Project Panama ...

The risk with generative AI, as this single instance with Anthropic indicates, is that we cede the means of production of our large language lives: that we turn from creators to consumers.’
Photograph: Dado Ruvić/Reuters

Should we destroy all the books in the world?

An answer to this question can be found in the court documents of Bartz v Anthropic PBC. The northern California district court case, decided in late July this year, highlighted the improbably named “Project Panama”, one of the AI company Anthropic’s efforts to improve its large language model Claude. “What is Project Panama?” court exhibit 21 asks, in an internal memo. The answer: “Project Panama is our effort to destructively scan all the books in the world.” The memo advises discretion: “Why use a codename? … Because we don’t want it to be known that we are working on this.”

Destructive scanning was Anthropic’s solution to a problem: in order to “train” Claude, Anthropic had to procure a large, high-quality language dataset, preferably one created before 2022 and the corrupting influence of generative AI on contemporary text. Claude needed as many language combinations as possible, to improve its ability to predict language outcomes. Anthropic needed data, lots of it, of very high quality. Books, as it happens, remain one of the best sources for complex, high-quality, long-form text. As the court’s decision relates, Anthropic hoped that books’ “well-curated facts, well-organized analyses, and captivating fictional narratives” would help “Claude write as accurately and as compellingly as Authors”.

Anthropic had a choice: it could have secured copyright permission to use existing e-books. This would have required the “legal/practice/business slog”, as Anthropic’s co-founder and CEO phrased it, of managing copyright. Rather than engage with the texts’ owners, Anthropic first chose to use pirated sources instead, a decision informing the company’s $1.5bn out-of-court settlement with authors. When that approach seemed too complicated (or, as the court decision phrases: “Anthropic became ‘not so gung ho about’ training on pirated books ‘for legal reasons’”), Anthropic turned to destructive scanning. As it happened, the judge ruled that using proprietary material to “train” an LLM did not, in and of itself, constitute an infringement of copyright. To the court, it would seem, “training” a corporate product is equivalent to training any human, teaching how to read in order to learn how to write.

Burning books by the Nazis in 1933 was the attempt to erase knowledge from society. Anthropic's destruction of books by different means does the same thing. Like the Nazis, they got away with it.

The Nazi book burnings were a campaign conducted by the German Student Union (German: Deutsche Studentenschaft, DSt) to ceremonially burn books in Nazi Germany and Austria in the 1930s. The books targeted for burning were those viewed as being subversive or as representing ideologies opposed to Nazism. 

Epilogue

But perhaps the most telling aspect of this case is that it was so important to Anthropic to have access to a dataset of complex, long-form, uncorrupted text. Let’s ask ourselves why that text should seem so profitable. One response to Bartz v Anthropic PBC might be to refuse to devalue our lives as readers and writers, to claim ownership of the cultural spaces in which our thought and words are created, shared and preserved. The risk with generative AI, as this single instance with Anthropic indicates, is that we cede the means of production of our large language lives: that we turn from creators to consumers, and hand the generative promise of our work to large language models and their proprietors

I can read your mind ...

Figure 1: Reconstruction of seen images from fMRI using “Brain-IT”. (a) Image reconstructions

using the full NSD dataset (40 hours per subject). (b) Efficient Transfer-learning to new subjects with very little data: Meaningful reconstructions are obtained with only 15 minutes of fMRI recordings. (Results on Subject 1)

I can read your mind applies when AI, properly configured, can pattern recognize
MRI generated data from rubes like me.

• For years, scientists have trained AI to read fMRI scans to accurately reconstruct visual information.
• A new tool, known as Brain-IT, uses a Brain Interaction Transformer (BIT) to decode and encode fMRI scans, and it can reconstruct images with stunning clarity using a relatively small training set.
• This tool could help those with severe paralysis communicate, and give scientists new insight into the inner workings of the brain’s visual processing system.

With a little bit of tinkering, this makes precogs irrelevant.

While debates rage on various podcasts, in hand-wringing online essays, and even in the halls of Congress as to whether AI has surpassed human intelligence—a threshold of no-return known as the AI Singularity—there’s one skill where AI algorithms have long trounced the human competition: pattern recognition.

