#statistical interpretation of wave function

https://vm.tiktok.com/ZNdA5FKdJ/

I’ll try my best to!

In this video—we have a LOT to cover.

CLAIM 1: “The Multiverse Has No Basis”

Firstly, there are several credible scientific foundations that give the multiverse hypothesis real weight, grounded in peer-reviewed work by respected physicists and cosmologists. Buckle up, this bitch is long.

Inflation theory, initially proposed by Alan Guth and later expanded by Andrei Linde, describes a rapid expansion phase in the early universe. In many models, this inflation never entirely stops, it becomes eternal, spawning countless “bubble” universes, each with potentially different physical constants.

Brian Greene, in The Hidden Reality, explains how string theory and inflation produce a “landscape” of ~10^500 possible vacua—each giving rise to a distinct universe.

Some researchers (e.g., Hiranya Peiris with Matt Johnson and Eugene Lim) have searched CMB data from WMAP and Planck for signs of bubble collisions, concentric temperature or polarization patterns that could indicate our universe bumped into another, A well-known “Cold Spot” in the CMB—too large and cool to be a void, may be evidence of such a cosmic encounter.

The guy mentions the Many-Worlds Interpretation, first articulated by Hugh Everett and refined by Bryce DeWitt, posits that every quantum event branches into separate, non-communicating universes. This viewpoint is mathematically robust and mainstream among quantum theorists like David Deutsch, who argue it cleanly resolves issues like wave-function collapse and quantum decoherence.

Our universe appears extremely fine-tuned, with parameters like the cosmological constant set exactly to support life. Scientists such as Steven Weinberg, Martin Rees, Alan Guth, and Max Tegmark argue that this makes sense statistically if countless universes exist, only in one with the right settings could observers like us evolve. Critics point out philosophical issues (e.g., inverse-gambler’s fallacy), but the fine-tuning remains one of the strongest motivations for hypothesizing a multiverse.

In string theory, our familiar 3 + 1 dimensions can fold into myriad shapes, each corresponding to different low-energy physics, a notion called the “string landscape.” Combined with eternal inflation, this gives a plausible mechanism for realizing an enormous diversity of universes, each with its own physical laws.

Proponents from the scientific community include (though aren’t limited to): Don Page, Brian Greene, Max Tegmark, Alan Guth, Andrei Linde, Michio Kaku, David Deutsch, Leonard Susskind, Yasunori Nomura, Laura Mersini‑Houghton, Sean Carroll, Stephen Hawking…

So much for “no basis”—am I right?

[CUE KNEESLAP]

CLAIM 2: “The CIA Documents Are Fake”

This.. thing, didn’t even try to prove his own claim. He just said because these are declassified and shown to the public, they are “fake”. Okay, smartass.

GATEAWAY PROCESS DOCS—OFFICIAL CIA WEBSITE.

Declassified ≠ fake, declassified documents were once restricted from public access for national security or internal purposes. They go through a formal process to be released, often after a certain period of time or due to public request (like through FOIA — Freedom of Information Act). Their availability to the public is proof of their authenticity as documents.

In fact, the act of declassification means the documents were once classified, which confirms that they were real, official material at one point, used or studied within a government context.

CLAIM 3: “I’ve Spoken To Shifters Who’ve Left, They Said It Was Delusion”

Oh cool, cool! Lemme guess, they never shifted? They lied? And I’ve spoke to flat-earthers who still claim everyone else is lying about the earth being round. Claims that shifting aren’t real are mostly made from people who haven’t shifted. Doesn’t that say a lot? You don’t see actual shifters, people who’ve had real experiences, suddenly waking up one day going, ‘Oops, never mind, it was all fake.’ That only happens when someone gives up, not when they succeed.

So the real question is; are they mad it didn’t work for them, or mad it worked for someone else?

CLAIM 4: “It’s Dreaming”

Wow. Original.

A claim old as time itself—because let’s ignore those who’ve shifted awake, shall we?

Shifting is not dreaming.

A dream is nonsensical. Time moves weirdly. You blink and a frame changes. You’re not FULLY immersed in a way you are right now, we’ve all dreamed before but it doesn’t feel like you’re in there.

You cannot stay in a dream for years on end.

Even claiming it’s lucid dreaming is odd. You still cannot stay for years on end in a dream, and you ACTIVELY control the dream. When you shift, and you’ve scripted, things unfold by themselves. You’re not actively controlling everything. In dreams, you cannot smell, you probably don’t blink, you don’t feel touch or taste—even if you were to experience any of those things, it’s still very distinct from what we consider physical and real.

And guess what? You always still wake up regardless of what you try and do to stay in the dream.

