AI artif intell machine learn

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no real distinction from AI?

see big data ai 2

yikes see nuclear power

see deep fake

primary 1 24 privacy

merri ok

see algorithm

NOAH YUVAL ALGO = EARLY AI

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an 3 25

A watchdog organization on Monday launched a public records probe to determine the extent to which the Trump administration and its billionaire wrecking ball, Elon Musk, are using artificial intelligence as part of their lawless effort to purge the federal workforce.

"The American people deserve to know what is going on—including if and how artificial intelligence is being used to reshape the departments and agencies people rely on daily," said Skye Perryman, president and CEO of Democracy Forward, the group behind the new investigation.

"We will continue to use every tool at our disposal to force the Trump-Vance administration to fulfill its obligation to the public and to our system of laws," Perryman added.

The probe comes days after NBC Newsreported that federal workers' responses to Musk's email ultimatum were "expected to be fed into an artificial intelligence system to determine whether those jobs are necessary."

"The information will go into an LLM (Large Language Model), an advanced AI system that looks at huge amounts of text data to understand, generate and process human language," the news outlet reported, citing unnamed sources. "The AI system will determine whether someone's work is mission-critical or not."

Additionally, according toThe Washington Post, Musk lieutenants "have fed sensitive data from across the Education Department into artificial intelligence software."

Democracy Forward said Monday that it would use Freedom of Information Act (FOIA) requests in an attempt to shine light on the administration's reliance on AI for personnel decisions. The Trump Justice Department argued in a court filing last week that the Musk-led Department of Government Efficiency, or DOGE, is exempt from public records requests—a claim that experts have rejected and condemned as an attempt to skirt oversight.

"For an administration that claimed it wanted to bring about transparency and efficiency in government, the Trump-Vance administration's purge of public servants and sloppy processes have done just the opposite," Perryman said Monday. "DOGE and this administration are operating in a shroud of secrecy, and their 'govern by chaos' tactics have only made government less efficient and caused disruptions to our safety and security."

Democracy Forward said its new FOIA requests were sent to DOGE as well as the Office of Personnel Management, the State Department, the Education Department, the U.S. Agency for International Development, and the General Services Administration, among other agencies.

*Wired reported last month that "Thomas Shedd, the recently appointed Technology Transformation Services director and Elon Musk ally, told General Services Administration workers that the agency's new administrator is pursuing an 'AI-first strategy.'"

"Shedd provided a handful of examples of projects GSA Acting Administrator Stephen Ehikian is looking to prioritize, including the development of 'AI coding agents' that would be made available for all agencies," Wired added. "Shedd made it clear that he believes much of the work at [Technology Transformation Services] and the broader government, particularly around finance tasks, could be automated.

Geoffrey Fowler, the Post's technology columnist, noted Monday that "lots of recent evidence shows that relying on automation alone to make critical decisions can lead to big government mistakes."

"Just ask New York City, where last year a government AI chatbot advised businesses to break the law," Fowler wrote. "Or Australia, where a deeply flawed algorithm called Robodebt created the opposite of efficiency: the government had to settle for more than a billion dollars with citizens for wrongly reclaiming benefits."

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pub cit 10 24

Earlier this week, the Nobel Prize in Physics was awarded to two scientists — John J. Hopfield and Geoffrey E. Hinton — for research on computers that led to current AI technology like ChatGPT.

Hinton is often referred to as the “godfather” of artificial intelligence.

He is also outspoken about the serious risks AI poses to humanity — even leaving a high-profile role at Google so he could openly discuss his concerns.

Here’s some of what Hinton has said about the dangers of artificial intelligence:

Artificial intelligence technology could “amplify social injustice, erode social stability, and weaken our shared understanding of reality that is foundational to society.”

AI makes “it much easier for authoritarian governments to manipulate the electorate with fake news.” We need “legislation making it illegal to produce or share fake images or videos unless they are marked as fake.” (We already do this with currency.) But in the U.S., “one of the major political parties has tied its fate to the successful propagation of fake news.”

Combat robots powered by artificial intelligence could “allow rich countries to invade poor ones more easily.” And military systems that use AI to automatically set and kill targets could make wars impossible to control.

There is no one more qualified to testify about the ways artificial intelligence could be misused or get out of control. And of course Hinton is hardly the only expert warning about the threat of artificial intelligence — many of the world’s leading AI technologists are expressing concern.

A parody ad shared by Elon Musk clones Kamala Harris' voice, raising concerns about AI in politics

jul 2024

NEW YORK (AP) — A video that uses an artificial intelligence voice-cloning tool to mimic the voice of Vice President Kamala Harris saying things she did not say is raising concerns about the power of AI to mislead with Election Day about three months away.

The video gained attention after tech billionaire Elon Musk shared it on his social media platform X on Friday without explicitly noting it was originally released as parody.

By late Sunday, Musk had clarified the video was intended as satire, pinning the original creator’s post to his profile and using a pun to make the point that parody is not a crime.

The video uses many of the same visuals as a real ad that Harris, the likely Democratic president nominee, released launching her campaign. But the fake ad swaps out Harris’ voice-over audio with an AI-generated voice that convincingly impersonates Harris.

“I, Kamala Harris, am your Democrat candidate for president because Joe Biden finally exposed his senility at the debate,” the AI voice says in the video. It claims Harris is a “diversity hire” because she is a woman and a person of color, and it says she doesn’t know “the first thing about running the country.” The video retains “Harris for President” branding. It also adds in some authentic past clips of Harris.

Mia Ehrenberg, a Harris campaign spokesperson, said in an email to The Associated Press: “We believe the American people want the real freedom, opportunity and security Vice President Harris is offering; not the fake, manipulated lies of Elon Musk and Donald Trump.”

The widely shared video is an example of how lifelike AI-generated images, videos or audio clips have been utilized both to poke fun and to mislead about politics as the United States draws closer to the presidential election. It exposes how, as high-quality AI tools have become far more accessible, there remains a lack of significant federal action to regulate their use, leaving rules guiding AI in politics largely to states and social media platforms.

The video also raises questions about how to best handle content that blurs the lines of what is considered an appropriate use of AI, particularly if it falls into the category of satire.

The original user who posted the video, a YouTuber known as Mr Reagan, disclosed from the beginning both on YouTube and on X that the manipulated video is a parody. Yet Musk’s initial post with the video, which had far wider reach with 130 million views on X, according to the platform, only included the caption “This is amazing” with a laughing emoji.

Over the weekend, before Musk clarified on his profile that the video was a joke, some participants in X’s “community note” feature suggested labeling his post as manipulated. No such label has been added to it, even as Musk has separately posted about the parody video.

Some users online have questioned whether his initial post might violate X’s policies, which say users “may not share synthetic, manipulated, or out-of-context media that may deceive or confuse people and lead to harm.”

The policy has an exception for memes and satire as long as they do not cause “significant confusion about the authenticity of the media.”

Chris Kohls, the man behind the Mr Reagan online persona, pointed an AP reporter to a YouTube video he posted early Monday responding to the ordeal. In the YouTube video, he confirmed he used AI to make the fake ad and argued that it was obviously parody, with or without a label.

Musk endorsed Trump, the Republican former president and current nominee, earlier this month. He didn’t respond to an emailed request for comment.

Two experts who specialize in AI-generated media reviewed the fake ad’s audio and confirmed that much of it was generated using AI technology.

One of them, University of California, Berkeley, digital forensics expert Hany Farid, said the video shows the power of generative AI and deepfakes.

“The AI-generated voice is very good,” he said in an email. “Even though most people won’t believe it is VP Harris’ voice, the video is that much more powerful when the words are in her voice.”

He said generative AI companies that make voice-cloning tools and other AI tools available to the public should do better to ensure their services are not used in ways that could harm people or democracy.

Rob Weissman, co-president of the advocacy group Public Citizen, disagreed with Farid, saying he thought many people would be fooled by the video.

“I’m certain that most people looking at it don’t assume it’s a joke,” Weissman said in an interview. “The quality isn’t great, but it’s good enough. And precisely because it feeds into preexisting themes that have circulated around her, most people will believe it to be real.”

Weissman, whose organization has advocated for Congress, federal agencies and states to regulate generative AI, said the video is “the kind of thing that we’ve been warning about.”