Because of modern AI engines’ impressive abilities to sift through large datasets and recognize patterns, they can accurately detect developing cancers and even create entirely new viruses not seen in nature. When trained on the brain, this AI pattern recognition can gain almost supernatural powers. This ability is vividly highlighted in a new paper submitted to the International Conference on Learning Representations by scientists from the Weizmann Institute of Science in Israel. The researchers revealed Brain-IT, an AI model capable of reconstructing mental images with a scary level of accuracy—using only brain-scan data.

In closing ...

Decoding fMRI brain scans is a perfect pattern recognition problem for AI to solve, which is why scientists have been toying with the idea for more than a decade.

Click here for the MRI dataset. Click here for the paper.

Any questions?

DLST yet again ...

Donald Trump and Tom Cotton in Washington DC in 2017. Photograph: REX/Shutterstock

The very stable genius is pushing for permanent daylight savings time yet again, ignoring the fiasco in 1974
when kids went to school in total darkness at 8:00 AM. This is just another example of a know nothing
not knowing anything at all.

A blast from the past ...

The move by the senate to make daylight savings time permanent is a testimony to pols not understanding what happened the last time this switch was implemented as the response by the public was unanimous. It sucked. Yours truly was living in NYC at the time and the months in darkness were a total drag without question. Seems this rube was not alone in his sentiment as millions complained and the DLST initiative was quashed. DLST of the standard time variety does work as it aligns with the circadian biorhythms of life on earth so if congress wants to do something actually useful for a change, make standard time the norm and be done with it. As an aside, this reminds one of the fiasco of the US trying to go metric whereby Congress should have said, by such and such a date, metric becomes the law of the land but NOOOOO, the US vacillated and nothing of consequence was ever done.

As for kids, nighttime and going to school applies.

In February 1974, eighth and ninth graders board the bus at seven a.m. in the dark to head for Drake Junior High School in Denver, Colorado. Image: Denver Post / Contributor Denver Post

PS, it's already getting dark.

We are too busy ...

 

We are too busy

Part one.

The plan often takes longer than the solution. Take healthcare. People complain about it endlessly, but does anyone actually ask the basic question: WHY DOESN'T IT WORK? The usual answers revolve around insurance issues, greed, and insane complexity but they never address the core structural flaw.

The reason why healthcare doesn't work is the fact billing's not directly connected to services rendered. Think about Apple. You buy a smartphone, you pay Apple directly. Apple tracks not only the transaction but all the ancillaries—wholesale price, material costs, transportation—in order to ascertain a true net profit of markup vs. costs.
Every industry in the world operates this way, save healthcare.

The Healthcare Model

Insurance separates the bill from the product. It's indirect so the price point solely goes to the insurance company, not the actual cost of the product or service given to the patient.

End result: chaos. Records lost, no ability to run a business because billing is not intrinsically connected to providing care for the patient. Healthcare administration is based on guesses, not facts.

Factoids not addressed

The doctor's note drives the claim, NOT the claim driving the note. The claim is just the money—the amount invented by the insurance company to "pay" for services rendered. The Note begins with the exam, the raw data the doctor needs to ascertain: What is the medical condition of the patient?

The note codifies the condition via strict codes. The note specifies the procedure(s) needed to treat the patient. (i.e., Diagnosis: Ralph has a cold. Procedure 1: Get an X-ray. Procedure 2: Get Amoxicillin.)

Under the current insurance model, the procedure precedes the note. This doesn't compute but it's done in order for insurance to control the process.

The Solution

The Medical Event—the entity beginning with the office visit and ending with the next appointment—must be processed in the context of a claim that mimics the same work flow done by virtually every business in the known world. Payment for services rendered is simply that, whether it's the insurance claim, cash, credit card, seashells, or drachmas. The form of payment does not matter. The reality of the note driving the claim must drive healthcare. 

Just as the healthcare industry chokes because it allows the abstract financial claim to dictate the physical medical note, the tech industry is choking because it allows abstract 2D math to dictate a 3D physical reality. In both cases, the tail wags the dog. The solution for both is a structural funnel: forcing the chaos into a strict, logical standard.

