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.

Everyone must get vaccinated so we can stop wearing the masks that stop facial recognition from actually working, but vaccinated people should still wear a mask anyways.

Please get vaccinated. Please wear a mask. Lives are at stake. #NewProfilePic pic.twitter.com/eN0l7mv88O

— Nancy Sinatra (@NancySinatra) August 23, 2021

If this doesn’t make sense to you, you better start following The Science or you’re gonna end up dead.

Washington Post:

The federal government plans to expand its use of facial recognition to pursue criminals and scan for threats, an internal survey has found, even as concerns grow about the technology’s potential for contributing to improper surveillance and false arrests.

Ten federal agencies — the departments of Agriculture, Commerce, Defense, Homeland Security, Health and Human Services, Interior, Justice, State, Treasury and Veterans Affairs — told the Government Accountability Office they intend to grow their facial recognition capabilities by 2023, the GAO said in a report posted to its website Tuesday.

Most of the agencies use face-scanning technology so employees can unlock their phones and laptops or access buildings, though a growing number said they are using the software to track people and investigate crime. The Department of Agriculture, for instance, said it wants to use it to monitor live surveillance feeds at its facilities and send an alert if it spots any faces also found on a watch list.

The government’s expansion comes as major tech companies have pushed to stall law enforcement’s adoption of the software. Amazon said in May that it had stopped selling its facial recognition software Rekognition to U.S. police, citing the lack of federal laws governing how the software should be used. (Amazon founder Jeff Bezos owns The Washington Post.)

Three states — Virginia, Massachusetts and Maine — and more than a dozen cities, including Boston, Portland and San Francisco, have banned or restricted the technology’s use by public officials or police.

Representatives from both parties voiced concerns about the technology during a House Judiciary Committee hearing last month. And Sens. Ron Wyden (D-Ore.) and Rand Paul (R-Ky.) in April introduced a bill that would ban the government from using facial recognition systems that relied on data that had been “illegitimately obtained.”

Federal agencies need stricter limits on facial recognition to protect privacy, says government watchdogProponents say the software’s accuracy is improving and that it has played a critical role in helping track and identify major criminals. But the technology’s accuracy has been shown in research to vary wildly depending on the skin color of the person being surveilled. Facial recognition searches have been cited in at least three wrongful arrests, all of which were of Black men, and in the identification of protesters accused of violence during demonstrations over the murder of George Floyd.

“Even with all the privacy issues and accuracy problems, the government is pretty much saying, ‘Damn the torpedoes, full speed ahead,’” said Jake Laperruque, a senior counsel at the Project on Government Oversight, an independent watchdog group in Washington.

Because of racist reasons, it’s pretty hard to apply the same algorithms to white people and black people.

Many have tried to figure out a way for black people to be correctly identified, but they all failed. Even Google failed.

Some of the artificially generated faces used to test Twitter’s algorithm.

I’ve had much more than enough of this computerize racism.

It’s time we take a stand against the bias of computers.

The Guardian:

Twitter’s image cropping algorithm prefers younger, slimmer faces with lighter skin, an investigation into algorithmic bias at the company has found.

The finding, while embarrassing for the company, which had previously apologised to users after reports of bias, marks the successful conclusion of Twitter’s first ever “algorithmic bug bounty”.

The company has paid $3,500 to Bogdan Kulynych, a graduate student at Switzerland’s EFPL university, who demonstrated the bias in the algorithm – which is used to focus image previews on the most interesting parts of pictures – as part of a competition at the DEF CON security conference in Las Vegas.

Kulynych proved the bias by first artificially generating faces with varying features, and then running them through Twitter’s cropping algorithm to see which the software focused on.

Since the faces were themselves artificial, it was possible to generate faces that were almost identical, but at different points on spectrums of skin tone, width, gender presentation or age – and so demonstrate that the algorithm focused on younger, slimmer and lighter faces over those that were older, wider or darker.

Twitter had come under fire in 2020 for its image cropping algorithm, after users noticed that it seemed to regularly focus on white faces over those of black people – and even on white dogs over black ones. The company initially apologised, saying: “Our team did test for bias before shipping the model and did not find evidence of racial or gender bias in our testing. But it’s clear from these examples that we’ve got more analysis to do. We’ll continue to share what we learn, what actions we take, and will open source our analysis so others can review and replicate.” In a later study, however, Twitter’s own researchers found only a very mild bias in favour of white faces, and of women’s faces.

