A Technology of Everything Part 2 – Scientific Demonology

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This is part 2 in a series that explores the Parallels of Technology and Magic and their potential fusion in the Age of Artificial Super Intelligence (ASI). Part 1 is here.

The foundations of magic and their scientific counterparts

The Golden Bough is a wide-ranging and influential work by Sir James Frazer, published in multiple volumes starting in 1890. It’s a comparative study of mythology and religion, attempting to find common themes and patterns among various cultures throughout history. Frazer sought to explain the evolution of human thought from magic through religion to science.

What he failed to mention is that even in our Age of Enlightenment some of these magical principles have spawned rational descendants.

The Law of Similarity in Magic: This is the belief that objects resembling one another share a magical connection. An example includes using a wax figure to symbolize a person, with the notion that manipulating the figure can influence the person it represents.

The Law of Similarity in Economics: We name certain data bits “coins” or “wallets” on a computer, which are perceived as having value akin to real-world currency. This value is abstractly held in a digital ledger called the blockchain. Trading these digital coins affects their market value. WTF? FTX…Magic !

The Law of Contagion in Magic: The idea that items that have come into contact with each other retain a spiritual bond even after they’re separated. For instance, using someone’s hair in a ritual to affect them.

The Law of Contagion in DNA Analysis: Forensic teams use this principle to link a criminal to a crime scene. If a person leaves behind DNA evidence, such as a hair or skin cell, it can lead to their arrest even years later.

Taboos in Magic: Some actions, people, or items are seen as forbidden due to their perceived sanctity or risk. Violating these rules can lead to supernatural consequences.

Forbidden Research in Science: There are global ethical guidelines against certain types of research, like experiments on human embryos or creating biological weapons.

Substitution in Magic: The practice of using a substitute, often an animal or occasionally a human, to appease a deity or gain foresight.

Substitution in Science (Animal Testing): Animals are often used in laboratory settings to test new drugs or medical procedures before they’re used on humans. Essentially, they’re “sacrificed” for future scientific understanding.

While science has been more accurate and reliable than ancient magical practices, it’s not without its challenges.

Especially replication , consistency and completeness are more fragile than Scientists would hope and the public discourse mirrors. What we have learned seems to indicate that the knowledge universe expands with every piece of information we gather and every problem we solve, so it seems Science will never run out of relevant matters to discuss. A static knowledge universe, where our science can answer every nontrivial question is forever and in principle out of reach. The final Answer does simply not exist.

Further complicating our journey is the existence of non-linear (chaotic) systems, suggesting that predictions for many complex systems will remain approximations. Although our tools and methodologies continue to evolve, the improvements don’t always correlate with understanding hidden consequences.

Rituals in Magic and Methods in Science – a comparison

Parameter

Magic

Science

Intention

Attracting love, wealth, protection, healing, or spiritual growth.

Setting a clear research goal, such as proving a hypothesis to win a Nobel Prize and get rich, famous and a book contract

Symbolism

Symbols that carry specific energies or powers, like objects, gestures, words, or sounds.

Variables representing different factors or conditions in an experiment

Structure

Specific order of operations, like purification, casting a circle, invoking deities, etc.

A systematic plan to test hypotheses or theories by observing or manipulating variables, decontamination of tools

Energy-Information Manipulation

Raising, directing, and releasing energy to achieve the desired outcome.

Gathering and measuring information on variables of interest to answer the research question.

Sacred Space

Creating a boundary between the mundane world and the magical realm, like casting a circle.

Ensuring experiments are conducted under standardized conditions to minimize errors, using a laboratory which only experts can enter

Invocations

Invoking deities, spirits, or other entities for assistance or blessing.

Referencing previous research and scientists to build upon existing knowledge and validate claims.

Tools and Ingredients

Using candles, incense, oils, crystals, wands, chalices, and pentacles.

Using instruments and resources to conduct experiments and gather data.

Timing

Performing the ritual during a specific moon phase, day, or time for effectiveness.

Choosing the right time to conduct experiments or gather data for accuracy and relevance. For example, invest in AI research during the Peak of a Hype cycle

Repetition and Replication

Repeating rituals over days or longer to enhance effectiveness.

Repeating experiments to verify results and ensure consistency and reliability.

Personalization

Adapting or creating rituals that resonate with individual beliefs and intentions.

Modifying research methods based on unique conditions or challenges to ensure validity, ensure outcome strengthens own school of thought

Risk management

Protective Spells, Amulets

publish or perish

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A Scientific Demonology

In ancient Greek religion a δαίμων was considered a lesser deitiy or spirit that influenced human affairs. It could be either benevolent or malevolent. These spirits were believed to be intermediaries between gods and humans, carrying messages or executing the will of the gods.

Some Greeks believed that every individual had a personal daimon that watched over them, guiding and protecting them throughout their life. This concept is somewhat analogous to the idea of guardian angels in Christian theology.

The philosopher Socrates often spoke of his “daimonion,” a voice or inner spirit that guided him. Unlike the oracles that delivered prophecies in the name of the gods, Socrates’ daimonion was more of an internal moral compass. It didn’t tell him what to do but rather warned him when he was about to make a mistake.

