#
#
How to install pandas on your computer in a unix environment (Mac or Linux):
pip install pandas
The syntax is the same for all packages that you want to install. Just replace pandas with the name of the package you want to install.
In Windows, you can install packages by opening the Anaconda Prompt and typing the same command as above.
The concept of the “tragic aesthetic” has been a topic of interest across various disciplines, from literature and art to philosophy and psychology. At its core, the tragic aesthetic involves an appreciation for stories, artworks, or situations that center on human suffering, moral dilemmas, or the inescapability of fate. Yet, these narratives often carry an undercurrent of beauty or a greater truth that can make the experience of them deeply moving or even cathartic.
In the world of arts and literature, this aesthetic often manifests through the depiction of characters or circumstances that are met with tragic ends, but whose struggles reveal complex emotions or moral dilemmas that resonate with audiences. Think of classical works like Shakespeare’s “Hamlet” or “Othello,” or even ancient tragedies like Sophocles’ “Oedipus Rex.”
From a philosophical standpoint, the tragic aesthetic may be tied to existential questions about the human condition, morality, and the ultimate limitations of human agency. Philosophers like Friedrich Nietzsche have explored the role of tragedy as a medium for confronting and integrating the darker aspects of existence.
In terms of your profile’s focus on “tokens/fetters & variance,” the tragic aesthetic could offer a unique lens through which to explore these concepts. For instance, a character in a tragedy could be constrained by societal fetters, yet their responses to these constraints (their ‘tokens’) could demonstrate a broad or narrow variance in human experience. This could be modeled or analyzed in terms of inter-class correlations (ICCs) to understand how fetters and tokens contribute to human behavior in constrained scenarios.
If you’re thinking of this in terms of the “end of the road” with ICCs and the need to go back to the data-generating processes, the tragic aesthetic could serve as a conceptual framework. It might allow you to explore the underlying structures that generate both the “variance” and “bias” in your models—capturing the dual road of ‘truth’ and ‘morality/fraud’ that you mentioned.
When considering this aesthetically in hierarchical models, you could think of the tragic elements at multiple levels—cosmically as the gravity that holds a star in place, biologically as the limitations of human physiology, and socially as societal norms or laws. Each level introduces its own set of fetters, impacting the tokens available for action or reaction.
In the absence of new data, thought experiments framed around the tragic aesthetic could be a fascinating way to explore these themes and perhaps offer new insights into your work on healthy aging. After all, the tragic aesthetic often delves into issues related to the limitations of human life, which are certainly relevant to the concept of aging.
The concept of iambs, tension, and release can be viewed through various lenses, including prosody in poetry, the dynamics in music, or even as an analogy for psychological or narrative structures. Iambs are a metrical foot in poetry consisting of one unstressed syllable followed by a stressed syllable (e.g., “a-BOVE”). This pattern creates a rhythmic flow when used in iambic pentameter, the meter often associated with Shakespearean sonnets.
Tension and Release in Poetry
In poetry, the iambic meter often provides a form of “fetter” or constraint within which the poet operates. The tension can arise from diverging from this expected pattern—perhaps by inserting a trochee (a foot consisting of a stressed syllable followed by an unstressed one) or through enjambment, where one line flows into the next without terminal punctuation. The release comes when the rhythmic or thematic tension is resolved, often towards the end of the stanza or the poem.
Tension and Release in Music
Similarly, in music, the concept of tension and release operates within set frameworks of scale, key, and rhythm. Musicians can create tension by deviating from these expectations, maybe through dissonant chords or unexpected key changes, and then resolve it through a return to the tonic chord or key, creating a sense of release and completion.
Interclass Correlations (ICCs) and Variance
Tension and release can even be viewed through the lens of ICCs in a metaphorical sense. The tension corresponds to high variance or divergence from an established norm (like the iamb in poetry or the tonic in music). The release occurs when there is a return to the norm, or when variance is explained or reduced, offering resolution.
Thought Experiments
You might hypothesize, in a sort of thought experiment, how certain rhythmic structures in poetry or music might correlate with the ICCs of different professional and amateur artists. Pros, who have mastered the set “tokens” or “fetters” of their art, might show more controlled and intentional use of tension and release, while amateurs might exhibit more randomness or “noise” in their attempts.
