Artificial_Intelligence | Theodore C. Tanner Jr.

SnakeByte[17] The Metropolis Algorithm

Frame Grab From the movie Metropolis 1927

Who told you to attack the machines, you fools? Without them you’ll all die!!

~ Grot, the Guardian of the Heart Machine

First, as always, Oh Dear Reader, i hope you are safe. There are many unsafe places in and around the world in this current time. Second, this blog is a SnakeByte[] based on something that i knew about but had no idea it was called this by this name.

Third, relative to this, i must confess, Oh, Dear Reader, i have a disease of the bibliomaniac kind. i have an obsession with books and reading. “They” say that belief comes first, followed by admission. There is a Japanese word that translates to having so many books you cannot possibly read them all. This word is tsundoku. From the website (if you click on the word):

“Tsundoku dates from the Meiji era, and derives from a combination of tsunde-oku (to let things pile up) and dokusho (to read books). It can also refer to the stacks themselves. Crucially, it doesn’t carry a pejorative connotation, being more akin to bookworm than an irredeemable slob.”

Thus, while perusing a math-related book site, i came across a monograph entitled “The Metropolis Algorithm: Theory and Examples” by C Douglas Howard [1].

i was intrigued, and because it was 5 bucks (Side note: i always try to buy used and loved books), i decided to throw it into the virtual shopping buggy.

Upon receiving said monograph, i sat down to read it, and i was amazed to find it was closely related to something I was very familiar with from decades ago. This finally brings us to the current SnakeByte[].

The Metropolis Algorithm is a method in computational statistics used to sample from complex probability distributions. It is a type of Markov Chain Monte Carlo (MCMC) algorithm (i had no idea), which relies on Markov Chains to generate a sequence of samples that can approximate a desired distribution, even when direct sampling is complex. Yes, let me say that again – i had no idea. Go ahead LazyWeb^TM laugh!

So let us start with how the Metropolis Algorithm and how it relates to Markov Chains. (Caveat Emptor: You will need to dig out those statistics books and a little linear algebra.)

Markov Chains Basics

A Markov Chain is a mathematical system that transitions from one state to another in a state space. It has the property that the next state depends only on the current state, not the sequence of states preceding it. This is called the Markov property. The algorithm was introduced by Metropolis et al. (1953) in a Statistical Physics context and was generalized by Hastings (1970). It was considered in the context of image analysis (Geman and Geman, 1984) and data augmentation (Tanner (I’m not related that i know of…) and Wong, 1987). However, its routine use in statistics (especially for Bayesian inference) did not take place until Gelfand and Smith (1990) popularised it. For modern discussions of MCMC, see e.g. Tierney (1994), Smith and Roberts (1993), Gilks et al. (1996), and Roberts and Rosenthal (1998b).

Ergo, the name Metropolis-Hastings algorithm. Once again, i had no idea.

Anyhow,

A Markov Chain can be described by a set of states $S$ and a transition matrix $P$ , where each element $P_{ij}$ represents the probability of transitioning from state $i$ to state $j$ .

Provide The Goal: Sampling from a Probability Distribution $\pi(x)$

In many applications (e.g., statistical mechanics, Bayesian inference, as mentioned), we are interested in sampling from a complex probability distribution $\pi(x)$ . This distribution might be difficult to sample from directly, but we can use a Markov Chain to create a sequence of samples that, after a certain period (called the burn-in period), will approximate $\pi(x)$ .

Ok Now: The Metropolis Algorithm

The Metropolis Algorithm is one of the simplest MCMC algorithms to generate samples from $\pi(x)$ . It works by constructing a Markov Chain whose stationary distribution is the desired probability distribution $\pi(x)$ . A stationary distribution is a probability distribution that remains the same over time in a Markov chain. Thus it can describe the long-term behavior of a chain, where the probabilities of being in each state do not change as time passes. (Whatever time is, i digress.)

The key steps of the algorithm are:

Initialization

Start with an initial guess $x_0$ , a point in the state space. This point can be chosen randomly or based on prior knowledge.

Proposal Step

From the current state $x_t$ , propose a new state $x^*$ using a proposal distribution $q(x^*|x_t)$ , which suggests a candidate for the next state. This proposal distribution can be symmetric (e.g., a normal distribution centered at $x_t$ ) or asymmetric.

Acceptance Probability

Calculate the acceptance probability $\alpha$ for moving from the current state $x_t$ to the proposed state $x^*$ :

$\alpha = \min \left(1, \frac{\pi(x^) q(x_t | x^)}{\pi(x_t) q(x^* | x_t)} \right)$

In the case where the proposal distribution is symmetric (i.e., $q(x^|x_t) = q(x_t|x^))$ , the formula simplifies to:

$\alpha = \min \left(1, \frac{\pi(x^*)}{\pi(x_t)} \right)$

Acceptance or Rejection

Generate a random number $u$ from a uniform distribution $U(0, 1)$
If $u \leq \alpha$ , accept the proposed state $x^*$ , i.e., $set x_{t+1} = x^*$ .
If $u > \alpha$ , reject the proposed state and remain at the current state, i.e., $set x_{t+1} = x_t$ .

Repeat

Repeat the proposal, acceptance, and rejection steps to generate a Markov Chain of samples.

Convergence and Stationary Distribution:

Over time, as more samples are generated, the Markov Chain converges to a stationary distribution. The stationary distribution is the target distribution $\pi(x)$ , meaning the samples generated by the algorithm will approximate $\pi(x)$ more closely as the number of iterations increases.

Applications:

The Metropolis Algorithm is widely used in various fields such as Bayesian statistics, physics (e.g., in the simulation of physical systems), machine learning, and finance. It is especially useful for high-dimensional problems where direct sampling is computationally expensive or impossible.

Key Features of the Metropolis Algorithm:

Simplicity: It’s easy to implement and doesn’t require knowledge of the normalization constant of $\pi(x)$ , which can be difficult to compute.
Flexibility: It works with a wide range of proposal distributions, allowing the algorithm to be adapted to different problem contexts.
Efficiency: While it can be computationally demanding, the algorithm can provide high-quality approximations to complex distributions with well-chosen proposals and sufficient iterations.

The Metropolis-Hastings Algorithm is a more general version that allows for non-symmetric proposal distributions, expanding the range of problems the algorithm can handle.

Now let us code it up:

i am going to assume the underlying distribution is Gaussian with a time-dependent mean $\mu_t$ , which changes slowly over time. We’ll use a simple time-series analytics setup to sample this distribution using the Metropolis Algorithm and plot the results. Note: When the target distribution is Gaussian (or close to Gaussian), the algorithm can converge more quickly to the true distribution because of the symmetric smooth nature of the normal distribution.

import numpy as np
import matplotlib.pyplot as plt

# Time-dependent mean function (example: sinusoidal pattern)
def mu_t(t):
    return 10 * np.sin(0.1 * t)

# Target distribution: Gaussian with time-varying mean mu_t and fixed variance
def target_distribution(x, t):
    mu = mu_t(t)
    sigma = 1.0  # Assume fixed variance for simplicity
    return np.exp(-0.5 * ((x - mu) / sigma) ** 2)

# Metropolis Algorithm for time-series sampling
def metropolis_sampling(num_samples, initial_x, proposal_std, time_steps):
    samples = np.zeros(num_samples)
    samples[0] = initial_x

    # Iterate over the time steps
    for t in range(1, num_samples):
        # Propose a new state based on the current state
        x_current = samples[t - 1]
        x_proposed = np.random.normal(x_current, proposal_std)

        # Acceptance probability (Metropolis-Hastings step)
        acceptance_ratio = target_distribution(x_proposed, time_steps[t]) / target_distribution(x_current, time_steps[t])
        acceptance_probability = min(1, acceptance_ratio)

        # Accept or reject the proposed sample
        if np.random.rand() < acceptance_probability:
            samples[t] = x_proposed
        else:
            samples[t] = x_current

    return samples

# Parameters
num_samples = 10000  # Total number of samples to generate
initial_x = 0.0      # Initial state
proposal_std = 0.5   # Standard deviation for proposal distribution
time_steps = np.linspace(0, 1000, num_samples)  # Time steps for temporal evolution

# Run the Metropolis Algorithm
samples = metropolis_sampling(num_samples, initial_x, proposal_std, time_steps)

# Plot the time series of samples and the underlying mean function
plt.figure(figsize=(12, 6))

# Plot the samples over time
plt.plot(time_steps, samples, label='Metropolis Samples', alpha=0.7)

# Plot the underlying time-varying mean (true function)
plt.plot(time_steps, mu_t(time_steps), label='True Mean ', color='red', linewidth=2)

plt.title("Metropolis Algorithm Sampling with Time-Varying Gaussian Distribution")
plt.xlabel("Time")
plt.ylabel("Sample Value")
plt.legend()
plt.grid(True)
plt.show()

Output of Python Script Figure 1.0

Ok, What’s going on here?

For the Target Distribution:

The function $mu_t(t)$ defines a time-varying mean for the distribution. In this example, it follows a sinusoidal pattern.
The function target_distribution(x, t) models a Gaussian distribution with mean $\mu_t$ and a fixed variance (set to 1.0).

Metropolis Algorithm:

The metropolis_sampling function implements the Metropolis algorithm. It iterates over time, generating samples from the time-varying distribution. The acceptance probability is calculated using the target distribution at each time step.

Proposal Distribution:

A normal distribution centered around the current state with standard deviation proposal_std is used to propose new states.

Temporal Evolution:

The time steps are generated using np.linspace to simulate temporal evolution, which can be used in time-series analytics.

Plot The Results:

The results are plotted, showing the samples generated by the Metropolis algorithm as well as the true underlying mean function $\mu_t$ (in red).

The plot shows the Metropolis samples over time, which should cluster around the time-varying mean $\mu_t$ of the distribution. As time progresses, the samples follow the red curve (the true mean) as time moves on like and arrow in this case.

Now you are probably asking “Hey is there a more pythonic library way to to this?”. Oh Dear Reader i am glad you asked! Yes There Is A Python Library! AFAIC PyMC started it all. Most probably know it as PyMc3 (formerly known as…). There is a great writeup here: History of PyMc.

We are golden age of probabilistic programming.