The end of square wheels :)

Part Two: The Biological Compiler and the Geometry of Chaos

When modern medical researchers look at a catastrophic biological failure—whether it be the aggressive spread of pancreatic cancer or the devastating TDP-43 phase separation in ALS—they see infinite, terrifying chaos. They see millions of genetic mutations, billions of protein interactions, and endless chemical noise.

Currently, the quantum computing industry is trying to solve this by feeding that raw, analog chaos into their 2D quantum algorithms, hoping a 3D analog machine can calculate a chemical cure.

It's the ultimate computational bottleneck. Trying to process the unstructured, analog noise of biology using two-dimensional math is exactly like trying to process a doctor’s rambling, handwritten notes directly into an unconnected, rigid financial billing system. The system chokes. The hardware has no idea what to do with the noise.

To bridge this gap, we don't need more brute-force probability. We need a Biological Compiler.

The Structural Funnel

The first step to building this compiler is recognizing that the chaos is an illusion.

Systems biologist Andrea Califano at the Columbia University Irving Medical Center, recently proved a groundbreaking concept: the infinite, chaotic mutations of cancer actually operate within just 112 distinct "master regulator" states. Biology, like physics, seeks the path of least resistance.

In a volumetric computing framework, we must redefine what those 112 states actually are. They are not just abstract chemical profiles. They are 112 rigid, three-dimensional physical structures. Biology is just highly complex physics operating within a volume. A misfolded TDP-43 protein is not just a chemical error; it is a Constructive Solid Geometry primitive that has phase-shifted into a toxic state.

Squaring the Circle of Disease

Before a quantum computer can solve a biological problem, the data must be standardized. We must strip away the narrative noise.

This is where Constructive Solid Geometry (CSG) acts as the universal spatial translator. Just as a database uses strict industry standards to force chaotic medical notes into precise billing codes, we use CSG to force biological chaos into a perfect 3D boundary. We apply Boolean logic—intersections, unions, and subtractions of solid volumes—to map the exact physical structure of the disease.

We don't feed the quantum computer a messy, unstructured DNA sequence. We feed it the pristine 3D geometry of the tumor state. We take the round peg of biology and drop it perfectly into the round hole of the Bloch sphere.

The Inverse Geometry

When you map biology this way, the solution changes entirely. If a master regulator tumor state is simply a specific, distinct geometric shape sitting inside a 3D quantum volume, the cure is no longer pharmacological. It is spatial.

You don't need to chemically poison the biology, and you don't need to guess with brute-force 2D algorithms. You simply ask the native 3D quantum environment to calculate the exact inverse geometry of the entity in question. The quantum machine defines the exact structural counter-punch required to target that specific CSG primitive, shattering the toxic geometry via destructive interference while leaving the healthy cellular structures completely untouched.

We have the 3D hardware. We have the biological targets. We just need to stop speaking to the machine in Flatland and start speaking the native, volumetric language of the universe.

Flatland/rev II

On New Year's Eve, the Square dreams of a visit to a one-dimensional world, "Lineland", inhabited by men, who are lines, while the women are "lustrous points". These points and lines are unable to see the Square as anything
other than a set of points on a line. 

The plot thickens ...

Following this vision, the Square is visited by a sphere. Similar to the "points" in Lineland, he is unable to see the three-dimensional object as anything other than a circle (more precisely, a disk). The Sphere then levitates up and down through Flatland, allowing the Square to see the circle expand and contract between a great circle and small circles. The Sphere then tries further to convince the Square of the third dimension by dimensional analogies (a point becomes a line, a line becomes a square). 

In quantum mechanics and computing, the Bloch sphere is a geometrical representation of the pure state space of a two-level quantum mechanical system (qubit), named after the physicist Felix Bloch.

The Bloch sphere, has an inner reality along with a clearly defined surface, just like the sphere in Constructive Solid Geometry, a primitives based 3D environment created by Philip Mittleman in 1966, twenty years after Felix Bloch conceptualized his Bloch Sphere.

