From Artificial General Intelligence (AGI) to Artificial Super Intelligence (ASI)

Google's From AGI to ASI Paper

On AGI/SGI/ASI

from Google AI:

AGI (Artificial General Intelligence) and SGI (Specialized Generative Intelligence or Superhuman General Intelligence) represent the next major evolutionary leaps in artificial intelligence, transitioning from narrow, single-task systems to highly capable, adaptable cognitive tools. [1, 2, 3]

Breaking Down the Concepts

• AGI (Artificial General Intelligence): Refers to a hypothetical AI system that possesses the cognitive ability to learn, understand, and perform any intellectual task that a human being can across a wide variety of domains. [1, 2]
• SGI (Specialized Generative Intelligence): A classification for AI models that are domain-specific but retain advanced generative and generalist abilities. Alternatively, some researchers refer to SGI as Symbiotic General Intelligence, highlighting recursive human-AI collaboration. [1, 2, 3]

Current Industry Trajectory

• Where we are: Today's models operate primarily in the SGI space—highly specialized yet possessing robust general knowledge. True AGI remains a major milestone that researchers are actively pursuing. [1, 2, 3, 4]
• The "Tipping Point": Reaching AGI requires balancing general knowledge with deep, reliable specialty in areas like complex math and iterative programming. [1, 2]

The Ultimate Goal: ASI

Beyond AGI lies ASI (Artificial Superintelligence). While AGI aims to simply match human capabilities, ASI refers to theoretical machines that exceed human intelligence across every conceivable domain, from creativity to problem-solving and social skills. [1, 2, 3, 4, 5]

Are you looking to explore how these models differ in processing data, evaluating hardware constraints, or understanding the safety and ethical standards surrounding their development? Let me know which angle you'd like to dive into!

R.I.P. ER=EPR

When the Panopticon's Central Control Tower Wants You to Know That They're Watching You

...so that you'll curb your curiosity and self-Police yourself

Pie's Rant Against 'the Accursed Share' (Part Maudite)

Productive Capitalism's Re-Capture of Elon Musk's Votive Offering to the Gods of Science

Black Hole Photon Rings and Gravitational Ringdown

General Relativity Magneto HydroDynamics (GRMHD)

from Google AI:

GRMHD (General Relativistic Magnetohydrodynamics) is the theoretical framework used to model the behavior of electrically conducting fluids (plasmas) in extreme gravitational fields. It merges General Relativity (how gravity affects space and time) with Magnetohydrodynamics (how magnetic fields affect moving, charged fluids). [1, 2, 3, 4, 5]

GRMHD is computationally intensive and relies on highly specialized numerical codes to solve complex systems of partial differential equations. [1, 2]

Core Physics at Play

• General Relativity: Accounts for extreme spacetime curvature (such as around black holes) using the Einstein Field Equations. It calculates effects like gravitational time dilation, light bending, and frame-dragging.
• Electrodynamics: Incorporates Maxwell's equations to track magnetic and electric fields within the plasma.
• Fluid Dynamics: Employs relativistic hydrodynamics to track the conservation of mass, momentum, and energy of the moving gas. [1, 2, 3, 4, 5]

Where It's Used

Astrophysicists rely on GRMHD simulations to model the most extreme environments in the Universe: [1, 2]

• Black Hole Accretion Disks: Modeling how gas spirals into black holes (like Sagittarius A* or M87), which helps interpret direct images from the Event Horizon Telescope.
• Relativistic Jets: Studying how magnetic fields launch and collimate ultra-fast streams of plasma blasting away from compact objects.
• Neutron Stars: Simulating the mergers of binary neutron stars and the resulting electromagnetic emissions (multi-messenger astronomy). [1, 2, 3, 4, 5, 6, 7]

Key Mechanisms and Effects

• Magnetorotational Instability (MRI): Drives turbulence in accretion disks. This turbulence acts as an effective viscosity, allowing gas to lose angular momentum and fall into the black hole. [1, 2]
• Blandford-Znajek Mechanism: A process where the rotational energy of a spinning black hole is extracted by magnetic fields, powering massive relativistic jets. [1, 2]
• Magnetic Arrest (MAD): A state where magnetic fields in the accretion disk become so strong that they halt the smooth flow of gas, funneling matter into the black hole at specific rates. [1, 2, 3]

To explore how these physics are computed, check out public computational infrastructure projects like the Einstein Toolkit or the astrophysical library.