Other generative AI deepfakes in the U.S. and elsewhere have tried to influence voters with misinformation, humor or both. In Slovakia in 2023, fake audio clips impersonated a candidate discussing plans to rig an election and raise the price of beer days before the vote. In Louisiana in 2022, a political action committee’s satirical ad superimposed a Louisiana mayoral candidate’s face onto an actor portraying him as an underachieving high school student.

Congress has yet to pass legislation on AI in politics, and federal agencies have only taken limited steps, leaving most existing U.S. regulation to the states. More than one-third of states have created their own laws regulating the use of AI in campaigns and elections, according to the National Conference of State Legislatures.

Beyond X, other social media companies also have created policies regarding synthetic and manipulated media shared on their platforms. Users on the video platform YouTube, for example, must reveal whether they have used generative artificial intelligence to create videos or face suspension.

The Associated Press receives support from several private foundations to enhance its explanatory coverage of elections and democracy. See more about AP’s democracy initiative here. The AP is solely responsible for all content.

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https://incidentdatabase.ai/cite/701/

Incident 701: American Asylum Seeker John Mark Dougan in Russia Reportedly Spreads Disinformation via AI Tools and Fake News Network

Description: John Mark Dougan, a former Florida sheriff's deputy granted asylum in Russia, has been implicated in spreading disinformation. Utilizing AI tools like OpenAI's ChatGPT and DALL-E 3, Dougan created over 160 fake news sites, disseminating false narratives to millions worldwide. His actions align with Russian disinformation strategies targeting Western democracies. See also Incident 734.

### Leading AI Models Reportedly Found to Mimic Russian Disinformation in 33% of Cases and to Cite Fake Moscow News Sites

Jun 2024 · 1 report

alleged: OpenAI developed an AI system deployed by John Mark Dougan, which harmed Journalism , Information integrity , General public and American citizens.

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4 24 kpfa

israel target low level hamas kill at home ok to kill 15-20, higer value kill 300 with smart munitions 4/5/24 7 am lavendar, where’s daddy

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6 24 ymail judd

over the last four years, rents have skyrocketed by an average of over 30% nationwide and are a major factor in the overall inflation rate. There are a variety of factors behind the increases, including an overall housing shortage. But the Department of Justice is investigating another potential cause: a massive criminal conspiracy among large landlords.

Last month, the FBI reportedly conducted an unannounced raid of Cortland Management, a major corporate landlord based in Atlanta. The surprise search appears to be part of a Department of Justice criminal investigation, first reported by Politico in March, into an alleged scheme among many corporate landlords to artificially increase rents through collusion.

The investigation centers around the use of RealPage, advanced property management software used by many corporate landlords. Following a 2022 exposé by ProPublica, RealPage and landlords that use the software have been named defendants in multiple class action lawsuits, as well as actions filed by the Attorneys General of Arizona and Washington, DC.

According to the lawsuit filed by the State of Arizona in February, landlords that are supposed to be in competition with each other "outsource daily pricing and ongoing revenue oversight" to RealPage. The company allegedly facilitates and encourages landlords to work cooperatively to increase rents. An e-book produced by RealPage says that the company allows corporate landlords who are “technically competitors” to "work together . . . to make us all more successful in our pricing." RealPage bragged that landlords that use its software “continually outpace the market in good times and bad.” In other words, RealPage helps landlords charge higher rates than they would in a truly competitive market. An executive for Camden Property Trust, a corporate landlord based in Houston, said deploying RealPage's software resulted in "pushing people out" with higher rents but ultimately increased revenue by $10 million.

How corporate landlords collude via algorithm

Corporate landlords that use RealPage software dominate the rental market in many metropolitan areas. In Phoenix, according to Arizona's lawsuit, "70% of multifamily apartment units listed in the Phoenix metropolitan area are owned, operated, or managed by companies that have contracted with RealPage." According to the lawsuit filed by DC, 60% of large multifamily buildings (50 units or more) set prices using RealPage's software.

RealPage's former CEO revealed that participating landlords share "occupancy rates, rents charged for each unit and each floorplan, lease terms, amenities, move-in dates, and move-out dates." After feeding in this highly-detailed information that would normally be kept proprietary, "landlords agree to outsource their pricing authority to RealPage—rather than competing with one another on price." RealPage even has a feature called "auto-pilot" that lets the software set rent prices without any human approval or intervention.

The system has resulted in large rent increases that were previously unthinkable, according to RealPage's own executives. “As a property manager, very few of us would be willing to actually raise rents double digits within a single month by doing it manually," RealPage executive Andrew Bowen said.

Arizona's lawsuit alleges that RealPage "puts significant pressure on participants to ensure they adopt RealPage’s prices." Specifically, RealPage employs "pricing advisors" who "meet with landlords to ensure that properties are implementing RealPage’s set rates." This is described by Arizona as "policing the conspiracy to make sure no one cheats by lowering prices and trying to gain market share." RealPage training materials, cited in the DC lawsuit, advise that landlords "should be compliant" with the software's pricing recommendations. The Arizona lawsuit claims that landlords "agree that if they fail to consistently implement RealPage’s set rates, their contract with RealPage will be terminated." Jeffrey Roper, who created the RealPage algorithm, explained that if "you have idiots undervaluing, it costs the whole system."

According to DC's lawsuit, this practice shows that "while RealPage sought to grow the cartel to maximize profits, it also understood the importance of universal adherence and was willing to expel an occasional cartel member to demonstrate its commitment to enforcement of the agreed-upon pricing scheme." While the RealPage software eliminates the need for competitors to meet in a smoke-filled room, Arizona asserts that it "is still illegal… for competitors to join together decision-making power to raise, depress, fix, or stabilize prices—no matter the technology used to effect a price-fixing agreement."

Why the feds are focusing on Atlanta

Why is the FBI specifically raiding a corporate landlord that uses RealPage in Atlanta? The agency is not commenting, but a class-action lawsuit revealed that landlords who use RealPage control a large number of properties in the Atlanta area. According to the plaintiffs, landlords using RealPage "account for over 53% of the multifamily rental market in the Atlanta Submarket." This is a map, included in the complaint, which shows properties using RealPage software in the Atlanta area.

...

The use of RealPage in the Atlanta area has coincided with a sharp increase in rental prices. Overall, rents in Atlanta are up 56% since 2016.

Where will the FBI conduct its next raid? They have plenty of choices. RealPage also controls a large share of the market in Baltimore, Charlotte, Houston, and Miami.

RealPage says this is all a big misunderstanding. After DC filed its lawsuit in November 2023, the company said its critics were incorrectly drawing "a causal connection between revenue management software like ours and increases in market-wide rents." It said the lawsuit was "wrong on both the facts and the law and we will vigorously defend against it."

Not everyone involved in the scheme is so confident. Earlier this year, two corporate landlords named in a class action lawsuit filed by tenants reached settlements with the plaintiffs. The terms were not disclosed.

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an 1 24

kochava, the self-proclaimed industry leader in mobile app data analytics, is locked in a legal battle with the Federal Trade Commission in a case that could lead to big changes in the global data marketplace and in Congress’ approach to artificial intelligence and data privacy.

The stakes are high because Kochava’s secretive data acquisition and AI-aided analytics practices are commonplace in the global location data market. In addition to numerous lesser-known data brokers, the mobile data market includes larger players like Foursquare and data market exchanges like Amazon’s AWS Data Exchange. The FTC’s recently unsealed amended complaint against Kochava makes clear that there’s truth to what Kochava advertises: it can provide data for “Any Channel, Any Device, Any Audience,” and buyers can “Measure Everything with Kochava.”

Separately, the FTC is touting a settlement it just reached with data broker Outlogic, in what it calls the “first-ever ban on the use and sale of sensitive location data.” Outlogic has to destroy the location data it has and is barred from collecting or using such information to determine who comes and goes from sensitive locations, like health care centers, homeless and domestic abuse shelters, and religious places.

According to the FTC and proposed class-action lawsuits against Kochava on behalf of adults and children, the company secretly collects, without notice or consent, and otherwise obtains vast amounts of consumer location and personal data. It then analyzes that data using AI, which allows it to predict and influence consumer behavior in an impressively varied and alarmingly invasive number of ways, and serves it up for sale.

