Life ⚓️#
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- What makes for a suitable problem for AI (Demis Hassabis, Nobel Lecture)?
- Space: Massive combinatorial search space
- Function: Clear objective function (metric) to optimize against
- Time: Either lots of data and/or an accurate and efficient simulator
- Guess what else fits the bill (Yours truly, amateur philosopher)?
- Space
- Intestines/villi
- Lungs/bronchioles
- Capillary trees
- Network of lymphatics
- Dendrites in neurons
- Tree branches
- Function
- Energy
- Aerobic respiration
- Delivery to "last mile" (minimize distance)
- Response time (minimize)
- Information
- Exposure to sunlight for photosynthesis
- Time
- Nourishment
- Gaseous exchange
- Oxygen & Nutrients (Carbon dioxide & "Waste")
- Surveillance for antigens
- Coherence of functions
- Water and nutrients from soil
From Ancient Grudge Break to New Mutiny? Racism as an Adversarial Reflex in the Human Information Infrastructure
1. Racism as Prejudice + Power: The Host Interrogates the Guest#
If racism is prejudice + power, then racism must be understood as a dynamic interrogation—a host interrogating a guest and deciding: “From ancient grudge brew new mutiny?” Is this guest friend or foe? A fellow traveler or an intruder? A negotiable identity or a speculative emergency?
The immune system has already solved this problem at a biological level. It does not welcome every molecular entity into the body; it runs interrogations. Antigen-presenting cells survey the landscape, holding molecules up to MHC complexes and asking: Is this an ancient grudge or a novel epitope? The answer is encoded in the ratio of noise to signal:
where \(X/Y\) represents noise-to-signal or molecule-to-epitope, determining whether a response is necessary.
In sociological terms, racism operates on the same adversarial reflex. The host society interrogates the guest: is this individual assimilable, a novel threat, or something still unknown? The lower the intelligence (or the more ancient the grudge), the more likely the system will misclassify benign guests as adversarial threats. High intelligence, on the other hand, allows for high noise-to-signal ratios, correctly identifying that not all novel information is harmful.
Thus, racism is not just about hostility; it is a system-level error in information processing—a hyperactive immune reflex that misidentifies harmless identities as dangerous intrusions.
Fig. 4 Veni-Vidi, Veni-Vidi-Vici. Yep, Red Queen Hypothesis all the way. This essay integrates your model’s adversarial framework, immune system parallels, and combinatorial search logic to explore racism as an error-prone interrogation mechanism. Source: Negotiated Identity#
2. Ancient Grudges vs. High Intelligence: The Problem of Low-Resolution Processing#
Keon West’s book The Science of Racism lays out the empirical evidence: racial bias persists not because people are unaware of prejudice but because the system defaults to low-resolution classifications. Teachers watching preschoolers unconsciously scrutinize black children more often. Crime stories are rated as more “terrorist-like” when the names are changed to Arab-sounding ones. These are not mere prejudices; they are error-prone pattern recognition mechanisms baked into social cognition.
The ancient grudge is the baseline—an old adversarial model that assumes the worst unless proven otherwise. In evolutionary terms, this was once useful; in small homogenous tribes, distrust of outsiders conferred survival advantages. But modern societies operate with higher cognitive bandwidth—or at least, they should. A high-functioning intelligence must tolerate high noise-to-signal ratios without defaulting to hostility. It must allow time to collect data, to differentiate rather than react reflexively.
However, systemic racism emerges when power (institutional control, media framing, historical inertia) keeps the interrogation system locked at a low intelligence setting. When the host system refuses to update its adversarial reflexes—when it keeps reacting to high-noise inputs as though they were all threats—it fails in precisely the way an autoimmune disease fails: by attacking its own body.
3. The Limits of Unconscious Bias: Systemic Racism as a Faulty Control Mechanism#
West’s analysis of unconscious bias is crucial. Many people believe that racism today is mostly implicit, unconscious, and accidental, but this ignores the real power structures that enforce bias even when personal prejudice is absent. A truly self-updating adversarial system—like a well-functioning immune response—should be able to recalibrate. Yet systemic racism persists even when explicit prejudice declines. Why?