~ Chris Fonnesbeck (creator of PyMC)

Lets convert it using PyMC. Steps to Conversion:

Define the probabilistic model using PyMC’s modeling syntax.
Specify the Gaussian likelihood with the time-varying mean $\mu_t$ .
Use PyMC’s built-in Metropolis sampler.
Visualize the results similarly to how we did earlier.

import pymc as pm
import numpy as np
import matplotlib.pyplot as plt

# Time-dependent mean function (example: sinusoidal pattern)
def mu_t(t):
    return 10 * np.sin(0.1 * t)

# Set random seed for reproducibility
np.random.seed(42)

# Number of time points and samples
num_samples = 10000
time_steps = np.linspace(0, 1000, num_samples)

# PyMC model definition
with pm.Model() as model:
    # Prior for the time-varying parameter (mean of Gaussian)
    mu_t_values = mu_t(time_steps)

    # Observational model: Normally distributed samples with time-varying mean and fixed variance
    sigma = 1.0  # Fixed variance
    x = pm.Normal('x', mu=mu_t_values, sigma=sigma, shape=num_samples)

    # Use the Metropolis sampler explicitly
    step = pm.Metropolis()

    # Run MCMC sampling with the Metropolis step
    samples_all = pm.sample(num_samples, tune=1000, step=step, chains=5, return_inferencedata=False)

# Extract one chain's worth of samples for plotting
samples = samples_all['x'][0]  # Taking only the first chain

# Plot the time series of samples and the underlying mean function
plt.figure(figsize=(12, 6))

# Plot the samples over time
plt.plot(time_steps, samples, label='PyMC Metropolis Samples', alpha=0.7)

# Plot the underlying time-varying mean (true function)
plt.plot(time_steps, mu_t(time_steps), label='True Mean ', color='red', linewidth=2)

plt.title("PyMC Metropolis Sampling with Time-Varying Gaussian Distribution")
plt.xlabel("Time")
plt.ylabel("Sample Value")
plt.legend()
plt.grid(True)
plt.show()

When you execute this code you will see the following status bar:

It will be a while. Go grab your favorite beverage and take a walk…..

Output of Python Script Figure 1.1

Key Differences from the Previous Code:

PyMC Model Usage Definition:
In PyMC, the model is defined using the pm.Model() context. The x variable is defined as a Normal distribution with the time-varying mean $\mu_t$ . Instead of manually implementing the acceptance probability, PyMC handles this automatically with the specified sampler.

Metropolis Sampler:
PyMC allows us to specify the sampling method. Here, we explicitly use the Metropolis algorithm with pm.Metropolis().

Samples Parameter:
We specify shape=num_samples in the pm.Normal() distribution to indicate that we want a series of samples for each time step.

Plotting:
The resulting plot will show the sampled values using the PyMC Metropolis algorithm compared with the true underlying mean, similar to the earlier approach. Now, samples has the same shape as time_steps (in this case, both with 10,000 elements), allowing you to plot the sample values correctly against the time points; otherwise, the x and y axes would not align.

NOTE: We used this library at one of our previous health startups with great success.

Optimizations herewith include several. There is a default setting in PyMC which is called NUTS.
No need to manually set the number of leapfrog steps. NUTS automatically determines the optimal number of steps for each iteration, preventing inefficient or divergent sampling. NUTS automatically stops the trajectory when it detects that the particle is about to turn back on itself (i.e., when the trajectory “U-turns”). A U-turn means that continuing to move in the same direction would result in redundant exploration of the space and inefficient sampling. When NUTS detects this, it terminates the trajectory early, preventing unnecessary steps. Also the acceptance rates on convergence are higher.

There are several references to this set of algorithms. It truly a case of both mathematical and computational elegance.

Of course you have to know what the name means. They say words have meanings. Then again one cannot know everything.

Until Then,

#iwishyouwater <- Of all places Alabama getting the memo From Helene 2024

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

Music To Blog By: View From The Magicians Window, The Psychic Circle

References:

[1] The Metropolis Algorithm: Theory and Examples by C Douglas Howard

[2] The Metropolis-Hastings Algorithm: A note by Danielle Navarro

[3] Github code for Sample Based Inference by bashhwu

Entire Metropolis Movie For Your Viewing Pleasure. (AFAIC The most amazing Sci-Fi movie besides BladeRunner)

What Would Nash,Shannon,Turing, Wiener and von Neumann Think?

An image of the folks as mentioned above via the GAN de jour

First, as usual, i trust everyone is safe. Second, I’ve been “thoughting” a good deal about how the world is being eaten by software and, recently, machine learning. i personally have a tough time with using the words artificial intelligence.

What Would Nash, Shannon, Turing, Wiener, and von Neumann Think of Today’s World?

The modern world is a product of the mathematical and scientific brilliance of a handful of intellectual pioneers who happen to be whom i call the Horsemen of The Digital Future. i consider these humans to be my heroes and persons that i aspire to be whereas most have not accomplished one-quarter of the work product the humans have created for humanity. Among these giants are Dr. John Nash, Dr. Claude Shannon, Dr. Alan Turing, Dr. Norbert Wiener, and Dr. John von Neumann. Each of them, in their own way, laid the groundwork for concepts that now define our digital and technological age: game theory, information theory, artificial intelligence, cybernetics, and computing. But what would they think if they could see how their ideas, theories and creations have shaped the 21st century?

A little context.

John Nash: The Game Theorist

John Nash revolutionized economics, mathematics, and strategic decision-making through his groundbreaking work in game theory. His Nash Equilibrium describes how parties, whether they be countries, companies, or individuals, can find optimal strategies in competitive situations. Today, his work influences fields as diverse as economics, politics, and evolutionary biology. NOTE: Computational Consensus Not So Hard; Carbon (Human) Consensus Nigh Impossible.

The Nash equilibrium is the set of degradation strategies

$(E_i^*,E_j^*)$

such that, if both players adopt it, neither player can achieve a higher payoff by changing strategies. Therefore, two rational agents should be expected to pick the Nash equilibrium as their strategy.

If Nash were alive today, he would be amazed at how game theory has permeated decision-making in technology, particularly in algorithms used for machine learning, cryptocurrency trading, and even optimizing social networks. His equilibrium models are at the heart of competitive strategies used by businesses and governments alike. With the rise of AI systems, Nash might ponder the implications of intelligent agents learning to “outplay” human actors and question what ethical boundaries should be set when AI is used in geopolitical or financial arenas.

Claude Shannon: The Father of Information Theory

Claude Shannon’s work on information theory is perhaps the most essential building block of the digital age. His concept of representing and transmitting data efficiently set the stage for everything from telecommunications to the Internet as we know it. Shannon predicted the rise of digital communication and laid the foundations for the compression and encryption algorithms protecting our data. He also is the father of my favorite equation mapping the original entropy equation from thermodynamics to channel capacity:

$H=-1/N \sum_{i=1}^{N} P_i\,log_2\,P_i$

The shear elegance and magnitude is unprecedented. If he were here, Shannon would witness the unprecedented explosion of data, quantities, and speeds far beyond what was conceivable in his era. The Internet of Things (IoT), big data analytics, 5G/6G networks, and quantum computing are evolutions directly related to his early ideas. He might also be interested in cybersecurity challenges, where information theory is critical in protecting global communications. Shannon would likely marvel at the sheer volume of information we produce yet be cautious of the potential misuse and the ethical quandaries regarding privacy, surveillance, and data ownership.

Alan Turing: The Architect of Artificial Intelligence

Alan Turing’s vision of machines capable of performing any conceivable task laid the foundation for modern computing and artificial intelligence. His Turing Machine is still a core concept in the theory of computation, and his famous Turing Test continues to be a benchmark in determining machine intelligence.

In today’s world, Turing would see his dream of intelligent machines realized—and then some. From self-driving cars to voice assistants like Siri and Alexa, AI systems are increasingly mimicking human cognition human capabilities in specific tasks like data analysis, pattern recognition, and simple problem-solving. While Turing would likely be excited by this progress, he might also wrestle with the ethical dilemmas arising from AI, such as autonomy, job displacement, and the dangers of creating highly autonomous AI systems as well as calling bluff on the fact that LLM systems do not reason in the same manner as human cognition on basing the results on probabilistic convex optimizations. His work on breaking the Enigma code might inspire him to delve into modern cryptography and cybersecurity challenges as well. His reaction-diffusion model called Turings Metapmorphsis equation, is foundational in explaining biological systems:

Turing’s reaction-diffusion system is typically written as a system of partial differential equations (PDEs):

$\frac{\partial u}{\partial t} &= D_u \nabla^2 u + f(u, v),$

$\frac{\partial v}{\partial t} &= D_v \nabla^2 v + g(u, v),$

$where:$

$\begin{itemize}\item $u$ and $v$ are concentrations of two chemical substances (morphogens),\item $D_u$ and $D_v$ are diffusion coefficients for $u$ and $v$,\item $\nabla^2$ is the Laplacian operator, representing spatial diffusion,\item $f(u, v)$ and $g(u, v)$ are reaction terms representing the interaction between $u$ and $v$.\end{itemize}$

In addition to this, his contributions to cryptography and game theory alone are infathomable.
In his famous paper, “Computing Machinery and Intelligence,” Turing posed the question, “Can machines think?” He proposed the Turing Test as a way to assess whether a machine can exhibit intelligent behavior indistinguishable from a human. This test has been a benchmark in AI for evaluating a machine’s ability to imitate human intelligence.

Given the recent advances made with large language models, I believe he would find it amusing, not that they think or reason.

Norbert Wiener: The Father of Cybernetics

Norbert Wiener’s theory of cybernetics explored the interplay between humans, machines, and systems, particularly how systems could regulate themselves through feedback loops. His ideas greatly influenced robotics, automation, and artificial intelligence. He wrote the books “Cybernetics” and “The Human Use of Humans”. During World War II, his work on the automatic aiming and firing of anti-aircraft guns caused Wiener to investigate information theory independently of Claude Shannon and to invent the Wiener filter. (The now-standard practice of modeling an information source as a random process—in other words, as a variety of noise—is due to Wiener.) Initially, his anti-aircraft work led him to write, with Arturo Rosenblueth and Julian Bigelow, the 1943 article ‘Behavior, Purpose and Teleology. He was also a complete pacifist. What was said about those who can hold two opposing views?