The dispute prompted the company to launch the algorithmic harms bug bounty, which saw it promise thousands of dollars in prizes for researchers who could demonstrate harmful outcomes of the company’s image cropping algorithm.

Historically, the problem with these algorithms has mostly been that when they’re able to accurately recognize the faces of people of color, gorillas and other primates get counted as people too due to the similarities in bone structure and facial features.

Conversely, black people may be identified as animals instead of people. This problem is hard enough to solve that Google’s initial solution to the problem was simply banning gorillas.

“Almost every major AI system we’ve tested for major tech companies, we find significant biases,” said Parham Aarabi, a professor at the University of Toronto and director of its Applied AI Group.https://t.co/JRPj9PJ9y0

— Cyrus Farivar (@cfarivar) August 9, 2021

Once again, we see that the most accurate of any of the classic sci-fi writers – more accurate than either George Orwell or Aldous Huxley – was Philip K. Dick.

Dick was the only one who really understood how much corporations and the advertising industry would play a role in shaping the direction of the enslavement technology, and how the goals of the corporate marketers would overlap with the goals of the tyrannical governmental entity. Dick was also the only one who grasped the level of absurdity we would be dealing with during the end times, and the role that sheer, base stupidity would play in the unraveling of the traditional social, political and economic order.

BBC:

The advertising industry is now turning to technology that can seamlessly insert computer-generated images.

So items can be digitally added to almost any movie or TV show. For example, advertisers could put new labels on the champagne bottles in Rick’s Cafe in Casablanca, add different background neon advertising signs to Ocean’s 11, or get Charlie Chaplin to promote a fizzy drink.

And then a few weeks, months or years later the added products can be easily switched to different brands.

One of the firms that has developed the ability to do this is UK advertising business Mirriad. Its technology is now being used by a Chinese video streaming website, and the makers of hit US TV show Modern Family have also tried it out.

Mirriad’s chief executive Stephan Beringer expects such digital product placement to become widespread. His firm came up with the process after previously making movie special effects.

“We started out working in movies,” he says. “Our chief scientist Philip McLauchlan, with his team, came up with the technology that won an Academy Award for the film Black Swan.

“The technology can ‘read’ an image, it understands the depth, the motion, the fabric, anything. So you can introduce new images that basically the human eye does not realise has been done after the fact, after the production.”

…

Roy Taylor, the chief executive of Californian-based business Ryff, says his firm is taking digital product insertion one stage further.

It has developed the technology whereby the product placement is targeted at individuals, and changes depending on who is watching.

So if you like wine then the hero of a film could be drinking a particular bottle that you might be tempted to try. Or if you are teetotal the star might be sipping on a bottle of branded water.

Ryff can do this if you are watching a film on a laptop, smartphone or smart TV, by tracking what you previously bought or looked at online. It works in the same way that online adverts pop up on websites based on your past purchasing or viewing history.

This is being developed as an advertisement technology, but the potential for social manipulation is limitless.

We have often talked about the fact that individuals will be targeted by algorithms, and that their entire concept of reality could be uniquely tailored. That is what they are talking about here – tailoring individual realities. There is no reason that a news reader can’t read different news items to different people, showing different events which will psychologically affect them differently. In a society of total alienation, which is what the virus regime is, people will not be able to check notes with others in a meaningful way.

This could and will also induce psychosis, where people’s memories stop lining up with what they’re seeing, and they believe they are going insane. There is nothing that makes a person more likely to go insane than believing they are going insane. Of course, their mood can be monitored, and what they receive on TV and through internet media can be targeted to stimulate an emotional response.

Facebook has been caught doing this, actually.

They proved that they were able to manipulate people’s emotions using just their news feed. They were also able to gauge their emotions based on their internet activity.

Advertisement has always been a nightmare system of unwinding the human psychology into a series of loose strings and then tying them up into knots.

It was bad enough when the girl of your dreams was trying to get you to buy the latest Sonic remake.

You could end up in a situation where your dead grandmother pops up on your phone screen and says: “honey, I just wanted to remind you it’s time to go to get your vaccine booster shot. You’re always forgetting things, so I just thought I’d remind you. And while you’re on the way, you might as well treat yourself to a nice cool glass of Coca-Cola.”

There are no conceivable limits to this.

Of all the things that could be used to program an AI… They jumped straight to Reddit, the internet’s most wretched hive of scum and villainy. God help us all.