The answer lies in a failure of regulatory feedback mechanisms. Just as PD-1 and CTLA-4 suppress excessive immune activation, a healthy society should have checks against overactive adversarial reflexes. Yet, institutions often reinforce rather than dampen these biases. Consider West’s example of voter ID laws: they restrict minority participation not because of personal racial animus, but because the system itself has been optimized to favor existing power structures.
This is what makes systemic racism so insidious: even in the absence of personal hostility, the interrogation framework remains biased at a structural level.
4. Moving Beyond the Autoimmune Loop: When Can We Collect Data?#
If racism is an overactive adversarial reflex, then what is the corrective mechanism? West points to increased intergroup contact under conditions of equal status and shared goals. But this is only part of the solution. The real question is: When does the system decide it can afford to collect data rather than react adversarially?
Again, the immune system provides an analogy. There are moments when an inflammatory response is necessary—when immediate action is required to neutralize a pathogen. But there are also moments when the system must engage in tolerance, suppressing an immune response to avoid unnecessary destruction. This is the challenge of racism at the systemic level: when does a society recognize that it is safe to stop reacting and instead engage in data collection?
Racism thrives when the system defaults to emergency mode—when it always assumes that noise = threat. The shift away from racism requires retraining the adversarial reflex, so that novel identities are not automatically coded as foreign invasions but as potential sources of social enrichment.
5. Conclusion: The Reweighting of Reflexes#
In the end, racism is a misallocation of intelligence and power. It is the failure to move beyond an adversarial heuristic that was once evolutionarily useful but is now destructive. The challenge is not merely about changing individual minds but about reprogramming the system’s interrogation reflexes.
West’s empirical approach provides evidence that this is possible. The same way that adaptive immunity learns to distinguish friend from foe, society can learn to reweight its adversarial reflexes. But this requires structural intelligence, the capacity to tolerate noise without reverting to hostility.
In an age of global interconnection, we can no longer afford the errors of low-resolution processing. The question we must ask, as a system, is no longer just “friend or foe?” but “is there room for negotiating identity?”—not as a threat, but as an opportunity.
Show code cell source
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
# Define the neural network layers
def define_layers():
return {
'Suis': ['DNA, RNA, 5%', 'Peptidoglycans, Lipoteichoics', 'Lipopolysaccharide', 'N-Formylmethionine', "Glucans, Chitin", 'Specific Antigens'],
'Voir': ['PRR & ILCs, 20%'],
'Choisis': ['CD8+, 50%', 'CD4+'],
'Deviens': ['TNF-α, IL-6, IFN-γ', 'PD-1 & CTLA-4', 'Tregs, IL-10, TGF-β, 20%'],
"M'èléve": ['Complement System', 'Platelet System', 'Granulocyte System', 'Innate Lymphoid Cells, 5%', 'Adaptive Lymphoid Cells']
}
# Assign colors to nodes
def assign_colors():
color_map = {
'yellow': ['PRR & ILCs, 20%'],
'paleturquoise': ['Specific Antigens', 'CD4+', 'Tregs, IL-10, TGF-β, 20%', 'Adaptive Lymphoid Cells'],
'lightgreen': ["Glucans, Chitin", 'PD-1 & CTLA-4', 'Platelet System', 'Innate Lymphoid Cells, 5%', 'Granulocyte System'],
'lightsalmon': ['Lipopolysaccharide', 'N-Formylmethionine', 'CD8+, 50%', 'TNF-α, IL-6, IFN-γ', 'Complement System'],
}
return {node: color for color, nodes in color_map.items() for node in nodes}