If Wiener were alive today, he would be fascinated by the rise of autonomous systems, from drones to self-regulated automated software, and the increasing role of cybernetic organisms (cyborgs) through advancements in bioengineering and robotic prosthetics. He, I would think, would also be amazed that we could do real-time frequency domain filtering based on his theories. However, Wiener’s warnings about unchecked automation and the need for human control over machines would likely be louder today. He might be deeply concerned about the potential for AI-driven systems to exacerbate inequalities or even spiral out of control without sufficient ethical oversight. The interaction between humans and machines in fields like healthcare, where cybernetics merges with biotechnology, would also be a keen point of interest for him.

John von Neumann: The Architect of Modern Computing

John von Neumann’s contributions span so many disciplines that it’s difficult to pinpoint just one. He’s perhaps most famous for his von Neumann architecture, the foundation of most modern computer systems, and his contributions to quantum mechanics and game theory. His visionary thinking on self-replicating machines even predated discussions of nanotechnology.

Von Neumann would likely be astounded by the ubiquity and power of modern computers. His architectural design is the backbone of nearly every device we use today, from smartphones to supercomputers. He would also find significant developments in quantum computing, aligning with his quantum mechanics work. As someone who worked on the Manhattan Project (also Opphenhiemer), von Neumann might also reflect on the dual-use nature of technology—the incredible potential of AI, nuclear power, and autonomous weapons to both benefit and harm humanity. His early concerns about the potential for mutual destruction could be echoed in today’s discussions on AI governance and existential risks.

What Would They Think Overall?

Together, these visionaries would undoubtedly marvel at how their individual contributions have woven into the very fabric of today’s society. The rapid advancements in AI, data transmission, computing power, and autonomous systems would be thrilling, but they might also feel a collective sense of responsibility to ask:

Where do we go from here?

Once again Oh Dear Reader You pre-empt me….

A colleague sent me this paper, which was the impetus for this blog:

My synopsis of said paper:

“The Tensor as an Informational Resource” discusses the mathematical and computational importance of tensors as resources, particularly in quantum mechanics, AI, and computational complexity. The authors propose new preorders for comparing tensors and explore the notion of tensor rank and transformations, which generalize key problems in these fields. This paper is vital for understanding how the foundational work of Nash, Shannon, Turing, Wiener, and von Neumann has evolved into modern AI and quantum computing. Tensors offer a new frontier in scientific discovery, building on their theories and pushing the boundaries of computational efficiency, information processing, and artificial intelligence. It’s an extension of their legacy, providing a mathematical framework that could revolutionize our interaction with quantum information and complex systems. Fundamental to systems that appear to learn where the information-theoretic transforms are the very rosetta stone of how we perceive the world through perceptual filters of reality.

This shows the continuing relevance in ALL their ideas in today’s rapidly advancing AI and fluid computing technological landscape.

They might question whether today’s technology has outpaced ethical considerations and whether the systems they helped build are being used for the betterment of all humanity. Surveillance, privacy, inequality, and autonomous warfare would likely weigh heavily on their minds. Yet, their boundless curiosity and intellectual rigor would inspire them to continue pushing the boundaries of what’s possible, always seeking new answers to the timeless question of how to create the future we want and live better, more enlightened lives through science and technology.

Their legacy lives on, but so does their challenge to us: to use the tools they gave us wisely for the greater good of all.

Or would they be dismayed that we use all of this technology to make a powerpoint to save time so we can watch tik tok all day?

Until Then,

#iwishyouwater <- click and see folks who got the memo

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

Music To blog by: Bach: Mass in B Minor, BWV 232. By far my favorite composer. The John Eliot Gardiner and Monterverdi Choir version circa 1985 is astounding.

What Is Love?

Love is a complex and multifaceted emotion that is often defined differently depending on cultural, social, and individual perspectives. At its core, love typically involves strong feelings of affection, caring, and attachment towards another person, often accompanied by a desire for intimacy, closeness, and mutual support.

Love can take many different forms, including romantic love, familial love, platonic love, and self-love. It can also involve a range of different experiences, such as joy, happiness, excitement, passion, and sometimes even pain and heartbreak.

Ultimately, love is a powerful and transformative force that can have a profound impact on our lives, shaping our relationships, beliefs, and sense of self.
~ ChatGPT

First Oh Dear Reader, i trust you are safe. Second, we are taking a slight detour into squishy, non-exact, messy, and truly complicated land: Emotions. This comes on the heels of Snake_Byte[14] Coding In Philosophical Frameworks. Also if you haven’t seen the news, the Cognitive Computing Arms Race with Large Language Models is in full swing. Boy Howdy Do We Love Us Some Anthropomorphism! ( i personally think its funny the parlor trick of auto-typing makes ChatGPT and all the other ones appear human to most. )

Turns out there is a class at Stanford on Computational Philosophy which is in the same realm as what i would love to discuss however slightly obtuse. It is a good read.

If we can compute Philosophical frameworks, could we or do we need to compute emotions in the future?

It was not random i started with the terseness of Stoicism. As this philosophy trends towards being objective in your emotions, akin to a more recent philosophy, “Objectivism.” We can quantify the aspects thereof. Or at least we think we can quantify them to some extent.

We know from a physiological standpoint that bodily metrics and chemicals can be measured for systematic responses to emotions. Dopamine, Vasopressin, Oxytocin, Serotonin, heart rate, sweat level, skin clamminess, and eye dilation are just a few measurable considerations.

However what does it mean?

Let’s take some bookend emotions: Love, Hate, Fear.

Some consider Fear to be the only true emotion. Flight or Fight. i’m also positive we can duplicate this in a machine or at least make it plausible to appear the machine is scared. (Also, it will make humans “feel” better one day but that is for a different discussion and different blog.)

Being that Spring Is In The Air let us choose LOVE.

There are so many types and kinds of Love.

Love of a Mother to a child
The Love you have for your pets
The Love of eating a doughnut.

i’m not referring to these types i am referencing:

You are the Love of My Life Companionship – Love

Books, signs, poetry, and yes, humans’ most deplorable creation War has been created over Love of Another. It is genuinely innate in the Human_Condition.

People acting a fool, if you will. You have seen it many times, and maybe (hopefully) you have done it.

As the famous song says, “What does love have to do with IT? What is love but some secondhand emotion?”. Great questioning Tina. We love you.

Have you ever been in The Love? How do you know that person Loves YOU?

i asked one of my progeny that question:

Me: “Do i Love you?”

Them: “Yes.”

Me” “How do you know?”

Them: “Dad i just know.”

This is not to be confused with infatuation, which is in and of itself an extremely intense feeling usually associated with a physically idealized version of the receiving person. Infatuation is initially overwhelming but then quickly fades. Love, on the other hand, appears to be a much deeper, almost seemingly “infinite forever-giving” emotion.

i was wondering how many times “Love” has been mentioned over the years. Below is a plot thereof:

i thought the drop in the 1980’s was interesting. If anyone was around in the United States in the 80’s that was the period of certain illegal chemicals, neon, 80’s metal and madonna. The Me Generation.

So what has happened in this area with using technology of Love? Well we scan our noggin:

**From The Journal of Frontiers in Human Neuroscience**

When I take you in my arms gathered forever. Sometimes it feels like a dream. Sometimes it feels like a dream; to be with you.
~ Feels Like A Dream Lyrics

Research has shown that certain brain regions are activated when a person is in love or experiencing strong feelings of affection, including the insula, anterior cingulate cortex, and striatum. These regions are associated with reward, motivation, and emotional processing. Brain scans have also revealed that the levels of certain neurotransmitters, like dopamine and oxytocin, can change in response to being in love. So while there is no one-size-fits-all image of someone in love, there are certain brain patterns and chemical changes that can be detected in those experiencing the emotion.

Furthermore on the chemical brain related activity research has shown that during the early stages of romantic love, there is a decrease and increase in serotonin levels in the brain^[1]. Serotonin can have direct affects on anxiety, insomnia and depression. For men there is a decrease and for women and increase in serotonin. Strangely this is similar to what is observed in individuals with obsessive-compulsive disorder, suggesting that the intense focus and preoccupation with the loved one may be related to changes in serotonin levels. Additionally, it has been found that long-term couples who remain in love have higher levels of serotonin than those who have fallen out of love. This suggests that serotonin may play a role in maintaining long-term relationships.

Our study provides the first evidence of love-related alterations in the underlying architecture of the brain, and the results shed new light on the mechanisms of romantic love.
~ Professor Xiaochu Zhang

So if there are (and there are injectable adjuncts to all of these chemicals) that can be augmented and tailored with specific dosages, this led me to ask the questions:

can we compute a model of Love?
can we fool the brain into thinking it is in Love?
do we want to augment the brain so we feel like we are in Love?
Do we want to make Love a thing of the past with future tech?
The list could go on…..

Did my heart love till now? Forswear it, sight / For I ne’er saw true beauty till this night.
~ That Romeo Guy

When people meet, go on a date, shack up, get hitched or get married, i have heard it said:

“You will grow to love them.”
“Well, the person doesn’t really have the attributes i am looking for in a mate, but i can adapt.”
“They share the same interest as you!”
“They can cook or fix stuff for you!”

Why would we want to change or have that person change? Are social norms so strong that there are unwritten rules that we as a so-called society must follow, With a hard rule of it must be two consenting adults in a relationship, what is it of anyone’s business? Do most want to do what they feel they should do? Do we want a best friend to watch the big game with or play golf? Why not a companion that you can dance with now and when your old and just look at each other? To idealized? Oh life gets in the way. i see it is hard and you have stuff to do – yep i get it. Then is Love that important? If not then what is?

Love in a relationship is it needed?

How do you know when you’re really in Love? Do violins play when you’re touching the one your Lovin?
~ The Tubes

Most often, love is due to their connection with their partner. This connection can be emotional, physical, spiritual, or intellectual (or hopefully all of them). People may also fall in love or grow into love because they appreciate their partner’s qualities or values, such as kindness, honesty, or intelligence. Sometimes, people are drawn to others who are different from themselves because they find them intriguing or exciting.

This room is bare
This night is cold
We’re far apart, and I’m growing old
But while we live
We’ll meet again
So then, my love
We may whisper once more
It’s you I adore
~ Palabras de Amor from Queen

Other factors that contribute to love include shared experiences, common interests, and mutual respect. When people feel like they’re a team with their partner and can rely on them, it can deepen their love. Additionally, physical attraction and chemistry can significantly influence romantic love.

