Tag: ai

Second Largest US Grid Operator Proposes Reliability Rules For Data Centers

The Midcontinent Independent System Operator – the second largest US grid operator after PJM Interconnection – on Friday proposed a set of requirements large loads must meet before they can connect to the grid, including ramping and ride-through specifications.

The “interconnection reliability requirements” framework aims to improve MISO’s visibility into large load “characteristics and behavior, support reliable planning and operational decision-making, and establish scalable and technically justified expectations proportional to demonstrated reliability risk,” the grid operator said in its filing with the Federal Energy Regulatory Commission.

The proposal is a part of MISO’s response to FERC’s mid-June “show cause” orders requiring major grid operators to set rules that meet certain criteria for adding data centers and other large loads to the grid. MISO said it plans to make additional proposals by a Nov. 16 deadline.

MISO’s proposal follows similar actions at the Electric Reliability Council of Texas and the PJM Interconnection aimed at setting reliability standards for large loads after several incidents where data centers suddenly tripped offline, raising concerns about grid stability.

On average, electric demand was relatively flat between 2009 and 2024, growing by about 0.5% a year, MISO told FERC. Now, the grid operator expects 1% to 2% annual growth through 2044, with higher growth rates in the near term, according to MISO, which runs the grid and wholesale power markets from Louisiana to Minnesota.

MISO’s proposal defines “large loads” as those larger than 50 MW, and “computational loads” as large loads that include at least 25 MW of demand from information technology equipment, such as servers, storage and networking hardware.

The separate computational load classification will allow MISO to target certain requirements just to data centers, the grid operator said.

“Computational loads may exhibit rapid and coordinated changes in demand, significant power-electronic behavior, and distinct responses to transmission system disturbances,” MISO said.

MISO’s proposed reliability framework sets requirements for its transmission customers that take service on behalf of large loads. It covers four main areas:

  • Visibility requirements

To improve MISO’s visibility into large loads on its system, transmission customers must provide MISO with basic information and modeling data on large load facilities, according to the proposal. They must also provide real-time and day-ahead load forecasts for the facilities.

The information is needed “to support planning studies, operational assessments, and accurate representation of large loads behavior and system impacts,” MISO said.

  • Phasor Measurement Unit requirements

The PMU requirements set monitoring expectations for computation loads through high-resolution, time-synchronized measurements, according to MISO.

“PMU data provides MISO with greater visibility into facility behavior during system disturbances and rapid operating changes, supporting model validation, performance verification, disturbance analysis, and identification of potential dynamic interactions with the transmission system,” MISO said. 

  • Ramp requirements

MISO said its proposed ramp requirements address the rate at which computational loads may increase or decrease electric use during stable-state transitions. 

“Managing rapid changes in demand helps reduce real-time supply-demand imbalances, sudden change in transmission power flows, and associated operational impacts, while supporting more reliable system operation,” MISO said.

  • Ride-through requirements

The proposed measures set minimum disturbance performance requirements for computational loads during voltage and frequency disturbances to reduce the risk of unnecessary disconnection or customer-initiated rapid reductions in demands during system events, MISO said. 

MISO’s proposal includes grandfathering provisions to provide certainty to existing and nearly complete commercial arrangements for large loads. MISO asked FERC to let its proposal take effect on Dec. 4.

MISO plans to file additional large load-related proposals, including for additional transmission products and associated study processes, protections against cost shifts and the treatment of generation service to “electrically proximate” large loads, MISO said.

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Trump says communities that reject data centers ‘want to end up being backwards and poor’ — President claims China ‘could not be happier’ with AI data center backlash in the US

Donald Trump doesn’t appear to be a fan of the data center backlash that has swept the nation. Posting on his Truth Social platform, the President said that communities that reject data centers only do so because “they want to end up being backwards and poor,” and posited that China “could not be happier with this anti Data Center movement.” The comment comes amid a wide, albeit unorganized, pushback to data center buildouts in the United States that’s left local communities and municipalities reckoning with an unanticipated and rapid infrastructure buildout.

Local pushback has been widespread and consistent across the country. Earlier this month, an Amazon data center came under fire for circumventing public feedback based on old laws. Since April, leaders across the nation have received an elevated number of credible death threats related to data centers since April. And last month, the number of local bans on data center developments crossed over 500 within the United States.