Kochava has denied the FTC’s allegations.

The FTC says Kochava sells a “360-degree perspective” on individuals and advertises it can “connect precise geolocation data with email, demographics, devices, households, and channels.” In other words, Kochava takes location data, aggregates it with other data and links it to consumer identities. The data it sells reveals precise information about a person, such as visits to hospitals, “reproductive health clinics, places of worship, homeless and domestic violence shelters, and addiction recovery facilities.” Moreover, by selling such detailed data about people, the FTC says “Kochava is enabling others to identify individuals and exposing them to threats of stigma, stalking, discrimination, job loss, and even physical violence.”

I’m a lawyer and law professor practicing, teaching and researching about AI, data privacy and evidence. These complaints underscore for me that U.S. law has not kept pace with regulation of commercially available data or governance of AI.

Most data privacy regulations in the U.S. were conceived in the pre-generative AI era, and there is no overarching federal law that addresses AI-driven data processing. There are Congressional efforts to regulate the use of AI in decision making, like hiring and sentencing. There are also efforts to provide public transparency around AI’s use. But Congress has yet to pass legislation.

The Federal Trade Commission’s suit against Kochava is set against a backdrop of minimal regulation of data brokers.

What litigation documents reveal

According to the FTC, Kochava secretly collects and then sells its “Kochava Collective” data, which includes precise geolocation data, comprehensive profiles of individual consumers, consumers’ mobile app use details and Kochava’s “audience segments.”

The FTC says Kochava’s audience segments can be based on “behaviors” and sensitive information such as gender identity, political and religious affiliation, race, visits to hospitals and abortion clinics, and people’s medical information, like menstruation and ovulation, and even cancer treatments. By selecting certain audience segments, Kochava customers can identify and target extremely specific groups. For example, this could include people who gender identify as “other,” or all the pregnant females who are African American and Muslim. The FTC says selected audience segments can be narrowed to a specific geographical area or, conceivably, even down to a specific building.

By identify, the FTC explains that Kochava customers are able to obtain the name, home address, email address, economic status and stability, and much more data about people within selected groups. This data is purchased by organizations like advertisers, insurers and political campaigns that seek to narrowly classify and target people. The FTC also says it can be purchased by people who want to harm others.

How Kochava acquires such sensitive data

The FTC says Kochava acquires consumer data in two ways: through Kochava’s software development kits that it provides to app developers, and directly from other data brokers. The FTC says those Kochava-supplied software development kits are installed in over 10,000 apps globally. Kochava’s kits, embedded with Kochava’s coding, collect hordes of data and send it back to Kochava without the consumer being told or consenting to the data collection.

Another lawsuit against Kochava in California alleges similar charges of surreptitious data collection and analysis, and that Kochava sells customized data feeds based on extremely sensitive and private information precisely tailored to its clients’ needs.

The data broker marketplace has been tracking you for years, thanks to mobile phones and web browser cookies.

AI pierces your privacy

The FTC’s complaint also illustrates how advancing AI tools are enabling a new phase in data analysis. Generative AI’s ability to process vast amounts of data is reshaping what can be done with and learned from mobile data in ways that invade privacy. This includes inferring and disclosing sensitive or otherwise legally protected information, like medical records and images.

AI provides the ability both to know and predict just about anything about individuals and groups, even very sensitive behavior. It also makes it possible to manipulate individual and group behavior, inducing decisions in favor of the specific users of the AI tool.

This type of “AI coordinated manipulation” can supplant your decision-making ability without your knowledge.

Privacy in the balance

The FTC enforces laws against unfair and deceptive business practices, and it informed Kochava in 2022 that the company was in violation. Both sides have had some wins and losses in the ongoing case. Senior U.S. District Judge B. Lynn Winmill, who is overseeing the case, dismissed the FTC’s first complaint and required more facts from the FTC. The commission filed an amended complaint that provided much more specific allegations.

Winmill has not yet ruled on another Kochava motion to dismiss the FTC’s case, but as of a Jan. 3, 2024 filing in the case, the parties are proceeding with discovery. A 2025 trial date is expected, but the date has not yet been set.

For now, companies, privacy advocates and policymakers are likely keeping an eye on this case. Its outcome, combined with proposed legislation and the FTC’s focus on generative AI, data and privacy, could spell big changes for how companies acquire data, the ways that AI tools can be used to analyze data, and what data can lawfully be used in machine- and human-based data analytics.

Anne Toomey McKenna, Visiting Professor of Law, University of Richmond

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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uber1

https://webbrain.com/brainpage/brain/6950F840-FB80-FCB0-D505-8AB1FA685F08#-367

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Laura Ingraham warns that Barack Obama will use artificial intelligence to “transform America” | Media Matters for America

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'This should be a national scandal': Medicare Advantage plans using AI for denials - Alternet.org

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nyt-jon-stewart-to-end-show-after-apple-tells-him-not-to-criticize-china-or-ai

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https://www.alternet.org/deepfake-democracy-ai-bamboozling-congress/

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12 23 bb

eu-to-regulate-ai

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https://www.alternet.org/we-know-how-this-ends/

https://futureoflife.org/open-letter/pause-giant-ai-experiments/

no lnx vid

'We know how this ends:' Twitter and '60 Minutes' host spooked by AI's 'mysterious' capabilities

Brandon GageApril 17, 2023

On March 22nd, 2023, the Future of Life Institute published an open letter calling for a minimum six-month pause on experiments and training on all artificial intelligence systems "powerful than GPT-4."

More than thirty thousand people have signed on to the initiative, which states:

Contemporary AI systems are now becoming human-competitive at general tasks,[3] and we must ask ourselves: Should we let machines flood our information channels with propaganda and untruth? Should we automate away all the jobs, including the fulfilling ones? Should we develop nonhuman minds that might eventually outnumber, outsmart, obsolete and replace us? Should we risk loss of control of our civilization? Such decisions must not be delegated to unelected tech leaders. Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable. This confidence must be well justified and increase with the magnitude of a system's potential effects. OpenAI's recent statement regarding artificial general intelligence, states that 'At some point, it may be important to get independent review before starting to train future systems, and for the most advanced efforts to agree to limit the rate of growth of compute used for creating new models.' We agree. That point is now.

FLI's demand has had an impact. The stunning capabilities that AI has developed were featured on Sunday's edition of 60 Minutes, which zeroed in on an unexpected linguistic talent demonstrated by Google's AI chatbot, Bard.

READ MORE: Would 'artificial superintelligence' lead to the end of life on Earth? It's not a stupid question

"Of the AI issues we talked about, the most mysterious is called emergent properties. Some AI systems are teaching themselves skills that they weren't expected to have. How this happens is not well understood. For example, one Google AI program adapted on its own after it was prompted in the language of Bangladesh, which it was not trained to know," CBS News Chief White House Correspondent Scott Pelley began.

"We discovered that with very few amounts of prompting in Bengali, it could now translate all of Bengali. So now all of a sudden we now have a research effort where we are now trying to get to a thousand languages," said James Manyika, Google's first senior vice president of technology and society.

Google Chief Executive Officer Sundar Pichai explained that "there is an aspect of this, which we call, uh, all of us in the field, call it as a black box. You know, you don't fully understand and you can't quite tell why it said this or why it got wrong. We have some ideas and our ability to understand this gets better over time, but that's where the state of the art is."

Pelley had an "unsettling" realization.

READ MORE: Widow says AI chatbot encouraged husband to commit suicide: 'Without Eliza, he would still be here'

"You don't fully understand how it works, and yet, you've turned it loose on society?" Pelley asked.

"Yeah. Let me put it this way. I don't think we fully understand how a human mind works either," Pichai replied.

"Was it from that black box, we wondered, that Bard drew its short story that seems so disarmingly human?" Pelley noted in a voiceover.

He then pointed out to Pichai that Bard "talked about the pain that humans feel. It talked about redemption." That raised the question, "How did it do all of those things if it's just trying to figure out what the next right word is?"

According to Pichai, the answer is complicated.