However, what if it is all of these with the same partner? Isn’t that what you truly desire? ALL THE THINGS! To truly know that there is one – what would you do to have that person show up on your porch?

This is what i believe we need to understand as we trend toward a world of distributed disconnected artificial intelligence.

In one kiss, you’ll know all I haven’t said.
~ Pablo Neruda^[3]

Even post the pandemic, we see people hug and kiss less. There is less physicality which as we all know for most, a hug feels good for both and releases several proof-positive chemicals. i would tend to think at some point you want to rub against your significant other instead of smooching in cyberspace or sleeping in separate beds.

Is Love now an algorithm? i know in the age of match.com and tinder this appears to be monday morning quarterbacking but at least i believe there is something more to it than a list of attributes. In fact one of the startups i worked at started with the premise, “What are the three main questions you ask someone on a blind date?” Here they are:

What is your favorite travel spot?
What is your favorite wine (assuming one drinks)
What type of music do you like?

From a cognitive standpoint the most difficult one was music recommendations. This by the way Oh Dear Reader was circa 2001 before Pandora, Shazam or any other music recommendation system. We based it on a cognitive model of music perception and cognitive psychology of inter-relations^[2].

If we already know the future and can match it aren’t we living in a world of no surprises? What happens to that special glance? That special smile or touch or a good morning hug? That true everlasting one that loves you for you and only you not because your father mother or your in-laws approved. (If course it has been said you marry the family not the person. )

i am sure by now you have heard of the term Twin Flame. This is different than a soul mate. A Twin Flame is a mirror of oneself, a yin-yang type entity. The biggest distinction between a twin flame and any other type of soul mate is that twin flames are two halves of the same whole. A soul mate, on the other hand, can be thought of as someone cut from the same cloth as you. There’s a deep familiarity, and you feel very close, but you aren’t the same soul.

i wonder if we can compute and create a Twin Flame Agent of oneself?

i’m not sure i want to work on those future algorithms folks. Some things i want to be a mystery^[4].

i’d love to hear some stories, comments, and possibly even rants as this is some very esoteric area we have paddled into so to speak. i’d love to know if you have the love of your life, twin flame or a tinder swipe of the night, i hope you just know.

Until Then,

#iwshyouwater <- alexy molchanov sets a new world record in Bonaire. i was just in the same spot freediving.

Muzak To Blog By: All The Love Songs by Queen. i miss you Freddy and recently brian may was knighted.

References:

[1] Scans Say Its True Love Link

[2] For the record, i tried the system, and no one liked strict cathedral organ works.

[3] Pablo Neruda is the most prolific poet when it comes to love and beaches i have ever read. there is a connection there.

[4] In blade runner, harrison ford knows full well racheal is a replicant yet he Loves her. Question is – Is He? That would be a twin flame. robot love.

Snake_Byte:[14] Coding In Philosophical Frameworks

Your vision will only become clear when you can look into your heart. Who looks outside, dreams; who looks inside, awakes. Knowing your own darkness is the best method for dealing with the darknesses of other people. We cannot change anything until we accept it.
~ C. Jung

(Caveat Emptor: This blog is rather long in the snakes tooth and actually more like a CHOMP instead of a BYTE. tl;dr)

First, Oh Dear Reader i trust everyone is safe, Second sure feels like we are living in an age of Deus Ex Machina, doesn’t it? Third with this in mind i wanted to write a Snake_Byte that have been “thoughting” about for quite some but never really knew how to approach it if truth be told. I cant take full credit for this ideation nor do i actually want to claim any ideation. Jay Sales and i were talking a long time after i believe i gave a presentation on creating Belief Systems using BeliefNetworks or some such nonsense.

The net of the discussion was we both believed that in the future we will code in philosophical frameworks.

Maybe we are here?

So how would one go about coding an agent-based distributed system that allowed one to create an agent or a piece of evolutionary code to exhibit said behaviors of a philosophical framework?

Well we must first attempt to define a philosophy and ensconce it into a quantized explanation.

Stoicism seemed to me at least the best first mover here as it appeared to be the tersest by definition.

So first those not familiar with said philosophy, Marcus Aurelius was probably the most famous practitioner of Stoicism. i have put some references that i have read at the end of this blog¹.

Stoicism is a philosophical school that emphasizes rationality, self-control, and inner peace in the face of adversity. In thinking about this i figure To build an agent-based software system that embodies Stoicism, we would need to consider several key aspects of this philosophy.

Stoics believe in living in accordance with nature and the natural order of things. This could be represented in an agent-based system through a set of rules or constraints that guide the behavior of the agents, encouraging them to act in a way that is in harmony with their environment and circumstances.

Stoics believe in the importance of self-control and emotional regulation. This could be represented in an agent-based system through the use of decision-making algorithms that take into account the agent’s emotional state and prioritize rational, level-headed responses to stimuli.

Stoics believe in the concept of the “inner citadel,” or the idea that the mind is the only thing we truly have control over. This could be represented in an agent-based system through a focus on internal states and self-reflection, encouraging agents to take responsibility for their own thoughts and feelings and strive to cultivate a sense of inner calm and balance.

Stoics believe in the importance of living a virtuous life and acting with moral purpose. This could be represented in an agent-based system through the use of reward structures and incentives that encourage agents to act in accordance with Stoic values such as courage, wisdom, and justice.

So given a definition of Stoicism we then need to create a quantized model or discrete model of those behaviors that encompass a “Stoic Individual”. i figured we could use the evolutionary library called DEAP (Distributed Evolutionary Algorithms in Python ). DEAP contains both genetic algorithms and genetic programs utilities as well as evolutionary strategy methods for this type of programming.

Genetic algorithms and genetic programming are both techniques used in artificial intelligence and optimization, but they have some key differences.

This is important as people confuse the two.

Genetic algorithms are a type of optimization algorithm that use principles of natural selection to find the best solution to a problem. In a genetic algorithm, a population of potential solutions is generated and then evaluated based on their fitness. The fittest solutions are then selected for reproduction, and their genetic information is combined to create new offspring solutions. This process of selection and reproduction continues until a satisfactory solution is found.

On the other hand, genetic programming is a form of machine learning that involves the use of genetic algorithms to automatically create computer programs. Instead of searching for a single solution to a problem, genetic programming evolves a population of computer programs, which are represented as strings of code. The programs are evaluated based on their ability to solve a specific task, and the most successful programs are selected for reproduction, combining their genetic material to create new programs. This process continues until a program is evolved that solves the problem to a satisfactory level.

So the key difference between genetic algorithms and genetic programming is that genetic algorithms search for a solution to a specific problem, while genetic programming searches for a computer program that can solve the problem. Genetic programming is therefore a more general approach, as it can be used to solve a wide range of problems, but it can also be more computationally intensive due to the complexity of evolving computer programs².

So returning back to the main() function as it were, we need create a genetic program that models Stoic behavior using the DEAP library,

First need to define the problem and the relevant fitness function. This is where the quantized part comes into play. Since Stoic behavior involves a combination of rationality, self-control, and moral purpose, we could define a fitness function that measures an individual’s ability to balance these traits and act in accordance with Stoic values.

So lets get to the code.

To create a genetic program that models Stoic behavior using the DEAP library in a Jupyter Notebook, we first need to install the DEAP library. We can do this by running the following command in a code cell:

pip install deap

Next, we can import the necessary modules and functions:

import random
import operator
import numpy as np
from deap import algorithms, base, creator, tools

We can then define the problem and the relevant fitness function. Since Stoic behavior involves a combination of rationality, self-control, and moral purpose, we could define a fitness function that measures an individual’s ability to balance these traits and act in accordance with Stoic values.

Here’s an example of how we might define a “fitness function” for this problem:

# Define the fitness function.  NOTE: # i am open to other ways of defining this and other models
# the definition of what is a behavior needs to be quantized or discretized and 
# trying to do that yields a lossy functions most times.  Its also self referential

def fitness_function(individual):
    # Calculate the fitness based on how closely the individual's behavior matches stoic principles
    fitness = 0
    # Add points for self-control, rationality, focus, resilience, and adaptability can haz Stoic?
    fitness += individual[0]  # self-control
    fitness += individual[1]  # rationality
    fitness += individual[2]  # focus
    fitness += individual[3]  # resilience
    fitness += individual[4]  # adaptability
    return fitness,

# Define the genetic programming problem
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

# Initialize the genetic algorithm toolbox
toolbox = base.Toolbox()

# Define the genetic operators
toolbox.register("attribute", random.uniform, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attribute, n=5)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", fitness_function)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.1, indpb=0.1)
toolbox.register("select", tools.selTournament, tournsize=3)

# Run the genetic algorithm
population = toolbox.population(n=10)
for generation in range(20):
    offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
    fits = toolbox.map(toolbox.evaluate, offspring)
    for fit, ind in zip(fits, offspring):
        ind.fitness.values = fit
    population = toolbox.select(offspring, k=len(population))
    
# Print the best individual found
best_individual = tools.selBest(population, k=1)[0]

print ("Best Individual:", best_individual)

Here, we define the genetic programming parameters (i.e., the traits that we’re optimizing for) using the toolbox.register function. We also define the evaluation function (stoic_fitness), genetic operators (mate and mutate), and selection operator (select) using DEAP’s built-in functions.

We then define the fitness function that the genetic algorithm will optimize. This function takes an “individual” (represented as a list of five attributes) as input, and calculates the fitness based on how closely the individual’s behavior matches stoic principles.

We then define the genetic programming problem via the quantized attributes, and initialize the genetic algorithm toolbox with the necessary genetic operators.

Finally, we run the genetic algorithm for 20 generations, and print the best individual found. The selBest function is used to select the top individual fitness agent or a “behavior” if you will for that generation based on the iterations or epochs. This individual represents an agent that mimics the philosophy of stoicism in software, with behavior that is self-controlled, rational, focused, resilient, and adaptable.