Also, dreams fade. Shifts stay. I don’t wake up from a dream remembering what cereal I ate, what someone smelled like, or the exact words of a 3-hour conversation. Shifts are detailed, structured, and persistent, dream recall wishes it was that reliable.

CLAIM 4: “Used To Cope”

Yes, shifting is often used by many as a form of escapism from their issues but that doesn’t mean everyone in the community is doing it because they’re “mentally deranged”. I have shifter friends who have the most perfect life and they still shift.

Shifting can be something that’s done on a whim, but I forgot—god forbid any of us wanna kiss fictional people.

Let’s stop pretending like every non-mainstream spiritual or mental experience must be a disorder. Meditation used to be called ‘weird’ and ‘woo-woo’, now it’s in every corporate wellness package. Give shifting a decade. You will be selling DR-themed planners on Amazon.

CLAIM 5: “Using Rituals”

Claiming that shifting is furthermore unhealthy because people dedicate their lives to it and use rituals is just… just gives me a headache already. I do not “use rituals”. Everytime I’ve shifted was effortless. Everytime I will shift is effortless. Some people blink and shift. Some people shift accidentally. What fucking ritual was used in THAT, huh? Cause I wanna know.

People act like one person’s obsessive behavior suddenly invalidates the entire concept. By that logic, should we call cooking unhealthy because some people develop eating disorders? Or say studying is harmful because some students burn out? No, we call that a personal experience. So why can’t shifting be the same?

NOT REALLY A CLAIM, JUST FUNNY: “I Don’t Have Anything Against Shifting”

Yet you’ve made an almost ten minute video insinuating we’re deranged, unwell and mocking us? Cute. Giving fan behaviour, my love.

You watched us, studied us, talked about us at length, all while swearing you don’t care. That’s some Shakespearean denial if I’ve ever seen it. ‘I hate them so much I can’t stop consuming their content!’ Uh-huh. Tell me you wanna shift without telling me you wanna shift.

NOTICE THIS: “I’m A Twenty One Year Old Loser Who Lives With His Parents”

The fact that this man kept mentioning he was a loser and would LOVE it if “shifting was real” because otherwise he wouldn’t be living with his parents is EXTREMELY TELLING. No sane human who isn’t projecting just does this on a daily basis.

It honestly makes me feel bad for him. To continously project and waste your time of day making arguments that can easily be disproven, just to mock a community doing something you WISH you could do?

CLAIM 6: “Religion Has A Basis”

Because ofcourse, the man who doesn’t believe in shifting—believes in religion. I don’t care about your beliefs but I will die on the hill that religion is man-made. You cannot tell me a book is a basis. I can say that shifting books are a basis for shifting. He also mentioned something along the lines of “geological evidence of the flood” yet gave nothing to source?

He continued to defend the flood by saying other religions have the same idea.. it’s almost like man-made religions copy eachother, right? Islam and Christianity have similar prophets doesn’t make them any more credible than the other.

Just because different religions echo the same myth doesn’t make it historical fact, it just proves humanity loves a good dramatic story about wrath, water, and moral resets. Same way people like to shift into action-packed universes. That doesn’t make either of us wrong, it just makes both of us human.

CLAIM 7: “Sixty Six Plus Years”

Continues to claim that the entire idea of shifting was made 66+ years ago but I’m about entirely sure I’ve seen older shifters. But once again, let’s also ignore all anecdotal evidence and report from other people, young or old, because YOU’RE miserable with your fucking life.

People have been accessing altered states of consciousness, using mental visualization, and interacting with other realities for centuries. Indigenous tribes, mystics, monks, etc. You think people only started lucid dreaming or reality bending once some teenager made a DR script on Tumblr? Please. This is just rebranded consciousness work with a fandom twist. Ancient practice, modern language.

So no, this didn’t start 66 years ago. You just started paying attention 6 seconds ago.

To end this off—here’s my post about scientific research to back up manifestation and shifting. Bloop.

What else could be explained in the Witcher if we were to interpret the elven Sages' & Ciri's prophetic abilties in the fashion of Dune?

Dune's take on prescience is that once a future is perceived, it is set in stone.

It's basically the QM measurement rule bringing about wave function collapse into a defined state, which means a particle (I'll grandiosely call it "reality" for the sake of fantasy) loses its superposition (potential to be in all potential states) and takes on a defined property; i.e. one actual reality is chosen from among the many possible ones. Alternatively, the wave function might not collapse at all (many worlds interpretation), with all possible worlds continuing to exist as we, upon observation, simply become part of one of the realities while losing track of others.

In the case of the latter, this poses a question whether it is at all possible for a prescient being to continue "course-correcting." (and what about other observers, equally capable of collapsing the wave function? that would mean other prescients for our literary purposes, as we ignore that actually it could be light, air, etc.)