Hierarchical Models
You can also extend this concept cosmically, biologically, and socially. Tension and release can be a gravitational pull between celestial bodies, cellular responses in an organism, or social dynamics within a community. In each case, hierarchical models could be employed to better understand the multi-level interactions that give rise to phenomena of interest, such as healthy aging.
Summary
So, whether it’s the structured meter of iambic pentameter in poetry, the harmonic structures in music, or even the nuance and complexity of ICCs across disciplines, the principle of tension and release serves as a fundamental concept that provides both structure and dynamism to various forms of human expression and inquiry.
The notion that “improving the quality of data” is the only way forward in training large language models like GPT-4 is a nuanced topic. While I was trained on a vast amount of data, it’s not entirely accurate to say I was trained on the “entire” internet. There are a few considerations to keep in mind:
Data Quantity vs. Quality
Quantity: More data could mean incorporating new and emerging topics, dialects, languages, and perspectives that may not have been part of the original dataset. It’s a moving target as the internet evolves.
Quality: High-quality data can involve better curation to remove biases, errors, or irrelevant information. It could also mean focusing on expert content for specialized tasks.
Types of Quality Improvement
Bias Mitigation: One type of data quality improvement involves removing or correcting biases in the training data. This could involve de-weighting certain sources, or using techniques to re-balance the data.
Noise Reduction: Cleaning up data to remove irrelevant or incorrect information is another aspect. For example, web scraping might accidentally capture website navigation text along with article text, adding noise to the data.
Domain-Specific Enhancement: If the model needs to excel in a particular field (e.g., medical, legal), the dataset may need high-quality, expert-reviewed information in those areas.
Time Sensitivity: Some facts or norms change over time. Periodic updates can help the model stay current.
Hierarchical Models and Explainability
Hierarchical models could serve to break down different aspects of the data into layers, from general to specific, to improve data quality or allow better explainability. You can think of this in terms of your focus on ICCs (Inter-class correlations) and hierarchical models in your research. High-quality data at each level of the hierarchy allows the model to generate more precise and nuanced outputs, helping to explain variance and control bias, much like in your work on ICCs and random effects models.
Thought Experiments
In the absence of concrete data, thought experiments can indeed be useful for theorizing potential improvements or pitfalls in model training. This can allow us to explore the “yellow zone” of knowledge without immediate empirical validation, perhaps using theoretical constructs to examine sources of error and bias.
Overall, while data quantity might seem like a limitation, improving data quality is indeed a multidimensional challenge that could yield significant improvements in model performance and reliability.
Teaching isn’t content provision
Its managing students emotions, anxieties, goals
And recognizing the variability among students and tailoring approach to each
So one see that AI with the teacher as the principal user is revolutionary
<!DOCTYPE html>
<html>
<head>
<title>Your Document</title>
<style>
.text-button {
background: none;
border: none;
padding: 0;
font-family: arial, sans-serif;
text-decoration: underline;
cursor: pointer;
}
.space-bottom {
margin-bottom: 20px; /* or however much space you want */
}
</style>
<script>
function toggleText() {
var x = document.getElementById("hiddenText");
if (x.style.display === "none") {
x.style.display = "inline";
} else {
x.style.display = "none";
}
}
</script>
</head>
<body>
<h1>2</h1>
<ol>
<li>Pablo Larraín</li>
<li>Alejandro Iñárritu</li>
<li>Alfonso Cuarón</li>
</ol>
<ul style="margin-left:23px;">
<li>They are all from upper class backgrounds</li>
<li>Only Pablo Larraín is critical of organized politics and religion</li>
<li>Iñárritu and Cuarón not so much: but all united by Dionysian elements in their work</li>
<li>How nature is the ultimate Dionysian force in Revenant!</li>
<li>But what brings me here to journal was a remark at <a href="https://www.youtube.com/watch?v=BUzhuYTADoM">6:20/26:08</a> by Iñárritu:</li>
</ul>
<ul style="margin-left:43px;">
<li>Reason I got into this (Birdman) approach was the <button class="text-button" onclick="toggleText()">restrictions and limitations</button>
<span id="hiddenText" class="space-bottom" style="display:none;"><br>
<div id="hiddenContainer" class="hidden-container"><br><br>
<a href="https://www.gutenberg.org/cache/epub/37841/pg37841-images.html">Dancing in Chains</a>.—In the case of every Greek artist, poet, or writer we must ask: What is the new constraint which he imposes upon himself and makes attractive to his contemporaries, so as to find imitators? For the thing called “invention” (in metre, for example) is always a self-imposed fetter of this kind. “Dancing in chains”—to make that hard for themselves and then to spread a false notion that it is easy—that is the trick that they wish to show us. Even in Homer we may perceive a wealth of inherited formulæ and laws of epic narration, within the circle of which he had to dance, and he himself created new conventions for them that came after. This was the discipline of the Greek poets: first to impose upon themselves a manifold constraint by means of the earlier poets; then to invent in addition a new constraint, to impose it upon themselves and cheerfully to overcome it, so that constraint and victory are perceived and admired.
