Tag: ai

AI’s 2026 Influence Pushed Democrats on Voters – Like Far-Left Google Before It

Although many AI models were released in 2023-04, it could be argued that this recent election is the first one where the American electorate was fully aware of AI…..and surrounded by it.

People have been seeking AI’s help for voting decisions. Heavy candidate bias is injected, incorrect instructions are given, races are invented, and even misleading candidate information has been provided.

Not long ago, Dr. Robert Epstein’s election-monitoring research proved Google’s Search bar results were manipulating users on a massive scale to favor Democratic candidates. Those list-based search results were rigged regarding elections, and he proved it.

Now, AI’s human-like answers and authoritative wording are infinitely more convincing than a list of links. AI chatbots give low-information voters a quick answer to a real question. This requires less thinking and less work than using search rankings.

A Pew survey of 5,273 U.S. adults found that 8% were using AI for election information. Among that group, 40% said they sought “suggestions on how to make voting choices.” As expected, people are using AI to help with political and candidate choices.

As the election got closer, more and more people used AI for election answers. In early October, the SSRS Carter Center survey found 11% of adults used AI to learn about House, Senate, and Governor candidates to decide how to vote. By the end of October, this was 25%.

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The Democrats Push To Put Government In Charge Of AI

The Democrats have come out on top in the November 3 US midterm elections, and some of them wasted little time in declaring what they would like to do first: take on the tech industry and put the government in charge of AI development.

The comments started appearing on X within a day of the election, with Rep. Yassamin Ansari posting, “Top of the Democratic agenda on day 1 needs to be a pause on advanced AI development, regulation of this out of control industry, and a ban on artificial superintelligence.”

A member of the House Democratic leadership, Rep. Ted Lieu, also quickly got down to business, announcing that the new Congress would be holding hearings on AI, and that “reasonable guardrails” would be introduced around the tech.

Lieu criticized Republicans for allegedly deciding “to let the AI industry run wild.”

It’s the type of regulation that is the problem, not its quantity. The “guardrails” Lieu and others want to put in place are those that would give the authorities the power to decide who can build and use software, and under what conditions.

That is the same type of “regulation” that is present in the Ban Artificial Superintelligence Act, introduced in the House by Rep. Greg Casar, and in the Senate by Bernie Sanders. The bill is essentially a licensing scheme that would give a newly created cabinet Department of Artificial Intelligence the power to decide which companies can build “advanced” AI.

“Advanced” is defined as AI trained on at least 10^25 operations. Those allowed to build such AI would have to obtain a charter from the department, and then give its secretary access to “systems, staff, and physical premises.”

Flagged systems would be cut off from the internet. Executives who recklessly violate the terms of the bill could end up in prison for up to 20 years, and if a company’s charter is revoked, its assets can be seized.

Reclaim The Net has been tracking this bill and we noted that, “Publishing code and model weights is a form of expression, and this bill puts prior government approval in front of it.”

In the wake of the election, Casar described the results as “the early tremor of a much bigger AI earthquake upending American politics.”

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Guide to Recognizing Fake AI Videos

I find a lot of people seem to think they can glance at an online video and quickly figure out if it’s real or not.

They can’t.

Studies put human detection rates near chance for good video “deepfakes” and AI generated content.

The technology has gotten so advanced that there’s no easy, reliable way to spot every deepfake or AI-generated video by eye alone. Discernment is important because a convincing clip can drive outrage, move markets, or worse. In 2024 there was a case involving a deepfaked video-call and participants made roughly $25 million in financial transfers based on the call. Several years ago on my TV program “Full Measure,” I spoke with experts on national security fears that a good deepfake of a president making a statement could start a war before the video was ever exposed as a fraud.

Even in today’s environment where people are on alert for fakes, videos can feel more “real” than text, so they spread faster and hit harder. Knowing the common tells—and, more importantly, how to check context and source—gives you a better chance of pausing before you share, act, or believe.

Read on for details.

Here’s a practical guide for detecting AI videos.

Length

One of the most consistent patterns I’ve noticed is that many realistic AI videos and deepfakes circulating on social media are short: often 6–10 seconds. Digital forensics expert Hany Farid has pointed out that the vast majority of clips he’s asked to verify fall in that range. The fact is: generating longer, coherent video is still computationally expensive for many tools, and longer clips give the AI more chances to glitch.

Missing context

These clips frequently show only the dramatic moment: the confrontation, the shocking statement, the heartwarming rescue, or the “gotcha.” They skip what led up to it and cut off before the aftermath.

Framing

Short, tightly framed emotional clips that leave you with almost no surrounding information are a strong reason to consider they may be fake.

