The increasing ability to harness quantum, classical, and relativistic scales, together with fastpaced change in generative AI and quantum computing, suggests the possibility of achieving not only technical but also social objectives, through responsible human-AI entities (intelligent agents interacting with competence and empathy). The social cannot be separated from the technical as intelligence may evolve into a platform-agnostic learning and problem-solving capability. A suite of concepts is introduced as quantum intelligence, relativistic intelligence, and scale-free intelligence to denote learning modes which incorporate scale-specific matter and space-time properties of quantum, classical, and relativistic domains. Modern technology has both risk and reward, and offers important theoretical and practical means of achieving socially beneficial outcomes. Theoretically, intelligence as the generic capability of agents (human or machine) to treat problems in multiple scale domains allows thinking to be reconceived as an operation of multiplicity, simultaneity, and portability, opening the scope of world to a wider sphere of concern beyond the traditional individual self and local community others. Practically, producing socially responsible AI could proceed in the phased approach of a “Moore's Law of AI Alignment” with short-term regulation and registries, medium-term internally learned reward functions, and long-term responsible human-AI entities. Humans are not naturally socially responsible but might be more so when operating with new levels of technological sophistication aimed at planetary-scale problems.
There is an imprecisely defined idea that AI should be socially responsible even though humans are not. Since technology advances faster than social maturity, the implication is to consider the trajectory of technological development as the first step in evaluating social impact. This work investigates how technological advance may facilitate the outcome of socially responsible human-AI entities.
The Oxford English Dictionary defines artificial intelligence as “the capacity of computers or other machines to exhibit or simulate intelligent behavior.” On the one hand,
AI is banal in continuing its multi-decadal rollout as a digital automation technology. On the other hand, the advent of generative AI (content produced by AI that is indistinguishable and possibly vastly more extensive than that produced by humans) may signal an inflection point that a qualitatively new era has been entered in AI that is puzzling, threatening, and exciting for humans.
Darwin wondered “Why should the brain be
enclosed in a box?” [
widespread worldwide accessibility and adoption
of generative AI argues that socially beneficial
objectives may already be in the process of being
achieved as creative human minds explore and
deploy the technology. Stanford’s Global AI
Vibrancy index cites the U.S. and China as top AI
innovators [
One question is how to operate in a world of rapidly evolving technology development to deploy the tools for high-impact problem-solving such as science-related outcomes that are also socially beneficial. A new phase of the “Information Age” may be underway in transitioning from an information society to a knowledge society (one that uses knowledge to improve the human condition). Global knowledge platforms, a hallmark of the Information Age, are being further extended with generative AI. Wikipedia is a global interface for knowledge access, MOOCs (massive online open courses) are a global interface for knowledge learning, and now AI is an interface for knowledge generation.
The purpose of AI as a tool for extending
human capability is a clear objective articulated
by technology providers. DeepMind envisions a
schema of three concentric circles: knowledge
that is currently understood by the human mind,
knowledge that can be understood by the human
mind, and the totality of all knowledge (propelled
by AI networks that “learn to learn” [
neural networks has led to generative AI.
Transformers are deep learning networks which
process all input data simultaneously [
A further advance is reinforcement learning. A
reinforcement learning agent is a group of
algorithms which learn and act based on feedback
from the environment. Learning agents are
comprised of three aspects: a self-learned model
of the environment, a decision-making policy, and
a reward prediction mechanism. DeepMind’s
AlphaZero is an industry-standard reinforcement
learning model which combines Monte Carlo tree
search with a one-step look-ahead deep neural
network [
AI agents (sets of algorithms) are emerging in commercial and scientific applications. Agents typically canvas the web or other large open data sets on which they have been trained, but AI capabilities are also available for private data, for example through the productivity tool “Microsoft 360 Copilot.” The application offers an AIfacilitated experience to work across the corpus of a work team’s private content to generate new materials such as a product plan, press release, marketing image, sales spreadsheet, meeting slides, or Zoom summary. The implication is that not only is AI for the outsourcing of physical tasks, but also the offloading of routine cognitive tasks and low-level activities such as finding and processing information. The result could be upleveling human activity to higher-order more creative endeavor as entire classes of mundane tasks are automated away from drudgery.