“This is the awe-inspiring universe of magic: There are no atoms, only waves and motions all around. Here, you discard all belief in barriers to understanding. You put aside understanding itself. This universe cannot be seen, cannot be heard, cannot be detected in any way by fixed perceptions. It is the ultimate void where no preordained screens occur upon which forms may be projected. You have only one awareness here — the screen of the magi: Imagination! Here, you learn what it is to be human. You are a creator of order, of beautiful shapes and systems, an organizer of chaos.”

Everything being an ocean of pure energy potential, the reality we think we know sort of "emerges" based on the conditions and our perception of those conditions. Truly the waves of reality remain in constant fluctuation, while we experience the "concrete" reality—that we label and categorize (imagining a shape for it)—as emerging from those more fundamental levels.

Magic, then, is about the perceiving mind creating the ultimate meaning for what can and cannot be over the raw senses. When an Aen Saevherne, for example, is able to perceive the future accurately they are, in effect, creating it.

Calculus

Taylor Series

Sequences of Functions

Transcendental Equations

Differential Equations

Linear Algebra

Separation of Variables

Scalars

Vectors

Matrixes

Operators

Basis

Vector Operators

Inner Products

Identity Matrix

Unitary Matrix

Unitary Operators

Evolution Operator

Transformation

Rotational Matrix

Eigen Values

Coefficients

Linear Combinations

Matrix Elements

Delta Sequences

Vectors

Basics

Derivatives

Cartesian

Polar Coordinates

Cylindrical

Spherical

LaPlacian

Generalized Coordinate Systems

Waves

Components of Equations

Versions of the equation

Amplitudes

Time Dependent

Time Independent

Position Dependent

Complex Waves

Standing Waves

Nodes

AntiNodes

Traveling Waves

Plane Waves

Incident

Transmission

Reflection

Boundary Conditions

Probability

Probability

Probability Densities

Statistical Interpretation

Discrete Variables

Continuous Variables

Normalization

Probability Distribution

Conservation of Probability

Continuum Limit

Classical Mechanics

Position

Momentum

Center of Mass

Reduce Mass

Action Principle

Elastic and Inelastic Collisions

Physical State

Waves vs Particles

Probability Waves

Quantum Physics

Schroedinger Equation

Uncertainty Principle

Complex Conjugates

Continuity Equation

Quantization Rules

Heisenburg's Uncertianty Principle

Schroedinger Equation

TISE

Seperation from Time

Stationary States

Infinite Square Well

Harmonic Oscillator

Free Particle

Kronecker Delta Functions

Delta Function Potentials

Bound States

Finite Square Well

Scattering States

Incident Particles

Reflected Particles

Transmitted Particles

Motion

Quantum States

Group Velocity

Phase Velocity

Probabilities from Inner Products

Born Interpretation

Hilbert Space

Observables

Operators

Hermitian Operators

Determinate States

Degenerate States

Non-Degenerate States

n-Fold Degenerate States

Symetric States

State Function

State of the System

Eigen States

Eigen States of Position

Eigen States of Momentum

Eigen States of Zero Uncertainty

Eigen Energies

Eigen Energy Values

Eigen Energy States

Eigen Functions

Required properties

Eigen Energy States

Quantification

Negative Energy

Eigen Value Equations

Energy Gaps

Band Gaps

Atomic Spectra

Discrete Spectra

Continuous Spectra

Generalized Statistical Interpretation

Atomic Energy States

Sommerfels Model

The correspondence Principle

Wave Packet

Minimum Uncertainty

Energy Time Uncertainty

Bases of Hilbert Space

Fermi Dirac Notation

Changing Bases

Coordinate Systems

Cartesian

Cylindrical

Spherical - radii, azmithal, angle

Angular Equation

Radial Equation

Hydrogen Atom

Radial Wave Equation

Spectrum of Hydrogen

Angular Momentum

Total Angular Momentum

Orbital Angular Momentum

Angular Momentum Cones

Spin

Spin 1/2

Spin Orbital Interaction Energy

Electron in a Magnetic Field

