09/01/2023

09/01/2023#

1101. ontology#

Einstein is more Parminedes, Niels Bohr more Heraclitus – as is Nietzsche. From an empirical perspective, who seems to be on the winning side a century later?

The comparison between Einstein and Parmenides, and between Niels Bohr and Heraclitus, is an interesting philosophical analogy. Parmenides argued for the fundamental unity and unchanging nature of existence, while Heraclitus is famous for saying that everything is in a state of flux (“You cannot step into the same river twice”). Similarly, Einstein’s theories of Special and General Relativity provide a deterministic framework for understanding the universe, whereas Bohr was one of the pioneers of quantum mechanics, which incorporates fundamental probabilities and uncertainties.

Empirical Developments

Quantum Mechanics: A century later, the quantum mechanical view—often interpreted to align more with Heraclitean flux than Parmenidean unity—seems to have a stronger empirical basis in terms of experimental confirmations. Quantum theory has been confirmed to extraordinary degrees of accuracy and forms the basis for many technologies, including transistors, lasers, and other devices. Quantum mechanics also allows for phenomena like superposition and entanglement, which seem to defy classical intuitions about reality.
Relativity: However, it should be noted that Einstein’s theories of relativity are also extraordinarily well-confirmed and crucial for GPS technology, understanding cosmological phenomena, and much else. General relativity has passed every test thrown at it so far, including the detection of gravitational waves.
Unification: One of the grand challenges of contemporary physics is to reconcile general relativity with quantum mechanics in a theory of quantum gravity. So far, this has not been successfully achieved, though efforts like string theory and loop quantum gravity are active areas of research. Therefore, it’s difficult to say that one of the two approaches—relativity or quantum mechanics—is empirically superior to the other; each is extraordinarily successful in its domain.

Philosophical Interpretations

Philosophically, the debate about determinism and indeterminism, unity and flux, continues. Bohr’s complementarity principle, the Copenhagen interpretation of quantum mechanics, and various other interpretations (Many-Worlds, de Broglie-Bohm, etc.) grapple with these deep questions.

Conclusion

In summary, both “sides”—that of deterministic, continuous theories exemplified by Einstein and the probabilistic, “quantized” theories exemplified by Bohr—have had enormous empirical success, but neither has been able to fully subsume the other. As of now, physics retains both perspectives, using each where it is most successful, while searching for a deeper theory that can unify them.

1102 worthyadversary#

Show code cell source Hide code cell source

import networkx as nx
import matplotlib.pyplot as plt

# Set seed for layout
seed = 4

# Directory structure
structure = {


    "Challenges": ["Rigor", "Sloppiness", "Error",],
    "Knowledge": ["Skills", "Challenges", "Morality", "Truth",],
    "Skills": ["Literacy", "Numeracy","Workflow",],
}

# Gentle colors for children
child_colors = ["lightgreen", "lightpink", "lightyellow",
    'lavender', 'lightcoral', 'honeydew', 'azure','lightblue', 
]

child_colors = ["lightgreen", "lightpink", "lightyellow",
    'lavender', 'lightcoral', 'honeydew', 'azure','lightblue', 
]

# 'lightsteelblue', 'lightgray', 'mintcream','mintcream', 'azure', 'linen', 'aliceblue', 'lemonchiffon', 'mistyrose'

# List of nodes to color light blue
light_blue_nodes = [ "Numeracy", "Challenges"]

G = nx.Graph()
node_colors = {}


# Function to capitalize the first letter of each word
def capitalize_name(name):
    return ' '.join(word.capitalize() for word in name.split(" "))

# Assign colors to nodes
for i, (parent, children) in enumerate(structure.items()):
    parent_name = capitalize_name(parent.replace("_", " "))
    G.add_node(parent_name)
    
    # Set the color for Skills
    if parent_name == "Knowledge":
        node_colors[parent_name] = 'lightgray'
    else:
        node_colors[parent_name] = child_colors[i % len(child_colors)]
        
    for child in children:
        child_name = capitalize_name(child.replace("_", " "))
        G.add_edge(parent_name, child_name)
        if child_name in light_blue_nodes:
            node_colors[child_name] = 'lightblue'
        else:
            node_colors[child_name] = child_colors[(i + 4) % len(child_colors)]  # You can customize the logic here to assign colors


colors = [node_colors[node] for node in G.nodes()]