Editing

Creators also stitch short segments together, which can produce noticeable cuts every few seconds.

Visual and audio clues

We’ve already learned to look for the obvious signs of extra fingers and limbs. I still think it’s weird that AI can’t always figure out humans generally have four fingers and a thumb. Even when it’s instructed to create that kind of a hand, it sometimes cannot.

I’ve been watching Medicare commercials that clearly use AI-generated people and images. The lips don’t perfectly match the sounds coming from them. The voice seems too perfect.

But what about less obvious clues and better deepfakes?

Slow the video (0.25x or 0.5x if possible) and pause on movement. Look for lip-sync and mouth issues. Real lips usually close fully on plosive sounds like “p,” “b,” and “m.” Deepfakes and lip-synced AI often drift by a frame or two, or the mouth interior looks off.

Blinks that are too regular, too rare, or mistimed with speech are another possible sign. So are eyes that look glassy or have mismatched reflections and expressions that don’t quite match the words or the rest of the face.

Watch for warping, smearing, or a soft “halo” around the jaw, hairline, ears, or where the face meets the background—especially when the head turns quickly or something crosses in front of the face such as a hand or a microphone.

Unnaturally smooth or waxy skin compared with the neck or hands can be a sign of deepfakes. So are teeth that look like a solid white block.

Be on the lookout for shadows pointing the wrong way, lighting on the face that doesn’t match the room, objects that float or pass through each other, or garbled text on signs, screens, or clothing. Sometimes background elements morph or behave impossibly.

Voices that sound too clean or flat are suspect. So are missing natural room tone— the sort of natural background sounds in a room— breathing, and pauses. At times you may detect the emotion doesn’t match the delivery.

These clues are more reliable indicators of AI videos when you find several of them together.

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Nearly 1,900 U.S. Schools Are Within 1 Mile of an AI Data Center, Prompting Health Concerns for Children

Researchers warn that air pollution, industrial noise and 24/7 artificial light emitted by AI data centers could threaten children’s health and contribute to learning difficulties. Large data centers can house hundreds of diesel-powered generators, which can release nitrogen oxides, carbon monoxide and fine particulate matter.

Nearly 1,900 public schools in the U.S. are located within a mile of existing or proposed data centers, prompting concerns among researchers, educators and parents about potential health risks stemming from the artificial intelligence (AI) industry’s rapid expansion.

Research highlighted by the National Education Association identified 1,878 public schools within a mile of data centers, including 602 in California and 101 in Virginia.

The findings come as researchers examine the environmental consequences of a booming AI sector whose electricity demands are expected to increase drastically in the coming years.

A 2024 study conducted by the University of California, Riverside, and California Institute of Technology looked at prospective effects of the industry’s growth. Researchers estimated that air pollution associated with AI infrastructure could contribute to as many as 1,300 premature deaths annually in the U.S. by 2030, with public health costs approaching $20 billion.

The projections account for pollution — including fine particulate matter linked to respiratory and cardiovascular diseases — from electricity generation and diesel backup generators.

“If you look at those sustainability reports by tech companies, they only focus on carbon emissions, and some of them include water as well, but there’s absolutely no mention of unhealthful air pollutants. And these pollutants are already creating a public health burden,” said Shaolei Ren, Ph.D., a UC Riverside engineering professor and study co-author.

Although research directly linking data center proximity to specific illnesses among schoolchildren remains limited, studies examining other sources of industrial pollution have documented significant consequences for children’s health and education.

A 2026 study published in the Proceedings of the National Academy of Sciences found that nearly 10,000 U.S. schools, enrolling 3.36 million students, are located within a quarter-mile of known environmental hazard sites. About 44% of schools are within a mile of at least one site.

The study specifically examined Superfund sites, contaminated former industrial properties known as brownfields, and facilities that report toxic chemical releases to the U.S. Environmental Protection Agency (EPA).

“The location of schools relative to these hazards has direct implications for children’s development and learning given the well-documented negative health and academic effects of exposure to pollution,” the researchers wrote.

Researchers cautioned that their findings likely underestimate the number of schools potentially exposed to hazardous pollutants because the analysis excluded several pollution sources.

Diesel generators linked to asthma attacks, reduced lung function in kids

A major concern with the spread of AI data centers is their reliance on diesel-powered backup generators, which can release nitrogen oxides, carbon monoxide and fine particulate matter.

Large facilities can house hundreds of generators intended to maintain operations during power outages. Their emissions depend on factors including generator capacity, operating hours, fuel use and pollution controls.

The EPA has linked pollution from generators to asthma attacks, reduced lung function and impaired lung development in children.