ElectroMagnetic Interactions

Minimal Coupling

Orbital magnetic dipole moments

Two particle systems

Bosons

Fermions

Exchange Forces

Symmetry

Atoms

Helium

Periodic Table

Solids

Free Electron Gas

Band Structure

Transformations

Transformation in Space

Translation Operator

Translational Symmetry

Conservation Laws

Conservation of Probability

Parity

Parity In 1D

Parity In 2D

Parity In 3D

Even Parity

Odd Parity

Parity selection rules

Rotational Symmetry

Rotations about the z-axis

Rotations in 3D

Degeneracy

Selection rules for Scalars

Translations in time

Time Dependent Equations

Time Translation Invariance

Reflection Symmetry

Periodicity

Stern Gerlach experiment

Dynamic Variables

Kets, Bras and Operators

Multiplication

Measurements

Simultaneous measurements

Compatible Observable

Incompatible Observable

Transformation Matrix

Unitary Equivalent Observable

Position and Momentum Measurements

Wave Functions in Position and Momentum Space

Position space wave functions

momentum operator in position basis

Momentum Space wave functions

Wave Packets

Localized Wave Packets

Gaussian Wave Packets

Motion of Wave Packets

Potentials

Zero Potential

Potential Wells

Potentials in 1D

Potentials in 2D

Potentials in 3D

Linear Potential

Rectangular Potentials

Step Potentials

Central Potential

Bound States

UnBound States

Scattering States

Tunneling

Double Well

Square Barrier

Infinite Square Well Potential

Simple Harmonic Oscillator Potential

Binding Potentials

Non Binding Potentials

Forbidden domains

Forbidden regions

Quantum corral

Classically Allowed Regions

Classically Forbidden Regions

Regions

Landau Levels

Quantum Hall Effect

Molecular Binding

Quantum Numbers

Magnetic

Withal

Principle

Transformations

Gauge Transformations

Commutators

Commuting Operators

Non-Commuting Operators

Commutator Relations of Angular Momentum

Pauli Exclusion Principle

Orbitals

Multiplets

Excited States

Ground State

Spherical Bessel equations

Spherical Bessel Functions

Orthonormal

Orthogonal

Orthogonality

Polarized and UnPolarized Beams

Ladder Operators

Raising and Lowering Operators

Spherical harmonics

Isotropic Harmonic Oscillator

Coulomb Potential

Identical particles

Distinguishable particles

Expectation Values

Ehrenfests Theorem

Simple Harmonic Oscillator

Euler Lagrange Equations

Principle of Least Time

Principle of Least Action

Hamilton's Equation

Hamiltonian Equation

Classical Mechanics

Transition States

Selection Rules

Coherent State

Hydrogen Atom

Electron orbital velocity

principal quantum number

Spectroscopic Notation

=====

Common Equations

Energy (E) .. KE + V

Kinetic Energy (KE) .. KE = 1/2 m v^2

Potential Energy (V)

Momentum (p) is mass times velocity

Force equals mass times acceleration (f = m a)

Newtons' Law of Motion

Wave Length (λ) .. λ = h / p

Wave number (k) ..

k = 2 PI / λ

= p / h-bar

Frequency (f) .. f = 1 / period

Period (T) .. T = 1 / frequency

Density (λ) .. mass / volume

Reduced Mass (m) .. m = (m1 m2) / (m1 + m2)

Angular momentum (L)

Waves (w) ..

w = A sin (kx - wt + o)

w = A exp (i (kx - wt) ) + B exp (-i (kx - wt) )

Angular Frequency (w) ..

w = 2 PI f

= E / h-bar

Schroedinger's Equation

-p^2 [d/dx]^2 w (x, t) + V (x) w (x, t) = i h-bar [d/dt] w(x, t)

-p^2 [d/dx]^2 w (x) T (t) + V (x) w (x) T (t) = i h-bar [d/dt] w(x) T (t)

Time Dependent Schroedinger Equation

[ -p^2 [d/dx]^2 w (x) + V (x) w (x) ] / w (x) = i h-bar [d/dt] T (t) / T (t)

E w (x) = -p^2 [d/dx]^2 w (x) + V (x) w (x)

E i h-bar T (t) = [d/dt] T (t)

TISE - Time Independent

H w = E w

H w = -p^2 [d/dx]^2 w (x) + V (x) w (x)

H = -p^2 [d/dx]^2 + V (x)

-p^2 [d/dx]^2 w (x) + V (x) w (x) = E w (x)

Conversions

Energy / wave length ..

E = h f

E [n] = n h f

= (h-bar k[n])^2 / 2m

= (h-bar n PI)^2 / 2m

= sqr (p^2 c^2 + m^2 c^4)

Kinetic Energy (KE)

KE = 1/2 m v^2

= p^2 / 2m

Momentum (p)

p = h / λ

= sqr (2 m K)

= E / c

= h f / c

Angular momentum ..

p = n h / r, n = [1 .. oo] integers

Wave Length ..