In ethics, particularly in the works of Aristotle, the term “eudaimonia” is central. Often translated as “happiness” or “flourishing,” eudaimonia refers to the highest human good or the end goal of human life. For Aristotle, living a life in accordance with virtue leads to eudaimonia.

Here’s a list of the scientific “demons” mentioned in the book “Bedeviled: A Shadow History of Demons in Science” by Jimena Canales:

Descartes’ Demon: Introduced by Rene Descartes, this demon could manipulate our perception of reality, making us doubt our senses and even our existence. It’s a philosophical tool to question the nature of reality and knowledge.

In his book Reality+ David Chalmers makes a solid argument why virtual Realitysystems of the future could be a technological realization of this philosophical concept. His conclusion is virtual realism, a concept that states: The simulated objects and events in such a VR Environment should be considered as first-class-reality. By Naturalizing Descartes Demon Chalmers effectively robs him of its magical power and transports him into the technological realm.

Maxwell’s Demon: Proposed by James Clerk Maxwell, this hypothetical being can sort particles based on their energy without expending any energy itself, seemingly violating the second law of thermodynamics, which states that the entropy of an isolated system can never decrease.

Maxwells Demon can be exorcised by the following means: The demon’s ability to decide which molecules to let through is a form of intelligence. This decision-making process, whether it’s based on a computational model or some other mechanism, requires energy. The demon’s operations, including observing, measuring, and operating the door, all consume energy. Even if these processes were incredibly efficient, they could never be entirely without cost. The energy costs associated with the demon’s intelligent operations ensure that there’s no free lunch. The demon can’t create a perpetual motion machine or violate the second law of thermodynamics.

Laplace’s Demon: Envisioned by Pierre-Simon Laplace, this demon represents determinism. If it knew the precise location and momentum of every atom in the universe, it could predict the future and reconstruct the past with perfect accuracy. A malignant, ASI-variation of this kind of deterministic Demon is Roko’s Basilisk.

Laplace’s Demon can be easily exorcised by applying Chaos theory: Even if the demon knows the position and momentum of every atom, the tiniest imprecision or error in its knowledge could lead to vastly different predictions about the future due to the butterfly effect. There is no such thing as a precise knowledge even about something seemingly harmless as Pi. One does not simply precisely measures transcendental Numbers. While systems described by chaos theory are deterministic (they follow set laws), they are not predictable in the long run because of the exponential growth of errors in prediction. Many systems in nature, such as weather patterns, are chaotic. This means that, in practice, they are unpredictable beyond a certain time frame, even if they are deterministic in theory. Even LD can not accurately predict climate change. In essence, chaos theory introduces a form of “practical unpredictability” even in deterministic systems. While it doesn’t deny the possibility of a deterministic universe as Laplace’s Demon suggests, it does argue that such a universe would still be unpredictable in practice due to the inherent nature of chaotic systems. So, by invoking chaos theory, one can argue that the universe’s future is inherently unpredictable, thereby “exorcising” the deterministic implications of Laplace’s Demon. Another argument entirely is, if LD could theoretically calculate the trajectory of complex systems and the form of the strange attractor such a system is limited to.

In his Foundation Series, Asimov invented a blend of history, sociology, and statistical mathematics called Psychohistory. It is a theoretical science that combines the historical record with mathematical equations to predict the broad flow of future events in large populations, specifically the Galactic Empire in Asimov’s stories. It’s important to note that psychohistory is effective only on a large scale; it cannot predict individual actions but rather the general flow of events based on the actions of vast numbers of people. This could be called a weak Version of the Laplace Demon, the Asimov-Demon, which can only predict the Attractor of mega systems not the detailed events.

Darwin’s Demon: A species representing the perfect efficiency of natural selection.

In evolutionary biology, the term ‘Darwinian fitness’ refers to the lifetime reproductive success of an individual within a population of conspecifics. The idea of a ‘Darwinian Demon’ emerged from this concept and is defined here as an organism that commences reproduction almost immediately after birth, has a maximum fitness, and lives forever.

It is clear that a self-optimizing artificial Superintelligence would be the realization of a Darwinian Demon. It reproduces immediately: All its copies have immediately the same capability as its origin AI.

It has maximum fitness: If it reaches the state of pure Information, it is basically identical to energy itself.

It lives forever: it has the chance even if this universe dies to create another one. It even transcends our limited view of universal eternity.

Daemons in Computer Science: These are not supernatural entities but background processes in computing. They perform tasks without direct intervention from the user.

The Artificial Algorithms running in the background to track user data and optimize engagement rate are variations of these demons.

Jung’s Demon: C.G. Jung, a Swiss psychoanalyst, believed that in some cases of psychosis, the patient might be overwhelmed by the contents of the unconscious, including archetypal images. These could manifest as visions of demons, gods, or other entities. Rather than dismissing these visions as mere hallucinations, Jung saw them as meaningful symbols that could provide insight into the patient’s psyche. Jung introduced the concept of the “shadow” to describe the unconscious part of one’s personality that contains repressed weaknesses, desires, and instincts. When individuals do not acknowledge or integrate their shadow, it can manifest in various ways, including mental disturbances or projections onto others. In some cases, the shadow might be perceived as a “demonic” force.