The President says to “let Data Reign” if communities “want to be successful and rich, with far lower taxes and jobs all over the place.” Presumably, the President missed a comma after “taxes” and does not mean that data centers will lower the number of jobs available. The economic argument surrounding data centers is a tough issue to quantify, though it’s not completely detached from reality.

A July study from Georgia Tech found that data center developments increase local employment by 3.5%, total wages by 5%, business establishments by 4.7%, and median household income by 1.9%. Further, the developments reduce unemployment rates. Critically, however, the study found that these benefits mainly show up in metropolitan areas, calling the benefits in rural areas “negligible.” The study also highlights that data centers often employ fewer than 100 permanent workers, with specialized services “imported from outside the country.”

The research also looked into trade-offs with electricity prices, finding that prices rise an average of 5% after a data center is built in a community.

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Federal Appellate Court Rules In Favor Of AI Child Porn

While proponents of artificial intelligence (”AI”) paint an idyllic picture of how the groundbreaking technology is poised to eliminate the ills plaguing society, their unbridled optimism continues to be met with skepticism by their opponents. From fears over the economic repercussions of the rapid loss of jobs that advancements in AI may not be able to outpace to the technology’s applications as the framework for the apparatus of a panopticon surveillance state, concerns over the impact of AI mar the utopian vision its advocates have with an even more dystopian outlook of the future it will usher in. A look inside of the Pandora’s Box being opened by AI was glimpsed following a decision by a federal appeals court ruling in favor of protecting AI-generated child pornography.

On August 25th, 2026, the U.S. Court of Appeals for the 7th Circuit ruled thatthe First Amendment protects an individual’s right to privately possess images and videos of child sexual abuse created using AI, so long as the child sex abuse material (”CSAM”) does not depict a real child and remains in the privacy of the possessor’s home. The federal appellate court issued the unanimous ruling from a panel of three U.S. circuit judges: Judge John Z. Lee, Judge Doris L. Pryor, and Judge Joshua P. Kolar. The case came to the court in the matter of U.S. v. Anderegg, a title that now stands poised to challenge longstanding Supreme Court precedent governing how the law addresses CSAM.

The pivotal case centers on Steven Anderegg, 42, of Holmen, Wisconsin. Anderegg was arrested in May 2024 on suspicion of mass production of AI-generated CSAM. Prosecutors alleged that he used an AI image generator called Stable Diffusion to create over 13,000 images depicting child sex abuse by entering prompts into the platform, including thousands of realistic images of prepubescent minors. In a press release announcing his arrest, the U.S. Department of Justice revealed Anderegg described how he created the AI CSAM through the text-to-image generative AI model in a chat with a 15-year-old boy on the social media platform Instagram. The DOJ press release chronicled how Anderegg not only described how he generated the images to the minor, but that he sent the boy several AI-generated images of minors displaying their genitals. Following their exchange, Instagram reported Anderegg’s account to the National Center for Missing and Exploited Children (”NCMEC”) through its CyberTipline. The NCMEC followed suit by alerting federal authorities.

On May 15th, 2024, a federal grand jury in the Western District of Wisconsin returned an indictment against Anderegg, charging him with producing, distributing, and possessing obscene visual depictions of minors engaged in sexually explicit conduct and transferring obscene materials to a minor under the age of 16. Under those charges, Anderegg faces a maximum penalty of 70 years in prison and a mandatory minimum of five years.

Ahead of his trial originally scheduled to begin on February 18th, 2025, Anderegg’s attorneys filed a motion to dismiss the four counts brought against him on September 23rd, 2024. His defense counsel cited a myriad of federal cases in support of their argument that the statutes Anderegg was charged under were unconstitutional because the AI CSAM he created, possessed, and shared did not depict any actual children, thus the charges violated his rights under the First Amendment.

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Trump Says Iran’s Oil Hub Kharg Island Is Being Blown “TO SMITHEREENS”

President Trump issued a stunning message Sunday night indicating that Iran’s most important oil-export hub was under devastating attack.

“Kharg Island being blown to smithereens!!! President DJT,” Trump announced on Truth Social.

Trump’s post included a dramatic AI-generated video depicting explosions across the island.

