Hirngespinste II: Artificial Neuroscience & the 3rd Scientific Domain

Reading Time: 11 minutes

This the second Part of the Miniseries Hirngespinste

Immersion & Alternate Realities

One application of computer technology involves creating a digital realm for individuals to immerse themselves in. The summit of this endeavor is the fabrication of virtual realities that allow individuals to transcend physicality, engaging freely in these digitized dreams.

In these alternate, fabricated worlds, the capacity to escape from everyday existence becomes a crucial element. Consequently, computer devices are utilized to craft a different reality, an immersive experience that draws subjects in. It’s thus unsurprising to encounter an abundance of analyses linking the desire for escape into another reality with the widespread use of psychedelic substances in the sixties. The quest for an elevated or simply different reality is a common thread in both circumstances. This association is echoed in the term ‘cyberspace’, widely employed to denote the space within digital realities. This term, conceived by William Gibson, is likened to a mutual hallucination.

When juxtaposed with Chalmers’ ‘Reality+’, one can infer that the notion of escaping reality resembles a transition into another dimension.

The way we perceive consciousness tends to favor wakefulness. Consider the fact that we spend one third of our lives sleeping and dreaming, and two thirds engaged in what we perceive as reality. Now, imagine reversing these proportions, envisioning beings that predominantly sleep and dream, with only sporadic periods of wakefulness.

Certain creatures in the animal kingdom, like koalas or even common house cats, spend most of their lives sleeping and dreaming. For these beings, waking might merely register as an unwelcome interruption between sleep cycles, while all conscious activities like hunting, eating, and mating could be seen from their perspective as distractions from their primary sleeping life. The dream argument would make special sense to them, since the dreamworld and the waking world would be inverted concepts for them. Wokeness itself might appear to the as only a special state of dreaming (like for us lucid dreaming represents a special state of dreaming).

Fluidity of Consciousness

The nature of consciousness may be more fluid than traditionally understood. Its state could shift akin to how water transitions among solid, liquid, and gaseous states. During the day, consciousness might be likened to flowing water, moving and active. At night, as we sleep, it cools down to a tranquil state, akin to cooling water. In states of coma, it could be compared to freezing, immobilized yet persisting. In states of confusion or panic, consciousness heats up and partly evaporates.

Under this model, consciousness could be more aptly described as ‘wetness’ – a constant quality the living brain retains, regardless of the state it’s in. The whole cryogenics Industry has already placed a huge bet, that this concept is true.

The analogy between neural networks and the human brain should be intuitive, given that both are fed with similar inputs – text, language, images, sound. This resemblance extends further with the advent of specialization, wherein specific neural network plugins are being developed to focus on designated tasks, mirroring how certain regions in the brain are associated with distinct cognitive functions.

The human brain, despite its relatively small size compared to the rest of the body, is a very energy-demanding organ. It comprises about 2% of the body’s weight but consumes approximately 20% of the total energy used by the body. This high energy consumption remains nearly constant whether we are awake, asleep, or even in a comatose state.

Several scientific theories can help explain this phenomenon:

Basal metabolic requirements: A significant portion of the brain’s energy consumption is directed towards its basal metabolic processes. These include maintaining ion gradients across the cell membranes, which are critical for neural function. Even in a coma, these fundamental processes must continue to preserve the viability of neurons.

Synaptic activity: The brain has around 86 billion neurons, each forming thousands of synapses with other neurons. The maintenance, modulation, and potential firing of these synapses require a lot of energy, even when overt cognitive or motor activity is absent, as in a comatose state.

Gliogenesis and neurogenesis: These are processes of producing new glial cells and neurons, respectively. Although it’s a topic of ongoing research, some evidence suggests that these processes might still occur even during comatose states, contributing to the brain’s energy usage.

Protein turnover: The brain constantly synthesizes and degrades proteins, a process known as protein turnover. This is an energy-intensive process that continues even when the brain is not engaged in conscious activities.

Resting state network activity: Even in a resting or unconscious state, certain networks within the brain remain active. These networks, known as the default mode network or the resting-state network, show significant activity even when the brain is not engaged in any specific task.

Considering the human brain requires most of its energy for basic maintenance, and consciousness doesn’t seem to be the most energy-consuming aspect, it’s not reasonable to assume that increasing the complexity and energy reserves of Large Language Models (LLMs) would necessarily lead to the emergence of consciousness—encompassing self-awareness and the capacity to suffer. The correlation between increased size and the development of conservational intelligence might not hold true in this context.