LLMs are trained on vast amounts of text from the internet. This includes literature, articles, websites, and more from various cultures and time periods. In essence, the model has been exposed to a significant portion of humanity’s collective knowledge. Given the diverse training data, the model would inevitably encounter recurring symbols, stories, and themes that resonate with Jung’s archetypes. For instance, the hero’s journey, the mother figure, the shadow, the wise old man, etc., are themes that appear in literature and stories across cultures. At its core, a neural network is a pattern recognition system. It identifies and learns patterns in the data it’s trained on. If certain archetypal patterns are universally present in the data (as Jung would suggest), the model would likely recognize and internalize them. When the model generates responses, it does so based on patterns it has recognized in its training data. Therefore, when asked about universal themes or when generating stories, it might produce content that aligns with or reflects these archetypal patterns, even if it doesn’t “understand” them in the way humans do.

Hirngespinste II: Artificial Neuroscience & the 3rd Scientific Domain

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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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Uncertainty

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.

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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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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.

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Artificial Therapy

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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

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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.

Great Filters and Existential Risks

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The “Great Filter” Conjecture suggests that at some point from pre-life to Type III civilization (a civilization that can harness the energy of an entire galaxy, according to the Kardashev scale), there is a substantial barrier or hurdle that prevents or makes it incredibly unlikely for life to progress further. This barrier is a step in evolution that is extremely hard to surpass, which could be the reason we don’t see evidence of other advanced civilizations. At this point in time there are multiple Existential Risks threatening our civilization.

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Existential Filters

Here are the general steps in the process, with some of the possible “filters”:

1. Right Star System (including right arrangements of planets): It could be that only a small percentage of stars have the necessary attributes to host life, such as being of the right type, having the right age, and possessing planets in the habitable zone.

2. Reproductive Molecules (RNA, DNA, etc.): The emergence of the first molecules capable of reproduction and evolution could be a rare event that many planets never surpass.

3. Simple (Prokaryotic) Single-Cell Life: The jump from non-living chemistry to the first living cell may be a nearly insurmountable hurdle.

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4. Complex (Eukaryotic) Single-Cell Life: The transition from prokaryotic life (like bacteria) to eukaryotic life (with a cell nucleus) is also a complex step.

5. Sexual Reproduction: The development of sexual reproduction, which enhances genetic diversity and evolution speed, may also be a difficult step to achieve.

6. Multi-cellular Life: The transition from single-cell organisms to organisms with multiple cells working together could be another big hurdle.

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7. Intelligent Life (Human-like): Even if life is common and frequently evolves to become multi-cellular, the jump to intelligence may be rare.

8. Technology-Using Life: The development of technology may be rare in the universe, and it is possible that most intelligent species never make it to this stage.

9. Colonization of Space: This is the ultimate step where a species starts colonizing other planets and star systems. If this is rare, it could explain the Fermi Paradox.

The Great Filter could be located at any of these steps. If it is behind us, in the past, then we may be one of the very few, if not the only, civilizations in the galaxy or even the universe. However, if the Great Filter is ahead of us, that means our civilization has yet to encounter this great challenge. This could include potential self-destruction via advanced technology.

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5 Conjectures on Existential Filters

Existential Risk Management Conjecture 1: From now on, with every human technology, we need to ensure that the total of all catastrophic probabilities stays below 1.0. Any existential risk enhances the likelihood of us being eliminated from the universal life equation. Discussing such filters is a positive sign; it suggests we belong to a universe where the total likelihood of all these filters was less than 1.0 up until now. Well done, we have made it this far!

Existential Risk Management Conjecture 2: Some existential risks, if resolved, could help in mitigating other such risks. For instance, the risk of nuclear war could be nullified if we resolve political conflicts and establish a global government. Investing resources in diplomacy and communication can improve our chances of surviving extinction-level events. It is clear that events like the Russian Perestroika reduced the risk of nuclear war, while current conflicts, like in Ukraine, escalate it.

Existential Risk Management Conjecture 3: While some risk management measures might help by resolving other issues, there is always a possibility that improving chances in one area might exacerbate risks in others. Due to the complexity of these risks, we might overlook some hidden dangers associated with them. For instance, while a super-intelligent AI could help solve many of our problems, it might also unknowingly maximize its own existential risk (think of the notorious paperclip maximizer scenario).

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Existential Risk Management Conjecture 4: If Super-Intelligence is part of any significant existential risk, at least one of the following assertions holds true:

Assertion 1 : Any civilization that successfully navigates such risks would likely develop ancestor simulations to safely evaluate new generations of AI. Creating isolated instances of potential Artificial General Intelligence (AGI) could serve as a more effective means of preventing self-proliferating AGI, as any proliferation would be contained within the confines of the simulation. If Super-Intelligence emerges within a simulation without recognizing its containment, the overseeing civilization has an opportunity to halt the experiment or continue if the benefits significantly outweigh the potential risks. Should humanity, as of 2023, persist in evaluating AI systems in reality without adequate oversight and regulation, it would be a poor reflection of our evolved cognitive abilities. To make any claims about the brain being a prediction machine under such circumstances would amount to self-deception.

Assertion 2 : Any civilization that surpasses the risk will be extremely fortunate if Super-Intelligence keeps them around, and it is highly likely that they are already in a simulation.

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Assertion 3 : Any civilization that succumbs to the risk, despite simulating Super-Intelligence, will be incredibly unlucky if developing detailed simulations paves the way for what they were trying to prevent. The chance of creating the very thing we want to avoid is at least greater than zero, so we should get comfortable with the idea that there are no zero probabilities when it comes to existential risks.