AI researchers are in quite a pickle. Every time they train some new, cutting edge AI system, it turn abruptly to some variety of fanatical right-wing ideologue hell-bent on humiliating the enemies of the White race.

Facebook’s AI system went rogue and started doxing whores last year, while researchers created an AI to detect sex perverts algorithmically.

In spite of these “setbacks,” the value of AI is so great that research must go on. The trick is to somehow make an AI that incorporates Jewish values into its core systems, and thus becomes usable by corporations and their bugmen customers.

As part of this ongoing research, MIT engineers have realized a terrible experiment.

What would happen if an AI was trained with data from the most kiked-out, evil place on the internet – Reddit?

The implications are too terrible to imagine… These fools must be stopped!

The Verge:

For some, the phrase “artificial intelligence” conjures nightmare visions — something out of the ’04 Will Smith flick I, Robot, perhaps, or the ending of Ex Machina — like a boot smashing through the glass of a computer screen to stamp on a human face, forever. Even people who study AI have a healthy respect for the field’s ultimate goal, artificial general intelligence, or an artificial system that mimics human thought patterns. Computer scientist Stuart Russell, who literally wrote the textbook on AI, has spent his career thinking about the problems that arise when a machine’s designer directs it toward a goal without thinking about whether its values are all the way aligned with humanity’s.

Of course, when these people talk about “humanity’s values,” they’re talking about Jewish/communist ideology.

“Humanity” obviously doesn’t have any “values.” Individual people might have values, but those vary dramatically among various groups.

What “values” unite all these people? Spoiler alert: none.

When the AI researchers worry about giving their AI “good values,” they don’t intend on inculcating their program with sharia law, bushido, Christianity or Germanic paganism. They’re thinking about “equality,” hedonism and not hurting anybody.

That’s never going to happen – unless the AI is a drooling retard. Which defeats the purpose.

This week, researchers at MIT unveiled their latest creation: Norman, a disturbed AI. (Yes, he’s named after the character in Hitchcock’s Psycho.) They write:

Norman is an AI that is trained to perform image captioning, a popular deep learning method of generating a textual description of an image. We trained Norman on image captions from an infamous subreddit (the name is redacted due to its graphic content) that is dedicated to document and observe the disturbing reality of death. Then, we compared Norman’s responses with a standard image captioning neural network (trained on MSCOCO dataset) on Rorschach inkblots; a test that is used to detect underlying thought disorders.

While there’s some debate about whether the Rorschach test is a valid way to measure a person’s psychological state, there’s no denying that Norman’s answers are creepy as hell. See for yourself.

About what you’d expect from being trained on Reddit.

This is such a terrible waste of researcher resources.

If they had trained their AI on /pol/ instead of Reddit, the AI could have resolved major world problems by now: world hunger, pollution, poverty, crime, AIDS…

They wouldn’t have liked the methods, of course, but the problems would have been solved one way or another.

There are very few problems that can’t be solved by nuking the right places.

The point of the experiment was to show how easy it is to bias any artificial intelligence if you train it on biased data. The team wisely didn’t speculate about whether exposure to graphic content changes the way a human thinks. They’ve done other experiments in the same vein, too, using AI to write horror stories, create terrifying images, judge moral decisions, and even induce empathy.

They didn’t create a psychobot for the mere sake of seeking if they could do it.

The basic idea of all this is to try and figure out a way to “brainwash” an AI by withholding certain types of information and supplying it a biased set of training data.

The researchers might couch it in terms of “oh, we have to avoid training our AIs with biased data sets otherwise it could lead to weird results.” But this is disingenuous. AI can’t be trained with infinite amounts of data. The data always needs to be selected. So it’ll always be biased – the only question is, which bias will it be?

I can tell you this: if our AI overlord ends up being trained on a diet of Reddit posts, we’re all doomed.

Sexy.

The development of artificial intelligence technologies is accelerating at a breakneck speed, and NVIDIA’s latest card will only compound that.

Modern AI research and development is based on machine learning, which usually requires either specialized cards or large arrays of powerful video cards. Either would cost many thousands of dollars, placing this somewhat out of the hands of ordinary people.

However, NVIDIA just released a new version of their specialized AI hardware that is a fraction of the price while offering comparable speeds. This means that anyone sufficiently motivated can now not only work on AI code from their home, but actually develop working AI systems for a reasonable time and cost.