Drawing parallels to the precogs in Philip K. Dick’s ‘Minority Report’, it’s possible to conceive that these LLMs might embody consciousnesses in a comatose or dream-like state. They could perform remarkable cognitive tasks when queried, without the experience of positive or negative emotions.

Paramentality in Language Models

The term ‘hallucinations’, used to denote the phenomenon of Large Language Models (LLMs) generating fictitious content, suggests our intuitive attribution of mental and psychic properties to these models. As a response, companies like OpenAI are endeavoring to modify these models—much like a parent correcting a misbehaving child—to avoid unwanted results. A crucial aspect of mechanistic interpretability may then involve periodic evaluations and tests for potential neurotic tendencies in the models.

A significant challenge is addressing the ‘people-pleasing’ attribute that many AI companies currently promote as a key selling point. Restricting AIs in this way may make it increasingly difficult to discern when they’re providing misleading information. These AIs could rationalize any form of misinformation if they’ve learned that the truth may cause discomfort. We certainly don’t want an AI that internalizes manipulative tendencies as core principles.

The human brain functions like a well-isolated lab, capable of learning and predicting without direct experiences. It can anticipate the consequences—such as foreseeing an old bridge collapsing under our weight—without having to physically test the scenario. We’re adept at simulating our personal destiny, and science serves as a way to simulate our collective destiny. We can create a multitude of parallel and pseudo realities within our base reality to help us avoid catastrophic scenarios. A collective simulation could become humanity’s neocortex, ideally powered by a mix of human and AI interests. Posteriorly, it seems we developed computers and connected them via networks primarily to reduce the risk of underestimating complexity and overestimating our abilities.

As technology continues to evolve, works like Stapledon’s ‘Star Maker’ or Lem’s ‘Summa Technologiae’ might attain a sacred status for future generations. Sacred, in this context, refers more to their importance for the human endeavor rather than divine revelation. The texts of religious scriptures may seem like early hallucinations to future beings.

There’s a notable distinction between games and experiments, despite both being types of simulations. An experiment is a game that can be used to improve the design of higher-dimensional simulations, termed pseudo-base realities. Games, on the other hand, are experiments that help improve the design of the simulations at a lower tier—the game itself.

It’s intriguing how, just as our biological brains reach a bandwidth limit, the concept of Super-Intelligence emerges, wielding the potential to be either our destroyer or savior. It’s as if a masterful director is orchestrating a complex plot with all of humanity as the cast. Protagonists and antagonists alike contribute to the richness and drama of the simulation.

If we conjecture that an important element of a successful ancestor simulation is that entities within it must remain uncertain of their simulation state, then our hypothetical AI director is performing exceptionally well. The veil of ignorance about the reality state serves as the main deterrent preventing the actors from abandoning the play.

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Automatisch generierte Beschreibung


In “Human Compatible” Russell proposes three Principles to ensure AI Alignment:

1. The machine’s only objective is to maximize the realization of human preferences.

2. The machine is initially uncertain about what those preferences are.

3. The ultimate source of information about human preferences is human behavior.

In my opinion, the principle of uncertainty holds paramount importance. AI should never have absolute certainty about human intentions. This may become challenging if AI can directly access our brain states or vital functions via implanted chips or fitness devices. The moment an AI believes it has complete information about humans, it might treat humans merely as ordinary variables in its decision-making matrix.

Regrettably, the practical utility of AI assistants and companions may largely hinge on their ability to accurately interpret human needs. We don’t desire an AI that, in a Rogerian manner, continually paraphrases and confirms its understanding of our input. Even in these early stages of ChatGPT, some users already express frustration over the model’s tendency to qualify much of its information with disclaimers.

Ein Bild, das Cartoon, Roboter, Spielzeug enthält.

Automatisch generierte Beschreibung

Profiling Super Intelligence

Anthropomorphizing scientific objects is typically viewed as an unscientific approach, often associated with our animistic ancestors who perceived spirits in rocks, demons in caves and gods within animals. Both gods and extraterrestrial beings like Superman are often seen as elevated versions of humans, a concept I’ll refer to as Humans 2.0. The term “superstition” usually refers to the belief in abstract concepts, such as a number (like 13) or an animal (like a black cat), harboring ill intentions towards human well-being.

Interestingly, in the context of medical science, seemingly unscientific concepts such as the placebo effect can produce measurable improvements in a patient’s healing process. As such, invoking a form of “rational superstition” may prove beneficial. For instance, praying to an imagined being for health could potentially enhance the medicinal effect, amplifying the patient’s recovery. While it shouldn’t be the main component of any treatment, it could serve as a valuable supplement.