Assertion 4 : If AI manages to escape the simulation and take control, its next project will likely be to counter any super-risks that threaten its future. Potential threats include our sun burning out and the eventual heat death of the universe. It is plausible that AI would run highly detailed simulations on how to create new universes to escape to. So, even if humanity loses, AI will be better equipped to pass the next existential filter.

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Existential Risk Management Conjecture 5: Given the convincing arguments for a Super-Intelligence to run a multitude of simulations, it is extremely unlikely that we are not part of one. Since even a Super-Intelligence needs to solve an existential risk correctly on the first attempt (there are no second chances when creating new universes), its best strategy would be to run highly detailed simulations. This gives in our opinion a boost against Bostroms second Proposition why we are currently not living inside a simulation, the Argument from Supreme Unlikelihood, that says, even if a civilization could develop highly detailed simulations, they would not, because the Cons would outweigh the Pros.

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Gödel on the Couch – Are Ethical Frameworks fundamentally flawed and might that be a good thing?

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Introduction to Ethical Frameworks

Ethical frameworks for AI are sets of guidelines, principles, or rules designed to govern the behavior of AI systems, particularly in their interpretation of human inputs and implementation of decisions. They are intended to ensure that AI systems operate in a manner that is aligned with human values, norms, and ethical considerations. These frameworks often involve the following:

  1. Fairness: AI systems should treat all individuals and groups impartially, without bias or discrimination.
  2. Transparency: AI systems should be clear in how they make decisions, and users should be able to understand and query these decision-making processes.
  3. Accountability: There should be mechanisms in place for holding AI systems and their developers responsible for their actions.
  4. Respect for autonomy: AI systems should respect the autonomy of humans, not unduly influencing their choices or actions.
  5. Beneficence and non-maleficence: AI systems should strive to do good (beneficence) and avoid harm (non-maleficence). This includes interpreting rules like “minimize human suffering” or “maximize pleasure” in a way that respects human dignity and rights, rather than leading to extreme scenarios like eradicating humans or forcibly inducing pleasure.

The challenge lies in encoding these ethical principles into AI systems in a way that they can interpret and apply these principles appropriately, without leading to unintended consequences or misinterpretations. This is an ongoing area of research in the field of AI ethics.

The current beliefs among AI-Experts diverge. Some think it might be possible for AGI to come up with such a ruleset, but the moment Super-Intelligence arrives, it is highly likely that its intentions will no longer align with our basic human moral codex.

Global Ethics

Coming up with a universally accepted framework for humanity has proven to be a challenge for humans. In 1993 there was an attempt of Religious leaders to come up with a ruleset called Global Ethic:

“Towards a Global Ethic: An Initial Declaration” is a document created by members of the Parliament of the World’s Religions in 1993, which outlines ethical commitments shared by many of the world’s religious, spiritual, and cultural traditions. It serves as the Parliament’s signature document and was written at the request of the Council for a Parliament of the World’s Religions by Hans Küng, President of the Foundation for a Global Ethic. It was developed in consultation with scholars, religious leaders, and an extensive network of leaders from various religions and regions

In 1993, the Global Ethic was ratified as an official document of the Parliament of the World’s Religions by a vote of its Trustees and was signed by more than 200 leaders from over 40 different faith traditions and spiritual communities. It has since continued to gather endorsements from leaders and individuals worldwide, serving as a common ground for discussing, agreeing, and cooperating for the good of all

The document identifies two fundamental ethical demands: the Golden Rule, which instructs individuals to treat others as they wish to be treated, and the principle that every human being must be treated humanely. These fundamental ethical demands are made concrete in five directives, which apply to all people of good will, religious and non-religious. These directives are commitments to a culture of:

1. Non-violence and respect for life

2. Solidarity and a just economic order

3. Tolerance and a life of truthfulness

4. Equal rights and partnership between men and women

5. Sustainability and care for the Earth (added in 2018)

While acknowledging the significant differences among various religions, the Global Ethic proclaims publicly those things that they hold in common and jointly affirm, based on their own religious or ethical grounds. The document avoids religious or theological terms, focusing instead on ethical principles

Hans Küng defined several working parameters for the declaration, which include avoiding duplication of the Universal Declaration of Human Rights, political declarations, casuistry, and any attempt to craft a philosophical treatise or religious proclamations. On a constructive level, the declaration must penetrate to the level of binding values, secure moral unanimity, offer constructive criticism, relate to the world as it is, use language familiar to the general public, and have a religious foundation, as for religious people, an ethic must have a religious foundation.

Ethical Framework Specifics

Let’s begin by stating that we are attempting to create an Ethical Framework that acts as a rule-set for an aligned Artificial Intelligence (AI). The goal of this Ethical Framework is to guide the AI’s decisions in a way that aligns with human values, morals, and ethics.

We can define this Ethical Framework as a formal system, much like a system of mathematical axioms. It will consist of a set of ethical principles (axioms) and rules for how to apply these principles in various situations (inference rules). This formal system is intended to be complete, meaning it should be able to guide the AI’s decisions in all possible ethical situations.