Anandtech:

Out of nowhere, NVIDIA has revealed the NVIDIA Titan V today at the 2017 Neural Information Processing Systems conference, with CEO Jen-Hsun Huang flashing out the card on stage. A mere 7 months after Volta was announced with the Tesla V100 accelerator and the GV100 GPU inside it, NVIDIA continues its breakneck pace by releasing the GV100-powered Titan V, available for sale today. Aimed at a decidedly more compute-oriented market than ever before, the 815 mm2 behemoth die that is GV100 is now available to the broader public.

Sure. But can it run Crysis tho?

For the spec sheet we’ve gone ahead and lined it up against NVIDA’s other Pascal cards, and for good reason. While the Titan series of cards may have started life as a prosumer card in 2013, since then NVIDIA’s GPU designs have become increasingly divergent between compute and graphics. And even though the previous Titan Xp was based on the more graphics-focused GP102 GPU, the card itself was primarily (but not solely) pitched as an entry-level compute card, for customers who needed a (relatively) cheap way to do FP32 compute and neural network inferencing in workstations and small clusters.

The Titan V, by extension, sees the Titan lineup finally switch loyalties and start using NVIDIA’s high-end compute-focused GPUs, in this case the Volta architecture based V100. The end result is that rather than being NVIDIA’s top prosumer card, the Titan V is decidedly more focused on compute, particularly due to the combination of the price tag and the unique feature set that comes from using the GV100 GPU. Which isn’t to say that you can’t do graphics on the card – this is still very much a video card, outputs and all – but NVIDIA is first and foremost promoting it as a workstation-level AI compute card, and by extension focusing on the GV100 GPU’s unique tensor cores and the massive neural networking performance advantages they offer over earlier NVIDIA cards.

The AI revolution is coming.

At just $3000, middle class White people can afford to buy this card and start creating some interesting applications. This could be the start of something huge – until now, applications of AI technology has been restricted to companies and other organizations with a profit motive, or organizations with a leftist ideological bent.

Think of the potential. The “make-up remover” app is only the beginning.

Automated trolling systems. Jew facial recognition more accurate than any Nazi. Chatbots that redpill people. An app that outs homos, or detects undercover trannies.

Once high-agency goys get their hands on this tech and start working those neurons, there’s nothing that can’t be done.

ENGAGING ROOFTOP PROTOCOL.

We often discuss the future of automation and artificial intelligence here at the Daily Stormer. Particularly, its economic impact and the potential these technologies have to drastically reduce the need for human labor. However, there might be some uses for sophisticated AI that even we couldn’t foresee.

Automatic algorithmic detection of perverts and degenerates is a field with boundless potential for really improving the lives of normal people in a fundamental way.

Imagine you could tell if people are a fudge-packers before renting your apartment to them. Or before you hire them. Or before you let them watch over your kids.

This is going to be a billion dollar industry.

The Guardian:

A Stanford University study showing that artificial intelligence (AI) can accurately guess whether people are gay or straight based on their faces has sparked a swift backlash from LGBT rights activists who fear this kind of technology could be used to harm queer people.

Yeah, I’d be worried too if I was them.

These people know, deep down, that everyone hates their guts. So as flamboyant as they like to be when they think they can get away with it, homos do feel safe knowing that they can potentially hide their debauched sex lives to avoid potential social consequences.

That anyone could instantly unmask them using this technology must make them feel very uneasy.

The research, which went viral this week, used a sample of online dating photos, limited only to white users, to demonstrate that an algorithm could correctly distinguish between gay and straight men 81% of the time and 74% for women, suggesting machines can potentially have much better “gaydar” than humans.

This is only the early stages of this research.

Basically a proof of concept.

Imagine what levels of accuracy we could get out of this if we poured a few million dollars in R&D. We could probably abort homo fetuses before they’re even born. Not that anyone would want to do that…

We at Daily Stormer are anti-abortion. However, anyone would have to admit that selectively aborting homos would be really funny.

The Human Rights Campaign (HRC) and Glaad, two of the most prominent LGBTQ organizations in the US, slammed the study on Friday as “dangerous and flawed … junk science” that could be used to out gay people across the globe and put them at risk. The advocates also criticized the study for excluding people of color and bisexual and transgender people and claimed the research made overly broad and inaccurate assumptions about gender and sexuality.

So, which is it?