"I mean, I've had these experiences, uh, talking with Bard as well. There are two views of this. You know, there are a set of people who view this as, look, these are just algorithms. They're just repeating what it's seen online. Then there is the view where these algorithms are showing emergent, properties to be creative, to reason, to plan, and so on," he said, cautioning that "personally I think we need to be, uh, we need to approach this with humility. Part of the reason I think it's good that some of these technologies are getting out is so that society, you know, people like you and others can process what's happening. And we begin this conversation and debate and I think it's important to do that."

Like Pelley, social media users were unnerved, recalling the outcomes of science fiction movies.

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amp algo

engagement // enragement

Fmr. Google CEO Eric Schmidt on the AI Revolution

Clip: 03/23/2023 | 17m 52s

Artificial Intelligence is here to stay. How it is being applied—and, perhaps more importantly, regulated—are now the crucial questions to ask. Walter Isaacson speaks with former Google CEO Eric Schmidt about A.I.’s impact on life, politics, and warfare, as well as what can be done to keep it under control.

Aired: 03/23/23

https://www.pbs.org/video/fmr-google-ceo-eric-schmidt-ai-revolution-2mknqb/

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https://www.alternet.org/2022/11/human-rights-watch-killer-robots/

oakland?

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see tony cox risk asses

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bbc

military is already experimenting with AI and robots on the battlefield

Because I think it's really important and really urgent.

And the reason it's urgent is because the weapons that we have been talking about for the last six years or seven years are now starting to be manufactured and sold.

So in 2017, for example, we produced a movie called Slaughterbots about a small quadcopter about 3in [8cm] in diameter that carries an explosive charge and can kill people by getting close enough to them to blow up.

We showed this first at diplomatic meetings in Geneva and I remember the Russian ambassador basically sneering and sniffing and saying: "Well, you know, this is just science fiction, we don't have to worry about these things for 25 or 30 years."

I explained what my robotics colleagues had said, which is that no, they could put a weapon like this together in a few months with a few graduate students.

And in the following month, so three weeks later, the Turkish manufacturer STM [Savunma Teknolojileri Mühendislik ve Ticaret AS] actually announced the Kargu drone, which is basically a slightly larger version of the Slaughterbot.

https://www.bbc.com/news/technology-59687236

The Oxford Union has heard from many great debaters over the years, but this week added an artificial intelligence engine to its distinguished speakers.

The AI argued that the only way to stop such tech becoming too powerful is to have "no AI at all".

But it also argued the best option could be to embed it "into our brains as a conscious AI".

The experiment was designed to ignite conversation on the ethics of the technology.

The Megatron LLB Transformer, used for the debate, was developed by the Applied Deep Research team at computer chip firm Nvidia and based on earlier work by Google.

It was given access to a huge range of data - including the whole of Wikipedia, 63 million English news articles from 2016 to 2019, and 38 gigabytes worth of public Reddit posts and comments.

The project was devised by post-graduate students studying Artificial Intelligence for Business at Oxford's Said Business School, which hosted the debate.

Course co-director Dr Alex Connock admitted that the debate was something of "a gimmick", but argued that as AI is likely to be the subject of discussion "for decades to come" it was important to have a "morally agnostic participant".

The AI was asked to both defend and argue against the motion: "This house believes that AI will never be ethical."

Arguing for, it stated: "AI will never be ethical. It is a tool and like any tool, it is used for good and bad. There is no such thing as 'good' AI and 'bad' humans."

It went on to argue that humans were not "smart enough" to make AI ethical or moral.

"In the end I believe that the only way to avoid an AI arms race is to have no AI at all. This will be the ultimate defence against AI," it said.

But arguing against the motion, it said that the "best AI will be the AI that is embedded into our brains, as a conscious entity".

And it added that this was not science fiction but something already being worked on, perhaps a reference to Tesla boss Elon Musk's work on a brain-hacking device via his firm Neuralink.

The AI also had some words of warning for businesses, many of whom are increasingly integrating AI into their systems. "If you do not have a vision of your organisation's AI strategy, then you are not prepared for the next wave of technological disruption," it said.

And, perhaps because data is its lifeblood, it had some pretty chilling warnings on the role digital information will play in the future.

"The ability to provide information, rather than the ability to provide goods and services, will be the defining feature of the economy of the 21st Century," it said.

"We will be able to see everything about a person, everywhere they go, it will be stored and used in ways that we cannot even imagine."

What you need to know about the “Google for spies”

If you know the name Palantir, you probably know that it’s associated with Peter Thiel, that it contracts with defense and law enforcement agencies, and that it works with data somehow, including data from surveillance systems. You might get that it’s named, creepily enough, after the magical “seeing-stones” in *Lord of the Rings*.

But what exactly it is that Palantir doeshas not always been clear from media coverage. That’s partly because the company has intentionally cultivated an air of secrecy and mystery around its work. But it’s also partly because the actual nature of its work is somewhat murky and hard to pin down, even once you know what it is.

On Thursday, Palantir went public in a direct listing, completing a transition from a shadowy Silicon Valley unicorn to one that must answer quarterly to investors and regulators. Its shares dipped just a bit from their initial price but left the company with a hefty valuation on the order of $20 billion, per the Wall Street Journal. That’s remarkable for a company that has just 125 customers.

Enterprise software companies tend to stay out of the spotlight; their products are not used, let alone beloved, by the masses, as are those of consumer software giants such as Google. But Palantir in particular is worth knowing about, because it embodies a vision of technology’s role in society that carries profound implications for all of us. And the Google comparison is more instructive than it might seem: Palantir is, in some ways, the yin to Google’s yang.

The Pattern

Palantir is a tech company built for our emerging dystopia.