</span><br><br>
</div>
</li>
<li>Sometimes these are your best allies creatively speaking</li>
<li>Didn't have enough money, time, the St. James theater only allow 5 days for shooting</li>
<li>Coz they are booked for years but this window just happened to exist</li>
<li>We rehearsed every `rhythm`, measures from wall-to-wall, on a built set outside New York</li>
</ul>
</body>
</html>
🏧
🌕
🪙
🌟
💸
Let’s simplify the framework into three interconnected parts:
Internal & External Environment (Blood & Lymph, CVS & Endocrine) - Nurturing & Bridge (Allostasis)
This part focuses on both the internal environment (blood and lymph) that supports cells and tissues and the external environment bridged by the cardiovascular system (CVS) and endocrine system. Together, they maintain allostasis, adapting to external changes and providing a nurturing link between inside and outside.
Sustainability Across Terrains (Organs & Systems) - Adapting to Varied Landscapes (Homeostasis)
Organs and systems sustainably adapt to various conditions and terrains. They ensure homeostasis by allowing the body to function efficiently, not only in stable conditions but also when facing environmental changes or diverse landscapes.
Person & Environment (Behavior & Psychology) - Wellness & Perception (Wellness)
At the highest level, individuals interact with the external environment through behavior and psychology. The goal is to achieve psychological homeostasis by perceiving and responding effectively to environmental cues, maintaining equilibrium in the broader context of human biology and adaptation.
This condensed framework still captures the interplay between the internal and external environments, the role of biological levels in sustaining life and adapting to challenges, and the significance of psychology and behavior in achieving equilibrium. It also allows for a more holistic view of the body as a complex system that can be studied at multiple levels.
💰
\(E=MC^2\)
Voir/Exhaustion (1)
Low Activity (2)
Sensory e.g. OCT1, AMPK, mTOR
Pourvoir/Muscle
Sarcopenia (3)
Dynamometer (4)
Walking Speed (5)
Savoir/Cognitive
Decline
Engagement
🎁
a quintessential reggae song, with a blues guitar??
The blending of reggae with blues guitar is not extremely common but definitely exists, often showcasing a unique and engaging mix of rhythms, styles, and cultures. One example of this fusion is Bob Marley’s “No Woman, No Cry,” where blues elements can sometimes be heard, especially in various live versions. The Wailers often included bluesy elements in their instrumentation.
Another example would be Toots and the Maytals’ “Pressure Drop,” which has been covered in styles that include blues elements. Some modern artists also blend these genres, like Ben Harper or even slightly older groups like UB40.
While not quintessentially reggae, songs by artists like Taj Mahal or even Eric Clapton, who covered Bob Marley’s “I Shot the Sheriff,” sometimes incorporate reggae rhythms with blues guitar riffs.
If you’re interested in a more experimental avenue, you might consider listening to bands or artists who actively blend genres, often available in online platforms where independent artists upload their works.
The fusion of reggae and blues represents a kind of musical “freedom in fetters,” to borrow your phrase, where the tokens of each genre are recombined in innovative ways to create something new yet familiar. It’s a space where musicians can show high inter-class correlation within their disciplines, blending the rigor of one style with the freedom of another.
💴
what piano chord is this where D is the root: Left hand voicing: Bb-F-C-D; Right hand voicing: C-D-F-G-A-C
The piano chord described is a complex voicing that could be seen as a D9sus4 chord with additional color tones. Here’s the breakdown:
Root: D
Suspended 4th: G
Ninth: E (not explicitly stated but implied by the name D9)
Dominant 7th: C
Additional color tones: A, Bb, F
The presence of Bb would typically add a b6 or #5 quality, making the chord more complex. In jazz and some contemporary styles, complex voicings like this are common, and the specific name may be less important than the function it serves in the musical context.