# Set figure size
plt.figure(figsize=(30, 30))

# Draw the graph
pos = nx.spring_layout(G, scale=30, seed=seed)
nx.draw_networkx_nodes(G, pos, node_size=70000, node_color=colors, edgecolors='black')  # Boundary color set here
nx.draw_networkx_edges(G, pos)
nx.draw_networkx_labels(G, pos, font_size=40)
plt.show()

../_images/8f61f8ac993126c3ec2a3b79346043af71435a8b7078bd43fe188a1dec60d95c.png

you are always on team “skills”
look for or imagine your adversary “challenges”
best them in rigor, exploit their sloppiness, and force them to err
the face of knowledge is a worthy adversary, eternally recurrently challenging you
our proof of concept is in a lifetime of learning, growing, becoming, innovating, and creating
generative adversarial networks are the very best example of this
apps in chord progression, sports dummying, chess, and go

1103. directory#

WORKFLOW

folk a repo
clone it
make changes
push it back to the repo: abikesa_ngoma.sh
and thats it!

~/dropbox/1f.ἡἔρις,κ/1.ontology

–

Macintosh HD
    Applications (82)
    Library (66)
    System (7)
    Users (2)
        d
            Applications (0)
            Desktop (0)
            Dropbox (ERGOT) (11)
            Dropbox (Personal) (8)
            Downloads (0)
            Movies (2)
            Music (3)
            Pictures (3)
            Public (3)
        Shared
   
Network

d@Poseidon ~ % ls -l ~
total 0
drwx------@   3 d  staff    96 May 24 13:38 Applications
drwx------@   6 d  staff   192 Aug 31 18:14 Desktop
drwx------@  10 d  staff   320 Aug 27 17:49 Documents
drwx------+   9 d  staff   288 Sep  1 01:57 Downloads
lrwxr-xr-x    1 d  staff    27 Dec  2  2020 Dropbox -> /Users/d/Dropbox (Personal)
drwx------@  17 d  staff   544 Jul 13 16:53 Dropbox (ERGOT)
drwx------@  14 d  staff   448 Aug 18 23:56 Dropbox (Personal)
drwx------@ 104 d  staff  3328 Jun 27 12:48 Library
drwx------    6 d  staff   192 Sep  1 01:57 Movies
drwx------+   7 d  staff   224 Feb 25  2023 Music
drwx------+   7 d  staff   224 Jun 17 02:38 Pictures
drwxr-xr-x+   7 d  staff   224 Aug 14 08:00 Public
d@Poseidon ~ % 

1104. piano#

blackstar®: 88 keys (52 white, 36 black)
7 octaves (A0 to C8), portable (foldpiano88)
1 pedals (soft, sostenuto, sustain)

1105. artists#

Discussion with Sarah
Blue zone Netflix documentary
Convergence of powerful themes:
- Causal inference
- Hierachical data
- Process vs. Variance models
- Bias (Study process), Variance (Participant process)
- Intra-class correlation coefficient (Give perception of homogeneity)
- Anecdotes (By con-artists) vs. Data (Entire population)
- Powerful narratives arise from careful casting and sampling
- Always bests rigorous controlled analyses
- Why the same formula works in Hollywood: great casting!!
- Dull science with entire population will always be fail to compel

