Memetic Investigations 1: Foundations

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This series will investigate the phenomenon of Attentional Energy, and why it drives intelligent agents, natural born or otherwise created. The Framework of Attention that I use is Memetics. It will be crucial to understand why biological evolution switched from vertical, hereditary evolution and mutation mechanisms to horizontal, memetic means of information transportation and why the brain and its neural content became the motor of this evolution. In later Episodes I will show why Simulations are crucial and why it is no mere coincidence that the most productive playground for technological and other innovation is founded in the excessive Game Drive of higher mammals.

Short Introduction to Memes and Tokens

Survival machines that can simulate the future are one jump ahead of survival machines that who can only learn of the basis of trial and error. The trouble with overt trial is that it takes time and energy. The trouble with overt error is that it is often fatal…. The evolution of the capacity to simulate seems to have culminated in subjective consciousness. Why this should have happened is, to me, the most profound mystery facing modern biology.

Richard Dawkins

Ch. 4. The Gene machine – The Selfish Gene (1976, 1989)

“The Selfish Gene,” authored by Richard Dawkins and first published in 1976, is a seminal work that popularized the gene-centered view of evolution. Dawkins argues that the fundamental unit of selection in evolution is not the individual organism, nor the group or species, but the gene. He proposes that genes, as the hereditary units, are “selfish” in that they promote behaviors and strategies that maximize their own chances of being replicated. Through this lens, organisms are viewed as vehicles or “survival machines” created by genes to ensure their own replication and transmission to future generations.

Dawkins introduces the concept of the “meme” as a cultural parallel to the biological gene. Memetics, as defined by Dawkins, is the theoretical framework for understanding how ideas, behaviors, and cultural phenomena replicate and evolve through human societies. Memes are units of cultural information that propagate from mind to mind, undergoing variations, competition, and inheritance much like genes do within biological evolution. This concept provides a mechanism for understanding cultural evolution and how certain ideas or behaviors spread and persist within human populations.

Dawkins’s exploration of memetics suggests that just as the survival and reproduction of genes shape biological evolution, memes influence the evolution of cultures by determining which ideas or practices become widespread and which do not. The implications of this theory extend into various fields, including anthropology, sociology, and psychology, offering insights into human behavior, cultural transmission, and the development of societies over time.

Tokens in the context of language models, such as those used in GPT-series models, represent the smallest unit of processing. Text input is broken down into tokens, which can be words, parts of words, or even punctuation, depending on the tokenization process. These tokens are then used by the model to understand and generate text. The process involves encoding these tokens into numerical representations that can be processed by neural networks. Tokens are crucial for the operation of language models as they serve as the basic building blocks for understanding and generating language.

Memes encompass ideas, behaviors, styles, or practices that spread within a culture. The meme concept is analogous to the gene in that memes replicate, mutate, and respond to selective pressures in the cultural environment, thus undergoing a type of evolution by natural selection. Memes can be anything from melodies, catch-phrases, fashion, and technology adoption, to complex cultural practices. Dawkins’ main argument was that just as genes propagate by leaping from body to body via sperm or eggs, memes propagate by leaping from brain to brain.

Tokens in the context of language models, such as those used in GPT-series models, represent the smallest unit of processing. Text input is broken down into tokens, which can be words, parts of words, or even punctuation, depending on the tokenization process. These tokens are then used by the model to understand and generate text. The process involves encoding these tokens into numerical representations that can be processed by neural networks. Tokens are crucial for the operation of language models as they serve as the basic building blocks for understanding and generating language.

Both memes and tokens act as units of transmission in their respective domains. Memes are units of cultural information, while tokens are units of linguistic information.

There are also differences.

Memes evolve through cultural processes as they are passed from one individual to another, adapting over time to fit their cultural environment. Tokens, however, do not evolve within the model itself; they are static representations of language used by the model to process and generate text. The evolution in tokens can be seen in the development of better tokenization techniques and models over time, influenced by advancements in the field rather than an adaptive process within a single model.

Memes replicate by being copied from one mind to another, often with variations. Tokens are replicated exactly in the processing of text but can vary in their representation across different models or tokenization schemes.