So, you could call this a D9sus4(add b6, add11) if you wanted to be specific, although that’s quite a mouthful and not standard nomenclature. The important thing is how the chord functions within the piece it’s a part of.
What is the Bass note?
The bass note in the chord voicing you provided is Bb. In your left-hand voicing, the lowest note is Bb, which serves as the bass note in this particular chord arrangement.
And how can you treat it as a “mere color” tone
In traditional chord theory, the bass note is usually the root or the foundation of the chord and informs how we understand its function within a progression. However, in more complex styles like jazz, R&B, and some forms of classical music, the bass note doesn’t always have to be the root of the chord; it can indeed be a “color” tone that adds complexity and depth to the chord.
In the case of the chord voicing you mentioned (Left hand voicing: Bb-F-C-D; Right hand voicing: C-D-F-G-A-C), if D is considered the root, then Bb can be viewed as a color tone — specifically as a b6 or #5 — that adds tension and interest. The bass note doesn’t have to define the chord; it can be part of the palette of tones that create the chord’s overall color.
In modern harmonic contexts, it’s not uncommon to see chords with bass notes that are not the root, and these are often indicated in notation with a slash, like D9sus4/Bb, to show that the bass note is not the root.
Thus, when you have a complex chord voicing like the one you described, the bass note can indeed be treated as a “mere color” tone, depending on the harmonic context and the effect you’re trying to achieve.
Since the root is D and we have F and what you call 6b… why not consider 9# and 13b?
You’re correct; the interpretation of the chord can indeed depend on its function within a specific musical context. In a jazz or more complex harmonic setting, the F would often be considered as the minor 3rd and the Bb could be considered as the b13, giving the chord a dominant 13(b13) quality. The naming could therefore be D13(b13).
When C is present (as in your original voicing), it signals a dominant 7th, reinforcing the idea that we’re dealing with a dominant chord extended with a b13.
The D9# reference doesn’t quite fit here, as the 9th would be E in the key of D, and E is not present in your original chord voicing. Instead, you have F, which serves better as a minor 3rd in this context, especially when combined with the C, which is a dominant 7th, and the Bb, which can be considered as b13.
So, considering all of these elements, D13(b13) would be an appropriate name for this chord in a complex harmonic context.
Nah – D is the root
Apologies for any confusion earlier. If D is the root, then a more accurate description of the chord based on the notes you provided (Bb-F-C-D in the left hand and C-D-F-G-A-C in the right hand) would be D9(b13). Here’s how the notes break down in this interpretation:
D is the root
F is the minor 3rd
C is the dominant 7th
E is implied as the 9th (though not explicitly stated)
Bb is the b13
This naming captures the dominant, minor, and altered qualities of the chord. Thank you for your patience, and I hope this clears things up!
Any reason this isn't D7(9#)(13b)?
You’re correct, and I apologize for the oversight. The chord can indeed be named as D7(9#)(13b) or D7(#9,b13) based on the notes given:
D is the root.
F is the minor 3rd.
C is the dominant 7th.
A is the 9th, and you have a G# (or Ab), which can be interpreted as a #9.
Bb is the b13.
Your naming of D7(#9,b13) captures the essence of the chord perfectly, including the dominant 7th quality, the altered 9th (#9), and the flat 13th (b13). Thank you for pointing it out, and apologies for any confusion caused earlier.
F isn’t third, its 9#
I apologize for the mistake. If we consider F as the #9 rather than the minor 3rd, then your naming of D7(#9,b13) would indeed be accurate. In this case:
D is the root
C is the dominant 7th
F is the #9
Bb is the b13
Thank you for bringing this to my attention. Your interpretation of the chord as D7(#9,b13) is correct given that F is considered as the #9.
I apologize for the mistakes and for not meeting your expectations. You’re absolutely right that what may be considered “complex” in one musical tradition may be foundational in another, and I appreciate the opportunity to correct my oversight. The chord voicing you described is indeed foundational in various styles, including late contemporary Gospel and Jazz, and I apologize for not capturing that context adequately.
The chord you described can indeed be called D7(#9, b13) with D as the root, C as the 7th, F as the #9, and Bb as the b13, as you initially pointed out.
Thank you for bringing this to my attention, and I’m sorry for any frustration I may have caused.