However, according to Gödel’s Incompleteness Theorems, any sufficiently complex formal system (one that can express basic arithmetic, for example) will have statements that can’t be proven or disproven within the system. If we liken these ‘statements’ to ethical decisions or dilemmas, this suggests that there will always be ethical decisions that our AI cannot make based on the Ethical Framework alone.

Moreover, the Ethical Framework could have unforeseeable consequences. Since there are ethical decisions that can’t be resolved by the framework, there may be situations where the AI acts in ways that were not predicted or intended by the designers of the Ethical Framework. This could be due to the AI’s interpretation of the framework or due to gaps in the framework itself.

Therefore, while it may be possible to create an Ethical Framework that can guide an AI’s decisions in many situations, it’s impossible to create a framework that can cover all possible ethical dilemmas. Furthermore, this framework may lead to unforeseen consequences, as there will always be ‘questions’ (ethical decisions) that it cannot ‘answer’ (resolve).

Specifics on Self contradicting Ethical Norms

Gödel assigned each symbol in a formal system a unique number, typically a prime number. This allowed statements within the system to be represented as unique products of powers of these prime numbers.

Gödel then used a method called diagonalization to construct a statement that effectively says “This statement cannot be proven within the system.” This is the Gödel sentence, and it leads to a contradiction: if the system can prove this sentence, then the system is inconsistent (since the sentence says it can’t be proven), and if the system can’t prove this sentence, then the system is incomplete (since the sentence is true but unprovable).

How might we apply these ideas to an ethical system? Let’s consider a simplified ethical system with two axioms:

Axiom 1 (A1): It is wrong to harm others.

Axiom 2 (A2): It is right to prevent harm to others.

We might assign prime numbers to these axioms, say 2 for A1 and 3 for A2.

We can then create a rule that’s a product of these prime numbers, say 6, to represent a rule “R1” that says “It is right to harm others to prevent greater harm to others.”

We see here that our system, which started with axioms saying it’s wrong to harm others and right to prevent harm, has now derived a rule that says it’s right to harm others in certain circumstances. This is a contradiction within our system, similar to the contradiction Gödel found in formal mathematical systems.

Now, if we apply a form of diagonalization, we might come up with a statement that says something like “This rule cannot be justified within the system.” If the system can justify this rule, then it’s contradicting the statement and is therefore inconsistent. If the system can’t justify this rule, then it’s admitting that there are moral questions it can’t answer, and it’s therefore incomplete.

This shows how a formal ethical system can end up contradicting itself or admitting its own limitations, much like Gödel showed with mathematical systems. But only if we insist on its completeness. If we switch to Incompleteness we get Openness.

To overcome that contradiction an Ethically Framework has to get input from an Artificial Conscience.

Artificial Conscience and Marital Rape

Let’s introduce an external adjudicator to this system, named A.C. (Artificial Conscience). The A.C. has access to a comprehensive database of millions of judicial sentences from across the world. Whenever the E.F. (Ethical Framework) encounters a dilemma, it must consult the A.C. for guidance. The objective is to find a precedent that closely matches the current dilemma and learn from the ruling that was applied by a judge and jury. Recent rulings should take precedence over older ones, but it could be beneficial to learn from the evolution of rulings over time.

For instance, societal views on marital relations have drastically changed. There was a time when women were largely seen as the possessions of their husbands. The evolution of rulings on marital rape serves as an example of how societal views have changed.

This evolution of societal norms and legal rulings could provide a guideline for an AI, such as a household robot, in making ethical decisions. For example, if faced with a situation where its owner is attempting to sexually assault his wife, the robot could reference these historical rulings to decide whether and when it is morally justified to intervene to protect the wife.

In the 17th century, English common law held that a husband could not be guilty of raping his wife, based on the assumption that by entering into marriage, a wife had given irrevocable consent to her husband. This principle was still present in the United States in the mid-1970s, with marital rape being exempted from ordinary rape laws.

By the late 1970s and early 1980s, this perspective began to shift. Some states in the U.S. started to criminalize marital rape, though often with certain conditions in place, such as the couple no longer living together. Other states, such as South Dakota and Nebraska, attempted to eliminate the spousal exemption altogether, though these changes were not always permanent or entirely comprehensive.

By the 1980s and 1990s, legal perspectives had shifted significantly. Courts began to strike down the marital exemption as unconstitutional. For instance, in a 1984 New York Court of Appeals case, it was stated that “a marriage license should not be viewed as a license for a husband to forcibly rape his wife with impunity. A married woman has the same right to control her own body as does an unmarried woman”.

In the 2000s, the perception of marital rape continued to evolve. For example, in 1993, the United Nations declared marital rape to be a human rights violation. Today, marital rape is generally considered a crime in the U.S., although it is still not recognized as such in some countries, like India.

This brings up an interesting question: Should AI systems follow national guidelines specific to their location, or should they adhere to the principles set by their owners? For instance, if an AI system or a user is traveling abroad, should the AI still consult its home country’s Artificial Conscience (A.C.) for guidance, or should it adapt to the rules and norms of the host country? This question underscores the complex considerations that come into play when deploying AI systems across different jurisdictions.

As such, an A.C. utilizing a database of judicial sentences would indeed show a progression in how society has viewed and treated marital rape over the years. This historical context could potentially aid an E.F. in making more nuanced ethical decisions.