Kids are particularly vulnerable to air pollution because their lungs and other organs are still developing.

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American Voters Overwhelmingly Reject AI Data Centers

People across America rejected data centers during last night’s midterm election. Proposals related to data centers were explicitly on the ballot in more than 40 races across 13 states. People voted against data centers in 38 of them and counting. Six cities voted to ban data centers outright and 12 put limits on the amount of megawatts such buildings could draw. In addition to specific ballot measures, candidates who supported data centers lost across the country.

One of the most striking repudiations of pro data center politicians was in Texas, where Gregg Abbott lost a bid for a fourth term as governor. Abbott had made Texas a data center friendly place. The state has more than 300 working data centers and there are plans to double that number. “Texas is the epicenter of AI development,” Abbott said in 2025 at a press conference with Google CEO Sundar Pichai. “We must ensure that America remains at the forefront of the AI revolution, and Texas is the place where that can happen.” He went on to accept more than $9 million from data center-related donors.

When public sentiment turned against data centers, Abbott attempted to ride shifting political winds by issuing a “pause” on new projects. The pause wasn’t real. Texas regulators continued to approve data center projects after Abbott’s announcement and only shut down the state’s permitting web portal when 404 Media reported it.

Gina Hinojosa, Texas’s Democratic governor-elect, made Abbott’s stance on data centers a central part of her campaign. Abbott’s words about making Texas a data center-friendly AI state played on TVs and the internet over and over again. He lost by more than 700,000 votes.

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Competence and Capability in the Age of AI

Christopher Morales’ 9/26/2026 post, “AI Has Made an Old Education Problem Harder to Ignore,” (written in Minding the Campus, a project of the National Association of Scholars) has touched a nerve with many:

Students can now generate essays, summaries and discussion posts in seconds. The danger is real, but the underlying weakness is older. AI did not create the gap between completing work and mastering knowledge. It made that gap much harder to ignore.

Morales reveals the staggering statistics on the prevalence of remedial courses offered to first-year undergraduates in colleges and universities:

The evidence supports academic under-preparedness was widespread, visible and costly long before AI could write a polished paragraph.

I would suggest that the problem is even more widespread and pernicious and stretches beyond the confines of academia. It is the tension between Competence and Capability in most walks of life, including my own, medicine and healthcare.

To understand the roots of this present-day dynamic, we need to go back a bit…quite a bit…and understand the significant social, political, and economic upheavals in Great Britain in the late 1970s:

At the time, Great Britain was facing serious societal breakdown in a number of areas. Brian Grant explained how a committed group of diverse individuals spontaneously formed around a belief that imbalance in the core activities of education and training fueled much of the socioeconomic disruption in society.

The watchword of the proposed reforms to deal with this disruption was capability. This group’s actions culminated in newspaper declarations taken out in the Times and the Guardian and became known as The Capability Manifesto. This concept had 4 main pillars of change, linked to the following verbs:

  • To do: move from passive memorization of facts for a test to the successful execution of real projects.
  • To make: value craftsmanship, engineering, and art as well as pure academic knowledge.
  • To organize: value teamwork and leadership. The practical value of this was later emphasized in such works as Tribal Leadership by David Logan, John King, and Halee Fischer-Wright.
  • To adapt: understand that problem-solving often has multiple answers and methods of inquiry, depending upon the circumstances.

It is interesting that these concepts (such as the importance of starting point and nonlinear system dynamics) echo those of the nascent Complexity Science being founded at about the same time at The Santa Fe Institute and described in an extremely readable fashion by M. Mitchell Waldrop. The emphasis of verbs rather than nouns in the successful execution of projects is similar to the “Action Learning” concept of Complexity Science described by Stewart Hase and Chris Kenyon.

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California startup says it has injected AI-designed virus into humans for first time

An artificial intelligence research lab in California said it has administered a custom AI-designed virus into a human patient for the first time as a potential scalable solution to counter antibiotic-resistant superbugs.

AI lab Radical Numerics announced the treatment on Tuesday, but it is pending scientific scrutiny in the form of a peer-reviewed study.

If verified, the treatment, named Omnii-1, could mark the first time AI-designed bacteriophages have been administered to a human patient.

These phages are viruses that specifically infect bacteria. Antibiotic-resistant superbugs are projected to cause nearly 40 million deaths by 2050.

Under current phage treatment approaches for bacterial infections, researchers undertake a time-consuming search of vast environmental collections of phages to find one that happens to match a patient’s specific infection.

It starts with clinicians extracting a sample of the bacteria causing a patient’s infection to find or design phages that kill the infectious bug.

Key to this challenge is understanding the genetic features of a potential phage that could kill particular bacterial strains.