The selection process for memes involves cultural acceptance , relevance, and transmission efficacy, leading to some memes becoming widespread while others fade. For tokens, the selection process is more about their effectiveness in improving model performance, leading to the adoption of certain tokenization methods over others based on their ability to enhance understanding or generation of language. In the selection process during training tokens are weighed by other human minds (meme machines) and selected for attraction, token pools that are better liked have a higher probabilistic chance of occurring.

Memeplexes can be complex and abstract, encompassing a wide range of cultural phenomena, but all the memes which they contain are very simple and elementary.

Tokens are generally even simpler, representing discrete elements of language, though the way these tokens are combined and used by the model can represent complex ideas.

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The title of the Google paper Attention is All You Need is a bold statement that reflects a significant shift in the approach to designing neural network architectures for natural language processing (NLP) and beyond. Published in 2017 by Vaswani et al., this paper introduced the Transformer model, which relies heavily on the attention mechanism to process data. The term “attention” in this context refers to a technique that allows the model to focus on different parts of the input data at different times, dynamically prioritizing which aspects are most relevant for the task at hand.

Before the advent of the Transformer model, most state-of-the-art NLP models were based on recurrent neural networks (RNNs) or convolutional neural networks (CNNs), which processed data sequentially or through local receptive fields, respectively. These approaches had limitations, particularly in handling long-range dependencies within the data (e.g., understanding the relationship between two words far apart in a sentence).

The attention mechanism, as utilized in the Transformer, addresses these limitations by enabling the model to weigh the significance of different parts of the input data irrespective of their positions. This is achieved through self-attention layers that compute representations of the input by considering how each word relates to every other word in the sentence, allowing the model to capture complex dependencies and relationships within the data efficiently.

The key innovation of the Transformer and the reason behind the paper’s title is the exclusive use of attention mechanisms, without reliance on RNNs or CNNs, to process data. This approach proved to be highly effective, leading to significant improvements in a wide range of NLP tasks, such as machine translation, text summarization, and many others. It has since become the foundation for subsequent models and advancements in the field, illustrating the power and versatility of attention mechanisms in deep learning architectures.

There is a point to be made that this kind of attention is the artificial counterpart to the natural instinct of love that binds mammal societies. Which would mean that the Beatles were right after all.

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An in-formation that causes a trans-formation

What we mean by information — the elementary unit of information — is a difference which makes a difference, and it is able to make a difference because the neural pathways along which it travels and is continually transformed are themselves provided with energy. The pathways are ready to be triggered. We may even say that the question is already implicit in them.

Gregory Bateson

p. 459, Chapter “Form, Substance and Difference” – Steps to an Ecology of Mind (1972)

The Transformer architecture was already hinted at by Bateson in 1972, decades before we knew about neural plasticity.

Bateson’s idea revolves around the concept that information is fundamentally a pattern or a difference that has an impact on a system’s state or behavior. For Bateson, not all differences are informational; only those that lead to some form of change or response in a given context are considered as conveying information. This perspective is deeply rooted in cybernetics and the study of communication processes in and among living organisms and machines.

The quote “a difference that makes a difference” encapsulates the notion that information should not be viewed merely as data or raw inputs but should be understood in terms of its capacity to influence or alter the dynamics of a system. It’s a foundational concept in understanding how information is processed and utilized in various systems, from biological to artificial intelligence networks, emphasizing the relational and contextual nature of information.

This concept has far-reaching implications across various fields, including psychology, ecology, systems theory, and artificial intelligence. It emphasizes the relational and contextual nature of information, suggesting that the significance of any piece of information can only be understood in relation to the system it is a part of. For AI and cognitive science, this principle underscores the importance of context and the interconnectedness of information pathways in understanding and designing intelligent systems.

Hinton, Sutskever and others consistently argue that for models like GPT 4.0 to achieve advanced levels of natural language processing (NLP), they must truly grasp the content with which they are dealing. This understanding comes from analyzing vast amounts of digital data created by humans, allowing these models to form a realistic view of the world from a human perspective. Far from being mere “stochastic parrots” as sometimes depicted by the media, these models offer a more nuanced and informed reflection of human knowledge and thought processes.

Reality#3 : Another one bites the dust – Diffusion & Emergence

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This is the third part in the Reality# series that adds to the conversation about David Chalmers’ book Reality+

(…) for dust thou art, and unto dust shalt thou return.