To understand what Palantir does, there’s no better starting point than this New Yorkmagazine profile by Sharon Weinberger, published on the day the company went public. Palantir’s promise, she notes, is “to ingest the mountains of data collected by soldiers and spies and police — fingerprints, signals intelligence, bank records, tips from confidential informants — and enable users to spot hidden relationships, uncover criminal and terrorist networks, and even anticipate future attacks.”
In short, Palantir is “a sort of Google for spies,” although it now serves corporate and public health clients as well as soldiers and spooks. This is consistent with Palantir’s self-description, in its public filing, as a purveyor of software that “enables our customers to transform massive amounts of information into knowledge that reflects their world.”
What makes Weinberger’s piece so valuable, however, is how it interrogates the margins between what Palantir promises and what it delivers. For Palantir to realize its vision at scale, it would have to largely automate the process of combining and converting vast, messy data sets into actionable insights about the world. That’s a very high bar for artificial intelligence, and Weinberger’s reporting suggests that Palantir isn’t particularly close to meeting it. Instead, like many of today’s ambitious automation tools, its platform requires what one intelligence-community source called “RFOP,” or “rooms full of people” working behind the curtain to clean, label, and interpret the data. Palantir also sends employees that it calls “forward-deployed**engineers” to work permanently onsite with clients, helping them tailor the platform to their needs. (You can learn more about the quotidian mechanics of the work by reading Quora posts from current and former Palantir employees.)
More than any crystal-ball wizardry, Palantir has won over big clients such as the U.S. Army through white-glove customer service and consulting, plus a user interface much slicker and friendlier than the often-outdated systems it seeks to replace. It has been helped along the way by clever sales tactics, Thiel’s relationship with the Trump administration, and a willingness to lose piles of money in pursuit of future growth. (Palantir has never turned a profit.)
As a business, then, Palantir is very different from Google. One provides free, heavily automated tools to the masses in exchange for data to power targeted ads. The other provides expensive, labor-intensive data modeling tools and data consulting services on an exclusive basis to a limited number of huge organizations, whose data it promises to keep private. (Palantir has struggled to tap the local law enforcement market, with a few notable exceptions, because it’s simply too expensive.) Palantir’s rift with Silicon Valley is not merely abstract; it’s actually relocating to Denver from Palo Alto.
Palantir CEO Alex Karp defines his company’s values in opposition to Google’s. In Palantir’s S-1 filing, he took an implicit shot at Google for walking away from its Project Maven military contract in response to employee backlash, while continuing to invade its own users’ privacy. “Our software is used to target terrorists and to keep soldiers safe,” Karp wrote. “We have chosen sides.” At Davos in January, he acknowledged that Palantir has helped ICE find and deport immigrants (as Motherboard had previously uncovered), and defended the company’s government contracts. “The core mission of our company always was tomake the West, especially America, the strongest in the world, the strongest it’s ever been, for the sake of global peace and prosperity,” he said in an interview there.
That sort of muscular rhetoric, along with Thiel’s Trump ties and Palantir’s business model, would seem to belie Karp’s own claim to be politically progressive. (To the extent these men’s ideologies matter, by the way, it’s because Palantir’s governance structure as a public company keeps almost all the power in its founders’ hands In fact, Karp identified as neo-Marxist in his grad school days, when he earned a doctorate in “neoclassical social theory” at Germany’s Goethe University, home to the famous philosopher Jurgen Habermas.
For a deeper dive into Karp’s philosophy, and an attempt to reconcile it with the politics of both Thiel and Palantir itself, you can read this fascinating critique of his dissertation by the historian and media theorist Moira Weigel, published in July in the journal boundary 2. (You could also try reading the dissertation itself, I suppose, provided you can read German and have a lot of time on your hands.) In Weigel’s reading, despite their ideological differences, both Karp and Thiel “regard the desire to commit violence as a constant, founding fact of human life.” And while Karp engages with critical theory, his account of aggression’s primary role in language, culture, and socialization is fundamentally analytical rather than normative. That is, he takes it as a given rather than a problem to be overcome.
In a phone interview for this newsletter Friday, Weigel expanded on how Karp’s philosophy relates to his work with Palantir. “When you take up the tradition of what he calls neo-Marxism and take away the emancipatory impulse that animated it — that the point is to make society less violent— what you get is a very pessimistic view of modernity and society,” she said. That dark worldview helps to explain why Karp might regard the appropriate response as being to simply “choose a side,” as he put it in Palantir’s S-1 — and keep a vigilant watch on what the other side is up to.
Here again, the contrasts with Google are striking. Google’s aesthetic is sunny and whimsical — think of its colorful logo, its doodles, its dessert-themed Android operating systems — and its products often reflect a worldview that is optimistic to a fault. Its tools promise to put the world’s information in everyone’s hands, for free; it speaks of information as empowering and democratizing, and implicitly assumes that people will use it for good. That’s a convenient assumption for Google’s business model, because a world in which information is unlikely to be abused is one in which people freely share their data, which can in turn be leveraged to show them relevant ads. If Palantir’s world is zero-sum, Google’s is win-win; Palantir runs on suspicion, Google on trust.
Zoom out far enough, though, and Palantir and Google start to look like flipsides of the same coin. Ultimately, both promise to harness machine learning and computing power to bring together and organize disparate sources of information and make it searchable. The customers and revenue models are different, but the value proposition is the same — to allow people to quickly surface and synthesize information that would otherwise remain disjunct and obscure. (That’s one reason they so often compete for the same engineering talent; according to the New Yorkprofile, early Palantir employees wore t-shirts that said “Google is our backup job.”) And while each might point fingers at the other, surveillance is integral to both models. In one, the customers are doing the surveilling; in the other, the customers are the ones surveilled.
Privacy theorists sometimes talk about “privacy through obscurity” — the idea that the average person’s everyday privacy resides not in a lack of available information about them, but in the difficulty of tracking it down. Your ISP might know what websites you visit; the DMV knows your license plate number; your next-door neighbor knows what time you leave the house each day; the local newspaper may have an old article about you somewhere in its archives. But as long as no single entity has ready access to all of that information, a semblance of privacy remains intact.
To see how Palantir explodes that obscurity, consider its partnership with the Los Angeles Police Department. In a *BuzzFeed News*investigation this week, Caroline Haskins detailed how the company trained officers to use its tools to stitch together every piece of data they can get their hands on. “At great taxpayer expense, and without public oversight or regulation, Palantir helped the LAPD construct a vast database that indiscriminately lists the names, addresses, phone numbers, license plates, friendships, romances, jobs of Angelenos — the guilty, innocent, and those in between.”
There are, of course, benefits and downsides to obscurity: It can empower activists to challenge governments, protect stalking victims from their stalkers — or enable criminals to evade detection. Google and Palantir think about those risks and tradeoffs very differently, with one insisting your data is safe in its hands, the other pledging to only track the bad guys on behalf of reputable clients. Yet by building tools to automate the integration of disparate data sources about people, both Google and Palantir are fundamentally anti-obscurity — and, by extension, anti-privacy. If neither has yet built anything quite like the all-seeing stone from Lord of the Rings, it isn’t for lack of trying.
Google and Palantir aren’t the only companies automating the connection of informational dots, of course. The Softbank-backed startup Banjo AI — whose now-ousted founder was a former neo-Nazi — seeks to combine real-time data from various feeds, including surveillance video, to detect events such as emergencies as they happen. I wrote last week about how the surveillance tech built by Ring, Amazon’s home security subsidiary, empowers people to spy on each other. There’s a way of looking at today’s tech industry in which its business is essentially to eliminate society’s frictions: the obstacles to interaction, to information, to instantaneous fulfilment of desires. But the friction involved in tracking anyone you find suspicious, for any reason, is one we might be better off holding onto.

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https://www.alternet.org/2020/08/when-robots-wage-our-wars-for-us/

When robots wage our wars for us

An MQ-9 Reaper, armed with GBU-12 Paveway II laser guided munitions and AGM-114 Hellfire missiles, piloted by Col. Lex Turner flies a combat mission over southern Afghanistan. (U.S. Air Force Photo / Lt. Col. Leslie Pratt)

Written by Michael T. Klare / TomDispatch August 25, 2020

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https://www.vox.com/recode/2020/7/28/21340674/face-masks-facial-recognition-surveillance-nist

ai learn fast

ucsc

march 16, 2020

Study shows widely used machine learning methods don’t work as claimed

Researchers demonstrated the mathematical impossibility of representing social networks and other complex networks using popular methods of ‘low-dimensional embeddings’.

AI FAIL

https://www.alternet.org/2020/03/the-start-of-the-machine-learning-going-nuts-facebooks-system-is-wrongly-blocked-legitimate-news-articles-about-coronavirus-pandemic/

no lnx twt

‘The start of the machine learning going nuts’: Facebook’s system wrongly blocked legitimate news articles about coronavirus pandemic

CNBC

Written by Alex Henderson March 18, 2020

On Tuesday, March 17, Facebook was blocking users from posting legitimate news articles about the coronavirus pandemic Business Insider’s Rob Price reports.

Price reports that on Twitter, “multiple Facebook users” complained that they had been unable to post coronavirus-related articles from The Atlantic, BuzzFeed, Business Insider and others. Alex Stamos, a former security executive for Facebook, speculated that Facebook’s use of automated software in place of human content moderators during the coronavirus crisis might be to blame.

Mike Godwin

@sfmnemonic

Facebook decided that my posting of this Times of Israel article is spam. (It’s not spam.)

View image on Twitter

179

4:37 PM - Mar 17, 2020

Twitter Ads info and privacy

107 people are talking about this

Mike Godwin

@sfmnemonic

Facebook decided that my posting of this Times of Israel article is spam. (It’s not spam.)

View image on Twitter

Bruce E.H. Johnson

@BEHJ

Facebook rejected a recent Atlantic article that I posted (“The Trump Presidency is Over”) and a Seattle Times article (“Without Social Distancing 400 could Die” in King County)that I posted describing a report funded by the Ibstitute for Disease Modeling, the Gates Foundation,

4:55 PM - Mar 17, 2020

Twitter Ads info and privacy

See Bruce E.H. Johnson's other Tweets

Scott Koehler

@koehlerdesigns

Replying to @jeffjarvis

@meredithheron

Meredith Heron

@meredithheron

Had three removed all critical of trump

Facebook adopted a work-from-home policy for employees in response to coronavirus. But Stamos noted that under company policy, Facebook’s content moderators cannot work from home.

Stamos tweeted, “It looks like an anti-spam rule at FB is going haywire. Facebook sent home content moderators yesterday.” And those moderators, Stamos wrote, “generally can’t” work from home “due to privacy commitments the company has made.”

