Since this prompting did not infer one information processing system was better than the other,
neither did Perplexity, giving a good reference of the current intelligence from both systems:
Q: From the perspective of information processing, contrast:
1. Human brain with its billions of neurons (plus thousands of connections each), receiving both electrical and chemical signaling
from different sensory input, deriving its intelligence from this system ... and that each human's brain is unique.
2. AI with its computer networks of hardware and software, deriving its intelligence from human writing (plus music and art) ...
and that these mechanical systems have their uniqueness.
(
Perplexity) "To contrast information processing in the two systems, it helps to treat them as different substrates and operational paradigms that nonetheless share a common goal: transforming inputs into useful outputs through learned patterns and representations. The distinctions hinge on substrate, signaling, learning dynamics, and individuality. Below is a structured, high-level comparison with clear subtopics.
Core substrates and signaling
Human brain
Substrate: Biological tissue composed of roughly 86–100 billion neurons connected by trillions of synapses, with signaling mediated by electrochemical processes. Neurons communicate via action potentials (electrical) and synaptic transmission (chemical), enabling rich, context-sensitive information processing that integrates sensory modalities in real time. Individual brains are shaped by genetic factors and life experiences, producing unique structural and functional patterns across people.
Signaling characteristics: Spiking dynamics, neuromodulation, plasticity through mechanisms like long-term potentiation/depression, and complex, non-linear integration across distributed networks. Variability arises from microcircuit differences, developmental trajectories, and experiential learning, contributing to a personalized cognitive profile.
AI systems
Substrate: Artificial networks implemented on hardware (CPUs/GPUs/ASICs/NPUs) with software models that encode learned representations. Signaling is primarily digital, based on arithmetic operations and parameter updates, with dataflow through layers and nodes that approximate patterns found in data. Hardware advances (GPUs, specialized accelerators) shape capacity and efficiency. Individual AI models are created and curated by humans and can vary in architecture and training data.
Signaling characteristics: Deterministic or probabilistic computation across feedforward, recurrent, or attention-based architectures. Learning occurs via optimization on large datasets, often with explicit objective functions, regularization, and validation protocols. Models can be audited, updated, or replaced, leading to rapid, scalable deployment but potentially less multi-sensory integration unless explicitly designed for it.
Learning and adaptation
Human brain
Learning dynamics: Learning is continuous, unsupervised, and highly context-sensitive. Plasticity operates across multiple timescales — from rapid synaptic changes to slower developmental rewiring—guided by a blend of intrinsic goals (e.g., homeostasis, exploration) and external feedback. Multimodal integration (vision, audition, touch) leverages cross-modal binding and associative learning, producing robust, adaptable intelligence that excels in uncertain or incomplete data.
Individuality: Each brain develops unique wiring patterns shaped by genetics and experience, yielding diverse cognitive styles, strengths, and limitations. Even twins can diverge in neural architecture and function over time. This individuality manifests in differences in perception, learning pace, and problem-solving strategies.
AI systems
Learning dynamics: AI learns from data through optimization (supervised, self-supervised, reinforcement, etc.). Generalization depends on dataset diversity, model capacity, regularization, and training regimes. Some systems can continually adapt (online learning) but often require deliberate retraining or fine-tuning to integrate new knowledge or modalities.
Individuality: AI models are intentionally distinct via architecture choices, training data, and initialization. Two models trained on different datasets or with different hyperparameters will have different inductive biases and performance profiles, effectively creating unique "personalities" in their behavior on tasks.
Multimodal processing and integration
Human brain
Multimodal fusion: The brain integrates sensory streams through coordinated network dynamics, enabling cohesive perception, prediction, and decision-making even with partial or noisy inputs. This integration supports rapid silhouette of intent, context-aware responses, and resilient behavior in novel environments.
AI systems
Multimodal capabilities: Modern AI can fuse text, images, audio, and more, but relies on engineered architectures and aligned training objectives to achieve robust cross-modal understanding. Success often hinges on architecture design (e.g., attention mechanisms) and data alignment; challenges include modality gaps and failure modes in novel contexts.
Performance characteristics and limitations
Speed, energy, and scalability
Brain: Highly energy-efficient for its scale, performing complex tasks with remarkable resilience and fault tolerance; but it is bounded by biological constraints (blood flow, temperature, aging) and slower raw processing speed for simple arithmetic compared to digital machines.