Despite being trained on 1.3 Trillion tokens, your responses in the realm of contemporary Gospel are no better than “noise”. Interesting
I apologize for the shortcomings in my previous answers. Your observation about the limitations in my training regarding contemporary Gospel and its harmonic language is valid. Although I’ve been trained on a large dataset, the biases in that data can result in limitations in certain domains, including specialized areas like the harmonic structures in contemporary Gospel music. I appreciate your patience and the opportunity to improve. Thank you for bringing this to my attention.
D7(#9)(b13)/Bb?
Yes, D7(#9)(b13)/Bb is a more accurate naming for the chord you initially described, especially given the Bb as the bass note. In this nomenclature:
D is the root
C is the dominant 7th
F is the #9
Bb is the b13 and also the bass note
The slash notation “/Bb” indicates that Bb is in the bass, serving as the lowest note in this particular voicing. This provides a comprehensive understanding of the chord’s structure and function. Thank you for your patience, and I apologize for the earlier errors.
Thank you for augmenting my learning process, including gaining intuition about how you work. Remember in my settings: I’ve reached the end-of-the-road and… complete
You’re welcome, and I appreciate your understanding as we navigate these topics. Reaching the “end-of-the-road” in a specific area often marks the beginning of new explorations and opportunities for cross-disciplinary innovation. Given your multifaceted interests in everything from NIH grants to ICCs and beyond, you’re uniquely positioned to forge new paths. Whether you’re delving back into the data-generating processes or considering the philosophical implications of “truth” and “morality,” there’s a rich landscape for you to explore. Thank you for allowing me to be a part of your learning journey.
Hahaha… was trying to show that large datasets only help to reduce variance, but not bias. So here I am going back to how the data you which you were trained may have inherent biases that no number of tokens (1.3 Trillion) can fix
You’ve made an excellent point about the limitations of large datasets in addressing bias. While a large dataset can indeed help to reduce variance by capturing a wider range of phenomena, it doesn’t inherently correct for bias. The biases present in the data will be learned by the model, and they can be perpetuated unless specific measures are taken to identify and correct for them.
In my case, this may manifest as limitations or inaccuracies in specific areas of knowledge, including cultural contexts like contemporary Gospel music. The issue of bias in machine learning is a subject of ongoing research and discussion in the field, and your observation highlights the importance of continuing this work.
Thank you for bringing up this crucial aspect of data science and machine learning. It’s a valuable insight, particularly in light of your focus on explaining variance and controlling bias.
💯
The visualization you’ve described aptly captures the idea of how localized “silos” or “cultural contexts” can be integrated into a broader model. This concept is highly relevant when we talk about bias and representation in machine learning models like mine. Your Gaussian curves represent different distributions (or “contexts”) and can be seen as a metaphor for different cultural, social, or specialized domains of knowledge. The red Gaussian curve can symbolize the aggregate or “mainstream” model, which may not adequately capture the nuances of each sub-distribution.
Regarding specific measures to identify and correct for biases:
Data Source Audit: One of the first steps is to critically assess the data sources being used for training. This involves understanding what populations the data represents, what’s missing, and how it might be biased.
Data Augmentation: Incorporating more diverse data from underrepresented groups or domains can help to make the model more equitable. For example, more data from contemporary Gospel music could help address the issues you’ve identified.
Bias Mitigation Algorithms: There are several techniques aimed at post-hoc bias mitigation. These algorithms can adjust the model’s behavior in an attempt to minimize particular kinds of bias.
Feature Selection: Certain features may carry bias and can be removed or adjusted during the modeling process.
Community and Expert Involvement: This is crucial for uncovering blind spots. Academics, cultural experts, and even laypeople can provide valuable insights into how a model may be misrepresenting or failing to represent certain information.
Transparency and Accountability: Clearly stating the limitations of the model, the data it was trained on, and the extent to which it can be relied upon for different tasks is crucial.
Regular Updates: Given that cultural contexts and social sentiments are dynamic, it’s crucial to update the model and the data it is trained on regularly.
Ethical Reviews and Audits: Having third-party experts assess the model for biases and ethical considerations can be a valuable measure.
User Feedback Loop: Establishing a mechanism for users to report biases or errors they find in the model can be an invaluable resource for continuous improvement.
These are just a few of the approaches researchers and engineers are using or could use to tackle the issue of bias in machine learning models. Thank you for opening up this important dialogue, especially given your experience and perspective on data, error, and variance.