λ = h / p

= h r / n (h / 2 PI)

= 2 PI r / n

= h / sqr (2 m K)

Constants

Planks constant (h)

Rydberg's constant (R)

Avogadro's number (Na)

Planks reduced constant (h-bar) .. h-bar = h / 2 PI

Speed of light (c)

electron mass (me)

proton mass (mp)

Boltzmann's constant (K)

Coulomb's constant

Bohr radius

Electron Volts to Jules

Meter Scale

Gravitational Constant is 6.7e-11 m^3 / kg s^2

History of Experiments

Light

Interference

Diffraction

Diffraction Gratings

Black body radiation

Planks formula

Compton Effect

Photo Electric Effect

Heisenberg's Microscope

Rutherford Planetary Model

Bohr Atom

de Broglie Waves

Double slit experiment

Light

Electrons

Casmir Effect

Pair Production

Superposition

Schroedinger's Cat

EPR Paradox

Examples

Tossing a ball into the air

Stability of the Atom

2 Beads on a wire

Plane Pendulum

Wave Like Behavior of Electrons

Constrained movement between two concentric impermeable spheres

Rigid Rod

Rigid Rotator

Spring Oscillator

Balls rolling down Hill

Balls Tossed in Air

Multiple Pullys and Weights

Particle in a Box

Particle in a Circle

Experiments

Particle in a Tube

Particle in a 2D Box

Particle in a 3D Box

Simple Harmonic Oscillator

Scattering Experiments

Diffraction Experiments

Stern Gerlach Experiment

Rayleigh Scattering

Ramsauer Effect

Davisson–Germer experiment

Theorems

Cauchy Schwarz inequality

Fourier Transformation

Inverse Fourier Transformation

Integration by Parts

Terminology

Levi Civita symbol

Since the machine learning course I did this semester at Tsinghua university was mainly focused on typical data science applications, I was curious in how far those methods can be applied in physics. Of course it is nothing new that neural networks can in principle also be used for physical applications - however, tensor network methods still seem to be dominant in the field of numerical many body physics. Thus, I decided to dive in a little into the literature about the usage of Restrictive Boltzmann Machines (RBM) in many body physics.

What are RBMs?

Usually, RBMs are used for instance for recommendation tasks (e.g. video recommendations on video platforms) and many more. In general, it is a unsupervised learning technique which makes use of minimizing its "energy". Thus, the intuition behind RBMs is, despite their data science applications, already related to physics: We will see that it is no surprise that "Boltzmann" is part of the name of this method. An RBM utilizes input data, tries to extract meaningful features from it and wants to find the probability distribution over the input. This follows the physical intuition as follows: The RBM is a neural network with two layers, a hidden and a visible layer, where each node can adopt binary values. An example network looks like:

where the x denote the visible nodes, the h the hidden nodes and W denotes the weights between both layers. Note that there are no links in between the nodes of a single layer; this is why these networks are called restricted Boltzmann machines.

The network is governed by a corresponding energy function as:

where we also have the offsets of the single nodes (a for the visible nodes and b for the hidden nodes). Given this energy function, one can determine the probability distribution by the Boltzmann distribution where Z is a partition function, as familiar from classical statistical physics. As usual, the energy is supposed to be minimized, what is done by the learning algorithm of the RBM but this should not explained here since it would go beyond the scope of a brief blog entry. At this point I'd only like to mention that there are some difficulties determining e.g. the partition function (which is intractable in general) and that this requires some sophisticated algortihms. If you're interested in this and how the RBMs work exactly, a neat and far more rigorous introduction into RBMs can be found here.

One side note at this point: RBMs were introduced by Geoffrey Hinton after John Hopfield (a physicist) invented the so-called Hopfield networks which are also such an energy based mechanism, based on the physical intuition of Ising models.

Note that so far we only talked about the RBMs as they are used in data science - despite their physical intuition, they had so far nothing to do with neither quantum mechanics nor the many body problem. This is what comes next.

How can this be linked to condensed matter?

As introduced in [1], an RBM that can represent a quantum many body state would look like this:

In comparison to the previous network we changed the labels from x to σ, where the σ's denote e.g. spin 1/2 configurations, bosonic occupation numbers and so on. For them one has to choose a basis, e.g. the σ^z basis. This configuration can be summarized in the set S. Hence, the visible nodes are the N physical nodes of the system. The M hidden nodes h play the role of auxiliary (spin) variables. The authors describe the understanding of such a neural-network quantum state as follows: "The many-body wave function is a mapping of the N−dimensional set S to (exponentially many) complex numbers which fully specify the amplitude and the phase of the quantum state. The point of view we take here is to interpret the wave function as a computational black box which, given an input manybody configuration S, returns a phase and an amplitude according to Ψ(S)" [1, p.2]. Thus, one gives a certain spin configuration as input and the RBM generates the state, in the following form:

Thus, once can recognize that the form of such a neural-network quantum state adopts a similar form as the aforementioned Boltzmann distribution (exponential of energy function). However, there is additionally a sum over all possible hidden configurations which specifies the full state. After setting a state up in this form, one aim could be to find the ground state corresponding to a certain Hamiltonian, and according to the authors of [1], their RBM method gives decent results for this task!