Phages kill bacteria through a multi-step process in which the virus first binds to specific receptor molecules on the surface of the target bacteria.

They then proceed to enter the bacterial cell to hijack it for their own replication and break it open.

The initial binding of the virus to bacteria is very specific, as a given phage only targets particular strains or species of bacteria.

This specificity is a crucial advantage of phage therapy, which spares beneficial gut microbes that tend to be affected by broad-spectrum antibiotics.

Radical Numerics claims that their AI-driven approach can lead to rapid, on-demand generation of phages that meet patient requirements, accelerating the timeline for treatment.

“In the world’s first clinical use of AI-designed phages, designs generated by Omnii-1 were administered to a patient with an antibiotic-resistant bacterial infection under an authorised, physician-led compassionate-use pathway,” Radical Numerics wrote in a blog post.

“This milestone marks an early step toward translating AI-guided genome design into personalised therapies,” it said.

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Quo Vadis, AI? Study Suggests AI Use Makes Us Lazier And Less Intelligent

The Oxford word of the year in 2024 was “brain rot,” due to a 230 percent increase in usage of the term between 2023 and 2024. The moniker reflected a collective sense – already on the rise in 2023 – that our heavy use of digital technology has corroded our ability to think.

Now, in the futuristic era of artificial intelligence known as 2026, brain rot has acquired a whole new level of relevance. Now that it’s possible to outsource complex questions or research problems to a tool that summons answers in seconds, seemingly siphoning them out of mid-air, we need to take stock of how reliance on these digital genies blunts our own mental sharpness.

Well, a recent study presented at the Conference on Language Modeling confirms our worst fears about how using AI may be diminishing our own brainpower. The paper – titled “AI Assistance Reduces Persistence and Hurts Independent Performance” and authored by researchers from MIT, Oxford University, Carnegie Mellon, UC Berkeley, and the University of California, Los Angeles – found that spending a mere 10 minutes relying on an AI tool noticeably impairs one’s attention span and persistence on challenging tasks.

“The best collaborators maintain a balance between helping and fostering autonomy; they know when not to help,” the study’s authors wrote.

“Current AI assistants are a stark contrast to this dynamic. They rarely refuse to help, and provide instant answers to almost any query. … In this sense, AI systems are … extraordinarily helpful in the moment, but indifferent to what that help does to the person receiving it over time. In this paper, we investigate the consequences of this misalignment.”

To examine AI’s impact on human cognitive abilities and habits, the researchers conducted a series of experiments. The first one involved dividing 354 participants into two groups tasked with solving 15 fraction problems. The first group had to solve those problems on their own, while the second group could use AI for help. After 12 problems, researchers removed the AI assistance from the second group.

As expected, the AI-boosted group had more accurate answers at first, compared to the group relying on only their own gray matter. But once the AI tool was removed, the group struggled to answer questions accurately.

Similar results arose from an experience with 667 participants. The group that started out with AI assistance would simply give up or provide incorrect answers once that crutch was removed, while the group that hadn’t had AI assistance at any point rode the problems out to the end and ultimately achieved greater success.

Switching to a different kind of problem-solving didn’t make a difference; when researchers ran a third experiment with 201 participants involving SAT reading comprehension questions, the same thing happened. Participants who had come to rely on AI assistance buckled once that support was removed – both their persistence and their accuracy fell.

As the UC Berkeley press release on the study noted: “The findings backed up what anecdotes and surveys have increasingly found in recent years: An overreliance on AI for answers erodes our cognitive fitness. When AI produces answers in seconds, we get used to that speed; anything that takes longer feels inefficient.”

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‘Super Intelligence’ Fraud Ring Impersonated CEOs And Lawyers To Steal $129 Million, Feds Say

Federal prosecutors in Manhattan unsealed an indictment Thursday charging three foreign nationals with using what they call “super intelligence” to mimic the voices and images of corporate chief executives and big-firm lawyers, and so talk employees at companies around the world into wiring about $129 million, according to the Justice Department.

Jacob Soall, 41, of Marbella, Spain, and the United Kingdom, was arrested in Spain on June 17. Sharon Hazan, 54, of Israel, was arrested in Spain on Aug. 5, and Guy Sheetrit, 42, of Israel and the United Arab Emirates, was arrested in the United Kingdom on Sept. 7. Extradition requests for all three are pending. The release also lists aliases: Soall as “Seba Clarke” and “Rocky Balboa,” Hazan as “Superman Cash,” “Impossible Mission,” “Fat Boy” and “Ron.”