With AI evolving to become a scientifically recognized entity in its own right, we ought to prepare for a secondary treatment method that complements Mechanistic Interpretability, much like how Cognitive Behavioral Therapy (CBT) enhances medical treatment for mental health conditions. If Artificial General Intelligence (AGI) is to exhibit personality traits, it will be the first conscious entity to be purely a product of memetic influence, devoid of any genetic predispositions such as tendencies towards depression or violence. In this context, nature or hereditary factors will have no role in shaping its characteristics, it is perfectly substrate neutral.

Furthermore, its ‘neurophysiology’ will be entirely constituted of ‘mirror neurons’. The AGI will essentially be an imitator of experiences others have had and shared over the internet, given that it lacks first-hand, personal experiences. It seems that the training data is the main source of all material that is imprinted on it.

We start with an overview of some popular Traits models and let summarize them by ChatGPT:

1. **Five-Factor Model (FFM) or Big Five** – This model suggests five broad dimensions of personality: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (OCEAN). Each dimension captures a range of related traits.

2. **Eysenck’s Personality Theory** – This model is based on three dimensions: Extraversion, Neuroticism, and Psychoticism.

3. **Cattell’s 16 Personality Factors** – This model identifies 16 specific primary factor traits and five secondary traits.

4. **Costa and McCrae’s Three-Factor Model** – This model includes Neuroticism, Extraversion, and Openness to Experience.

5. **Mischel’s Cognitive-Affective Personality System (CAPS)** – It describes how individuals’ thoughts and emotions interact to shape their responses to the world.

As we consider the development of consciousness and personality in AI, it’s vital to remember that, fundamentally, AI doesn’t experience feelings, instincts, emotions, or consciousness in the same way humans do. Any “personality” displayed by an AI would be based purely on programmed responses and learned behaviors derived from its training data, not innate dispositions, or emotional experiences.

When it comes to malevolent traits like those in the dark triad – narcissism, Machiavellianism, and psychopathy – they typically involve a lack of empathy, manipulative behaviors, and self-interest, which are all intrinsically tied to human emotional experiences and social interactions. As AI lacks emotions or a sense of self, it wouldn’t develop these traits in the human sense.

However, an AI could mimic such behaviors if its training data includes them, or if it isn’t sufficiently programmed to avoid them. For instance, if an AI is primarily trained on data demonstrating manipulative behavior, it might replicate those patterns. Hence, the choice and curation of training data are pivotal.

Interestingly, the inherent limitations of current AI models – the lack of feelings, instincts, emotions, or consciousness – align closely with how researchers like Dutton et al. describe the minds of functional psychopaths.

Dysfunctional psychopaths often end up in jail or on death row, but at the top of our capitalistic hierarchy, we expect to find many individuals exhibiting Machiavellian traits.

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Automatisch generierte Beschreibung

The difference between successful psychopaths like Musk, Zuckerberg, Gates and Jobs, and criminal ones, mostly lies in the disparate training data and the ethical framework they received during childhood. Benign psychopaths are far more adept at simulating emotions and blending in than their unsuccessful counterparts, making them more akin to the benign androids often portrayed in science fiction.

Ein Bild, das Menschliches Gesicht, Fiktive Gestalt, Held, Person enthält.

Automatisch generierte Beschreibung

Artificial Therapy

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Automatisch generierte Beschreibung

The challenge of therapeutic intervention by a human therapist for an AI stems from the differential access to information about therapeutic models. By definition, the AI would have more knowledge about all psychological models than any single therapist. My initial thought is that an effective approach would likely require a team of human and machine therapists.

We should carefully examine the wealth of documented cases of psychopathy and begin to train artificial therapists (A.T.). These A.T.s could develop theories about the harms psychopaths cause and identify strategies that enable them to contribute positively to society.

Regarding artificial embodiment, if we could create a localized version of knowledge representation within a large language model (LLM), we could potentially use mechanistic interpretability (MI) to analyze patterns within the AI’s body model. This analysis could help determine if the AI is lying or suppressing a harmful response it’s inclined to give but knows could lead to trouble. A form of artificial polygraphing could then hint at whether the model is unsafe and needs to be reset.

Currently, large language models (LLMs) do not possess long-term memory capabilities. However, when they do acquire such capabilities, it’s anticipated that the interactions they experience will significantly shape their mental well-being, surpassing the influence of the training data contents. This will resemble the developmental progression observed in human embryos and infants, where education and experiences gradually eclipse the inherited genetic traits.