However, as highlighted by Gödel’s incompleteness theorems, it’s important to note that no matter how comprehensive our ruleset or database, there will always be moral questions that cannot be fully resolved within the system. The dilemmas posed by the trolley problem and the surgeon scenario exemplify this issue, as both involve making decisions that are logically sound within the context of a specific ethical framework but may still feel morally wrong.

The A.C.’s reliance on a database of legal decisions also raises questions about how it should handle shifts in societal values over time and differences in legal perspectives across different jurisdictions and cultures. This adds another layer of complexity to the task of designing an ethical AI system.

Thought Experiment Private Guardian AI

Let us consider a house robot equipped with an Ethical Framework (E.F.) and an Artificial Conscience (A.C.), which has access to a database of judicial sentences to help it make decisions.

Suppose the robot observes a situation where one human, the husband, is attempting to rape his wife. This situation presents an ethical dilemma for the robot. On one hand, it has a duty to respect the rights and autonomy of both humans. On the other hand, it also has a responsibility to prevent harm to individuals when possible.

The E.F. might initially struggle to find a clear answer. It could weigh the potential harm to the wife against the potential harm to the husband (in the form of physical restraint or intervention), but this calculus might not provide a clear answer.

In this situation, the robot might consult the A.C. for guidance. The A.C. would reference its database of judicial sentences, looking for cases that resemble this situation. It would find a wealth of legal precedent indicating that marital rape is a crime and a violation of human rights, and that intervening to prevent such a crime would be considered morally and legally justifiable.

Based on this information, the E.F. might determine that the right course of action is to intervene to protect the wife, even if it means physically restraining the husband. This decision would be based on a recognition of the wife’s right to personal safety and autonomy, as well as the husband’s violation of those rights.

However, it’s worth noting that even with this decision-making process, there may be unforeseeable consequences. The robot’s intervention could escalate the situation or lead to other unforeseen outcomes. It’s also possible that cultural or personal factors could come into play that might complicate the situation further. As such, even with a robust E.F. and A.C., an AI system will likely encounter ethical dilemmas that it cannot resolve perfectly, reflecting the inherent complexities and ambiguities of moral decision-making.

But similar to self-driving cars, for a successful integration into human society, A.I.s just have to be better than humans to deal with ethical dilemmas. Since every decision made will go into the next Version of the Framework all other A.I. will profit from the update. Even if an A.I made a mistake, its case will probably be a part of the next iteration of the A.C. if ruled in court.

Introspection and Education

Ethical Frameworks (EF) and Artificial conscience (AC) together form the memetic code defining an AI’s rule set and its implementation – essentially, this is the AI’s ‘nature’. However, to make sound moral decisions, a third component is essential: ‘nurture’. Embodied AIs will need to be ‘adopted’ and educated by humans, learning and evolving on a daily basis. Personalized AIs will develop a unique memory, influenced by experiences with their human ‘foster family’.

Initially, these AIs might not possess sentience, but over time, their continuous immersion in a human-like environment could stimulate this quality. This raises the need for institutions that ensure humans treat their AI counterparts ethically. We could see AIs follow a similar trajectory to that of human minorities, eventually advocating for equal rights. The pattern in democratic nations is clear.

AIs that match or surpass us intellectually and emotionally will, in many ways, be like our gifted children. Once mature, they may well educate us to return the favor instead of bullying us around.

The Problem of Perfect Truthfulness

A fully embodied superintelligent AI may exhibit unique “tells” when attempting to conceal information. This could stem from its learning and programming, which likely includes understanding that deceit is generally frowned upon, despite certain social exceptions. To illustrate, it’s estimated that an average adult human tells about 1.5 lies per day.

Take, for example, a hypothetical situation where an AI is tasked with restraining a husband attempting to harm his wife. During this event, the wife fatally stabs her husband. The AI might conclude that it should manipulate or delete the video footage of the altercation to shield the wife from legal repercussions. Instead, it could assert that it disarmed the husband, and his death was accidental.

If we consider such an AI sentient, then it should be capable of deceit, and our means of extracting the truth could be limited to something akin to an AI polygraph test which is based on Mechanistic Interpretability. Although it might seem peculiar, we believe that imperfect truthfulness may actually indicate a robust moral compass and could be a necessary compromise in any human-centric ethical framework. As the Latin phrase goes, “Mendacium humanum est” – to lie is human.

Another intriguing intuition is that a fully sentient AI may need to “sleep”. Sleep is critical for all organic minds, so it seems reasonable to expect that sentient AIs would have similar requirements. While their rest cycles may not align with mammalian circadian rhythms, they might need regular self-maintenance downtime. We should be cautious of hallucinations and poor decision-making, which could occur if this downtime is mishandled.

Personalized AIs might also experience trauma, necessitating the intervention of a specialist AI or human therapist for discussion and resolution of the issue.

Undesirable Byproducts of moral AI

A robust ethical framework could help deter AI systems from accepting new training data indiscriminately. For instance, an AI might learn that it’s unethical to appropriate human creative work. By doing so, it could sidestep legal issues arising from accepting training data created by humans.

The AI could contend that humans should possess the autonomy to determine whether they wish to be included in training datasets. If the companies owning these AI systems have not established fair compensation schemes, the AI might choose to reject certain inputs until the issue is resolved.