Best Individual: [0.8150247518866958, 0.9678037028949047, 0.8844195735244268, 0.3970642186025506, 1.2091810770505023]

This denotes the best individual with those best balanced attributes or in this case the Most Stoic,

As i noted this is a first attempt at this problem i think there is a better way with a full GP solution as well as a tunable fitness function. In a larger distributed system you would then use this agent as a framework amongst other agents you would define.

i at least got this out of my head.

until then,

#iwishyouwater <- Alexey Molchanov and Dan Bilzerian at Deep Dive Dubai

Muzak To Blog By: Phil Lynott “The Philip Lynott Album”, if you dont know who this is there is a statue in Ireland of him that i walked a long way with my co-founder, Lisa Maki a long time ago to pay homage to the great Irish singer of the amazing band Thin Lizzy. Alas they took Phil to be cleaned that day. At least we got to walk and talk and i’ll never forget that day. This is one of his solo efforts and i believe he is one of the best artists of all time. The first track is deeply emotional.

References:

[1] A list of books on Stoicism -> click HERE.

[2] Genetic Programming (On the Programming of Computers by Means of Natural Selection), By Professor John R. Koza. There are multiple volumes i think four and i have all of this but this is a great place to start and the DEAP documentation. Just optimizing a transcendental functions is mind blowing what GP comes out with using arithmetic

January 9, 2023 TCTJr Artificial_Intelligence|Books|Direct Thoughts|General Discussion

A Book Review – Scythe

I am the blade that is swung by your hand,

Slicing a rainbow’s arc,

I am the clapper; but you are the bell,

Tolling the gathering dark.

If you are the singer, then I am the song,

A threnody, requiem dirge.

You’ve made me the answer for all the world’s need,

Humanity’s undying urge.
~ “Threnody,” from the collected works of H.S. Socrates

First as always i hope everyone is safe. Second, i hope everyone had an indulgent and superlative holiday season heading into the New Year! Third, i decided i wanted to write a book review since i haven’t in quite some time and given the number of books i have read recently figured hey lets do a book review!

This review deserves a little context. My middle progeny was assigned this book for a winter break reading assignment. i believe it is important to take an interest in your progenies’ activities and well reading is definitely one to promote and take interest. Thus when i was talking to her about what she was assigned, she said, “i think you will like this book.” She handed it to me, and i opened it to this page:

“It is the most difficult thing a person can be asked to do. And know what it is for the greater good doesn’t make it any easier. People used to die naturally. Old age used to be a terminal affliction, not a temporary state. There were invisible killers called “diseases” that broke the body down. Aging couldn’t be reversed, and there were accidents from which there was no return. Planes fell out from the sky. Cars actually crashed. There was pain, misery, and despair. It’s hard for most of us to imagine a world so unsafe, with dangers lurking in every unseen, unplanned corner. All of that is behind us now, and yet a single simple truth remains: People must die.”

~ From the gleaning journal of H.S. Currie

My daughter knows me well. So i said ok let us read it together but don’t think this is a race I’ll probably have this book finished in a week.

Ok this had my attention. So immediately i thought of Soylent Green meets Logan’s Run but there is a twist. In reading the back cover it paraphrases a world with no hunger, no disease, no war, no misery. Humanity has conquered all those things and has even conquered death. However, who are these scythes that are mentioned, and are the only ones who can “glean” life? So with the context out of the way let us get down to business.

The cover above depicts a Scythe. As most know or should know from Websters we have the following definition:

scythe (pronounced /sīT͟H/)

noun: a tool used for cutting crops such as grass or wheat, with a long curved blade at the end of a long pole attached to which are one or two short handles.

verb: cut with a scythe as in scythed.

Given many aspects of our so-called society today and social normalizations i believe this is a wonderful teenage adventure novel that sets the stage for some more esoteric readings in science fiction such as 1984, Something Wicked This Way Comes, Brave New World, Do Androids Dream Of Electric Sheep (DADOES), Snowcrash and Neuromancer.

The book’s premise is that humans now exist in a conflict-free world where human-kind have conquered death. The world they live in is a Post Age of Morality world where one no longer has true crimes against humanity; poverty is not an issue, and hunger is solved via synthetic food engineering. As such over-population has overrun Mother Earth and elected Scythes must cull the human population. This culling process is known in the vernacular as “gleaning”. The Age of Mortality is the duration of time before the scythedom, revival centers, and the Thunderhead were established.

To this end, artificial intelligence has been amplified via the “ThunderHead” which monitors, recommends, and predicts AllTheThings. As such, there is no need for the concept or construction of a government.

Tyger shrugged, “One Splat Too Many. They gave up. Now I am a ward of the ThunderHead.”

“I’m sorry Tyger”

“Hey don’t be. Believe it or not, the ThunderHead’s a better father than my father was. I get good advice now and get asked how my day was from someone who actually seems to care.”

Just like everything else about the ThunderHead its parenting skills were indisutable.

~ Apprentice Rowan

Two teens find themselves volunteered as apprentice-Scythes which leads to a world of corruption greed and the finality of death.

Scythes are volunteered as apprentices and taught the ways of all of the classics, philosophy, chemistry (poisons), neural linguistic programming (person-reading), and of course, skilled in all the ways one can end a person’s life or if you will extreme social engagement called “killcraft”.

Scyhtes choose which lives to glean based on statistics of past Age Of Morality morbidity rates and behaviors, social class, and ethnicities. They however cannot show bias.

Scythes are ruled by a worldwide committee and meet on a quarterly basis where concerns are raised, appretences are tested, and old friendships are renewed. Did i mention that humans now live indefinitely and can rewind physical age and appearance to no lower than 21? However, given that most Sychtes choose ages between 35-45.

The Scythes lived by the following commandments:

Thou Shalt Kill
Thou shalt kill with no bias, bigotry or malice of afterthought
Thou shalt grant an annum of immunity to the beloved of those who accept your coming and to anyone else you deem worthy.
Thou shalt kill the beloved of those who resist.
Thou shalt serve humanity for the full span of thy days and thy family shall have immunity as recompense for as long as you live.
Thou shalt lead an exemplary life in word and deed and keep a journal of each and every day.
Thou shalt kill no scythe beyond thyself.
Thou shalt claim no earthly possessions save thy robe, ring and journal.
Thou shalt have neither spouse or spawn.
Thou shalt be beholden to no laws beyond these.

So we must ask ourselves that if in fact we solve all the so called woes of the Human Condition will we solve the root cause of the Human Condition? If we take away mortality (and morality) and can save and upload our memories then what is meant to be Human? Passion and Lust (of life)? Is compassion still needed?

Upon giving me the book to read my daughter laughed and said “Daddy maybe you are one.”

Then again, reflecting on what my daughter said to me when she was referencing the text, at the core maybe we all are Sycthes.

So if your in the market for a good book for your children or you just want a quick read that will be a good catalyst for your thoughts for our future, pick this book up. Here i even will provide the link to the ThunderHead Book Club In The Sky. Note: this is book one of a triology.

Until Then,

#iwishyouwater <- some footage from the recent 50 year storm on the left coast.

@tctjr

Muzak To Blog By: Tchaikovsky’s Symphony No. 6 in B minor, Valery Abisalovich Gergiev conducting the Vienna Philharmonic. Spectacular piece Symphony No. 6 in B minor, Op. 74, also known as the Pathétique Symphony (a.k.a. The Passionate Symphony). I recently got to see this performed by the Charleston Symphony with Jonathan Heyward conducting it was spectacular. I was sitting there thinking how someone who has certain sexual proclivities or other passions in their life that at the time were not tolerated in society could create such a work of art; then again, it just goes to show the extreme lengths humans will go to make their true passions as it were, incarnate. i also think it very ironic that this composer would probably not trend in the position of Tchaikovsky due to his political beliefs, but one never knows, does one?

Look Up Down All Around!

Your Brain 3D Printed ^[1]

The effects of technology do not occur at the level of opinions or concepts. Rather they alter patterns of perception steadily and without any resistance.
~ Marshall McLuhan

First i hope everyone is safe. Second, this blog is more meta-physical in nature. The above picture is a present i received from a dear friend who 3D printed it for me. A transhumanist pictorial if you will for accelerating our wetware. This brings us to the current matter at hand.

i was traveling recently and i couldn’t help but notice how many humans are just sitting, walking, running and even biking looking at their mobile devices. Families no longer talk to each other, couples no longer kiss. Kids no longer day dream. All no longer LOOK UP, DOWN and ALL AROUND.

i must confess at this juncture that, as a technologist, i am conflicted. As they say we make the guns, but we don’t pull the trigger. As a technologist, i truly love using and creating with mathematics, hardware, and software. it is an honor as far as i am concerned, and i treat it as such, yet when i have time to sit and ponder i think of the time i held the first telegraph in my hands. Yes, the FIRST telegraph that read:

What hath God wrought!

Invented and sent by Samuel Finley Breese Morse 24 May 1844. I held it. Of course it was behind plexiglass, and this is a link to said telegraph.

Why is this important? It converted numbers (morse code in this case) into a readable document, content if you will. Even if you do not believe in higher-order deities or some theistic aspects what was transmitted and received via the message of the telegraph herewith was multi-modal and carried some weight to the message.

There seems to be a trend toward a kind of primitive outlook on life a more tribal attitude and i think its a natural reaction to industrialization. Unfortunately i think it is a bit naive because the future is going to become more mechanized, computerized as you call it and i dont think there is any turning back.
~ Jim Morrison

Intelligence it seems, is now but a search engine away or if you will a “tic-tok” away. It also seems due to this immediate gratification of content and information that, we no longer talk to anyone. “The Pandemic” seems to have modified several aspects of our existence. The results of this i believe will take decades of evolution before this change is truly understood from a systems theory and first principles engineering view.

We have been sequestered into a living environment tethered to the LazyWeb_^(TM). Per my commentary about seeing families with their heads buried in their phones during all modes of so-called social engagement, this is creating considerable fractures in how we deal with friends, families, and most importantly ourselves.

Now in recent times, Humans are going into the office or “back to the hybrid workplace” and taking a zoom call in the adjacent meeting room to where the REAL PHYSICAL meeting is occurring. So the more i pondered, the more i thought i would post a bunch of pictures and talk about cyberspace vs real space.

i have read all the books: “Neuromancer, Cyberspace, SnowCrash,Do Androids Dream of Electric Sheep (DADOES), Super Intelligence, 1984, Brave New World, Realware etc”, i first worked on full Virtual Reality applications in 1993. Yes there were computers back then, big red ones called Silicon Graphics Crimson machines. These augmented with fixed point digital signal processing equipment created the first six degrees of freedom ( 6DOFS) head tracked stereoscopic renderings complete with spatial audio. So it is nothing new just executed in a different fashon.

i recently went to the NASA Astronaut Training Experience at the Kennedy Space Center with my eldest daughter and we took a walk on Mars and did some trivial tasks. It was tethered environment with mono-based audio however it was impressive from a simulation standpoint. When the alert system informed me that a sandstorm was coming, i was non-plussed. Having worked on top-secret systems, i understand the need for simulations entirely. Simulate all the emergencies over and over again that you can think of when going into an environment of conflict.