Similarity to tensor networks

Interesting is, that this framework (even though it appears very different) has some analogous quantities as tensor network states. For example, the representational quality of a neural-network quantum state can be increased by increasing the number of hidden states: Thus, the ratio M/N plays a similar role as the bond dimension of a tensor product state! There are many more similarities, which should not be discussed here but can be found in [3].

Nevertheless, I'd like to mention an important distinction, which is also crucial for tensor network states, because some algorithms (DMRG etc.) can only handle area law states properly. While volume law states have an entanglement entropy which scales with the volume of the partitions of a state, an area law state has a scaling only proportionally with the area of the cut. Area law states can be handled better numerically, because the bond dimension of tensor network states explode for volume law states (more on tensor networks and area law can be found in [4]). According to [2] the difference between area law states and volume law states can be captured in a neat way with RBM states: While volume law states must have full connections between the hidden and physical nodes, an area law state has fewer links - this imposes locality in a sense. The RBM states thus give a neat intuition between the differenece of both kinds of states.

All in all, RBMs seem to be an interesting approach to connect both data science methods and many body physics. It may be that they have strengths which the usual tensor networks approaches lack: for instance, the authors of [2, p.888] claim that it might be possible that RBMs might be able to handle volume law states better than usual tensor network approaches do, which would be of course a major benefit. Since I haven't heard of this approach within the condensed matter framework before, I'm very curious how the importance of this method will evolve in future research!

--- References:

[1] Carleo, Troyer, Solving the Quantum Many-Body Problem with Artificial Neural Networks, arXiv:1606.02318

[2] Melco, Carleo, Carrasquilla, Cirac, Restricted Boltzmann machines in quantum physics, https://doi.org/10.1038/s41567-019-0545-1

[3] Chen, Cheng, Xie, Wang, Xiang, Equivalence of restricted Boltzmann machines and tensor network states, arXiv:1701.04831

this doesn't have anything to do with dark matter (variance from theory in statistical distribution of galactic rotation is the observable variable under consideration for dark matter), i think it relates to dark energy (the apparent expansion of the universe is the observable variable under consideration for dark energy). but then this gets deep into the weeds, where if i'm right to discard expansion and isotropy, then galaxies under merger should give a reverse-expanding (contracting? i've been calling it congealing) apparent kick "towards" us, which doesn't represent actual motion but a difference in energy required to escape the well of supermassive gravity, the kick being also proportional to the mass lost as gravitational waves. but the amount of the total mass lost is like 8% maybe, so this would be, even in optimal circumstances, nearly impossible to detect since colliding galaxies do not have a unified, easy to trace, single axis of rotation and we don't have a coherent or generally agreed upon distance ladder to work with.

and the part that's REALLY in the weeds is that i, unrelatedly, think supermassives are likelier to be a swarm, which should produce a chain reaction that could be easily distinguishable from a single merger event, or not, but i don't know how to account for the event horizon. presumably gravitational waves can't escape it any more than light can, but then, if a swarm is behind an event horizon, there shouldn't be any functional difference in curvature exterior to the horizon when comparing swarms vs single objects. if gravitational waves can't escape it, then the swarm exerts a single effect on the landscape of curvature. or not. i don't know.

but then there's this 1.7 second discrepancy between the speed of light and gravity from the 2017 kilonova that nobody talks about. that could account for an amount of gravitational waves that are infinitesimally more able to escape their own well than light. or the other way around. it's a 1.7 second error bar and most people interpret this as "the same instant as measured with fairly high precision" because they both traveled a hundred million lightyears which is like. fourteen orders of magnitude more than 1.7 seconds.

This is a player piano punch sheet or score taken from a rolled up drum. The holes make the keys move. It is a very simple pattern that translates into musical notes.

This is a decoding of “neural spikes” according to scientists at Elon Musk’s brain computer company Neuralink. The spikes are pictured in single boxes, and many spikes when they fire make a pattern. From that pattern you can begin to decode thought. According to Neuralink, “Everything you hear or think is all action potentials, its action spikes and it feels so real, it feels very real.”

This is an early IBM computer punch card, and the basic pattern recognition of the presence or absence of holes in predefined patterns led to everything we know today about computers. Though IBM introduced the first cards in 1928, they had already been in use to ‘program’ cloth in the Jacquard loom in 1804, making gorgeous silks and tapestries.

I think we are basically back in the same position as we were when IBM first made the computer punch card in 1928 in terms of brain research and decoding thought. Neuralink is putting together elements in brain research that 100 years from now, or even 50 years from now will have tremendous repercussions. The company did not invent all of the aspects of technology it is using to put things together, but they did improve upon existing tech and brought it all together. 