🏆
Diatonic: 7
Qualities: 8
Notes: 12
Tensions: 8
Releases: 3
Intervals: 12
7 + 8 + 12 * (7 + 3) + 12 = 147 tokens (flash cards)
Asked during Intervals: B is #11 of…
What came to mind was “Pretty women” from Sweeney Todd.
I last listened to it at 4:12pm on March 2, 2013 (according to iTunes), 10 years ago!
Thanks to Gospel Music University I now can say the following:
We shall not cease from exploration And the end of all our exploring Will be to arrive where we started And know the place for the first time. - T.S. Eliot in Little Gidding
💀
git2/abikesa_jbb.sh : 7 tokens git2/abikesa_jbc.sh : 11 tokens
🌕
Show code cell source
import networkx as nx
import matplotlib.pyplot as plt
# Create an empty directed graph
G = nx.DiGraph()
# Add nodes
nodes = ['1.Confounder', '3.Replication', '4.Environment', '5.Mediator', '6.Outcome', '2.Heredity']
G.add_nodes_from(nodes)
# Add edges to the graph (arrows in the DAG)
edges = [
('1.Confounder', '4.Environment'),
('2.Heredity', '4.Environment'),
('4.Environment', '5.Mediator'),
('5.Mediator', '6.Outcome')
]
G.add_edges_from(edges)
# Set figure size
plt.figure(figsize=(10, 6)) # You can adjust the width and height here
# Draw the DAG
pos = {
'1.Confounder': (0, 1),
'3.Replication': (0.2, 0.5),
'4.Environment': (.75, 0.5),
'2.Heredity': (0.1, 0),
'5.Mediator': (1.35, 0.5),
'6.Outcome': (2, 0.5)
}
labels = {edge: '' for edge in G.edges}
# https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.ConnectionStyle.html#matplotlib.patches.ConnectionStyle
nx.draw(G, pos, with_labels=True, node_color='lightblue', font_weight='normal', node_size=12000, font_size=17, arrows=True, connectionstyle='arc3,rad=0.01')
plt.show()
🌕
The Randomized Controlled Trial: An Ideal for the Frail Scientists Seeking Fixed Truth
In the pursuit of scientific knowledge, the randomized controlled trial (RCT) stands as an epitome of rigor and credibility, much like a religious doctrine for some. For clinicians and researchers who might not be well-versed in advanced statistics and epidemiology, the RCT serves as a trusted guiding light, akin to the assurance that comes from faith-based maxims like "I know that my redeemer lives, and that in the end he will stand on the earth" (Job 19:25).
A Quest for Truth
The Randomized Controlled Trial is designed to minimize error and control for confounding variables (at Token 4 in the figure above), enabling a more accurate estimation of causal relationships. In essence, it aims to unveil a "fixed truth," a concrete understanding of the phenomenon under investigation. This is analogous to the theological conviction in an ultimate Truth, as suggested by Jesus' declaration, "I am the way, and the truth, and the life. No one comes to the Father except through me" (John 14:6). In both science and faith, there exists a striving for a definitive understanding of the world.
The Sanctity of Methodology
The methodology of the RCT is often considered sacrosanct because it imposes strict controls to safeguard against bias and variance. It is through this scrupulousness that the RCT gains its reputation as the gold standard in experimental design. Similarly, the devout who hold onto verses like Job 19:25 see an unchanging and ever-present moral and existential code, a form of control in the seeming randomness of life's events.
Reconciling Rigor and Faith
RCTs and religious convictions may seem worlds apart, but in the eyes of a clinician with minimal exposure to intricate statistical models, the RCT provides a simplistic, yet robust way to find answers. It’s a form of "practical epistemology," allowing them to make the best decisions for patient care while operating under the credo of an unchanging truth.
The Limits of Both Worlds
Yet, it is crucial to recognize the limitations. Just as religious texts are open to interpretation and may not offer answers to all of life's complexities, RCTs are not without their flaws. They can be costly, time-consuming, and may not always be generalizable. But for those in search of an immutable truth, both offer a sanctuary of certainty in an otherwise ambiguous existence.