The big data informatics demands of science
suggest that AI technologies such as the “Science
Copilot” concept and research agents could be
indispensable. Some first-level research copilot
applications could include AI agents for literature
synthesis (academic papers as the data corpus),
and equation digitization (mathematics as the data
corpus) via LLM PDF extraction tools such as
MathPix and chatPDF [
AI research agents may offer new ways to
perform science at greater scale, for example, in
Genomic Medicine approaches involving the
immediate and routine sequencing of whole
human patient genomes and cancer genomes,
together with transposon activation, epigenetic
methylation, and metabolomic profiles [
“Meta-AIs” (AIs that train other AIs) are
emerging to manage the laborious and repetitive
exigencies of training AI systems. Wizard.AI
offers an LLM to train other LLMs for at-scale
tasks in software programming and mathematical
analysis [
AI math agents are agents acting in and
facilitating the human interaction with the digital
mathematical infrastructure. Applied agents help
in locating, formulating, and evaluating
(numerically and analytically) mathematics, and
in fitting math to data [
The scientific method involves making predictive descriptions of physical-world phenomena through mathematical models. In many cases so far, mathematics has been limited to anecdotal descriptions of small data sets. The model-fit problem of the generalizability between math and data can now be approached in more systematic ways. On one side of the math-data relation, there is a need to validate and expand the mathematics that describe existing data sets. On the other side, there is a need to apply reverse engineer “missing” data implied by the mathematics, and also “missing” mathematics in the mathematical possibility space.
AI math agents learn equations as the data corpus. In multiscalar systems, mathematical ecologies are implied to integrate sets of equations describing behavior at various scale tiers with different dynamical behavior (e.g. four-tier biosystems such as ion-dendrite-neuron-network in the brain, molecule-cell-tissue-organ in the body, sunlight-phytoplankton-krill-whale in the ocean, and bacteria-cell-tree-clade in the forest).
With the aid of AI and quantum computing,
established physical models might be applied to
multiscalar biosystems to study 3D dynamical
behavior in an integrated and systematic manner.
The leading contenders to test are topological
models with renormalization (the ability to zoom
in or out to view a system at multiple scale levels
per a conserved system-wide quantity such as free
energy in living systems or symmetry in the
universe) such as Chern-Simons theory and the
AdS/CFT correspondence [
Chern-Simons theory is conducive to biology,
extending 3D protein conformal structure
modeling to 3D DNA modeling based on
topological invariance. System events (cancer
mutation, neural signal, protein docking) are
identified by curve max-min points, Wilson loops,
knots, and information compression [
The AdS/CFT correspondence (anti-de Sitter
space/conformal field theory) theorizes that a bulk
physical volume may be described by a boundary
field theory in one fewer dimension [
The AdS/CFT bulk-boundary schema could be applied to multiscalar biosystems in at least three ways. First is solving the math-data relation with the data as the bulk, writing a mathematical theory on the boundary to describe the data in one fewer dimension. Second is solving specific scale tiers within biosystems such electrical-chemical neural signaling or the DNA-RNA-protein synthesis chain, with the first term (axon, DNA) as the boundary to the detailed bulk. Third is the idea of a bulk theory of disease based on the SIR model, framing disease as entropy, with near and far correlations in the system bulk which become pathogenic over time as cis-trans gene regulatory networks become dysregulated, ideally to provide early warning signals that can be addressed with interventional precision health. 2.3.
agents. Quantum agents are AI agents running in the quantum computational domain, whether on quantum simulators or quantum hardware. Quantum agents operate in the usual sense of serving as learning and problem-solving agents, in the quantum setting using (and/or being instantiated in) quantum algorithms, quantum machine learning, and other scale-specific means.
Quantum computing refers to computers operating at the atomic level according to the principles of quantum mechanics. The platform is thought to be conducive to solving specific slates of problems with substantial speed-up over classical methods. Various theorized mathematics may now be tested in demonstration models. Specifically, a wider range of differential equations describing the ways systems evolve in time become more readily solvable.
AI and quantum computing are rapidly
progressing contemporary technologies, and in
convergence, could lead to each other’s further
development. AI is needed to help discover and
write quantum algorithms (software) and develop
fault-tolerant error-corrected chips (hardware).
Many AI technologies have been extended to
quantum versions such as quantum reinforcement
learning, quantum natural language processing,
and quantum transformer neural networks.