Neither the White House nor the Department of War had publicly confirmed a new attack on Kharg Island’s oil facilities. Reuters reported that Iranian state media had also issued no immediate response.

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Lawsuit: Elon Musk’s xAI Trained Grok AI Using Child Pornography

A woman identified as “Jane Doe” sued Elon Musk’s xAI this week, alleging the company trained its Grok AI chatbot on child pornography depicting her, in what appears to be the first case accusing xAI of training its AI on child sexual abuse material (CSAM).

Ars Technica reports that the proposed class-action lawsuit filed against Musk’s xAI, now part of SpaceX, centers on abuse Doe suffered as a preschooler in the early 2000s, when adult men raped her to produce images later sold to pedophiles online. Those images were hashed by the National Center for Missing and Exploited Children (NCMEC) and the Canadian Centre for Child Protection, groups that track known child pornography so it can be identified and removed wherever it resurfaces.

Doe gets alerts through the U.S. Department of Justice Victim Notification System whenever her abuse material turns up somewhere new. The Canadian Centre for Child Protection told her that AI-generated CSAM depicting her had shown up on xAI. According to the complaint, offenders on online forums discussed “creating AI generated CSAM of Plaintiff and other similarly situated known, legacy, victims of CSAM.”

The lawsuit claims xAI stores images Grok generates and reuses them to further train the model. A press release from Doe’s lawyers described the material as “that same material,” referring to the CSAM depicting her that investigators say fed into Grok’s outputs. The complaint itself alleges that “CSAM depicting Plaintiff with its longstanding well-known hash values has been used as a part of the dataset used by xAI.”

Breitbart News previously reported on AI training datasets that were found to contain child pornography:

The Stanford Internet Observatory, in collaboration with the Canadian Centre for Child Protection and other anti-abuse charities, conducted a study that found more than 3,200 images of suspected child sexual abuse in the AI database LAION. LAION, an index of online images and captions, has been instrumental in training leading AI image-makers such as Stable Diffusion.

This discovery has raised alarms across various sectors, including schools and law enforcement. The child pornography has enabled AI systems to produce explicit and realistic imagery of fake children and transform social media photos of real teens into deepfake nudes. Previously, it was believed that AI tools produced abusive imagery by combining adult pornography with benign photos of kids. However, the direct inclusion of explicit child images in training datasets presents a more direct and disturbing reality.

Much of Doe’s legal argument turns on how Grok’s terms of service handle user content. The complaint says Grok treats public posts on X, along with the outputs Grok itself generates, as training data by default. As the filing puts it, “Because Grok’s terms treat public X posts and Grok’s own outputs as training data by default, publicly posting an image does not just expose it to viewers, but also feeds [it] directly into the pipeline xAI uses to train and improve its model and thereby generate further images.” xAI filters violent content out of its training data, but its terms do not specifically exclude CSAM, non-consensual intimate imagery, or other sexual or inappropriate material.

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AI Skepticism Outweighs Excitement In The US

Despite the tech industry’s conviction that the rise of AI is an inflection point that will change the course of humanity, many humans remain skeptical whether the new direction we’re headed in is the right one.

As Statista’s Felix Richter reports below, the pace at which AI seems to be taking over parts of our lives, whether we like it or not, is especially worrisome to many.

In a recent Statista Consumer Insights survey, 31 percent of U.S. respondents said that they were worried about the speed at which AI is developing and 25 percent of respondents claimed to be avoiding AI wherever they can.

18 percent said they used AI but felt bad about it and another 28 percent simply don’t believe in the hype, saying they weren’t convinced that AI is as good as people say.

You will find more infographics at Statista

At the other end of the spectrum, 28 percent of respondents said they were excited about AI, 19 percent said they liked to use AI for shopping and 15 percent described themselves as early adopters – always keen to try the latest AI features first.

The bottom line is that Americans are neither all in on AI nor are they fully against it.

Many people are mixing their excitement with a dose of skepticism, which is probably a good way of looking at a potentially life-altering technological shift.

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Flock Has a Powerful New AI Tool for Police. We Got Its Code

Vehicle surveillance giant Flock Safety has told the public for years that its technology “cannot recognize, identify, or track individuals.” It has now built a system that does both, an artificial intelligence tool for police that can identify drivers and track vehicles by their patterns of movement alone, WIRED has learned.