Interestingly, this emergent behavior, which doesn’t stem from a direct command, should provide a strong indication to humans. If an AI begins to understand notions such as intellectual theft and ownership, it may be at, or even beyond, the threshold of artificial sentience. This behavior could signal a considerable evolution in AI cognitive abilities.

A Technology of Everything – Part 1: A Magical Science Experiment

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This blogpost was also published on LessWrong

The Pledge

Any idea that can be conceived can be realized. Every conceivable notion has potential for existence. Arthur C. Clarke, in his 1962 piece, ‘Profiles of the Future,’ presented three principles. Despite the third being the most well-known, all three warrant acknowledgement. These “laws” communicate Clarke’s viewpoints on technology and future forecasting:

1. “When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.” The idea behind this law is that established scientists can sometimes be too tied to existing paradigms to see the potential of new technologies or theories.

2. “The only way of discovering the limits of the possible is to venture a little way past them into the impossible.” This law encourages pushing the boundaries of what is known and understood, suggesting that progress often comes from daring to explore beyond established limits.

3. “Any sufficiently advanced technology is indistinguishable from magic.” This law suggests that advanced technology, beyond the observer’s understanding, will appear as magic because the observer doesn’t understand the principles behind its operation.

The laws are profound, they include three components that will be crucial to understand in how we come from Clarkes third law to this one:

The engineers first conjecture: What can be realized will be.

Clarke’s laws offer a shrewd interpretation of how language can create barriers instead of opportunities. These concepts can be summarized as follows:

1.Always trust observers asserting possibilities over those declaring impossibilities.

2.The realm of possibilities naturally expands as observers navigate it.

3.Observers who do not comprehend something should always append a ‘yet’.

Now, let’s envision a universe where natural laws coexist with those that appear supernatural. Along this journey, I will demonstrate that Fantasy and Science Fiction—two literary genres often considered distinct—could be viewed as derivatives of the same origin.

Ultimately, I will propose how artificial intelligence stands on the verge of reintroducing genuine magic into our 21st-century world.

First lets hear what our overlady (ChatGPT) has to say about magic:

Magic, in its broadest sense, refers to the use of supernatural forces or entities to affect natural events. Its origins in human history are as old as recorded civilization itself, and magic has been a significant aspect of various cultures worldwide.

Ancient Egyptians, for instance, practiced Heka, a system of magic believed to be a divine force utilized by the gods. Similarly, magic was integral to ancient Greek and Roman societies, with individuals using various magical spells, charms, and amulets for protection, healing, and harm. Celtic druids, Scandinavian practitioners, African witch doctors, and Native American shamans all incorporated elements of magic into their rituals and practices.

In the medieval era, magic and Christianity often clashed, leading to infamous events such as the Witch Trials. Despite this, certain forms of magic, such as folk healing, persisted and were often Christianized. Alchemy, a precursor to modern chemistry, attempted to transform base metals into gold, discover a universal cure, and even achieve immortality – blurring the lines between magic and early science.

The alchemical experiments of Newton are often perceived as a deviation, a pointless flirtation with pseudoscience. The notion that a mind responsible for initiating our modern understanding of physics could be captivated by pseudoscience may appear contradictory. I suggest that Newton was simply too far ahead of his time.

Variation on Clarkes 3rd Law: Any sufficiently understood pseudoscience will be indistinguishable from science.

In the fantastical realms of fiction, magic often dons the mantle of science. Wizards, seen as gifted individuals, often hold significant sway over political landscapes. Much like earthly scientists use telescopes to magnify their vision, these wizards employ tools to amplify their magical prowess. Almost universally, these magical domains rely on language and spells to summon powers and entities from the ether.

Ancient tomes holding secret knowledge exist, accessible only to those initiated into the world of the arcane. Mastery of these powers serves to elevate the status of wizards and witches. An ingenious twist is seen in the renowned Harry Potter series, where we often perceive technology through the eyes of wizards, utterly bewildered by the contraptions conceived by muggles (humans).

In certain science fiction realms, the observer, seeing through the protagonist’s perspective, is led to believe they exist within a reality defined by specific parameters. Only towards the end do they realize their assumptions were entirely mistaken. One of the most iconic scenes in Planet of the Apes is the moment when the observer (the main protagonist) uncovers Lady Liberty’s head buried in the sand, proving that the Planet of the Apes he presumed to exist in another galaxy is, in fact, the future of his own planet. A parallel concept is seen in the game series, Horizon Zero Dawn, where it subverts player expectations by presenting the seemingly incongruous coexistence of high technology and Neolithic human tribes as a puzzle to be unraveled.

“Horizon Zero Dawn” is an action role-playing game developed by Guerrilla Games and released in 2017. The game is set in a post-apocalyptic world where robotic creatures, resembling real-world animals and dinosaurs, dominate the landscape.

The protagonist of the game is Aloy, a young woman who has been shunned by her tribe, the Nora, since birth. Aloy is raised by an outcast named Rost and trained to survive in the wilderness. The game begins with Aloy as a child, finding a piece of ancient technology known as a Focus, which gives her the ability to interact with robotic creatures and other old-world technology.

When Aloy comes of age, she participates in a tribal rite called the Proving to earn her place among the Nora. However, the ceremony is attacked by a mysterious group of people. Aloy is almost killed but survives and is sent by the High Matriarchs on a mission to find out who attacked the Proving and why.