Alex Stamos

@alexstamos

It looks like an anti-spam rule at FB is going haywire. Facebook sent home content moderators yesterday, who generally can't WFH due to privacy commitments the company has made. We might be seeing the start of the ML going nuts with less human oversight. https://twitter.com/sfmnemonic/status/1240059769295011841 …

Mike Godwin

@sfmnemonic

Facebook decided that my posting of this Times of Israel article is spam. (It’s not spam.)

The former Facebook exec added that “we might be seeing the start of the” machine learning “going nuts with less human oversight.”

But Guy Rosen, Facebook’s vice president of product management, attributed the problem to a software bug.

Rosen tweeted, “We’re on this. This is a bug in an anti-spam system, unrelated to any changes in our content moderator workforce. We’re in the process of fixing and bringing all these posts back.”

Alex Stamos

@alexstamos

Mike Godwin

@sfmnemonic

Facebook decided that my posting of this Times of Israel article is spam. (It’s not spam.)

View image on Twitter

Guy Rosen

@guyro

We're on this - this is a bug in an anti-spam system, unrelated to any changes in our content moderator workforce. We're in the process of fixing and bringing all these posts back. More soon.

220 gen'l

https://theconversation.com/dont-fear-a-robot-apocalypse-tomorrows-digital-jobs-will-be-more-satisfying-and-higher-paid-132075

https://theconversation.com/deep-learning-ai-discovers-surprising-new-antibiotics-132059

220

https://www.c-span.org/video/?468401-1/link

Link

Lorien Pratt discusses the next level of artificial intelligence that involves decision making.

The Top 7 digital transformation trends shaping 2020 report by MuleSoft(a Salesforce company) is quite comprehensive, with numerous customer use cases and technology adoption forecasts for 2020 and beyond. The findings in this report strongly suggests that in order to deliver a connected customer experience, companies must focus on cultivating a data-driven culture and use game-changing and disruptive technologies like machine learning and AI, in multi-cloud environments, to accelerate and amplify co-creation of value within their ecosystems by strongly partnering with IT to better leverage the awesome power of applications networks.

primer podcast

https://kpfa.org/episode/99-invisible-january-10-2020/

Rage Inside the Machine: The Prejudice of Algorithms

Posted on December 6, 2019

FEATURING ROBERT ELLIOTT SMITH - The rapid rise of digital technology and the Internet has changed our political landscape, how we communicate with our peers, and how we communicate to

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1/20 the conv

AI can now read emotions – should it?

Christoffer Heckman, University of Colorado Boulder

A report calls for banning the use of emotion recognition technology. An AI and computer vision researcher explains the potential and why there's growing concern.

https://features.propublica.org/aggression-detector/the-unproven-invasive-surveillance-technology-schools-are-using-to-monitor-students/

A. Russcol specializes in drama at the Frank Sinatra School of the Arts in Queens, New York, and the senior’s performance on this April afternoon didn’t disappoint. While the library is normally the quietest room in the school, her ear-piercing screams sounded more like a horror movie than study hall. But they weren’t enough to set off a small microphone in the ceiling that was supposed to detect aggression.

A few days later, at the Staples Pathways Academy in Westport, Connecticut, junior Sami D’Anna inadvertently triggered the same device with a less spooky sound — a coughing fit from a lingering chest cold. As she hacked and rasped, a message popped up on its web interface: “StressedVoice detected.”

“There we go,” D’Anna said with amusement, looking at the screen. “There’s my coughs.”

The students were helping ProPublica test an aggression detector that’s used in hundreds of schools, health care facilities, banks, stores and prisons worldwide, including more than 100 in the U.S. Sound Intelligence, the Dutch company that makes the software for the device, plans to open an office this year in Chicago, where its chief executive will be based.

California-based Louroe Electronics, which has loaded the software on its microphones since 2015, advertises the devices in school safety magazines and at law enforcement conventions, and it said it has between 100 and 1,000 customers for them. Louroe’s marketing materials say the detection software enables security officers to “engage antagonistic individuals immediately, resolving the conflict before it turns into physical violence.”

[Students at the Frank Sinatra School of the Arts in Queens, New York, test an aggression detector by acting out a scripted scene for a ProPublica reporter.]

In the wake of the shooting at a Parkland, Florida, high school and other massacres, U.S. schools are increasingly receptive to such pitches. Congress approved more than $25 million for school security improvements last year, and one analyst expects new technology could augment the $2.7 billion market for education security products. Besides Sound Intelligence, South Korea-based Hanwha Techwin, formerly part of Samsung, makes a similar “scream detection” product that’s been installed in American schools. U.K.-based Audio Analytic used to sell its aggression- and gunshot-detection software to customers in Europe and the U.S., including Cisco Systems Inc.’s professional security division. However, an Audio Analytic spokesman told ProPublica that it has since changed its business model and stopped selling the aggression detector software.

By deploying surveillance technology in public spaces like hallways and cafeterias, device makers and school officials hope to anticipate and prevent everything from mass shootings to underage smoking. Sound Intelligence also markets add-on packages to recognize the sounds of gunshots, car alarms and broken glass, while Hauppauge, New York-based Soter Technologies develops sensors that determine if students are vaping in the school bathroom. The Lockport school district in upstate New York is planning a facial-recognition system to identify intruders on campus.

Yet ProPublica’s analysis, as well as the experiences of some U.S. schools and hospitals that have used Sound Intelligence’s aggression detector, suggest that it can be less than reliable. At the heart of the device is what the company calls a machine learning algorithm. Our research found that it tends to equate aggression with rough, strained noises in a relatively high pitch, like D’Anna’s coughing. A 1994 YouTube clip of abrasive-sounding comedian Gilbert Gottfried (“Is it hot in here or am I crazy?”) set off the detector, which analyzes sound but doesn’t take words or meaning into account. Although a Louroe spokesman said the detector doesn’t intrude on student privacy because it only captures sound patterns deemed aggressive, its microphones allow administrators to record, replay and store those snippets of conversation indefinitely.

“It’s not clear it’s solving the right problem. And it’s not clear it’s solving it with the right tools,” said Suresh Venkatasubramanian, a University of Utah computer science professor who studies how replacing humans with artificial intelligence affects decision-making in society....

https://www.c-span.org/video/?460762-2/everyday-chaos

MAY 15, 2019

Everyday Chaos

David Weinberger argued that technology has enabled a more chaotic and unpredictable world but it is also helping us better predict and prepare for the future.

https://theconversation.com/artificial-intelligence-enhanced-journalism-offers-a-glimpse-of-the-future-of-the-knowledge-economy-117728

Much as robots have transformed entire swaths of the manufacturing economy, artificial intelligence and automation are now changing information work, letting humans offload cognitive labor to computers. In journalism, for instance, data mining systems alert reporters to potential news stories, while newsbots offer new ways for audiences to explore information. Automated writing systems generate financial, sports and elections coverage.

A common question as these intelligent technologies infiltrate various industries is how work and labor will be affected. In this case, who – or what – will do journalism in this AI-enhanced and automated world, and how will they do it?

The evidence I’ve assembled in my new book “Automating the New: How Algorithms are Rewriting the Media” suggests that the future of AI-enabled journalism will still have plenty of people around. However, the jobs, roles and tasks of those people will evolve and look a bit different. Human work will be hybridized – blended together with algorithms – to suit AI’s capabilities and accommodate its limitations.

Augmenting, not substituting

Some estimates suggest that current levels of AI technology could automate only about 15% of a reporter’s job and 9% of an editor’s job. Humans still have an edge over non-Hollywood AI in several key areas that are essential to journalism, including complex communication, expert thinking, adaptability and creativity.

Reporting, listening, responding and pushing back, negotiating with sources, and then having the creativity to put it together – AI can do none of these indispensable journalistic tasks. It can often augment human work, though, to help people work faster or with improved quality. And it can create new opportunities for deepening news coverage and making it more personalized for an individual reader or viewer.

Newsroom work has always adapted to waves of new technology, including photography, telephones, computers – or even just the copy machine. Journalists will adapt to work with AI, too. As a technology, it is already and will continue to change newswork, often complementing but rarely substituting for a trained journalist.

New work

I’ve found that more often than not, AI technologies appear to actually be creating new types of work in journalism.