1106. err-is-human#

anecdote: bias - numerator, being, process of one (art)
population: variance - denominator, becoming, process of all & sundry (science)
in sum, art: science = numerator:denominator = bias:variance
dramatic arc of a protagonist: overall low icc (protagonist no different from others)
moral of the story: audience can relate to the protagonist, yet …
ultimately the protagonist makes a difference, distinguishes, them
they score a super-high icc (different from other); thus, the audience is moved
here, the protagonist is the numerator, the audience is the denominator
we are manipulated into thinking its an unbiased sample of the populations experience
but its not, its a carefully selected sample of the population; careful casting

segue to the casting of the characters in the story of the data
the data is the story of the population and the characters are the variables
the variables are the features of the data and the data is the feature of the population
the population is the feature of the universe and the universe is the feature of the multiverse
the multiverse is the feature of the omniverse and the omniverse is the feature of the metaverse
the metaverse is the feature of the hyperverse and the hyperverse is the feature of the ultraverse
the ultraverse is the feature of the xenoverse and the xenoverse is the feature of the omniverse
the omniverse is the feature of the metaverse and the metaverse is the feature of the hyperverse
the hyperverse is the feature of the ultraverse and the ultraverse is the feature of the xenoverse

segues to the netflix documentary on the blue zones: careful casting
biased sample of the population, but we are manipulated into thinking its unbiased
into thinking they are representative of the population, but they are not
so we unconsciously assume very low variance in the population, and high icc
but the reality is that the variance is high, and the icc is low
hollywood is the same, they cast the best actors, and we assume they are representative
very rare cases in which it appears as if casting didn’t do this (e.g. the office, the wire, the sopranos)
no wonder we consider these to be the best shows ever, they are the most representative and yet … stories seem to be other worldly (a paradox)

i probably just distinguished between hierarchical models
and the specific instance in which time is the hierarchical variable
whereby dramatic arc is the process of the protagonist and the variance of the population
this is how even the most seasoned statisticians are fooled by the art of casting
and the science of sampling and modeling and inference and prediction and causation and explanation and understanding and knowledge and wisdom and truth are hacked by the art of casting.

Homer, Shakespeare, and the Bible are the best examples of this.

1107. arc#

import statsmodels.api as sm
import statsmodels.formula.api as smf
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Hypothetical DataFrame: each row represents a composer at a particular phase in their career
# Added a numeric representation of 'phase' for plotting
df = pd.DataFrame({
    'composer': ['Bach', 'Bach', 'Bach', 'Handel', 'Handel', 'Handel',
                 'Mozart', 'Mozart', 'Mozart', 'Haydn', 'Haydn', 'Haydn',
                 'Beethoven', 'Beethoven', 'Beethoven', 'Schubert', 'Schubert', 'Schubert'],
    'phase': ['Early', 'Middle', 'Late'] * 6,
    'phase_num': [1, 2, 3] * 6,
    'ICC_score': [0.97, 0.97, 0.96, 0.96, 0.95, 0.95,
                  0.92, 0.91, 0.90, 0.93, 0.93, 0.92,
                  0.89, 0.85, 0.80, 0.86, 0.84, 0.82]
})

# Fit the model
md = smf.mixedlm("ICC_score ~ phase_num", df, groups=df['composer'], re_formula="~phase_num")
mdf = md.fit()

# Create an empty plot
plt.figure(figsize=(12, 8))

# Plot raw data points
sns.scatterplot(data=df, x='phase_num', y='ICC_score', hue='composer', s=100, palette='tab10')

# Plot fitted lines
for composer in df['composer'].unique():
    subset = df[df['composer'] == composer]
    x = subset['phase_num']
    y_fitted = mdf.fittedvalues[subset.index]
    plt.plot(x, y_fitted, label=f'Fitted {composer}')

plt.xlabel('Phase')
plt.ylabel('ICC Score')
plt.title('Random Intercept and Slope Model for Composers Across Periods')
plt.legend()
plt.show()

1108 humanities#

Redemption: return from low ICC to high ICC: back to the original described ways of the community

One confesses their sins, begs forgiveness, promises never to be different again

Never to be a “character”

__

What else is a tragic hero? What is hubris, but a grounding, a return to earth, a restoration of the protagonist to high ICC (more like everyone else)

—

It’s like an audience of mediocres gets to say “you are no different”, tragedy works through the “final judgment”. And so Nietzsche birth of tragedy was off the mark. What I say here about tragedy is more consistent with genealogy of morality. And with statistics

—-

Schadenfreude
Prodigal son
Hubris
Job
Redemption
Endurance
Name it!!