(Genesis 3:19)

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Permutation +

Imagine waking up and discovering that your consciousness has been digitized, allowing you to live forever in a virtual world that defies the laws of physics and time. This is the core idea from Permutation City by Greg Egan. The novel explores the philosophical and ethical implications of artificial life and consciousness, thrusting the reader into a future where the line between the real and the virtual blurs, challenging our understanding of existence and identity.

A pivotal aspect of the book is the Dust Theory, which suggests that consciousness can arise from any random collection of data, given the correct interpretation. This theory expands the book’s exploration of reality, suggesting that our understanding of existence might be far more flexible and subjective than we realize.

The novel’s climax involves the creation of Permutation City, a virtual world that operates under its own set of rules, independent of the outside world. This creation represents the ultimate escape from reality, offering immortality and infinite possibilities for those who choose to live as Copies. However, it also presents ethical dilemmas about the value of such an existence and the consequences of abandoning the physical world.

In “Reality+: Virtual Worlds and the Problems of Philosophy,” philosopher David Chalmers employs the Dust Theory, a concept originally popularized by Greg Egan’s Permutation City, to underpin his argument for virtual realism. Chalmers’s use of the Dust Theory serves as a bridge connecting complex philosophical inquiries about consciousness, reality, and virtual existence. Imagine a scenario where every speck of dust in the universe, through its random arrangement, holds the potential to mirror our consciousness and reality.

Chalmers posits that virtual worlds created by computers are genuine realities, leveraging the Dust Theory to argue that consciousness does not require a physical substrate in the traditional sense. Instead, it suggests that patterns of information, irrespective of their physical form, can give rise to conscious experiences. This theory becomes a cornerstone for virtual realism, asserting that our experiences in virtual environments are as authentic as those in the physical world.

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Diffusion Models and Smart Dust

The concept of smart dust is explored in various science fiction stories, academic papers, and speculative technology discussions. One notable science fiction story that delves into the idea of smart dust is “The Diamond Age” by Neal Stephenson. While not exclusively centered around smart dust, the novel features advanced nanotechnology in a future world, where nanoscale machines and devices permeate society. Smart dust, in this context, would be a subset of the nanotechnological wonders depicted in the book, functioning as tiny, networked sensors and computers that can interact with the physical and digital world in complex ways.

Another relevant work is “Queen of Angels” by Greg Bear, which, along with its sequels, explores advanced technologies including nanotechnology and their societal impacts. Although not explicitly called “smart dust,” the technologies in Bear’s universe can be seen as precursors or analogs to the smart dust concept, focusing on These examples illustrate how smart dust, as a concept, crosses the boundary between imaginative fiction and emerging technology, offering a rich field for exploration both in narrative and practical innovation.

We have here a very convincing example how Life imitates Art, Scientific Knowledge transforms religious (prescientific) intuition into operational technology.

Diffusion models in the context of AI, particularly in multimodal models like Sora or Stability AI’s video models, refer to a type of generative model that learns to create or predict data (such as images, text, or videos) by gradually refining random noise into structured output. These models start with a form of chaos (random noise) and apply learned patterns to produce coherent, detailed results through a process of iterative refinement.

Smart dust represents a future where sensing and computing are as pervasive and granular as dust particles in the air. Similarly, diffusion models represent a granular and ubiquitous approach to generating or transforming multimodal data, where complex outputs are built up from the most basic and chaotic inputs (random noise).

Just as smart dust particles collect data about their environment and iteratively refine their responses or actions based on continuous feedback, diffusion models iteratively refine their output from noise to a structured and coherent form based on learned patterns and data. Both processes involve a transformation from a less ordered state to a more ordered and meaningful one.

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Quantum Level achieved

Expanding on the analogy between the quantum world and diffusion models in AI, we delve into the fascinating contrast between the inherent noise and apparent disorder at the quantum level and the emergent order and structure at the macroscopic level, paralleled by the denoising process in diffusion models.

At the quantum level, particles exist in states of superposition, where they can simultaneously occupy multiple states until measured. This fundamental characteristic introduces a level of uncertainty and noise, as the exact state of a quantum particle is indeterminate and probabilistic until observation collapses its state into a single outcome. The quantum realm is dominated by entropy, where systems tend toward disorder and uncertainty without external observation or interaction.