Jeff Bezos Hand's Caught in the "Cookie" Jar? Customer Spots the Tip of the Technofeudal Iceberg.

David Manney, "The Washington Post’s Credibility Crisis Hits the Checkout Page"

Chelsea Bink thought she was buying a subscription. The lawsuit says she was also feeding a pricing machine. From the Independent:

A Washington Post reader has sued the Jeff Bezos-owned newspaper, accusing it of spying on its own subscribers to jack up their subscription prices.

Chelsea Blink’s class action complaint alleges that The Post began "covertly harvesting" data from its subscribers' phones, computers and tablets after the billionaire Amazon founder bought it for $250 million in 2013.

The Post then aggregated and analyzed the "deeply personal information" to "weaponize" it and maximize profits, according to the 28-page lawsuit filed in Superior Court in Washington, D.C.

"The more loyal a reader became, the more data The Post could gather to estimate how much more that person might tolerate paying at renewal," the court filing says. "Rather than rewarding loyalty, The Post’s system converted Subscribers’ engagement into leverage against them. Longtime Subscribers would end up paying more than new customers simply because the company knew more about them."

Blink's lawsuit, first reported by Mediaite, accuses The Post of violating local consumer protection law through its alleged "unfair and deceptive acts."

Blink, a Washington D.C. subscriber, is the named plaintiff in a class-action complaint accusing The Washington Post of using personal data to set renewal prices through “surveillance pricing.”

For a newspaper that sells trust, the allegation cuts deeper than billing.

The complaint, filed in Superior Court of the District of Columbia, seeks class-action status for current and former subscribers, claiming the Post used reader behavior, engagement, and personal information to estimate how much subscribers would tolerate paying at renewal. From Courthouse News:

The proposed class of readers argue in the suit The Post turned its audience’s reading habits into a “pricing profile” in 2024 to offer different prices to subscribers based on the demographics and their activities, like reading the morning headlines, checking an election update or following a favorite columnist.

“The Post has been monitoring usage and implementing this pricing practice, often referred to as ‘surveillance pricing’ since at least December 2024, at which point not a single subscriber was aware of The Post’s surveillance pricing or secret harvesting of subscriber data,” the readers wrote.

“The law does not allow this conduct. State attorneys general across the country along with the Federal Trade Commission have begun investigating companies that engage in ‘surveillance pricing’ (also referred to as ‘algorithmic pricing’) using consumer personal information instead of market forces to set individualized prices,” they added.

The proposed class is led by Chelsea Blink, a subscriber to billionaire Jeff Bezos’ Post who says she would have unsubscribed had she known her activity and data were being tracked for pricing purposes.

According to the readers, The Post had to disclose the surveillance policy in when New York required companies that set prices using algorithms based on consumer personal data to do so. That law took effect in late 2025, but the Post only made the disclosure in March 2026 via a renewal email to subscribers.

Loyalty became leverage.

The lawsuit says readers expected their data to be used for account service, analytics, or advertising. It claims they didn't knowingly consent to having that data used to raise subscription prices. If the complaint is right, the insult isn't only the price; it's the quiet calculation behind it.

The paper already faces business trouble, layoffs, subscriber anger, and an identity crisis. A lawsuit accusing it of covert pricing practices adds another crack.

Ryan Clarkson, founder and managing partner of Clarkson Law Firm, is representing the subscribers. Tim Giordano, a partner at the firm, has said potential damages could reach into the millions or even billions if the claims survive and the class is certified. Courts decide the facts, but readers can already understand the breach.

Surveillance pricing has already reached Congress. House Oversight Committee Chairman James Comer (R-Ky.) opened an investigation in March into AI and consumer data being used to set prices. The concern is simple: personal data can become a weapon against the customer who supplied it.

News organizations ask for a special kind of trust; they want readers to believe their judgment, accept their corrections, defend their independence, and pay for their work.