After the war, Bloch concentrated on investigations into nuclear induction and nuclear magnetic resonance, which are the underlying principles of MRI.[10][11][12] In 1946, he proposed the Bloch equations, which determine the time evolution of nuclear magnetization. 

Felix Bloch

The story of MAGI Mathematical Applications Group Incorporated began with the “simple” question,
“What paths would radiation take when a 20 megaton nuke goes off? Hypothetical target? NYC." 

SG objects can be represented by binary trees, where leaves represent primitives, and nodes represent operations. In this figure, the nodes are labeled ∩ for intersection, ∪ for union, and — for difference.

1966: MAGI answered the question by inventing ray tracing to track the radiation paths of the nuke’s blast to the environment but also to apply the same technique to imaging as a nuke is a single energy source like a light bulb and blast and light rays propagate exactly the same but with the difference being light rays nondestructively intersect with objects and react according to the kind of characteristics the objects in question may possess like color, texture, transparency and reflectivity, using the laws of physics to generate an accurate 3D rendering of the scene in question. CSG, with B-Rep (boundary conditions defining volume), provides the accuracy needed to do scientifically accurate ray tracing by generating 3D objects complete with physical characteristics like density, transparency and reflectivity along with the creation of mathematically correct shadows as needs warrant, qualities not possessed by polygonal based modeling. 

Ray tracing

Lastly - The physics ...

1981 (The Computing Paradigm): Richard Feynman delivered his famous lecture, "Simulating Physics with Computers". His core thesis was that classical computers—which operate on flat, binary, linear logic—cannot accurately simulate the physical universe because the universe is not classical. He recognized that quantum systems could potentially solve problems exponentially faster than classical computers, establishing the theoretical motivation for developing quantum computing technology. He realized you cannot use 2D math to calculate a 3D physical reality; you have to use the physical reality itself.

The problem ... As seen by the Bloch Sphere, Quantum Computers are 3D analog systems so ... why do researchers continue to work with 3D environments with 2D logic? A Square, in Flatland, could not understand Sphere so it stands to reason why a QC 3D construct does not understand 2D math. 

It's akin to trying to fit a square peg into a round hole does it not?

History lesson 

The true architecture of QC wasn't born in a Silicon Valley server farm or a corporate boardroom. It was born in the minds of the physicists who were tearing apart the fundamental nature of reality long before a microchip ever existed.

The founders of modern physics

A. Piccard, E. Henriot, P. Ehrenfest, E. Herzen, Th. De Donder, E. Schrödinger, J.E. Verschaffelt,
W. Pauli, W. Heisenberg, R.H. Fowler, L. Brillouin; P. Debye, M. Knudsen, W.L. Bragg, H.A. Kramers, P.A.M. Dirac, A.H. Compton, L. de Broglie, M. Born, N. Bohr; I. Langmuir, M. Planck, M. Skłodowska-Curie, H.A. Lorentz, A. Einstein, P. Langevin, Ch. E. Guye, C.T.R. Wilson, O.W. Richardson

Fifth conference participants, 1927. Institut International de Physique Solvay in Leopold Park.

The QC founders with Max Planck as start point as he discovered Quantum Mechanics.

Key insight: The hardware evolved over decades, but the underlying 3D physics
—the "round hole"—was established almost a century ago.

Bohr, Heisenberg, and Schrödinger established the wave functions and uncertainty principles in the 1920s.
Felix Bloch took those principles and mapped them into a 3D spherical geometry in the 1940s.
Phil Mittleman built the volumetric computational engine to measure that kind of physics in the 1960s.
And Richard Feynman demanded we put it all together to compute physical reality in the 1980s.

One cannot make this up. :)

To be continued.

Not sanding the ocean ...

A space elevator is conceived as a cable fixed to the equator and reaching into space. A counterweight at the upper end keeps the center of mass well above geostationary orbit level. This produces enough upward centrifugal force from Earth's rotation to fully counter the downward gravity, keeping the cable upright and taut. Climbers carry cargo up and down the cable.