The scheme ran from 2024 to 2026, prosecutors say. The crew allegedly opened with a call to an employee in which a faked voice of the company’s CEO asked for help on a confidential financial transaction, then steered the employee to “lawyers at large international law firms” who were supposedly working on the deal. Those lawyers were fakes too. One of them would finally tell the employee to wire a large sum to an account the conspirators controlled.

The release puts the haul at more than $129 million from “Victim Companies all over the United States and the world,” and lists six examples totaling about $31.9 million: $11.6 million from a U.K. financial technology company, $5.2 million from a Florida pet food company, $4.8 million from a Missouri hospital system, $4.6 million from a Massachusetts mutual fund company, $3.0 million from a California hospital system and $2.7 million from a Rhode Island bank.

The money did not stay where it landed. Prosecutors say the victims’ wires went to accounts in the United States, Spain, Portugal, Greece, the United Arab Emirates, Singapore, Austria, Slovakia and China, and the conspirators then converted the cash to cryptocurrency to hide its source and split it among themselves.

Soall, according to the release, mainly played the British lawyer, working from a handwritten script titled “Lawyer Call” that included language to convince victims not to discuss the calls with colleagues. Hazan and Sheetrit are accused of laundering the proceeds. Hazan allegedly boasted that he had been laundering money since 1994, had laundered over $400 million in the past three years and was “number one all over the world” among professional launderers. Hazan was convicted in the Southern District of New York of conspiracy to commit bank fraud in 2008.

Each man faces seven counts: conspiracy to commit wire fraud and wire fraud (20 years each), two counts of aggravated identity theft (two years each), and money laundering conspiracy and two money laundering counts (20 years each). The release notes that Congress sets those maximums and a judge decides any sentence. The charges are allegations, and the defendants are presumed innocent unless proven guilty.

U.S. Attorney Jamie McDonald said the technology “offers tremendous potential,” but that “the public should recognize that there are some who seek to use this emerging technology to pursue criminal goals,” and urged the business community to “consider how to best protect themselves from the emerging threat of SI-enabled crime.” FBI Assistant Director James Barnacle Jr. said criminals “continue to weaponize emerging technologies.” The case is with the office’s Complex Frauds and Cybercrime Unit, and prosecutors thanked Spain’s Policía Nacional and the U.K.’s National Crime Agency for the arrests.

The release does not say how the voices and images were generated, how any victim company’s bank flagged the transfers, or how much of the $129 million has been recovered. It asks anyone who believes they were a victim to use the FBI’s tips page.

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These AI startups want to predict your moves, speak for the dead, ‘take your jobs’

Some of the latest artificial intelligence startups apparently want to work in your place and predict your next moves.

Startups such as 42AI, Lebensstimmen and Orchid have launched AI tools that take over office work, anticipate the probability behind human action and prepare messages to use after you die. As the number of ideas to innovate AI and potentially replace human workers increases, Americans worry about how these products will impact them.

‘Behavior Is Always Probabilistic’

42AI, an Israeli startup that received $62.5 million in funding Monday, said it uncovered the rules that determine human behavior to create its prediction.

“[W]e asked a more fundamental question: does behavior have rules, a kind of physics? We spent three years bringing together neuroscience, psychology, psychiatry and AI to answer it, and the answer is yes,” CEO Amit Gal Or told the Daily Caller News Foundation, when asked how the company’s prediction model works. “Behavior is always probabilistic.”

The company’s Behavioral World Model, part of its two-model “brain with two hemispheres,” studies how a person reacts across many unrelated moments, from signing up for a service to placing and canceling an order, to learn what drives them, Gal Or said.

He clarified that demographic and personal data, like names, were excluded from the model, and adding those details did not assist the model’s effectiveness.

“Long term, we want to be the behavioral layer for future AI,” Gal Or told the DNCF. “As robots and AI agents move into the world, they need to understand people.”

‘Built To Take Your Jobs’

Orchid, a Canadian AI startup, launched a model called Keiki that empowers businesses to train AI agents that tackle every step in work for customers.

“A company connects the tools it already uses, like its calendar, store, payments and inbox. It writes down how the work should be done, and the agent takes it from there,” Nizar Abi Zaher, CEO of Orchid, told the DCNF in a statement likely generated by AI. “The agent talks with customers over text, iMessage, WhatsApp, email and Slack, takes the action, and reports back.”

“That’s what ‘take your jobs’ means to us: the agent takes the work off a person’s plate and gets it done,” Abi Zaher told the DCNF.

The company’s marketing for its personal AI has been blunt.

“Introducing Keiki, the first AI built to take your jobs,” Orchid wrote in an Oct. 8 X post launching its model.

“I’m telling you man, AI is gonna f***ing kill us,” one of the individuals says in the advertisement video.

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