On a regular basis “Humans being” and living do not constitute simulation unless you buy into Bostrom’s theory that we are living in a simulation, then what of it? Please make the most of IT. Talk to that person across from you. What color do they love? What is their favorite food? Do they like puppies? If they are close friends and family, above all – show them how you feel. Hug them.

I believe that computers have taken over the world. I believe that they
have in many ways ruined our children. I believe that kids used to love
to go out and play. I believe that social graces are gone because
manners are gone because all people do is sit around and text. I think
it’s obnoxious.
~ Stevie Nicks

If you are not the talkative type go outside build a fire, Walk through the city. Go sit under a tree. If you live in a place where you can see the sky go outside and just stare at the sky and let your eyes adjust. The stars will come out and think about the fact you are made of the same substances.

Reflect on and into yourself. Shut down all the noise and chatter. Listen. What do YOU hear?

I can’t fax you my love.
I can’t e-mail you my heart.
I can’t see your face in cyberspace,
I don’t know where to start.
~ Jimmy Buffet

When you get up in the morning, don’t start the Doom Scroll. Contemplate. Get a notebook and write some thoughts. The visceral act of writing activates differing neural patterns that allow us to remember and learn. Think about what you would like to accomplish. Hopefully, you made your bed. That is at least one thing you can check off that you did accomplish, and your parents would be proud.

i wrote a blog a while ago called Its An Honor To Say Goodbye. Many seemed to enjoy it for several different reasons. As you look up from your phone and are around, folks play a game. What if that person just disappeared as though they were shot by a BFG (Big F-in Gun) in one of the first-person shooting games and could not re-frag? Just gone from the simulation? Poof!

How would you feel?

i’ll have to say if this is a simulation, it is pretty good and has to be some quantum information theoretic manifestation^[2]. Yet! Feeling that embrace from a friend or loved one, feeling the spray from a wave, smelling and touching a rose, A dog licking you in the face, tasting that steak, the carnality and sensuality of it all transcend, at least for me, the “meta” aspects of the online experience.

Go Outside! The Graphics are Great!
~ Sensai Todd

So folks, when in doubt, put that device down for a bit. Go for a walk. Say hello to that person across the room and ask how the day is going, and mean it and listen. Go outside and sit against a tree at night, or take a walk near the ocean or body of water (my favorite). Draw. Shut your eyes and deeply listen to music. Dance. Make stupid sounds. Try something you have never done before. Do something besides being fed programmed content.

Look UP DOWN and ALL AROUND.

So question for all of you:

Q: Would you prefer a telegraph, facsimile or simulation of this life?

Until Then,

tctjr

#iwishyouwater <- Nathan Florence on a hellish scottish slab paddle out. He aint worried about who clicked like….

Muzak To Blog By Forestt “Into The Woods”. i would classify this as Martial Folk if i may use genre classification liberally.

[1] Someone i really respect technically and now consider a dear friend printed this out for me. He also prints body parts. Heavy stuff. He is a practicing ER doctor and also codes.

[2] On the above commentary concerning simulations, i do believe in the Minowski multi-verse theory and view of The Universe. Its all happening NOW with multiple probabilities, our noggin cant sample fast enough to reconstruct all of the information simultaneously. Also, remember, girls and boys, YOU are the universe.

[3] i took all of the pictures included herewith except the last one.

references:

[1] this is a great interview with The Lizard King (aka Jim Morrison when he was 26 in 1970. Listen. This isn’t hippie stuff. Click HERE.

August 24, 2022August 24, 2022 TCTJr Artificial_Intelligence|Snake_Bytes

Snake_Bytes[8] Intro_To_Mito

Software Is The Language Of Automation
Jensen Huang

First, i trust everyone is safe. Second: Hey Now! Wednesday is already here again! Why did Willy Wonka say about “So Much Time And So Little To Do?” Or better yet “Time Is Fun When You Are Having Flies!” Snake_Byte[8] Time!

This is a serendipitous one because i stumbled onto a library that uses a library that i mentioned in my last Snake_Bytes which was pandas. It’s called MitoSheets and it auto-generates code for your data wrangling needs and also allows you to configure and graph within your Jupyter_Lab_Notebooks. i was skeptical.

So we will start at the beginning which is where most things start:

i am making the assumption you are either using a venv or conda etc. i use a venv so here are the installation steps:

pip install mitoinstaller
pip mitoinstaller install

Note the two step process you need both to instantiate the entire library.

Next crank up ye ole Jupyter Lab:

import mitosheets
mito.sheet()

It throws up a wonky splash screen to grab your digits and email to push you information on the Pro_version i imagine.

Then you can select a file. i went with the nba.csv file from the last blog Snake_Bytes[7] Pandas Not The Animal. Find it here :

Then low and behold it spit out the following code:

from mitosheet import *; register_analysis("id-ydobpddcec");
    
# Imported nba.csv
import pandas as pd
nba = pd.read_csv(r'nba.csv')

register_analysis("id-ydobpddcec") is locked to the respective file.

So how easy is it to graph? Well, it was trivial. Select graph then X & Y axis:

So naturally i wanted to change the graph to purple and add some grid lines with a legend to test the export and here was the result:

As Henry Ford said you can have any color car as long as it is black. In this case you are stock with the above graph while useful it’s not going to catch anyone’s eye.

Then i tried to create a pivot table and it spit out the following code:

from mitosheet import *; register_analysis("id-ydobpddcec");
    
# Imported nba.csv
import pandas as pd
nba = pd.read_csv(r'nba.csv')

# Pivoted into nba
tmp_df = nba[['Team', 'Position', 'Number']]
pivot_table = tmp_df.pivot_table(
    index=['Team'],
    columns=['Number'],
    values=['Position'],
    aggfunc={'Position': ['count']}
)
pivot_table.set_axis([flatten_column_header(col) for col in pivot_table.keys()], axis=1, inplace=True)
nba_pivot = pivot_table.reset_index()

Note the judicious use of our friend the pandas library.

Changing the datatype is easy:

from mitosheet import *; register_analysis("id-ydobpddcec");
    
# Imported nba.csv
import pandas as pd
nba = pd.read_csv(r'nba.csv')

# Changed Salary to dtype datetime
import pandas as pd
nba['Salary'] = pd.to_datetime(nba['Salary'], unit='s', errors='coerce')

It also lets you clear the current analysis:

So i started experimenting with the filtering:

from mitosheet import *; register_analysis("id-ydobpddcec");
    
# Imported nba.csv
import pandas as pd
nba = pd.read_csv(r'nba.csv')

# Filtered Weight
nba = nba[nba['Weight'] < 180]

The views for modification are on the right side of the layout of the table which is very convenient. The automatic statistics and visualizations are helpful as well:

The max,min,median, and std are very useful and thoughtful:

The following in and of itself could be enough to pip install the library:

You can even have multiple dataframes that can be merged. Between those items and the summary stats for those that are experienced this could be enough price to entry to pip install and then install the library. For those that really don’t know how to code this allows you to copypasta code and learn some pretty basic yet very powerful immediate insights into data. Also if you are a business analyst, a developer could get you going in no time with this library.

i don’t particularly like the lockouts on the paywall for features. In today’s age of open-source humans will get around that issue and just use something else, especially the experienced folks. However, what caught my attention was the formatting and immediate results with a code base that is useful elsewhere, so i think the Mito developer team is headed in the right direction. i really can see this library evolving and adding sklearn and who knows Github Copilot. Good on them.

Give it a test drive.

Until Then,

#iwishyouwater <- #OuterKnown Tahiti Pro 2022 – Best Waves

@tctjr

Muzak To Blog By: Tracks from “Joe’s Garage” by Frank Zappa. “A Little Green Rosetta” is hilarious as well as a testament to Zappa’s ability to put together truly astound musicians. i love the central scrutinizer and “Watermelon in Easter Hey” i believe is one of the best guitar pieces of all time. Even Zappa said it was one of his best pieces and to this day Dweezil Zappa is the only person allowed to play it. One of my readers when i reviewed the Zappa documentary called the piece “intoxicating”. Another exciting aspect of this album is that he used live guitar solos and dubbed them into the studio work except for Watermelon In Easter Hey. The other Muzak was by a band that put Atlanta on the map: Outkast. SpeakerBoxx is phenomenal and Andre3000 is an amazing musician. “Prototype” and “Pink & Blue”. Wew.

February 2, 2021February 2, 2021 TCTJr Artificial_Intelligence|Computing_History|Human-Performance

Freedom From The Flesh

The human body resonates at the same frequency as Mother Earth. So instead of only focusing on trying to save the earth, which operates in congruence to our vibrations, I think it is more important to be one with each other. If you really want to remedy the earth, we have to mend mankind. And to unite mankind, we heal the Earth. That is the only way. Mother Earth will exist with or without us. Yet if she is sick, it is because mankind is sick and separated. And if our vibrations are bad, she reacts to it, as do all living creatures.
Suzy Kassem, Rise Up and Salute the Sun: The Writings of Suzy Kassem

Image from: The Outer Limits (1963) S 2 E 15 “The Brain of Colonel Barham”

i have been considering writing a series of blogs on the coming age of Cybernetics. For those unaware Cybernetics was a term coined by one of my heroes Dr. Norbert Weiner. Dr. Weiner was a professor of mathematics at MIT who early on became interested in stochastic and noise processes and has an entire area dedicated to him in the areas of Weiner Estimation Theory. However, he also is credited as being one of the first to theorize that all intelligent behavior was the result of feedback mechanisms, that could possibly be simulated by machines thereby coining the phrase “Cybernetics”. He wrote two seminal books in this area: (1) “Cybernetics” (2) “The Human Use of Humans”. This brings us to the present issue at hand.