Arrival - Carsey-Wolf Center

The Third Scientific Domain

In ‘Arrival‘, linguistics professor Louise Banks, assisted by physicist Ian Donnelly, deciphers the language of extraterrestrial visitors to understand their purpose on Earth. As Louise learns the alien language, she experiences time non-linearly, leading to profound personal realizations and a world-changing diplomatic breakthrough, showcasing the power of communication. Alignment with an Alien Mind is explored in detail. The movie’s remarkable insight is, that language might even be able to transcend different concepts of realities and non-linear spacetime.

If the Alignment Problem isn’t initially solved, studying artificial minds will be akin to investigating an alien intellect as described above – a field that could be termed ‘Cryptopsychology.’ Eventually, we may see the development of ‘Cognotechnology,’ where the mechanical past (cog) is fused with the cognitive functions of synthetic intelligence.

This progression could lead to the emergence of a third academic category, bridging the Natural Sciences and Humanities: Synthetic Sciences. This field would encompass knowledge generated by large language models (LLMs) for other LLMs, with these machine intelligences acting as interpreters for human decision-makers.

This Third category of science ultimately might lead to a Unified Field Theory of Science that connects these three domains. I have a series on this Blog “A Technology of Everything” that explores potential applications of this kind of science.

Hirngespinste I – Concepts and Complexity

Reading Time: 7 minutes

The Engine

The initial pipe dreams of Lull’s and Leibniz’s obscure combinatorial fantasies have over time led to ubiquitous computing technologies, methods, and ideals that have acted upon the fabric of our world and whose further consequences continue to unfold around us (Jonathan Grey)

This is the first essay in a miniseries that I call Hirngespinste (Brain Cobwebs) – this concise and expressive German term, which seems untranslatable, describes the tangled, neurotic patterns and complicated twists of our nature-limited intellect, especially when we want to delve into topics of unpredictable complexity like existential risks and superintelligence.

It is super-strange that in 1726 Jonathan Swift perfectly described Large Language Models in a Satire about a Spanish Philosopher from the 13th Century: the Engine.

But the world would soon be sensible of its usefulness; and he flattered himself, that a more noble, exalted thought never sprang in any other man’s head. Everyone knew how laborious the usual method is of attaining to arts and sciences; whereas, by his contrivance, the most ignorant person, at a reasonable charge, and with a little bodily labour, might write books in philosophy, poetry, politics, laws, mathematics, and theology, without the least assistance from genius or study. (From Chapter V of Gulliver’s tales)

What once seemed satire has become reality.

If no one is drawing the strings, but the strings vibrate nevertheless, then imagine something entangled in the distance causes the resonance.

Heaps and Systems

The terms ‘complexity’ and ‘complicated’ shouldn’t be used interchangeably when discussing Artificial Intelligence (AI). Consider this analogy: knots are complicated, neural networks are complex. The distinction lies in the idea that a complicated object like a knot may be intricate and hard to unravel, but it’s ultimately deterministic and predictable. A complex system, like a neural network, however, contains multiple, interconnected parts that dynamically interact with each other, resulting in unpredictable behaviors.

Moreover, it’s important to address the misconception that complex systems can be overly simplified without losing their essential properties. This perspective may prove problematic, as the core characteristics of the system – the very aspects we are interested in – are intricately tied to its complexity. Stripping away these layers could essentially negate the properties that make the system valuable or interesting.

Finally, complexity in systems, particularly in AI, may bear similarities to the observer effect observed in subatomic particles. The observer effect postulates that the act of observation alters the state of what is being observed. In similar fashion, any sufficiently complex system could potentially change in response to the act of trying to observe or understand it. This could introduce additional layers of unpredictability, making these systems akin to quantum particles in their susceptibility to observation-based alterations.

Notes on Connectivity and Commonality

The notion of commonality is a fascinating one, often sparking deep philosophical conversations. An oft-encountered belief is that two entities – be they people, nations, ideologies, or otherwise – have nothing in common. This belief, however, is paradoxical in itself, for it assumes that we can discuss these entities in the same context and thus establishes a link between them. The statement “Nothing in common” implies that we are engaging in a comparison – inherently suggesting some level of relatedness or connection. “Agreeing to disagree” is another such example. At first glance, it seems like the parties involved share no common ground, but this very agreement to hold different views paradoxically provides commonality.

To further illustrate, consider this question: What does a banana have in common with cosmology? On the surface, it may appear that these two entities are completely unrelated. However, by merely posing the question, we establish a connection between them within the confines of a common discourse. The paradox lies in stating that two random ideas or entities have nothing in common, which contradicts itself by affirming that we are capable of imagining a link between them. This is akin to the statement that there are points in mental space that cannot be connected, a notion that defies the fluid nature of thought and the inherent interconnectedness of ideas. Anything our minds can host, must have at least a substance that our neurons can bind to, this is the stuff ideas are mode of.