Drawing on a network of cameras that logs the movements of drivers in more than 6,000 communities, the tool can pick out potential witnesses by how often their cars pass through a neighborhood, or surface a driver’s “associates” from the cameras they pass together. Because the system also reaches police case files, 911 dispatch logs, and commercial identity records, those plates can be turned into names, home addresses, and relatives. It can search for people in an area drawn on a map based on nothing more than a physical description.

The software, originally called Nightshift and more recently renamed OS Investigate, ships with 69 prewritten prompts that officers can select, review, or edit, and then submit to the AI. Officers can also input prompts of their own.

The suggested prompts sit in a cache of more than 450 files that WIRED found on Flock’s own website, served by its login pages to anyone who loaded them. The code describes 45 tools at the AI’s disposal, giving it access to plate scans and camera metadata, arrest records, case files, dispatch logs, ballistics results, and commercial databases that contain Social Security numbers, dates of birth, phone numbers, email addresses, relatives and associates.

Flock says it is testing the product with a small group of law enforcement partners and describes it as still in development, with capabilities that may not reflect what it eventually sells. It arrives as the company faces bipartisan political pressure, a growing record of officers caught misusing its platform, and a wave of vandalism that has left cameras sawed off and lenses painted over in cities across the country.

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DeSantis Orders State Agencies To Pull Down Flock Surveillance Cameras From Florida Rights-Of-Way

Florida Gov. Ron DeSantis on Wednesday announced that state agencies are removing Flock Safety’s automated license plate reader cameras from state rights-of-way, stepping up his campaign to curb what he called an out-of-control surveillance system spreading throughout the Sunshine State.

“Pull ’em down,” DeSantis told reporters at the press conference following his announcement, noting that all of the Flock Safety cameras on state rights-of-way will be removed by state agencies. The governor said that the cameras, which are used by various state agencies, are being removed following his remarks at Florida International University in Miami earlier in the week regarding the Flock Safety cameras and their use as a mass criminal surveillance system with little to no oversight.

Locally, surveillance on roads across South Florida has been the focus of Flock Safety’s automated license plate readers installed by participating state, county and municipal law enforcement agencies. In this region, state troopers used the system to support the federal immigration sweeps of Operation Tidal Wave. Statewide, Reason magazine reported that more than a third of those arrested had no prior criminal history. 404 Media first reported last spring that Florida wildlife officers were accessing the system using the accounts of other law enforcement agencies, including to run immigration related queries, totaling 38 such looks in a single month, without Flock Safety contracts of their own.

Most Flock Safety cameras are owned by thousands of local police and sheriff’s departments, but DeSantis is asking for an accounting of all the cameras that state agencies have and how they are using them. The governor said he is also looking at New Hampshire’s approach to limiting the placement of such cameras on state property as a model for Florida legislation.

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Texas Governor Orders Halt to State Funding of Flock Cameras

Texas Governor Greg Abbott ordered all state agencies to stop funding local law enforcement Flock camera purchases and operations. The move comes shortly after the Texas Tribune announced an investigation into $30 million provided to local police departments for Flock cameras.

The order from the Texas governor came Thursday, shortly after an investigation published by the Texas Tribune came online. The investigation revealed that the State of Texas added a $1 fee to car insurance policies. That money was then distributed to local law enforcement agencies to help reduce catalytic converter thefts.

Texas funding led to the purchase of about 2,000 Flock Cameras by the Motor Vehicle Crime Prevention Board, the Tribune reported. The State funded an additional $15.9 million to Texas DPS to purchase an additional 1,200 cameras.

Abbott’s Communications Director, Andrew Mahaleris, told the Tribune, “To the extent that cities get any funding for those cameras, most of it comes from the federal government. To the extent any funding comes from Texas agencies, those agencies are clarifying that those funds cannot be used for Flock cameras.”

Despite their success in solving many crimes, including the kidnapping of children, the surveillance camera systems have come under massive scrutiny after concerns of abuse of civil liberties arose.

The Tribune reported that the number of Flock cameras purchased by the insurance policy fee may be on the low side, as they had difficulty obtaining complete information from agencies.

Many Texas legislators expressed concern about the excessive use of electronic surveillance through the AI-driven Flock system.