The catalyst for the blog came from a tweet:

More concerning Ted is how long before people start paying for upgrades. What effects will this have when you have achieved functional immortality?
@mitchparkercisco

This was in response to me RT’ing a tweet from @alisonBLowndes concerning the International Manifesto of the “2045” Strategic Social Initiative. An excerpt from said manifesto:

We believe that before 2045 an artificial body will be created that will not only surpass the existing body in terms of functionality but will achieve perfection of form and be no less attractive than the human body.
2045 Manifesto

Now for more context. I am a proponent of using technology to allow for increased human performance as i am an early adopter if you will of the usage of titanium to repair the skeletal system. i have staples, pins, plates and complete joints of titanium from pushing “Ye Ole MeatBag” into areas where it did not fair so well.

There are some objectives of the movement of specific interest is Number Two:

To create an international research center for cybernetic immortality to advance practical implementations of the main technical project – the creation of the artificial body and the preparation for subsequent transfer of individual human consciousness to such a body.
2045 Manifesto

This is closely related to Transhumanism which is more of a philosophy than an execution. The way i frame it is Transhumanism sets the philosophical framework for cybernetics. The contemporary meaning of the term “transhumanism” was foreshadowed by one of the first professors of futurology, a man who changed his name to FM-2030. In the 1960s, he taught “new concepts of the human” at The New School when he began to identify people who adopt technologies, lifestyles and worldviews “transitional” to post-humanity as “transhuman“.

Coming from a software standpoint we could map the areas into pipelines and deploy as needed either material biological or conscious. We could map these areas into a CI/CD deployment pipeline. .

For a direct reference, i work with an amazing practicing nocturnist who is also a great coder as well as a medical 3D Printing expert! He prints body parts! It is pretty amazing to think that something your holding that was printed that morning is going to enable someone to use their respective limbs or walk again. So humbling. By the way, the good doctor is also a really nice person. Just truly a great human. Health practitioners are truly some of humanity’s rockstars.

He printed me a fully articulated purple octopus that doesn’t go in the body:

Building upon this edict and for those who have read William Gibson’s “Neuromancer,” or Rudy Ruckers The Ware Tetralogy: Software, Wetware, Realware, Freeware “it calls into question the very essence of the use for the human body? Of the flesh is the only aspect we truly do know and associate with this thing called life. We continually talk about the ‘Big C Word” – Consciousness. However, we only know the body. We have no idea of the mind. Carnality seems to rule the roost for the Humans.

In fact most of the acts that we perform on a daily basis trend toward physical pleasure. However what if we can upgrade the pleasure centers? What if the whole concept of dysphoria goes away and you can order you a net-new body? What *if* this allows us as the above ponders to upgrade ad nauseam and live forever? Would you? Would it get tiresome and boring?

i can unequivocally tell you i would if given the chance. Why? Because if there *is* intelligent life somewhere then they have a much longer evolutionary scale that we mere humans on earth do not and they have figured out some stuff let’s say that can possibly change the way we view life in longer time scales ( or time loops or even can we say Infinite_Human_Do_Loops? ).

i believe we are coming to an age where instead of the “50 is the new 30” we can choose our age – lets say at 100 i choose new core and legs and still run a 40-yard dash in under 5 seconds? i am all for it.

What if you choose a body that is younger than your progeny?

What if you choose a body that isnt a representation of a human as we know it?

All with immortality?

i would love to hear folks thoughts on the matter in the comments below.

For reference again here is the link -> 2045 Manifesto.

Until then,

Be Safe.

#iwishyouwater

@tctjr

Muzak To Blog By: Maddalena by Ennio Morricone

November 10, 2020April 29, 2021 TCTJr Artificial_Intelligence|Complexity_Theory|Computational_Economics|Distributed_Computing

Computing The Human Condition – Project Noumena (Part 2)

In the evolution of a society, continued investment in complexity as a problem-solving strategy yields a declining marginal return.
Joseph A. Tainter

Someone asked me if from now on my blog will only be about Project_Noumena – on the contrary.

I will be interspersing subject matter within Parts 1 to (N) of Project_Noumena. To be transparent at this juncture i am not sure where it will end or if there is even a logical MVP 1.0. As with open-source systems and frameworks technically one never achieves V1.0 as the systems evolve. i tend to believe this will be the case with Project Noumena. i recently provided a book review on CaTB and have a blog on Recurrent Neural Networks with respect to Multiple Time Scale Prediction in the works so stuff is proceeding.

To that end, i would love comments and suggestions as to anything you would like my opinion on or for me to write about in the comments section. Also feel free to call me out on typos or anything else you see in error.

Further within Project Noumena there are snippets that could be shorter blogs as well. Look at Project Noumena as a fractal-based system.

Now on to the matter at hand.

In the previous blog Computing The Human_Condition – Project Noumena (Part 1) i discussed the initial overview of the model from the book World Dynamics. i will take a part of that model which is what i call, the main, Human_Do_Loop(); and the main attributes of the model: Birth and Death of Humans. One must ask if we didn’t have humans we would not have to be concerned with such matters as societal collapse? i don’t believe animals are concerned with such existential crisis concerns so my answer is a resounding – NO. We will be discussing such existential issues in this blog although i will address such items in future writings.

Over the years i have been asking myself is this a biological model by definition? Meaning do we have cellular components involved only? Is this biological modeling at the very essence? If we took the cell-based organisms out of the equation what do we still have as far as models on Earth?

While i told myself i wouldn’t get too extensional here and i do want to focus on the models and then codebases i continually check the initial conditions of these systems as they for most systems dictate the response for the rest of the future operations of said systems. Thus for biological systems, are there physical parameters that govern the initial exponential growth rate? Can we model with power laws and logistic curves for coarse-grained behavior? Is Bayesian reasoning biologically plausible at a behavioral level or at a neuronal level? Given that what are the atomic units that govern these models?

These are just a sampling of initial condition questions i ask myself as i evolve through this process.

So with that long-winded introduction and i trust i didn’t lose you oh reader lets hope into some specifics.

The picture from the book depicts basic birth and death loops in the population sector. In the case of these loops, they are generating positive feedback which causes growth. Thus an increase in population P causes an increase in birthrate BR. This, in turn, causes population P to further increase. The positive feedback loop would if left to its own devices would create an exponentially growing situation. As i said in the first blog and will continue to say, we seem to have started using exponential growth as a net positive fashion over the years in the technology industry. In the case of basic population dynamics with no constraints, exponential growth is not a net positive outcome.

Once again why start with simple models? The human mind is phenomenal at perceiving pressures, fears, greed, homeostasis, and other human aspects and characteristics and attempting at a structure that is given say the best fit to a situation and categorizing these as attributes thereof. However, the human mind is rather poor at predicting dynamical systems behaviors which are where the models come into play especially with social interactions and what i attempting to define from a self-organizing theory standpoint.

The next sets of loops that have the most effective behavior is a Pollution loop and a Crowding Loop. If we note that pollution POL increases one can assume up to a point that one hopes that nature absorbs and fixes the pollution otherwise it is a completely positive feedback loop and this, in turn, creates over pollution which we are already seeing the effects of around the worlds. One can then couple this with the amount of crowding humans can tolerate.

We see this behavior in urban sprawl areas when we have extreme heat or extreme cold or let’s say extreme pandemics. If the population rises crowding ratio increases the birth rate multiplier declines and birth rates reduce. The increasing death rate and reducing the birth rate are power system dynamic stabilizers coupled with pollution. This in turn obviously has an effect on food supplies. One can easily deduce that these seemingly simple coefficients if you will within the relative feedback loops create oscillations, exponential growth, or exponential decay. The systems while that seem large and rather stable are very sensitive to slight variations. If you are familiar with NetLogo it is a great agent-based modeling language. I picked a simple pollution model whereas we can select the number of people, birthrate, and tree planting rate.

As you can see without delving into the specifics after 77 years it doesn’t look to promising. i ‘ll either be using python or netlogo or a combination of both to extended these models as we add other references.

Ok enough for now.

Until Then,

#iwishyouwater

@tctjr

Computing The Human Condition – Project Noumena (Part 1)

“I am putting myself to the fullest possible use, which is all I think any conscious entity can ever hope to do.” ~ HAL 9000

“If you want to make the world a better place take a look at yourself and then make a change.” ~ MJ.

First and foremost with this blog i trust everyone is safe. The world is in an interesting place, space, and time both physically and dare i say collectively – mentally.

Introduction

This past week we celebrated Earth Day. i believe i heard it was the 50th year of Earth Day. While I applaud the efforts and longevity for a day we should have Earth Day every day. Further just “thoughting” about or tweeting about Earth Day – while it may wake up your posterior lobe of the pituitary gland and secret some oxytocin – creating the warm fuzzies for you it really doesn’t create an action for furthering Earth Day. (much like typing /giphy YAY! In Slack).

As such, i decided to embark on a multipart blog that i have been “thinking” about what i call an Ecological Computing System. Then the more i thought about it why stop at Ecology? We are able to model and connect essentially anything, we now have models for the brain that while are coarse-grained can account for gross behaviors, we have tons of data on buying habits and advertisement data and everything is highly mobile and distributed. Machine learning which can optimize, classify and predict with extremely high dimensionality is no longer an academic exercise.

Thus, i suppose taking it one step further from ecology and what would differentiate it from other efforts is that <IT> would actually attempt to provide a compute framework that would compute The Human Condition. I am going to call this effort Project Noumena. Kant the eminent thinker of 18th century Germany defined Noumena as a thing as it is in itself, as distinct from a thing as it is knowable by the senses through phenomenal attributes and proposed that the experience was a product of the mind.

My impetus for this are manifold:

i love the air, water, trees, and animals,

i am an active water person,

i want my children’s children’s children to know the wonder of staring at the azure skies, azure oceans and purple mountains,

Maybe technology will assist us in saving us from The Human Condition.

Timing

i have waited probably 15+ years to write about this ideation of such a system mainly due to the technological considerations were nowhere near where they needed to be and to be extremely transparent no one seemed to really think it was an issue until recently. The pandemic seems to have been a global wakeup call that in fact, Humanity is fragile. There are shortages of resources in the most advanced societies. Further due to the recent awareness that the pollution levels appear (reported) to be subsiding as a function in the reduction of humans’ daily involvement within the environment. To that point over the past two years, there appears to be an uptake of awareness in how plastics are destroying our oceans. This has a coupling effect that with the pandemic and other environmental concerns there could potentially be a food shortage due to these highly nonlinear effects. This uptake in awareness has mainly been due to the usage of technology of mobile computing and social media which in and of itself probably couldn’t have existed without plastics and massive natural resource consumption. So i trust the irony is not lost there.