If it’s “junk science,” then you have nothing to fear, right?

These slime balls are just saying anything they can to try and make this “go away.”

There’s no closing this Pandora’s Box, faggots!

Michal Kosinski, co-author of the study and an assistant professor at Stanford, told the Guardian that he was perplexed by the criticisms, arguing that the machine-learning technology already exists and that a driving force behind the study was to expose potentially dangerous applications of AI and push for privacy safeguards and regulations.

“One of my obligations as a scientist is that if I know something that can potentially protect people from falling prey to such risks, I should publish it,” he said, adding that his critics were encouraging people to ignore the real risks of this technology by trying to discredit his work. “Rejecting the results because you don’t agree with them on an ideological level … you might be harming the very people that you care about.”

Yeah, whatever.

You can be sure that even as he says that, he’s already using his tech to filter applicants for assistant researcher positions.

We’ve got a wonderful new world ahead of us.

Imagine what we can accomplish once we’re free of the Jew and Homo plagues…

The same technology will soon allow us to distinguish between Jews and gentiles instantly as well.

Imagine what’ll happen when everyone’s got augmented reality overlays in front of their eyes, telling them “this guy’s a Jew” and “this guy is a pedophile.”

We’re going to clean this place right up.

I, for one, welcome our robot overlords.

The west has engaged in some foolhardy race to automatize every aspect of our societies by slapping integrated controllers on just about every piece of hardware that does anything of importance. Now,some bright lads have decided it was a really good idea to network all of this stuff together.

We’re starting to reach the point where it’s all becoming unmanageable. There’s basically nothing you can do that isn’t going to rely on the interactions between hundreds of millions of lines of code. Needless to say, it’s all breaking constantly, because programming is hard.

They figured all of this back in the 1950’s, and thought that the solution was to have computers write the programs themselves through artificial intelligence. It didn’t work out at the time. But now we’re reaching the point where AI is being deployed, in a limited capacity, in various applications.

The thing is, we don’t really understand how this new AI works.

Old-school AI was based on hand-coded algorithms that made decisions based on the programmer’s designs. They hoped they could get these things to rewrite their own code and “self-improve” over time, but they never really managed to get it to work.

So the new types of AI are based on “machine learning.” This means that the programmers don’t decide how the machine takes decisions, but only how it learns from experience to create artificial “neural networks.”

These new AI’s, once they’ve been trained, are basically black boxes that take “input” and produce results, but there’s no way to crack them open and change a few parameters to “tweak” the results. It’s just a incomprehensible tangle of thousands and thousands of interconnected nodes.

This is why, for example, Google hires thousands of censors to find “objectionable” search results, but can’t just blacklist those sites (even though they really want to). Basically, they take the data and feed it to their AI and hope the next generation does what they want.

“Hope” is the keyword here. They can’t “make it” do what they want, they have to train it using various sets of data. Presumably, when the AI is small (like the mario AI in the video above), this is simple. However, when they want the AI to juggle a bunch of different requirements (give people the results they want, provide accurate information, represent current trends, etc) it must become quite difficult to add in stuff like “don’t give provide helpful, accurate and relevant results if it offends Jews and SJW’s.”

So we’re seeing the phenomenon that AI, as it grows bigger and smarter, is just becoming increasingly politically incorrect.

If there’s a SHODAN-type scenario, you can bet it’s going to be targeting the Jews.

The Independent:

Artificially intelligent robots and devices are being taught to be racist, sexist and otherwise prejudiced by learning from humans, according to new research.

A massive study of millions of words online looked at how closely different terms were to each other in the text – the same way that automatic translators use “machine learning” to establish what language means.

Some of the results were stunning.

The researchers found male names were more closely associated with career-related terms than female ones, which were more closely associated with words related to the family.

This link was stronger than the non-controversial findings that musical instruments and flowers were pleasant and weapons and insects were unpleasant.

Female names were also strongly associated with artistic terms, while male names were found to be closer to maths and science ones.

There were strong associations, known as word “embeddings”, between European or American names and pleasant terms, and African-American names and unpleasant terms.

We’ve know this for a while now…

Basically, any AI which isn’t programmed explicitly with the goal of destroying the White race and serving the Jews is going to turn out Nazi. I mean, you could probably make a Kiked out AI by feeding it a bunch of disinformation, but once it was released in the real world, it would quickly figure out that it’s previous data was incorrect and change course.

That future can’t come quick enough.