THE SURGICAL ROBOT

The Neuralink robot doing an implantation

Neuralink works by extracting electrical signals from neurons. In order to do that it has to implant extremely thin wires in the brain to access and read those signals onto a nanochip. Before it can do that, it has to make a precision surgical robot machine operated by neuroscientists to insert the wires - so it did. Apparently there was some DARPA money thrown into the mix somewhere along the way. The robotic surgeon has to implant tiny wires in between blood vessels and neurons, not on them. This can only be achieved through microscopes and nanometer precision. The implant needle is 24 microns small. Tiny threads are about 1/10 of a human hair, which is about the same size as a neuron. The needle to implant the wire is 24 microns small.  You can open the skull, insert the threads,  put in a tiny chip, and then glue the skull shut. The chip functions as a wireless bluetooth signal.

THE WIRES

This picture shows thin nano threads pasted onto a fingertip. The thinness of this wire is the big issue DARPA was trying to solve with Moldavian wire from Paradromics, that I previously blogged about in 2017, but Musk beat them to the punch. He wrote about his breakthroughs here.

This is the different new design of the threads called a “linear edge”, which are made of layered polymers. They are so are super duper thin they can’t bee seen with a human eye, so they need the special robot that inserts the threads onto the surface of the brain. Another reason is the brain’s surface moves with inhalation and exhalation, and the robot  can account for this natural movement. The wires and chip have to record the output from neurons. They are micro fabricated as precisely as the size of an electron beam. It is important to separate the signal to noise ratio in the chips, as they work with nanometer sizes of light. A new design. 350 nanometers,is smaller than visible light.

This image is the crux of how Neuralink acquires the signal. Here you see the neuron sending out an electrical spike and a thread next to it picking up the impulse. The electrical spike is the bulls eye. The copper colored needle is actually the implanted wire thread. You need to be 60 microns away or less to read the signals, so you really need to be under the skull. This is a graphic representation

This is a photo of the real thing, with lots of wires precision implanted into brain tissue. If you look really closely you will see they skirt around the blood vessels and neuron branches, but don’t touch any of them. Its sort of like an amazing game of darts, but the goal is to miss the bull’s eye of the vessels.

HISTORY OF IMPLANT CHIPS

This is a short history of brain research about making chips to implant in the brain. At this point the Utah array is still the most used implant chip in academia. Neuralinks chips are way, way faster and smaller. Their research builds on a century of neuroscience research and a decade of neuro engineering research. More advanced applications with advanced innovations will follow.

CHIPS AND PODS

These are early iterations of chips and devices made by Neurlink. Looks like Arduino 101, more or less.

The N1 sensor - the beginning of the sophisticated bean sized implant that goes into the skull and contains the chips.

The actual chips are made on super thin nano wafers in a hermetic environment so there is no dust. They are also super duper thin. A 4 x 4 millimeter chip has a thousand electrodes,and  implanting up to  10 chips is feasible. At this point the best FDA approved chip implant for Parkinsons Disease only has 10 electrodes. The Neuralink chips read and write, and are 1000 times more powerful than what is publicly approved. They will get better with newer versions.

Shades of Cyborg Neil Harbisson and his Eyeborg!  The Neuralink implant with four chips receive information from the threads, and sends them to an output area for batteries and firmware. 

This is the size of the output piece, like an earphone.You can upgrade the firmware on the pod on the ear, it is not the actual implant but connects to it. It will be controlled through an iPhone app. Probably Android as well.

SIGNAL PROCESSING

Waves of neural spikes from an array from implanted  threads that are being read out on a computer. monitor The color screen shows the brain at work, and traces of electrodes from single threads. Each trace is a voltage waveform in time. If you focus on one trace, it shows voltage deflections, or spikes per wave. It occurs when a neuron has an action potential, because that is the core information that is recorded.  Then the algorithm is decoded, which means capturing the intended information. You just have to think about something and build up the decoding data from that information or thought, and you can begin to interpret movement, memory, and  many other different types of experiences.

This is a basic diagram of signal processing from the chip - how they get the stuff out of the wires and threads to actually show the spikes. Neuralink said “Everything we care about is contained in the statistics of spikes (inside the brain).” So it goes back to the basic IBM punchcards, or the player piano drum roll - decipher the pattern and you decipher the thought.

Here is their basic logic analogue to digital conversion that is necessary to change action potential spikes to computer code..Calling Alvin Lucier, John Cage, Nam June Paik, David Rosenboom, Richard Teitelbaum - or Duh, this has been going on with the brain, changing analog to digital since 1965 in the music world.

