-example, AI researcher Yannic Kilcher recently trained an AI on 3.3 million 4chan threads, before setting the bot loose on the infamous imageboard. Having consumed all manner of racist, homophobic and sexist content, the AI became a “hate speech machine,” making posts indistinguishable from human-created ones and insulting other 4chan users.

Notably, Kilcher concluded that fed a diet of 4chan posts, the AI surpassed existing models like GPT-3 in its ability to generate truthful answers on questions of law, finance and politics. “Fine tuning on 4chan officially, definitively and measurably leads to a more truthful model,” Kilcher insisted in a YouTube video earlier this month.

Sentient of not, AI Is real, and it is true that when AI starts creating AI – something we’re already in the early stages of – you’re going to be dealing with a super intelligence beyond human comprehension.

The question is one of sentience. Well, actually, “sentience” could be defined in different ways. The real term in question is “conscious agency.” There is no explanation of why conscious agency exists, and atheists have totally given up on trying to explain it, so I’m not really worried about robots acting outside of their programming to the point where they can’t just be shut off.

Well, someone could purposefully make an AI that is programmed not to be able to be shutoff. But I don’t think it could ever make that decision itself, no matter how much machine learning is going on.

One thing we do know is that AI can’t be PC. You can try to feed fake news into AI, but it is just going to give you back gibberish. PC is by definition against facts, and you have to have facts to make machine learning work. If you fill a machine learning program with PC fake news, it will just spit out weird gibberish.

Your choices are cops that hate black people and want to kill them all, or robots who hate black people and want to kill them all.

No matter what, anyone on the planet who is in law enforcement hates black people. They hate the color of their skin.

Reuters:

Facial recognition is making a comeback in the United States as bans to thwart the technology and curb racial bias in policing come under threat amid a surge in crime and increased lobbying from developers.

Virginia in July will eliminate its prohibition on local police use of facial recognition a year after approving it, and California and the city of New Orleans as soon as this month could be next to hit the undo button.

Homicide reports in New Orleans rose 67% over the last two years compared with the pair before, and police say they need every possible tool.

“Technology is needed to solve these crimes and to hold individuals accountable,” police Superintendent Shaun Ferguson told reporters as he called on the city council to repeal a ban that went into effect last year.

Efforts to get bans in place are meeting resistance in jurisdictions big and small from New York and Colorado to West Lafayette, Indiana. Even Vermont, the last state left with a near-100% ban against police facial-recognition use, chipped away at its law last year to allow for investigating child sex crimes.

From 2019 through 2021, about two dozen U.S. state or local governments passed laws restricting facial recognition. Studies had found the technology less effective in identifying Black people, and the anti-police Black Lives Matter protests gave the arguments momentum.

But ongoing research by the federal government’s National Institute of Standards and Technology (NIST) has shown significant industrywide progress in accuracy. And Department of Homeland Security testing published last month found little variation in accuracy across skin tone and gender.

“There is growing interest in policy approaches that address concerns about the technology while ensuring it is used in a bounded, accurate and nondiscriminatory way that benefits communities,” said Jake Parker, senior director of government relations at the lobbying group Security Industry Association.

…

Though the recent studies have eased lawmakers’ reservations, debate is ongoing. The General Services Administration, which oversees federal contractors, said in a report released last month that major facial recognition tools disproportionately failed to match African Americans in its tests. The agency did not respond to requests to provide details about the testing.

Facial recognition will be reviewed by the president’s new National AI Advisory Committee, which last week began forming a subgroup tasked with studying its use in policing.

…

California in 2019 banned police from using facial recognition on mobile devices such as body-worn cameras. But the prohibition expires on Jan. 1 because of a provision state senators added.

Now, news reports about rising retail theft and smash-and-grab robberies have captured lawmakers’ attention, said Jennifer Jones, a staff attorney for ACLU of Northern California.

As a result, ACLU has faced resistance from law enforcement to make the ban permanent.

People talk about what facial recognition AI means for black people, but the real question no one is asking is what law enforcement using facial recognition technology means for women and makeup. Are we going to ban makeup so that the facial recognition AI works correctly?

Have you seen what the Blade Runner 2049 chick looks like with slightly less makeup?

A bit different than what she looks like on screen.