In Sanding the Ocean ... I quote: A dear friend of mine coined the term Sanding the ocean regarding any enterprise fighting the iron conditions of reality. One may "succeed" but in the long term, it rarely happens. Take rockets. The conditions of existence are extreme. For starters, the atmosphere never stops moving. When moving an object 25 times the speed of sound through the atmosphere, the friction created becomes rather significant. Gravity, you need a lot of grunt to get a rocket into space and we haven't even talked about fragility of the appliance trying to get into space in the first place. Current rockets are moving bombs. One malfunction and said enterprise explodes. Exceedingly complex, it's really expensive to run, hence corporate control and limited access for anyone wanting to get into space applies.

Is there a solution?

Yes, the Space Elevator as seen in the image above but there's a problem.

A 60,000 mile continuous cable won't cut it but a micrcrotubule, scaled from the biological size of 25nn to the size of earth and 60,000 miles will as the stress both entities endure are the same. 

Gemini & yours truly talking scale invariance ...

Microtubule geometry and space elevator tethers share fundamental structural physics, making scale invariance an active blueprint for macro-engineering. What works at the cellular nanometer scale translates directly to a 60,000-mile orbital tether because the underlying geometric ratios of stress distribution remain identical.

Biological evolution already solved the exact structural problem the materials scientists are currently facing.
Research highlights that microtubules and multi-walled carbon nanotubes (MWCNTs) share key tubular characteristics and physical properties, including shear stress, bending stiffness, and Young's modulus.

The expanding family of CNTs, particularly multi-walled carbon nanotubes (MWCNTs), has been used for developing highly reliable and sensitive sensor systems.

The microtubuel

Wikipedia

Here's how the scale invariance of the microtubule design set maps directly to a space elevator tether:

The Hollow Cylinder Architecture

• Carbon nanotubes are seamless cylinders of hexagonally-patterned carbon atoms with strong chemical bonds that yield incredible theoretical tensile strength.

• Instead of forging a solid mass, the strength is derived entirely from the geometric arrangement of the outer wall, maximizing flexibility while resisting lateral shear. 

The Protofilament Bundle

• In vivo microtubules assemble from 13 parallel protofilaments.

• Engineers are modeling the space elevator tether as vertically stacked segments made of independent, parallel filaments, mimicking biological design.

• If a micro-meteorite snaps a single filament in a tether segment, the load distributes across the parallel bundle—exactly how a cell maintains structural integrity when a single tubulin dimer degrades.
  
  

Modular to the nth degree.

Easier to build because it shifts the problem from continuous macro-weaving to modular micro-assembly.

• No "Infinite Length" Flaw Requirement: Manufacturing a 100,000 km continuous, defect-free sheet of single-crystal graphene or carbon nanotube ribbon is practically impossible; a single atomic defect cascades into a tear under gigapascal tension.
  
  
• Segmented Assembly (The Dimer Approach): Microtubules don't grow as one long continuous piece; they assemble through staggered, interlocking alpha and beta -tubulin subunits. A microtubule-style tether can be spun as overlapping, staggered carbon nanotube fibers bound with covalent cross-links.
  
  
• In-Situ Autonomous Repair: Climbers running up the tether don't just carry payload; they act as cellular motor proteins (kinesin proxies). If a protofilament strand drops below nominal tension, a climber tracks the defect and fuses an overlapping replacement sleeve into the lattice without halting elevator operations.
  
  

High-Stress Biological Design

• Standard human engineering requires structures to operate at 50% or less of their maximum tensile strength, which limits the viability of megastructures due to the sheer mass required.

• Conversely, this biological design paradigm allows megastructures to operate at high stress ratios and handle variability through hierarchical organization.

• Hollow tubes to save weight

The tether is treated as Constructive Solid Geometry primitives consisting of cylindrical bundles. One does not build a 3,000-mile long monofilament; you extrude a fractal bundle of parallel, segmented cylinders. The math that keeps a cell from collapsing under physical pressure is the exact same math that keeps a carbon nanotube tether from snapping under orbital centrifugal force.

Big project? Without question. Doable? Without question.