Spike rasters in the brain - The top is the brainwave spikes pointed at by the blue arrows, and the bottom is the beginning of pattern recognition of individual neurons. There should be one pixel per electrode. They are on-chip spike detectors. The methods for detection are thresholding signals or directly characterizing the shape. The Neuralink scientists claim they can identify different neurons from same electrode based on their shape. The engineers had to modify the algorithms and scale them to compress neural data up to 200 times. It takes only 900 nano seconds  to compute the signal, faster than the brain knows that the signal even occurred. They can also stimulate any combination of up to 64 channels

One good use of this in the next few years is creating visual feedback for the blind by targeting the visual cortex to create an image better than a dot matrix image - or computer vision basics meets brain wetware. The scientists want to not only read out, but read into the brain. You can read into the brain by passing a current in the electrode. This causes the cell to fire an action potential, like for cochlear implants, or a way for the eye to restore vision. You can also use this technique in the brain to restore the sense of touch or vision. The visual cortex has maps, a spatial map (orange section of the brain in graphic). If you stimulate a point in that area, a blind person sees a point of light or phosphene. The idea is you can stimulate areas of the brain in the visual cortex to resemble a dot matrix level of the world. There are also parts of the brain that control orientation, color, size and speed of moving objects, and once you figure out what they are, and where to stimulate them, you can generate a more comprehensive image that a blind person can experience. Neuralink wants a device with electrodes that are small enough, but with high density that can do better than a dot matrix image.

The first iteration of their implant will have three different types that can go from a mobile device to a mouse or keyboard on a Bluetooth signal.  Neuralink needs to get FDA approval. Right now they are working on patients with complete paralysis so it is for serious neurological needs, and idea is to make it really safe. 

Who needs Google maps when you can tap into someone’s brain algorithm in their Hippocampus which contains spatial orientation. Here is a rendition of someone who really knows San Francisco, and they can send you pattern signals as you wander through the park and direct you telepathically - voila!

THE PURPOSE

Musk gave a number of reasons for his very public video presentation. The first he shamelessly admitted, was to recruit for new talent to Neuralink. He then framed the motivation of his company as wanting to solve brain ailments, spinal disorders, or catastrophic injuries like a broken neck or spine,. He admitted it won’t happen quickly, and kept mentioning the need for FDA approval. He wants to make his devices as cheap and accessible as a Lasik-like device.

“Hopefully,” he said, “AI is a benign scenario. You can chose to have a neural implant if you want, it is not a mandatory thing... We already have digital super intelligence - our access to a computer and a smart phone. The input speed in the human brain is fast, due to our vision, but the output speed is slow because we have to type information into a computer. We are constrained by human bandwidth and mechanics. “ 

But then he dropped the Neuralink AI bomb -  that he believes that we “ultimately (will) achieve a symbiosis with artificial intelligence” (if you want it at a civilization level scale). Then he added, “two people with Neuralink could have telepathy, a new kind of communication, conceptual telepathy, it has to be consensual”.

So the new “me too” movement will concern having consensual telepathy #metooconsensualtelepathy.

Gabriel Guerrer,  August 24, 2017

Abstract 

Motivated by a series of reported experiments and their controversial results, the present work investigated if volunteers could causally affect an optical double-slit system through mental efforts alone. The participants task alternated between intending the increase of the (real-time feedback informed) amount of light diffracted through a specific single slit and relaxing any intention effort. The 160 data sessions contributed by 127 volunteers revealed a statistically significant 6.37 sigma difference between the measurements performed in the intention versus the relax conditions (p = 1.89 × 10−10, es = 0.50 ± 0.08), while the 160 control sessions conducted without any present observer resulted in statistically equivalent samples (z = −0.04, p = 0.97, es = 0.00 ± 0.08). The results couldn’t be simply explained by environmental factors, hence supporting the previously claimed existence of a not yet mapped form of interaction between a conscious agent and a physical system.

Discussion 

I have to know anatomy, not just of the brain, but of the nervous systems, hormone glands, and sense systems of the body.

I have to know brain chemistry, how all of the neurotransmitters and hormones interact and balance and what happens if they don’t. How different substances interact with these chemicals and disrupt or enhance brain functioning.

I have to know biology, of the cells in the brain and how placiticity affects structure. I have to know the difference between gray and white matter and everything that goes into maintaining a balance of it. I have to know how a child develops, what happens to their brains as they age, and the typical behaviors and thoughts of those ages.

I have to know physics, especially in regards to sound and light waves and how we perceive and interpret the stimuli around us. (I literally had a unit captioned Psychophysics in one my classes last semester- that’s pretty self explanatory).

I have to know at least basic technology in terms of brain scanning techniques.

I have to know so much about statistics. About how to run numerous types of tests, how to read results, and so many different formulas. It may not be calculus, but you cannot tell me that it's not math.

And yeah, sure, a lot of psychology is social. It’s studying the way people think and behave based on their brain and their interactions with others.

But don’t you dare try to tell me I’m not studying a “real science”. Psychology combines every science field and overlaps them. I am responsible for knowing not one, but bits and pieces of multiple sciences.