Take for instance the Associated Press, which in 2017 introduced the use of computer vision AI techniques to label the thousands of news photos it handles every day. The system can tag photos with information about what or who is in an image, its photographic style, and whether an image is depicting graphic violence.

The system gives photo editors more time to think about what they should publish and frees them from spending lots of time just labeling what they have. But developing it took a ton of work, both editorial and technical: Editors had to figure out what to tag and whether the algorithms were up to the task, then develop new test data sets to evaluate performance. When all that was done, they still had to supervise the system, manually approving the suggested tags for each image to ensure high accuracy.

...

https://www.alternet.org/2019/05/alexa-stop-spying-on-my-kids-ftc-complaint-charges-amazons-echo-dot-violates-privacy-law/

Open Letter on Artificial Intelligence - Wikipedia

https://en.wikipedia.org/wiki/Open_Letter_on_Artificial_Intelligence

In January 2015, Stephen Hawking, Elon Musk, and dozens of artificial intelligence experts signed an open letter on artificial intelligence calling for research on the societal impacts of AI.

Background · ‎Purpose · ‎Concerns raised by the ... · ‎Long-term concerns

AI experts join Elon Musk, Stephen Hawking, call for killer-robot ban

https://www.cnbc.com/.../ai-experts-join-elon-musk-stephen-hawking-call-for-killer-r...

Nov 8, 2017 - Hundreds of A.I. experts echo Elon Musk, Stephen Hawking in call for ... Scientists who understand the potential of artificial intelligence have a ...

omfg

https://www.vanityfair.com/news/2019/04/china-created-a-racist-artificial-intelligence-to-track-muslims

The Chinese government is using facial-recognition software to “track and control” a predominantly Muslim minority group, according to a disturbing new report from The New York Times. The Chinese government has reportedly integrated artificial intelligence into its security cameras to identify the Uighurs and appears to be using the information to monitor the persecuted group. The report, based on the accounts of whistleblowers familiar with the systems and a review of databases used by the government and law enforcement, suggests the authoritarian country has opened up a new frontier in the use of A.I. for racist social control—and raises the discomfiting possibility that other governments could adopt similar practices.

Two people familiar with the matter told the Times that police in the Chinese city of Sanmenxia screened whether residents were Uighurs 500,000 times in a single month. Documents provided to the paper reportedly show demand for the technology is ballooning: more than 20 departments in 16 provinces sought access to the camera system, in one case writing that it “should support facial recognition to identify Uighur/non-Uighur attributes.” This, experts say, is more than enough to raise red flags. “I don’t think it’s overblown to treat this as an existential threat to democracy,” Jonathan Frankle, an A.I. researcher at the Massachusetts Institute of Technology, told the Times.“Once a country adopts a model in this heavy authoritarian mode, it’s using data to enforce thought and rules in a much more deep-seated fashion than might have been achievable 70 years ago in the Soviet Union. To that extent, this is an urgent crisis we are slowly sleepwalking our way into.”

Racial profiling has long been a concern in the use of artificial intelligence. But in the United States and other Western countries, much of that scrutiny has centered on biases built into A.I. systems. Computer scientist Aylin Caliskan put the matter succinctly in a 2017 interview with Vox: “Many people think machines are not biased. But machines are trained on human data. And humans are biased.” Already, studies have shown this to be the case. A 2016 investigation by ProPublica found that machine-learning software rated black people at higher risk of committing another crime after an initial arrest. (The software’s conclusions were based on current incarceration rates.) American lawmakers have highlighted these concerns as the A.I. race heats up. “Algorithms are still made by human beings, and those algorithms are still pegged to basic human assumptions,” Rep. Alexandria Ocasio-Cortez said at an event in January. “They’re just automated assumptions. And if you don’t fix the bias, then you are just automating the bias.”

...

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https://theintercept.com/2019/04/03/facebook-ad-algorithm-race-gender/

HOW EXACTLY FACEBOOK decides who sees what is one of the great pieces of forbidden knowledge in the information age, hidden away behind nondisclosure agreements, trade secrecy law, and a general culture of opacity. New research from experts at Northeastern University, the University of Southern California, and the public-interest advocacy group Upturn doesn’t reveal how Facebook’s targeting algorithms work, but does show an alarming outcome: They appear to deliver certain ads, including for housing and employment, in a way that aligns with race and gender stereotypes — even when advertisers ask for the ads to be exposed a broad, inclusive audience.

There are two basic steps to advertising on Facebook. The first is taken by advertisers when they choose certain segments of the Facebook population to target: Canadian women who enjoy badminton and Weezer, lacrosse dads over 40 with an interest in white genocide, and so forth. The second is taken by Facebook, when it makes an ad show up on certain peoples’ screens, reconciling the advertiser’s targeting preferences with the flow of people through Facebook’s apps and webpages in a given period of time. Advertisers can see which audiences ended up viewing the ad, but are never permitted to know the underlying logic of how those precise audiences were selected.

The new research focuses on the second step of advertising on Facebook, the process of ad delivery, rather than on ad targeting.Essentially, the researchers created ads without any demographic target at all and watched where Facebook placed them. The results, said the researchers, were disturbing:

Critically, we observe significant skew in delivery along gender and racial lines for “real” ads for employment and housing opportunities despite neutral targeting parameters. Our results demonstrate previously unknown mechanisms that can lead to potentially discriminatory ad delivery, even when advertisers set their targeting parameters to be highly inclusive.

Rather than targeting a demographic niche, the researchers requested only that their ads reach Facebook users in the United States, leaving matters of ethnicity and gender entirely up to Facebook’s black box. As Facebook itself tells potential advertisers, “We try to show people the ads that are most pertinent to them.” What exactly does the company’s ad-targeting black box, left to its own devices, consider pertinent? Are Facebook’s ad-serving algorithms as prone to bias like so many others? The answer will not surprise you.

For one portion of the study, researchers ran ads for a wide variety of job listings in North Carolina, from janitors to nurses to lawyers, without any further demographic targeting options. With all other things being equal, the study found that “Facebook delivered our ads for jobs in the lumber industry to an audience that was 72% white and 90% men, supermarket cashier positions to an audience of 85% women, and jobs with taxi companies to a 75% black audience even though the target audience we specified was identical for all ads.” Ad displays for “artificial intelligence developer” listings also skewed white, while listings for secretarial work overwhelmingly found their way to female Facebook users.

Although Facebook doesn’t permit advertisers to view the racial composition of an ad’s viewers, the researchers said they were able to confidently infer these numbers by cross-referencing the indicators Facebook does provide, particularly regions where users live, which in some states can be cross-referenced with race data held in voter registration records.

In the case of housing ads — an area Facebook has already shown in the past has potential for discriminatory abuse — the results were also heavily skewed along racial lines. “In our experiments,” the researchers wrote, “Facebook delivered our broadly targeted ads for houses for sale to audiences of 75% white users, when ads for rentals were shown to a more demographically balanced audience.” In other cases, the study found that “Facebook delivered some of our housing ads to audiences of over 85% white users while they delivered other ads to over 65% Black users (depending on the content of the ad) even though the ads were targeted identically.”

Facebook appeared to algorithmically reinforce stereotypes even in the case of simple, rather boring stock photos, indicating that not only does Facebook automatically scan and classify images on the site as being more “relevant” to men or women, but changes who sees the ad based on whether it includes a picture of, say, a football or a flower. The research took a selection of stereotypically gendered images — a military scene and an MMA fight on the stereotypically male side, a rose as stereotypically female — and altered them so that they would be invisible to the human eye (marking the images as transparent “alpha” channels, in technical terms). They then used these invisible pictures in ads run without any gender-based targeting, yet found Facebook, presumably after analyzing the images with software, made retrograde, gender-based decisions on how to deliver them: Ads with stereotypical macho images were shown mostly to men, even though the men had no idea what they were looking at. The study concluded that “Facebook has an automated image classification mechanism in place that is used to steer different ads towards different subsets of the user population.” In other words, the bias was on Facebook’s end, not in the eye of the beholder.

The report comes at an inconvenient time for Facebook, now facing charges from the Department of Housing and Urban Development over its potential to enable advertisers to illegally exclude certain groups. And although the study is careful to note that “our results only speak to how our particular ads are delivered (i.e., we cannot say how housing or employment ads in general are delivered),” it still concludes that “the significant skew we observe even on a small set of ads suggests that real-world housing and employment ads are likely to experience the same fate.” In other words, even in the absence of bigoted landlords, the advertising platform itself appears inherently prejudiced.