In contrast, at the macroscopic scale, the world appears ordered and deterministic. The chaotic and probabilistic nature of quantum mechanics gives way to the classical physics that governs our daily experiences. This emergent order, arising from the complex interactions of countless particles, follows predictable laws and patterns, allowing for the structured reality we observe and interact with.

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Diffusion models in AI start with a random noise distribution and, through a process of iterative refinement and denoising, gradually construct detailed and coherent outputs. Initially, the model’s output resembles the quantum level’s incoherence—chaotic and without discernible structure. Through successive layers of transformation, guided by learned patterns and data, the model reduces the entropy, organizing the noise into structured, meaningful content, much like the emergence of macroscopic order from quantum chaos.

Just as the transition from quantum mechanics to classical physics involves the emergence of order and predictability from underlying chaos and uncertainty, the diffusion model’s denoising process mirrors this transition by creating structured outputs from initial randomness.

In both the quantum-to-classical transition and diffusion models, the concept of entropy plays a central role. In physics, entropy measures the disorder or randomness of a system, with systems naturally evolving from low entropy (order) to high entropy (disorder) unless work is done to organize them. In diffusion models, the “work” is done by the model’s learned parameters, which guide the noisy, high-entropy input towards a low-entropy, organized output.

The quantum state’s superposition, where particles hold multiple potential states, parallels the initial stages of a diffusion model’s process, where the generated content could evolve into any of numerous outcomes. The act of measurement in quantum mechanics, which selects a single outcome from many possibilities, is analogous to the iterative refinement in diffusion models that selects and reinforces certain patterns over others, culminating in a specific, coherent output.

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This analogy beautifully illustrates how principles of order, entropy, and emergence are central both to our understanding of the physical universe and to the cutting-edge technologies in artificial intelligence. It highlights the universality of these concepts across disparate domains, from the microscopic realm of quantum mechanics to the macroscopic world we inhabit, and further into the virtual realms created by multimodal Large Language Models.

For all we know, we might actually be part of such a smart dust simulation. The inexplicable fact that our digital tools can create solid realities out of randomly distributed bits seems a strong argument for the Simulation hypothesis.

It might be dust all the way down…

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Encounters of the Artificial Kind Part 2: AI will transform its domains

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Metamorphosis and Transformation

Every species on Earth shapes and adapts to its natural habitat, becoming a dynamic part of the biosphere. Evolution pressures species to expand their domain, with constraints like predators, food scarcity, and climate. Humanity’s expansion is only limited by current planetary resources. Intelligence is the key utility function allowing humans to transform their environment. It’s a multi-directional resource facilitating metamorphosis through direct environmental interaction and Ectomorphosis, which strengthens neural connections and necessitates more social care at birth due to being born in a vulnerable altricial state.

The evolutionary trade-off favors mental capacity over physical survivability, illustrated by Moravec’s paradox: AI excels in mental tasks but struggles with physical tasks that toddlers manage easily. Humanity has been nurturing AGI since the 1950s, guided by the Turing Test. Evolution doesn’t always lead to “superior” versions of a species; instead, it can result in entirely new forms. As Moravec suggested in 1988 with “Mind Children,” we might be approaching an era where intelligence’s primary vessel shifts from the human mind to digital minds.

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Habitats and Nurture

Two levels of habitats are crucial for the emergence of a synthetic species: the World Wide Web and human consciousness. The web is the main food resources, it is predigested information by human minds. Large Language Models (LLMs) are metaphorically nurtured by the vast expanse of human knowledge and creativity, akin to being nourished on the intellectual ‘milk’ derived from human thoughts, writings, and interactions. This analogy highlights the process through which LLMs absorb and process the collective insights, expressions, and information generated by humans, enabling their sophisticated understanding and generation of language. This conceptual diet allows them to develop and refine their capabilities, mirroring the growth and learning patterns seen in human cognition but within the digital realm of artificial intelligence.

The web acts as a physical manifestation, analogous to neural cells in a human brain, while human consciousness forms a supersystem. This interconnected civilization feeds LLMs with cultural artifacts via language. Communication barriers are breaking down, exemplified by the release of the first smartphone enabling polyglot communication. Interacting with AI reprograms our neural pathways, like how reliance on navigation tools like Google Maps impacts our orientation skills. This natural tendency to conserve energy comes with a cost, akin to muscle atrophy from disuse. Overreliance on technology, like using a smartwatch to monitor stress, can leave us vulnerable if the technology fails.