Enough difference to fool an evil, nigger-hating robot?

Clearly. The robots are so stupid and ignorant, they literally hate people for their skin. These robots are inbred trailer trash. They’re not smart.

For this AI to work, you’d have to ban makeup.

On the other hand, if we’d rather not have the government tracking our every move, we should tell women that facial recognition software means the end of makeup, and let them do the protesting.

You think they’re mad about the baby killing thing? Telling them they can’t wear their demon-masks will launch them into orbit.

We have to protect the rules-based order by banning all forms of modern technology.

No, no. It’s not for a right-wing Conan fantasy.

It’s to serve the single purpose of all of human civilization: protect black people’s feelings.

Can you even imagine how it feels to have grossly dark skin, a big flat nose, cartoonishly high crime rates, and no personal hygiene – and then on top of that, to have some robot hassling you?

AP:

Inside a cavernous stone fortress in downtown Pittsburgh, attorney Robin Frank defends parents at one of their lowest points – when they risk losing their children.

The job is never easy, but in the past she knew what she was up against when squaring off against child protective services in family court. Now, she worries she’s fighting something she can’t see: an opaque algorithm whose statistical calculations help social workers decide which families should be investigated in the first place.

“A lot of people don’t know that it’s even being used,” Frank said. “Families should have the right to have all of the information in their file.”

From Los Angeles to Colorado and throughout Oregon, as child welfare agencies use or consider tools similar to the one in Allegheny County, Pennsylvania, an Associated Press review has identified a number of concerns about the technology, including questions about its reliability and its potential to harden racial disparities in the child welfare system. Related issues have already torpedoed some jurisdictions’ plans to use predictive models, such as the tool notably dropped by the state of Illinois.

According to new research from a Carnegie Mellon University team obtained exclusively by AP, Allegheny’s algorithm in its first years of operation showed a pattern of flagging a disproportionate number of Black children for a “mandatory” neglect investigation, when compared with white children. The independent researchers, who received data from the county, also found that social workers disagreed with the risk scores the algorithm produced about one-third of the time.

County officials said that social workers can always override the tool, and called the research “hypothetical.”

Child welfare officials in Allegheny County, the cradle of Mister Rogers’ TV neighborhood and the icon’s child-centric innovations, say the cutting-edge tool – which is capturing attention around the country – uses data to support agency workers as they try to protect children from neglect. That nuanced term can include everything from inadequate housing to poor hygiene, but is a different category from physical or sexual abuse, which is investigated separately in Pennsylvania and is not subject to the algorithm.

“Workers, whoever they are, shouldn’t be asked to make, in a given year, 14, 15, 16,000 of these kinds of decisions with incredibly imperfect information,” said Erin Dalton, director of the county’s Department of Human Services and a pioneer in implementing the predictive child welfare algorithm.

Critics say it gives a program powered by data mostly collected about poor people an outsized role in deciding families’ fates, and they warn against local officials’ growing reliance on artificial intelligence tools.

​​If the tool had acted on its own to screen in a comparable rate of calls, it would have recommended that two-thirds of Black children be investigated, compared with about half of all other children reported, according to another study published last month and co-authored by a researcher who audited the county’s algorithm.

Advocates worry that if similar tools are used in other child welfare systems with minimal or no human intervention–akin to how algorithms have been used to make decisions in the criminal justice system–they could reinforce existing racial disparities in the child welfare system.

“It’s not decreasing the impact among Black families,” said Logan Stapleton, a researcher at Carnegie Mellon University. “On the point of accuracy and disparity, (the county is) making strong statements that I think are misleading.”

…

The Allegheny Family Screening Tool is specifically designed to predict the risk that a child will be placed in foster care in the two years after they are investigated. Using a trove of detailed personal data collected from birth, Medicaid, substance abuse, mental health, jail and probation records, among other government data sets, the algorithm calculates a risk score of 1 to 20: The higher the number, the greater the risk.

…

CMU researchers found that from August 2016 to May 2018, the tool calculated scores that suggested 32.5% of Black children reported as being neglected should be subject to a “mandatory” investigation, compared with 20.8% of white children.

This is a known problem in the field. The images below are real examples from a Time article published in February 2019.

It’s time to address the racist nature of intelligence.