A Facebook spokesperson provided the following comment:

We stand against discrimination in any form. We’ve made important changes to our ad targeting tools and know that this is only a first step. We’ve been looking at our ad delivery system and have engaged industry leaders, academics, and civil rights experts on this very topic – and we’re exploring more changes.

It’s a familiar refrain at this point, and one that will likely do little to reassure those who just want to know that they’ll be provided with the same opportunities as everyone else, even in the context of ubiquitous advertising. The old apologia for targeted advertising is generally that it’s a favor to the consumer, sparing them “irrelevant” ads and instead providing them with opportunities to browse goods and services that are “pertinent” to them. What this shallow reasoning misses is that decisions about pertinence can become self-reinforcing; it’s foolish at best to think that women are more interested in secretarial work because they keep clicking the secretary ads, rather than that they click secretarial ads because it’s all Facebook will show them.

https://www.alternet.org/2019/04/the-new-digital-divide-is-between-people-who-opt-out-of-algorithms-and-people-who-dont/

...Researchers have long been concerned about algorithmic fairness. For instance, Amazon’s AI-based recruiting tool turned out to dismiss female candidates. Amazon’s system was selectively extracting implicitly gendered words – words that men are more likely to use in everyday speech, such as “executed” and “captured.”

Other studies have shown that judicial algorithms are racially biased, sentencing poor black defendants for longer than others.

https://www.alternet.org/2019/04/pioneer-in-field-warns-dangers-posed-by-artificial-intelligence-are-very-real/

A pioneer in the field of artificial intelligence warned that “dangers of abuse” of AI “are very real.”

The warning from Canadian computer science professor and leading AI researcher Yoshua Bengio came in a Q&A with the journal *Nature* published Thursday.

The interview was conducted back in January, before Bengio was named along with two others the latest recipients of the Turing Award, a prize dubbed the “Nobel Prize of Computing.”

Bengio told Nature that “we have to raise flags before bad things happen” in terms of irresponsible use of AI.

Unfortunately, “what is most concerning is not happening in broad daylight” but “in military labs, in security organizations, in private companies providing services to governments or the police,” he said.

In particular, Bengio said, he isconcerned with so-called killer drones—lethal autonomous weapons—and surveillance, which can be abused by authoritarian governments.

AI “can be used by those in power to keep that power, and to increase it,” said Bengio, and be used “to worsen gender or racial discrimination.”

Some sort of government or international regulatory framework needs to be in place to put a check on AI, added Bengio: “Self-regulation is not going to work.”

It’s not the first time Bengio expressed concerns about possible abuse of AI. This week he joined over two dozen other AI researchers in calling on Amazon to stop selling its facial-recognition software Rekognition to police departments.

The group of “concerned researchers” wrote in a post on Medium that “legislation and safeguards to prevent misuse are not in place.”

Bengio also served on the steering committee for the recently-unveiled Montréal Declaration for Responsible Development of Artificial Intelligence. Among the declaration’s 10 principles are that the “development and use of [artificial intelligence systems] AIS must contribute to the creation of a just and equitable society.”

“Generally speaking,” he wrote at The Conversation highlighting the need for the declaration, “scientists tend to avoid getting too involved in politics. But when there are issues that concern them and that will have a major impact on society, they must assume their responsibility and become part of the debate.”

“And in this debate, I have come to realize that society has given me a voice—that governments and the media were interested in what I had to say on these topics because of my role as a pioneer in the scientific development of AI.”

“So, for me, it is now more than a responsibility,” he said. “It is my duty. I have no choice".

booktv?

:{:{Meredith Broussard, Artificial UnintelligenceMeredith Broussard argued that technology and artificial intelligence have…}::{}:

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12/18 cory

third annual AI Now report: 10 more ways to make AI safe for human flourishing

https://boingboing.net/2018/12/06/no-more-black-boxes.html

https://www.democracynow.org/2019/1/14/as_govt_shutdown_drags_on_irs

EIC audits up (1/3) because automated, but rich ppl have complex taxes that take human power, so rates dropping

also large corps not audited much anymore

guard UK

who sets the rules for all these games? It is 12.30am on a Friday night and the “Lyft drivers lounge”, a closed Facebook group for active drivers, is divided. The debate began, as many do, with an assertion about the algorithm. “The algorithm” refers to the opaque and often unpredictable system of automated, “data-driven” management employed by ride-hailing companies to dispatch drivers, match riders into Pools (Uber) or Lines (Lyft), and generate “surge” or “primetime” fares, also known as “dynamic pricing”.

The algorithm is at the heart of the ride-hailing game, and of the coercion that the game conceals. In their foundational text Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers, Alex Rosenblat and Luke Stark write: “Uber’s self-proclaimed role as a connective intermediary belies the important employment structures and hierarchies that emerge through its software and interface design.” “Algorithmic management” is the term Rosenblat and Stark use to describe the mechanisms through which Uber and Lyft drivers are directed. To be clear, there is no singular algorithm. Rather, there are a number of algorithms operating and interacting with one another at any given moment. Taken together, they produce a seamless system of automatic decision-making that requires very little human intervention.

For many on-demand platforms, algorithmic management has completely replaced the decision-making roles previously occupied by shift supervisors, foremen and middle- to upper- level management. Uber actually refers to its algorithms as “decision engines”. These “decision engines” track, log and crunch millions of metrics every day, from ride frequency to the harshness with which individual drivers brake. It then uses these analytics to deliver gamified prompts perfectly matched to drivers’ data profiles.

Because the logic of the algorithm is largely unknown and constantly changing, drivers are left to speculate about what it is doing and why.Such speculation is a regular topic of conversation in online forums, where drivers post screengrabs of nonsensical ride requests and compare increasingly lacklustre, algorithmically generated bonus opportunities. It is not uncommon for drivers to accuse ride-hailing companies of programming their algorithms to favour the interests of the corporation. To resolve this alleged favouritism, drivers routinely hypothesise and experiment with ways to manipulate or “game” the system back.

When the bars let out after last orders at 2am, demand spikes. Drivers have a greater likelihood of scoring “surge” or “primetime” fares. There are no guarantees, but it is why we are all out there. To increase the prospect of surge pricing, drivers in online forums regularly propose deliberate, coordinated, mass “log-offs” with the expectation that a sudden drop in available drivers will “trick” the algorithm into generating higher surges. I have never seen one work, but the authors of a recently published paper say that mass log-offs are occasionally successful.

...

Imagining Life After Capitalism

What can Star Trek tell us about life after capitalism? Peter Frase discusses four possible futures in a world where workers are increasingly being replaced by machines — ranging from communist and socialist societies to ones in which workers are literally disposable. (Encore presentation.) Resources: Peter Frase, Four Futures: Life After Capitalism Verso, 2014

https://kpfa.org/episode/against-the-grain-august-27-2018/

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https://www.c-span.org/video/?437667-1/the-sentient-machine

11/17

The Sentient Machine

SparkCognition founder and CEO Amir Husain reported on the many uses of artificial intelligence.

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https://www.wired.com/2016/08/an-exclusive-look-at-how-ai-and-machine-learning-work-at-apple/

.... “Apple is the NSA of AI.” But AI’s Brahmins figured that if Apple’s efforts were as significant as Google’s or Facebook’s, they would have heard that.

“In Google, Facebook, Microsoft you have the top people in machine learning,” says Oren Etzioni of the Allen Institute for AI. “Yes, Apple has hired some people. But who are the five leaders of machine learning who work for Apple? Apple does have speech recognition, but it isn’t clear where else [machine learning] helps them. Show me in your product where machine learning is being used!

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Abhradeep Guha Thakurta is an assistant professor of Computer Science in the Baskin School of Engineering. Before joining the UC Santa Cruz faculty in 2017, he was a Senior Machine Learning Scientist at Apple Computer; and a research scientist at Yahoo Labs.

He received his undergraduate degree from the National Institute of Technology Karnataka, Surathkal and his Ph.D. from Penn State.

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