If a paper treats those same readers as data profiles priced by pain tolerance, it burns more than goodwill.

It's only fair that The Washington Post gets its day in court. A lawsuit doesn't prove guilt. Still, the allegation lands hard because it fits a larger frustration with elite institutions that preach transparency while building systems ordinary people can't see or challenge.

Readers are tired of being lectured by institutions that ask for trust and then act shocked when trust runs thin. A subscription should be a clean bargain: here is the product, here is the price, and here is how your data will be used.

If The Washington Post wanted a reminder that credibility starts at home, it just got one at the checkout page.

What's it Cost?  How Much Is the Data We've Collected on You and Analyzed, Worth?? 

Disclosure Day - Propaganda in the Waning Days of the Post WWII Open Society Consensus

Coming to Theaters Tomorrow!

Any Hype is Good Hype!

Another Hail Mary from the Hollywood CAA CIA Affiliates

Aliens (& Immigrants) GOOD!

Friedrich Georg Junger: The Perfection of Technology (1939+)

from Google AI:

Friedrich Georg Jünger’s The Perfection of Technology (originally Die Perfektion der Technik, 1946) is a seminal critique of the modern technological mindset. It arguues that the relentless pursuit of technical efficiency creates mass alienation, depletes natural resources, and replaces genuine human purpose with a destructive cycle of endless rationalization. [1, 2]

Jünger’s core arguments revolve around several foundational themes:

• The Illusion of Progress: Technical advancements promise wealth, freedom, and leisure, but actually deliver the exact opposite. Instead of reducing toil, technology replaces manual labor with "organizational" labor, creating mass moral and physical poverty. [1, 2]
• The Bureaucratic Trap: As technology advances, it requires vast administrative and bureaucratic structures to manage it. These organizations feed on resource extraction and human effort, prioritizing ruthless efficiency over human well-being. [1]
• The Pillage of the Earth: Technology views nature purely as a resource to be exploited, mined, and leveled. Jünger was one of the earliest critics to highlight the devastating environmental and planetary consequences of industrial "perfection". [1, 2, 3]
• The Devaluation of Human Life: The technician's mindset reduces human beings to mere cogs in an industrial machine. Technical perfection, which strives to make everything calculable and efficient, is fundamentally irreconcilable with true human perfection. [1, 2, 3]

Read more about the author's critical legacy on Wikipedia's The Failure of Technology page or explore an overview of Western mechanistic titanism on The Imaginative Conservative. [1, 2]

Prometheus

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The Technological Gaze Sees Everything in Utilitarian Terms

from Google AI:

The capitalist gaze evaluates the world based on exchange value, viewing people, time, and creations strictly as commodities to maximize profit. In contrast, the engineer gaze focuses on technology, evaluating systems based on physical laws, functional efficiency, and structural problem-solving. [1, 2, 3, 4, 5]

1. Primary Value Focus

• Capitalist Gaze: Sees things in terms of M-C-M' (Money → Commodity → More Money). The goal is to generate financial returns.
• Engineer Gaze: Sees things in terms of input, transformation, and output (Energy → Work → Function). The goal is optimization and utility. [1, 2, 3, 4]

2. Relationship to the "Object"

• Capitalist Gaze: Looks at what something can be sold for or how cheaply it can be produced. This often leads to planned obsolescence or the reduction of human labor into interchangeable resources. [1, 2]
• Engineer Gaze: Looks at how a thing works. Engineers care about the physical durability, technical specifications, and internal logic of a system. [1, 2, 3, 4, 5]

3. Response to Constraints

• Capitalist Gaze: Treats natural resources and human well-being as external, flexible limits to be bypassed for cost reduction.
• Engineer Gaze: Treats natural and physical laws as rigid boundaries to be respected. The engineer solves for material constraints (e.g., thermal limits, weight loads). [1, 2, 3]

4. Approach to Innovation

• Capitalist Gaze: Innovates to capture market share and create new consumer desires. Technology is a means of scaling production and capturing value. [1, 2, 3]
• Engineer Gaze: Innovates to solve physical or systemic problems. Technology is an end in itself—a tool built to reshape material reality more effectively