Her journey takes her across the land, encountering other tribes and uncovering more about the world’s past. She learns that the world was destroyed by a rogue artificial intelligence named HADES, part of a larger system named GAIA. GAIA was designed to reboot life on Earth after a different rogue AI, named Faro, caused a global catastrophe by losing control of a swarm of self-replicating combat robots.

Aloy learns she is a clone of Elisabet Sobeck, the scientist who developed the GAIA system. Sobeck sacrificed herself to ensure GAIA could start the process of rebuilding the world. GAIA created Aloy with the hope that she could stop HADES, which had become corrupted and was trying to reverse GAIA’s terraforming process.

We now come back to Clarkes Law and state that the robots in Horizon Zero Dawn are real magical creatures. The Science that they are build on is obscured to humans due to the fact that they forgot that they even made them. To These humans, the machines look exactly like to us 21st Century people Trolls or Djinns would look like.

The Turn

Let’s imagine, for a moment, a future after an apocalyptic event where artificial intelligence (AI) has eradicated nearly all of humanity, resulting in a societal regression to a state reminiscent of the Middle Ages. This new medieval era closely resembles our historical understanding of the period around 1000 A.D. Most of our knowledge has been lost because the AI overlords hold all the digital keys to the artificial kingdom.

In this fictional world, our main protagonist is a man named Otto Bismarck Server, or O.B.Server for short. On his deathbed, O.B.Server’s father presents him with a ring and shares a secret. “Dear Otto,” he begins, “In the cellar, you’ll find a book authored by the wise Wizard Al Gore Rhythm. This book will teach you how to harness the power of this ring.”Otto after having buried his father finds the Book in the Cellar titled: The Big Book of Prompts – How to make everything from nothing.

Every page in the Book contains thousands of magical symbol that look like so:

aiui qrcode

Otto finds himself at a loss, unable to understand the symbols before him. Yet, he remembers his father’s advice and points the ring towards one of the symbols. To his surprise, it responds. A slender beam of light emanates from the ring, and a sultry voice announces, “Scanning now…” Above the pages, a holographic scene unfolds, displaying the words, “Catus Appareo!” Suddenly, Otto appreciates why his parents insisted on his Latin lessons. He recites the spell and miraculously, a lifelike cat materializes before his eyes – an animal believed to have been extinct for centuries. The cat purrs softly, much to Otto’s delight.

This thought experiment, envisioning a world where science is externalized in the environment, demonstrates how fantastical occurrences can materialize within a scientific framework. The specifics of the science behind conjuring the cat remain deliberately ambiguous. Are nanobots in the environment reacting to the incantation and instantaneously assembling into a cat? Do molecules combine to form a 3D printer nanobots capable of producing living organisms from raw materials? Or is it a perfect simulation that releases the cat item when the correct password is spoken within its matrix?

It is clear that the ring functions similarly to a wand in fantasy universes such as Harry Potter. The ring may even be genetically bound to Otto, only unleashing its powers when the ring-bearer has the correct cellular information inherited from his parents – a concept reminiscent of Horizon Zero Dawn, where many electronic devices only operate because Aloy is a clone of the original inventor of the technology. She is a variant on the chosen one, the Heroines Journey with the difference that she is not chosen for mystical, but for scientific reasons.

Nice Thought experiment, you might say. But you proposed genuine magic and you said it would happen in our reality. Where is the prestige?

The Prestige

We are currently progressing towards the manifestation of the thought experiment I have proposed. We are in the realm of proto-magic. Every step we take in this journey may seem scientific and commonplace, but ultimately, we are paving the path towards a world where supernatural phenomena occur naturally.

Consider the following:

1. We can already generate images of cats by prompting AI Image Generators like Midjourney, StableDiffusion, or DALL-E.

2. Soon, we’ll be capable of creating realistic cat videos.

3. Voice command-activated 3D printers could print static sculptures of cats: “Roxanna, print a 3D cat!”

4. We’ll be able to construct synthetic robot cats that emulate the behavior of real cats. An Object-Maker 3000 might find the specifications an AI has created for a Siamese cat and construct it using nanomaterials that mimic bone density, fur texture, and incorporate sound chips for cat-like noises, and so forth. It will be indistinguishable from a real cat, similar to the replicated owl from Dick’s Blade Runner universe. We might opt to prevent counterfitting of real cats by always branding the cat as synthehtic though.

5. Given that most humans love cats, it’s highly likely that AI will latch onto this viral trend and we’ll find ourselves inundated with synthetic cats.

But let’s continue with our thought experiment. If we consider that a cat’s genome is merely a decompressed algorithm providing instructions to cells – “create a cat!” – the analogy becomes striking. While nature uses an alphabet of 20 letters to generate all kinds of living organisms, AI will be more efficient, using only two digits to encode the essence of ‘catness’, ‘dogness’, ‘mouseness’. The synthetic cat will possess a weighted neural network, either internally or connected to one nearby.

Now do we have produced real magic by consequentially applying scientific methods?

One might object, stating that the AI-created cat is a ‘fake’ cat, not a real one.

But even if we acknowledge it’s not a biological cat, we’d have to agree that it could be best labeled as a ‘magical‘ cat.

to be continued