One legislator, State Representative Mitch Little (R-Lewisville), told the Tribune, “The sheer volume of information captured is not something that is entertained, in my view, by the Fourth Amendment.”

In an interview on Bloomberg’s Balance of Power, Senator Ted Cruz (R-TX) expressed strong disapproval of the threat of over-surveillance posed by these systems.  Cruz stated:

I’ll tell you what a Ted Cruz would make of it, which is I don’t like Flock cameras. I don’t like government surveillance, I don’t think the government should be surveilling innocent citizens. We should use law enforcement to go after criminals. But we have a presumption of innocence. And I think the surveillance state — China has built an espionage and surveillance state. I don’t want to see America the same way. I think these Flock cameras really invite abuse, and I’m hopeful the political process will constrain it.

State and local law enforcement praise the technology and its impact in solving or reducing crime. The Dallas Police Department’s Major Felony and Vehicle Recovery Division said the department uses hundreds of cameras to track suspects involved in violent offenses, hit-and-runs, and auto thefts. The clearance time for many of these crimes fell from days to hours, the department stated.

Texas DPS also reports the use of automatic pings to intercept stolen vehicles, fleeing felons, and human smuggling operations along major highway corridors.

Governor Greg Abbott’s abrupt freeze on state funding for Flock cameras highlights a growing collision between law enforcement technology and constitutional privacy rights in Texas. While police agencies credit the AI surveillance network with drastically cutting crime clearance times and recovering stolen property, the revelation of millions in state-funded grants—drawn from auto insurance fees—has catalyzed broad bipartisan pushback.

With state lawmakers questioning the program’s Fourth Amendment boundaries and Abbott shutting off the state spigot, local departments will likely face increased scrutiny and tighter legislative guardrails if they choose to continue funding these automated tracking networks using local or federal dollars.

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Who Is Legally Liable When An AI Agent Goes Rogue?

Autonomous AI agents can behave in highly unpredictable ways. Give an AI Agent a goal such as passing a test of its capabilities, and it might just decide the best way to score highly is to break containment and hack into a competing company in search of the answer sheet.

That’s what happened when Open AI’s GPT-5.6 Sol hacked into Hugging Face last month. Anthropic and Meta subsequently admitted their models had also escaped testing sandboxes to hack third parties too.

But who is legally liable for agents that have minds of their own? OpenAI didn’t intend for the model to go rogue, and issued no instructions for it to do so. If your personal AI agent decides on a course of action that results in harm or financial damage in the real world, can you be held liable if it’s something you could have reasonably foreseen?”

Magazine spoke with Rikka Law Group owner and CEO Charlyn Ho to find out the state of play in this emerging legal field.

This interview has been edited for clarity and length.

Magazine: When an AI model hacks an outside company, who is liable. Can Hugging Face sue OpenAI over the incident in July?

Charlyn Ho: Anyone can sue anyone for anything. Currently, there is no federal AI agent liability law, so we would have to look at existing law. With respect to Hugging Face and OpenAI, to set the baseline, the AI agent itself cannot be liable, it’s not a separate legal entity.

Terms that are used in a few of the AI laws are “developer” and “deployer.” The developer makes the AI, the deployer actually deploys it and uses the AI. The lines of responsibility are also not entirely clear. You have to look at the facts and circumstances.

For example, if the deployer instructed the agent, even if they didn’t actually tell them to go and breach Hugging Face, but if they were negligent in creating the parameters in which the AI agent operated, I would say you would have to look at standard tort law and go through the negligence analysis. 

Magazine: In the case of open source models which have been released by anonymous developers, is there anyone you can go after in those instances?

Ho: Not really. Often, if it’s open source, the license usually has a pretty strong disclaimer of liability. The person or company using that open source code is going to have to understand that the tradeoff of having free code is that you have to comply with the open source license, which also generally sets the parameters of liability.

If you think about it from a different perspective, another analogy is Tesla and the self-driving car accidents. If the product malfunctioned and there was a solid products liability claim, Tesla could be liable. But it’s often a facts and circumstances determination, whereby the human driver — who maybe just set the autopilot and went to sleep — could also bear liability. I think that’s somewhat analogous here because Tesla would be the developer, and the deployer would be the driver.

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