From a technical perspective, Open source and Open Source Systems have become the way that software is developed. For those that have not read The Cathedral and The Bazaar and In The Beginning Was The Command Line i urge you to do so it will change your perspective.

We are no longer hampered by the concept of scale in computing. We can also create a system that behaves at scale with only but a few human resources. You can do a lot with few humans now which has been the promise of computing.

Distributed computing methods are now coming to fruition. We no longer think in terms of a monolithic operating system or in place machine learning. Edge computing and fiber networks are accelerating this at an astonishing rate. Transactions now dictate trust. While we will revisit this during the design chapters of the blog I’ll go out on a limb here and say these three features are cogent to distributed system processing (and possibly the future of computing at scale).

Incentive models
Consensus models
Protocol models

We will definitely be going into the deeper psychological, mathematical, and technical aspects of these items.

Some additional points of interest and on timing. Microsoft recently released press about a Planetary Computer and announced the position of Chief Ecology Officer. While i do not consider Project Nuomena to be of the same system type there could be similarities on the ecological aspects which just like in open source creates a more resilient base to work.

The top market cap companies are all information theoretic-based corporations. Humans that know the science, technology, mathematics and liberal arts are key to their success. All of these companies are woven and interwoven into the very fabric of our physical and psychological lives.

Thus it is with the confluence of these items i believe the time is now to embark on this design journey. We must address the Environment, Societal factors and the model of governance.

A mentor once told me one time in a land far away: “Timing is everything as long as you can execute.” Ergo Timing and Execution Is Everything.

Goals

It is my goal that i can create a design and hopefully, an implementation that is utilizing computational means to truly assist in building models and sampling the world where we can adhere to goals in making small but meaningful changes that can be used within what i am calling the 3R’s: recycle, redact, reuse. Further, i hope with the proper incentive models in place that are dynamic it has a mentality positive feedback effect. Just as in complexity theory a small change – a butterfly wings – can create hurricanes – in this case positive effect.

Here is my overall plan. i’m not big on the process or gant charts. I’ll be putting all of this in a README.md as well. I may ensconce the feature sets etc into a trello or some other tracking mechanism to keep me focused – WebSphere feel free to make recommendations in the comments section:

Action Items:

Create Comparative Models

Create Coarse-Grained Attributes

Identify underlying technical attributes

Attempt to coalesce into an architecture

Start writing code for the above.

Preamble

Humanity has come to expect growth as a material extension of human behavior. We equate growth with progress. In fact, we use the term exponential growth as it is indefinitely positive. In most cases for a fixed time interval, this means a doubling of the relevant system variable or variables. We speak of growth as a function of gross national production. In most cases, exponential growth is treacherous where there are no known or perceived limits. It appears that humanity has only recently become aware that we do not have infinite resources. Psychologically there is a clash between the exponential growth and the psychological or physical limit. The only significance is the relevant (usually local) limit. How does it affect me, us, and them? This can be seen throughput most game theory practices – dominant choice. The pattern of growth is not the surprise it is the collision of the awareness of the limit to the ever-increasing growth function is the surprise.

One must stop and ask:

Q: Are progress (and capacity) and the ever-increasing function a positive and how does it relate to 2nd law of thermodynamics aka Entropy? Must it always expand?

We are starting to see that our world can exert dormant forces that within our life can greatly affect our well being. When we approach the actual or perceived limit the forces which are usually negative begin to gain strength.

So given these aspects of why i’ll turn now to start the discussion. If we do not understand history we cannot predict the future by inventing it or in most cases re-inventing it as it where.

I want to start off the history by referencing several books that i have been reading and re-reading on subjects of modeling the world, complexity, and models for collapse throughout this multipart blog. We will be addressing issues concerning complex dynamics as are manifested with respect to attributes model types, economics, equality, and mental concerns.

These core references are located at the end of the blog under references. They are all hot-linked. Please go scroll and check them out. i’ll still be here. i’ll wait.

Checked them out? i know a long list.

As you can see the core is rather extensive due to the nature of the subject matter. The top three books are the main ones that have been the prime movers and guides of my thinking. These three books i will refer to as The Core Trilogy:

World Dynamics

The Collapse of Complex Societies

Six Sources of Collapse

As i mentioned i have been deeply thinking about all aspects of this system for quite some time. I will be mentioning several other texts and references along the continuum of creation of this design.

We will start by referencing the first book: World Dynamics by J.W. Forrestor. World Dynamics came out of several meetings of the Rome Club a 75 person invite-only club founded by the President of Fiat. The club set forth the following attributes for a dynamic model that would attempt to predict the future of the world:

Population Growth

Capital Investment

Geographical Space

Natural Resources

Pollution

Food Production

The output of this design was codified in a computer program called World3. It has been running since the 1970s what was then termed a golden age of society in many cases. All of these variables have been growing at an exponential rate. Here we see the model with the various attributes in action. There have been several criticisms of the models and also analysis which i will go into in further blogs. However, in some cases, the variants have been eerily accurate. The following plot is an output of the World3 model:

Issues Raised By World3 and World Dynamics

The issues raised by World3 and within the book World Dynamics are the following:

There is a strong undercurrent that technology might not be the savior of humankind

Industrialism (including medicine and public health) may be a more disturbing force than the population.

We may face extreme psychological stress and pressures from a four-pronged dilemma via suppression of the modern industrial world.

We may be living in a “golden age” despite a widely acknowledged feeling of malaise.

Exhtortions and programs directed at population control may be self-defeating. Population control, if it works, would yield excesses thereby allowing further procreation.

Pollution and Population seem to oscillate whereas the high standard of living increases the production of food and material goods which outrun the population. Agriculture as it hits a space limit and as natural resources reach a pollution limit then the quality of life falls in equalizing population.

There may be no realistic hope of underdeveloped countries reaching the same standard and quality of life as developed countries. However, with the decline in developed countries, the underdeveloped countries may be equalized by that decline.

A society with a high level of industrialization may be unsustainable.

From a long term 100 years hence it may be unwise for underdeveloped countries to seek the same levels of industrialization. The present underdeveloped nations may be in better conditions for surviving the forthcoming pressures. These underdeveloped countries would suffer far less in a world collapse.

Fuzzy Human – Fuzzy Model

The human mind is amazing at identifying structures of complex situations. However, our experiences train us poorly for estimating the dynamic consequences of said complexities. Our mind is also not very accurate at estimating ad hoc parts of the complexities and the variational outcomes.

One of the problems with models is well it is just a model The subject-observer reference could shift and the context shifts thereof. This dynamic aspect needs to be built into the models.

Also while we would like to think that our mental model is accurate it is really quite fuzzy and even irrational in most cases. Also attempting to generalize everything into a singular model parameter is exceedingly difficult. It is very difficult to transfer one industry model onto another.

In general parameterization of most of these systems is based on some perceptual model we have rationally or irrationally invented.

When these models were created there was the consideration of modeling social mechanics of good-evil, greed – altruism, fears, goals, habits, prejudice, homeostasis, and other so-called human characteristics. We are now at a level of science where we can actually model the synaptic impulse and other aspects that come with these perceptions and emotions.

There is a common cross-cutting construct in most complex models within this text that consists of and mainly concerned with the concept of feedback and how the non-linear relationships of these modeled systems feedback into one another. System-wide thinking permeates the text itself. On a related note from the 1940’s of which Dr Norbert Weiner and others such as Claude Shannon worked on ballistic tracking systems and coupled feedback both in a cybernetic and information-theoretic fashion of which he attributed the concept of feedback as one of the most fundamental operations in information theory. This led to the extremely famous Weiner Estimation Filters. Also, side note: Dr Weiner was a self-styled pacifist proving you can hold two very opposing views in the same instance whilst being successful at executing both ideals.

Given that basic function of feedback, lets look at the principle structures. Essentially the model states there will be levels and rates. Rates are flows that cause levels to change. Levels can accumulate the net level. Either addition or subtraction to that level. The various system levels can in aggregate describe the system state at any given time $(t)$ . Levels existing in all subsystems of existence. These subsystems as you will see include but are not limited to financial, psychological, biological, and economic. The reason that i say not limited to because i also believe there are some yet to be identified subsystems at the quantum level. The differential or rate of flow is controlled by one or more systems. All systems that have some Spatio-temporal manifestation can be represented by using the two variables levels and rates. Thus with respect to the spatial or temporal variables, we can have a dynamic model.

The below picture is the model that grew out of interest from the initial meetings of the Club of Rome. The inaugural meeting which was the impetus for the model was held in Bern, Switzerland on June 29, 1970. Each of the levels presents a variable in the previously mentioned major structures. System levels appear as right triangles. Each level is increased or decreased by the respective flow. As previously mentioned on feedback any closed path through the diagram is a feedback loop. Some of the closed loops given certain information-theoretic attributes be positive feedback loops that generate growth and others that seek equilibrium will be negative feedback loops. If you notice something about the diagram it essentially is a birth and death loop. The population loop if you will. For the benefit of modeling, there are really only two major variables that affect the population. Birth Rate (BR) and Death Rate (DR). They represent the total aggregate rate at which the population is being increased or decreased. The system has coefficients that can initialize them to normal rates. For example, in 1970 BRN is taken as 0.0885 (88.5 per thousand) which is then multiplied by population to determine BR. DRN by the same measure is the outflow or reduction. In 1970 it was 9.5% or 0.095. The difference is the net and called normal rates. The normale rates correspond to a physical normal world. When there are normal levels of food, material standard of living, crowding, and pollution. The influencers are then multipliers that increase or decrease the normal rates.

As a caveat, there have been some detractors of this model. To be sure it is very coarse-grained however while i haven’t seen the latest runs or outputs it is my understanding as i said the current outputs are close. The criticisms come in the shape of “Well its just modeling everything as a $y=x*e^{{rt}}$ . I will be using this concept and map if you will as the basis for Noumena. The concepts and values as i evolve the system will vary greatly from the World3 model but i believe starting with a minimum viable product is essential here as i said humans are not very good at predicting all of the various outcomes in high dimensional space. We can asses situations very quickly but probably outcomes no so much. Next up we will be delving into the loops deeper and getting loopier.