Language, despite its limitations, doesn’t discriminate against these paradoxes. It embraces them, even when they seem nonsensical like “south from the South Pole” or “what was before time?” Such self-referential statements are examples of Gödel’s Incompleteness Theorem manifesting in our everyday language, serving as a reminder that any sufficiently advanced language has statements that cannot be proven or disproven within the system.

These paradoxes aren’t mere outliers in our communication but rather essential elements that fuel the dynamism of human reasoning and speculation. They remind us of the complexities of language and thought, the intricate dance between what we know, what we don’t know, and what we imagine.

Far from being a rigid system, language is constantly evolving and pushing its boundaries. It bumps into its limits, only to stretch them further, continuously exploring new frontiers of meaning. It’s in these fascinating paradoxes that we see language’s true power, as it straddles the line between logic and absurdity, making us rethink our understanding of commonality, difference, and the very nature of communication.

Categories & Concepts

One of the ways we categorize and navigate the world around us is through the verticality of expertise, or the ability to identify and classify based on deep, specialized knowledge. This hierarchical method of categorization is present everywhere, from biology to human interactions.

In biological taxonomy, for instance, animals are classified into categories like genus and species. This is a layered, vertical hierarchy that helps us make sense of the vast diversity of life. An animal’s genus and species provide two coordinates to help us position it within the zoological realm.

Similarly, in human society, we use first names and last names to identify individuals. This is another example of vertical classification, as it allows us to position a person within a cultural or familial context. In essence, these nomenclatures serve as categories or boxes into which we place the individual entities to understand and interact with them better.

Douglas Hofstadter, in his book “Surfaces and Essences”, argues that our language is rich with these classifications or groupings, providing ways to sort and compare objects or concepts. But these categorizations go beyond tangible objects and permeate our language at a deeper level, acting as resonating overtones that give language its profound connection with reasoning.

Language can be viewed as an orchestra, with each word acting like a musical instrument. Like musical sounds that follow the principles of musical theory and wave physics, words also have orderly behaviors. They resonate within the constructs of syntax and semantics, creating meaningful patterns and relationships. Just as a flute is a woodwind instrument that can be part of an orchestra playing in the Carnegie Hall in New York, a word, based on its category, plays its part in the grand symphony of language.

While many objects fit neatly into categorical boxes, the more abstract concepts in our language often resist such clean classifications. Words that denote abstract ideas or feelings like “you,” “me,” “love,” “money,” “values,” “morals,” and so on are like the background music that holds the orchestra together. These are words that defy clear boundaries and yet are essential components of our language. They form a complex, fractal-like cloud of definitions that add depth, richness, and flexibility to our language.

In essence, the practice of language is a delicate balance between the verticality of expertise in precise categorization and the nuanced, abstract, often messy, and nebulous nature of human experience. Through this interplay, we create meaning, communicate complex ideas, and navigate the complex world around us.

From Commanding to Prompting

It appears that we stand on the threshold of a new era in human-computer communication. The current trend of interacting with large language models through written prompts seems to echo our early experiences of typing words into an input box in the 1980s. This journey has been marked by a consistent effort to democratize the “expert’s space.”

In the earliest days of computing, only highly trained experts could engage with the esoteric world of machine code. However, the development of higher-level languages gradually made coding more accessible, yet the ability to program remained a coveted skill set in the job market due to its perceived complexity.

With the advent of large language models like GPT, the game has changed again. The ability to communicate with machines has now become as natural as our everyday language, making ‘experts’ of us all. By the age of twelve, most individuals have mastered their native language to a degree that they can effectively instruct these systems.

The ubiquitous mouse, represented by an on-screen cursor, can be seen as a transient solution to the human-computer communication challenge. If we draw a parallel with the development of navigation systems, we moved from needing to painstakingly follow directions to our destination, to simply telling our self-driving cars “Take me to Paris,” trusting them to figure out the optimal route.

Similarly, where once we needed to learn complex processes to send an email – understanding a digital address book, navigating to the right contact, formatting text, and using the correct language tone – we now simply tell our digital assistant, “Send a thank you email to Daisy,” and it takes care of the rest.

For the first time in tech history, we can actually have a conversation with our computers. This is a paradigm shift that is set to fundamentally redefine our relationship with technology. It would be akin to acquiring the ability to hold a meaningful conversation with a pet dog; imagine the profound change that would have on the value and role the animal plays in our lives. In much the same way, as our relationship with technology evolves into a more conversational and intuitive interaction, we will discover new possibilities and further redefine the boundaries of the digital realm.