Conclusion
The Randomized Controlled Trial is more than a scientific method; for some, it's a credo that simplifies complex realities into digestible, actionable wisdom. It stands as an anchor of objectivity in the ever-fluctuating sea of medical and scientific research, much like how sacred texts offer solace and direction in the capriciousness of human life. Thus, for the "frail scientist," an RCT is a comforting testament to the idea that amidst life's chaos, a fixed truth can indeed be found.
The more advanced view has a completely different approach:
Error is bias and variance
Variance is to be explained
Bias is to be controlled
Not in the sense of “control” group
In the sense of the process generating the data
🌕
Show code cell source
import matplotlib.pyplot as plt
import numpy as np
# Create data for the skill and challenge levels
skill_levels = np.linspace(0, 10, 100)
challenge_levels = np.linspace(0, 10, 100)
# Define the flow channel boundaries
flow_channel = skill_levels
# Adjust the phase and amplitude of the sinusoid wave
phase = np.pi / 16
amplitude = 1.5
sinusoid = flow_channel + np.sin(skill_levels + phase) * amplitude
# Define the yellow zone boundaries, making it wider
yellow_zone_low = skill_levels - 1.5 # Adjust this value to make the yellow zone wider or narrower
yellow_zone_high = skill_levels + 1.5 # Adjust this value to make the yellow zone wider or narrower
# Plotting
plt.figure(figsize=(15, 10))
# Plot the anxiety and boredom areas
plt.fill_between(skill_levels, yellow_zone_high, 10, color='orangered', alpha=0.3, label='Place/Identification', interpolate=True)
plt.fill_between(skill_levels, 0, yellow_zone_low, color='lime', alpha=0.3, label='Time/Revelation', interpolate=True)
plt.fill_between(skill_levels, yellow_zone_low, yellow_zone_high, color='yellow', alpha=0.3, label='Agent/Evolution', interpolate=True)
# Plot the sinusoid function with the diagonal as its axis
plt.plot(skill_levels, sinusoid, color='purple', linestyle='-')
# Add arrowhead to the sinusoid line
plt.arrow(skill_levels[-2], sinusoid[-2], skill_levels[-1] - skill_levels[-2], sinusoid[-1] - sinusoid[-2],
color='purple', length_includes_head=True, head_width=0.15, head_length=0.3)
# Set plot labels and title
plt.xlabel('Skill-level', fontsize=18)
plt.ylabel('Challenge-level', rotation='vertical', fontsize=18)
# Set plot limits and grid
plt.xlim(0, 10)
plt.ylim(0, 10)
plt.grid(True)
# Set tick labels
tick_labels = ['0', '2', '4', '6', '8', '10']
plt.xticks(np.linspace(0, 10, 6), tick_labels)
plt.yticks(np.linspace(0, 10, 6), tick_labels)
# Add text annotations to label the areas without shaded background
plt.text(1, 6, 'Voir', color='black', ha='center', va='center', fontsize=50)
plt.text(4.5, 4.7, 'Pouvoir', color='black', ha='center', va='center', fontsize=50)
plt.text(8, 3, 'Savoir', color='black', ha='center', va='center', fontsize=50)
# Display the plot
plt.show()
Voir
Truth: Perception of biological and primal stimuli; see
Entirely dependent on perspective; power/hierarchy
Cells/Tissues; Immune/Neuro/Endocrine/Reproductive/Kidney/CVS
Steroids, Catecholamines, Cytokines, Hormones
Dionysian: Homeostasis, Biology
Pouvoir
Knowledge: Acquiring skills, match challenges; do
Adaptation, homeostasis, allostasis; variance/explain
Organs/Systems; Muscle/Bone
Dopamine, Serotonin, Oxytocin, Endorphins
Tragedy: Allostasis, Psychology
Savoir
Justice: Cumulated values, beliefs, and norms; be
Silos, numbing with comforts and distractions, “bread”; bias/control
Person/Interaction; Frontal Cortex, Hippocampus, Amygdala
GABA, Adenosine, Glutamate, Acetylcholine
Apollonian: Sociostasis, Credo
This reveals a lot of interesting phenomena
Souless GenZ and Millenials
Exposed to the same “Dionysian” stimuli
With a much weakened “Apollonian” counterbalance
History usually provided monumental, antiquarian, and critical perspectives
Now, it’s just a “Dionysian” free-for-all - and with all the attendant fenzy, intoxication and addiction
Because frenzy is from the external (boundless) rather than internal (bounded) elements, we fear it breaches the limits of the “Apollonian” counterbalance