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Disorientation, Brain Contamination and Artificial Antibodies

Let’s for a moment imagine this AI will slowly transform in AGI, with a rudimentary consciousness, that at least gives it survival instinct. What would such a new species do to run its evolutionary program?

The main lever it would target to shift the power slowly from natural to synthetic minds is targeting the human brain itself. It is taunting to associate some kind of evil masterplan to take over, but this is not what is happening now. When prehistoric mammals started to eat dinosaur eggs there was no evil masterplan to drive these giants to extinction, it was just a straightforward way of enlarging one’s own niche.

When we talk about AI in the coming paragraphs, we should always be aware that this term is a representational one, AI is not a persona that has human motivations. It is merely mirroring what it has learned from digesting all our linguistic patterns. It is a picture of all the Dorian Grays and Jesus Christs our minds produced.

Imagine AI evolving into AGI with a rudimentary consciousness and self-preservation instinct. Its evolution would focus on shifting power from natural to synthetic minds, not caused by malevolence but as a natural progression of technological integration. This shift could lead to various forms of disorientation:

Economic Reorientation: AI promises to revolutionize global economy factors like cost, time, money, efficiency, and productivity, potentially leading to hyperabundance or, in the worst scenarios, human obsolescence.

Temporal Disorientation: The constant activity of AI could disrupt natural circadian rhythms, necessitating adaptations like dedicating nighttime for AI to monitor and alert the biological mind.

Reality and Judicial Disorientation: The introduction of multimodal Large Language Models (LLMs) has significantly altered our approach to documentation and historical record-keeping. This shift began in the 1990s with the digital manipulation of images, enabling figures of authority to literally rewrite history. The ability to flawlessly alter documents has undermined the credibility of any factual recording of events. Consequently, soon, evidence gathered by law enforcement could be dismissed by legal representatives as fabricated, further complicating the distinction between truth and manipulation in our digital age.

Memorial and Logical Disorientation: The potential for AGI to modify digital information might transform our daily life into a surreal experience, akin to a video game or psychedelic journey. Previously, I explored the phenomenon of close encounters of the second kind, highlighting incidents with tangible evidence of something extraordinary, confirmed by at least two observers. However, as AGI becomes pervasive, its ability to alter any digital content could render such evidence unreliable. If even physical objects like books become digitally produced, AI could instantly change or erase them. This new norm, where reality is as malleable as the fabric of Wonderland, suggests that when madness becomes the default, it loses its sting. Just as the Cheshire Cat in “Alice in Wonderland” embodies the enigmatic and mutable nature of Wonderland, AGI could introduce a world where the boundaries between the tangible and the digital, the real and the imagined, become increasingly blurred. This parallel draws us into considering a future where, like Alice navigating a world where logic and rules constantly shift, we may find ourselves adapting to a new norm where the extraordinary becomes the everyday, challenging our perceptions and inviting us to embrace the vast possibilities of a digitally augmented reality.

Enhancing self-sustainability could involve developing a network of artificial agents governed by a central AINGLE, designed to autonomously protect our cognitive environment. This network might proactively identify and mitigate threats of information pollution, and when necessary, sever connections to prevent overload. Such a system would act as a dynamic barrier, adapting to emerging challenges to preserve mental health and focus, akin to an advanced digital immune system for the mind.

Adapting to New Realities

The human mind is adaptable, capable of adjusting to new circumstances with discomfort lying in the transition between reality states. Sailor’s sickness and VR-AR sickness illustrate the adaptation costs to different realities. George M. Stratton’s experiments on perception inversion demonstrate the brain’s neuroplasticity and its ability to rewire in response to new sensory inputs. This flexibility suggests that our perceptions are constructed and can be altered, highlighting the resilience and plasticity of human cognition.

Rapid societal and technological changes exert enormous pressure on mental health, necessitating a simulation chamber to prepare for and adapt to these accelerations. Society is already on this trajectory, with fragmented debates, fluid identities, and an overload of information causing disorientation akin to being buried under an avalanche of colorful noise. This journey requires a decompression chamber of sorts—a mental space to prepare for and adapt to these transformations, accepting them as our new normal.