# Define edge weights
def define_edges():
return {
('DNA, RNA, 5%', 'PRR & ILCs, 20%'): '1/99',
('Peptidoglycans, Lipoteichoics', 'PRR & ILCs, 20%'): '5/95',
('Lipopolysaccharide', 'PRR & ILCs, 20%'): '20/80',
('N-Formylmethionine', 'PRR & ILCs, 20%'): '51/49',
("Glucans, Chitin", 'PRR & ILCs, 20%'): '80/20',
('Specific Antigens', 'PRR & ILCs, 20%'): '95/5',
('PRR & ILCs, 20%', 'CD8+, 50%'): '20/80',
('PRR & ILCs, 20%', 'CD4+'): '80/20',
('CD8+, 50%', 'TNF-α, IL-6, IFN-γ'): '49/51',
('CD8+, 50%', 'PD-1 & CTLA-4'): '80/20',
('CD8+, 50%', 'Tregs, IL-10, TGF-β, 20%'): '95/5',
('CD4+', 'TNF-α, IL-6, IFN-γ'): '5/95',
('CD4+', 'PD-1 & CTLA-4'): '20/80',
('CD4+', 'Tregs, IL-10, TGF-β, 20%'): '51/49',
('TNF-α, IL-6, IFN-γ', 'Complement System'): '80/20',
('TNF-α, IL-6, IFN-γ', 'Platelet System'): '85/15',
('TNF-α, IL-6, IFN-γ', 'Granulocyte System'): '90/10',
('TNF-α, IL-6, IFN-γ', 'Innate Lymphoid Cells, 5%'): '95/5',
('TNF-α, IL-6, IFN-γ', 'Adaptive Lymphoid Cells'): '99/1',
('PD-1 & CTLA-4', 'Complement System'): '1/9',
('PD-1 & CTLA-4', 'Platelet System'): '1/8',
('PD-1 & CTLA-4', 'Granulocyte System'): '1/7',
('PD-1 & CTLA-4', 'Innate Lymphoid Cells, 5%'): '1/6',
('PD-1 & CTLA-4', 'Adaptive Lymphoid Cells'): '1/5',
('Tregs, IL-10, TGF-β, 20%', 'Complement System'): '1/99',
('Tregs, IL-10, TGF-β, 20%', 'Platelet System'): '5/95',
('Tregs, IL-10, TGF-β, 20%', 'Granulocyte System'): '10/90',
('Tregs, IL-10, TGF-β, 20%', 'Innate Lymphoid Cells, 5%'): '15/85',
('Tregs, IL-10, TGF-β, 20%', 'Adaptive Lymphoid Cells'): '20/80'
}
# Define edges to be highlighted in black
def define_black_edges():
return {
('Lipopolysaccharide', 'PRR & ILCs, 20%'): '20/80',
('N-Formylmethionine', 'PRR & ILCs, 20%'): '51/49',
("Glucans, Chitin", 'PRR & ILCs, 20%'): '80/20',
('Specific Antigens', 'PRR & ILCs, 20%'): '95/5',
('PRR & ILCs, 20%', 'CD8+, 50%'): '20/80',
('PRR & ILCs, 20%', 'CD4+'): '80/20',
('CD4+', 'TNF-α, IL-6, IFN-γ'): '5/95',
('CD8+, 50%', 'TNF-α, IL-6, IFN-γ'): '49/51',
}
# Calculate node positions
def calculate_positions(layer, x_offset):
y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
return [(x_offset, y) for y in y_positions]
# Create and visualize the neural network graph
def visualize_nn():
layers = define_layers()
colors = assign_colors()
edges = define_edges()
black_edges = define_black_edges()
G = nx.DiGraph()
pos = {}
node_colors = []
# Create mapping from original node names to numbered labels
mapping = {}
counter = 1
for layer in layers.values():
for node in layer:
mapping[node] = f"{counter}. {node}"
counter += 1
# Add nodes with new numbered labels and assign positions
for i, (layer_name, nodes) in enumerate(layers.items()):
positions = calculate_positions(nodes, x_offset=i * 2)
for node, position in zip(nodes, positions):
new_node = mapping[node]
G.add_node(new_node, layer=layer_name)
pos[new_node] = position
node_colors.append(colors.get(node, 'lightgray'))
# Add edges with updated node labels
edge_colors = []
for (source, target), weight in edges.items():
if source in mapping and target in mapping:
new_source = mapping[source]
new_target = mapping[target]
G.add_edge(new_source, new_target, weight=weight)
edge_colors.append('black' if (source, target) in black_edges else 'lightgrey')
# Draw the graph
plt.figure(figsize=(12, 8))
edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
nx.draw(
G, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors,
node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
)
nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
plt.title("OPRAH™: Dorsal", fontsize=18)
plt.show()
# Run the visualization
visualize_nn()
Fig. 5 Innovation: Veni-Vidi, Veni-Vidi-Vici. If you’re protesting then you’re not running fast enough. Thus spake the Red Queens#