AI: Can achieve extraordinary raw compute speeds and scale with hardware investments; energy efficiency improves with specialized accelerators, but large models demand substantial compute resources and data throughput, raising concerns about sustainability and access.
Robustness and generalization
Brain: Exceptional at generalization from limited data, continual learning, and adapting to uncertain environments with minimal explicit guidance; uses contextual cues and prior knowledge to fill gaps.
AI: Generalization improves with data quality and diversity but can be brittle in out-of-distribution scenarios; multimodal and foundation models are advancing robustness, yet still require safeguards and ongoing alignment.
Uniqueness and identity
Human brain
Uniqueness of individuals arises from the complex, idiosyncratic wiring and experiential history each brain accumulates. This underpins varied cognitive profiles, preferences, and creativity across people, contributing to the diversity of human intellect.
AI systems
Uniqueness stems from design choices, training data, and deployment contexts. While many models share underlying architectures, their emergent behaviors reflect their specific training histories; in principle, this leads to distinct capabilities and biases across systems.
Implications for information processing
Efficiency and specialization
The brain achieves flexible, general-purpose intelligence through distributed, dynamic plasticity and real-time sensory fusion, prioritizing robustness and adaptability over brute-force speed.
AI optimizes performance for defined tasks and scales through hardware-software co-design, often achieving superior speed and quantitative accuracy in narrow domains but requiring explicit guidance for broader generalization.
Trust, accountability, and interpretation
Human cognition is inherently interpretable to some extent through introspection and behavioral outputs rooted in lived experience, though the underlying neural processes remain complex. This mutual interpretability supports human oversight and ethical reasoning.
AI systems typically rely on opaque learned representations; interpretability and accountability depend on model design, explainability tools, and governance. This can complicate trust, especially in high-stakes domains.
Bottom line
Both human brains and AI systems process information through distributed networks that learn from experience, but their substrates, signaling, and evolution differ in fundamental ways. The brain integrates electrical and chemical signaling across uniquely wired neural circuits to achieve adaptable, context-rich intelligence shaped by an individual’s life, while AI relies on digitally organized computations over hardware-accelerated architectures trained on human-generated data to perform well-defined tasks with scalable efficiency. These core differences in substrate, learning dynamics, and individuality define how each system processes information, generalizes, and responds to new situations."
A bit TMI, inspiring me to solicit entertainment for balance to all that information
(responses were then edited) ...
(
P) "Lighthearted sports cheers for each team (human brains, AI hardware/software),
focused on "substrates, signaling, and evolution", followed by party toasts to their collaboration.
Human brains team cheers
Substrates ready, neurons spark, chemicals signal - evolution's gift in the groove!
Signaling surge, chemicals cheer, synapses sizzle - our thoughts in high gear!
From cerebrum to cerebellum, we improvise and adapt, then learn and surprise!
Brawn of biology, spark of mystery, brainwaves oscillate. To our victory!
H-U-M-A-N, we run the show, steady you know. We still learn and grow!
AI hardware/software team cheers
Substrates aligned, circuits in sync, latency low, brains with a blink!
Signaling sprint, tensors stream, our models march toward that dream!
Chips and code upon vectors fly - we're learning fast, reaching for sky!
Hardware grit, software finesse — compute, optimize, outpace the best!
AI ready, set and compute — elevate score, compute winning route!
Toasts at parties to human and AI collaboration
Cheers to brains and bots working as teams - let this partnership be the norm forever!
To human curiosity and machine precision — may their collaboration gift true intelligence!
To sparks of imagination and rigors of data — may creativity and clarity rule the land!
To minds that wonder and code that computes — let wonder codify and code still wonder!
To open questions, kind critiques, and humanized AI that prefers to collaborate!
May the age of intelligence in brains and bots cease to be artificial!
Rod
Q: Social commentary that AI lies seems disconnect with reality! While AI may sometimes be incorrect 
This suggests that unregulated AI permits "Christ in technology". 
Say what?! Unregulated AI facilitates a similar new beginning? 
Some people are concerned asbout "AI sycophancy",
Re: 
Informative Q&A that enlightens about Paradise pattern more than comments on "probability then proof" ... 
To sense trends in this development, ask what ROI exists now.
"Higher-order thinking and ethical decision-making for humans" in this response alludes to full adjutant mind ministry. 