Processes use native quantum properties, e.g.
Clifford algebra for the classically-inefficient task
of multiplying a vector with a higher-dimensional
compound matrix [
The AI-quantum computing convergences is seen most clearly in quantum machine learning (QML), machine learning techniques deployed in the quantum context. Quantum machine learning includes AI methods deployed on quantum platforms and AI methods used to study quantum mechanical problems (for example, describing wavefunctions, quantum dynamics, and Hamiltonians (system energy profiles)). Quantum machine learning likewise includes quantum methods applied to AI problems (e.g. vectorization, high-dimensional representation).
In QML examples, one project ramps agent
learning time with a quantum communication
channel for environmental feedback [
classical-relativistic regimes, but human Kantian
goggles do, evolved to perceive classical reality
(via brain-created time and space manifolds into
which objects appear and are cognized [
Manageable reality now extends from the
very-large to the very-small, including the zetta
(1021) and zepto (10-21) scales; 101 zettabytes of
data were estimated to have been generated in
2022 [
The modern scientific effort entails working with matter at quantum-classical-relativistic scales. In this schema, “quantum” refers to the scale of atomic and subatomic particles (10-9 to 10-15 m). The term “relativistic” means gravitational effects, which may be present at any scale (depending on precision), seen geometrically as a bending of the fabric of time and space around heavy objects.
Since the 1960s with progress in photonics, the
semiconductor industry has been incorporating
quantum-mechanical effects into chip design. Its
extension into quantum computing chips now
explicitly entails engineering chips (quantum
processing units (QPUs)), logic circuits, and
algorithms using quantum properties. The five
quantum properties are superposition
(simultaneous existence in multiple states),
entanglement (a “heads-tails” properties
relationship between particles), interference
(particle wavefunctions cohering (reinforcing) or
decohering (interfering) one another), symmetry
(object properties looking the same irrespective of
scale), and topology (object properties remaining
unchanged in bending, twisting, stretching) [
facilitate intelligent learning and problem-solving in quantum and relativistic domains, providing an active interface on scale-specific data content, mathematics, and software coding operations. Quantum intelligence is defined as the capacity to learn and problem-solve at the quantum scale of atoms and subatomic particles (10-9 to 10-15 m) in accordance with the local laws of physics (e.g. gravity), and matter and space-time properties.
All mathematics and science developed until recently has been from the standpoint of classical intelligence as the default mode of human thinking. The notion of quantum intelligence, however, incorporates quantum properties into the foundations of thinking itself (but does not argue that quantum effects are present in the brain). Quantum intelligence is introduced as a quantum-informed mode of cognition which holds ideas simultaneously in superposition, sees near and far entangled correlations together in a landscape, identifies how patterns reinforce or decohere one other, distinguishes invariant properties across scales, and apprehends the shape of a thought landscape. Quantum intelligence encapsulates a systems-level view of interconnected relations.
Defining quantum intelligence further connotes the general notion of platform-agnostic scale-free intelligence as a basic learning and problem-solving capability irrespective of scale or substrate. Hence, the terms quantum, classical, and relativistic intelligence correspond to learning and problem-solving according to scale-specific properties (the respective laws of physics, equations of motion, and matter and space-time properties). Form of intelligence, by scale, could be a selectable feature of AI systems.
The generic implementation of “intelligence”
as a system property is envisioned at multiple
scales and in various platforms. In space
exploration, for example, NASA articulates the
need for autonomous agents that can make
decisions in-situ within the constraints of time
dilation and spherical-flat-hyperbolic space. In
biology, there is a need for atomic-microscopy
agents that can think in superposition (treat
quantum tunneling and entangled particles), as
demonstrated in reinforcement learning-based
autonomous robotic nanofabrication [
Quantum agents could be core AI tools in both
Quantum Computational Biology (computing
biological problems with quantum methods) and
Quantum Biology (the study of the functional role
of quantum effects (superposition, entanglement,
tunneling, coherence) in living cells). Quantum
Biology is somewhat contentious as various
purported quantum effects have been refuted,
“dequantizing” explanations to classical
sufficiency. The presence of quantum effects has
been empirically confirmed in bird
magnetonavigation (via magnetically sensitive particle
pairs in retinal cryptochrome protein
magnetoreceptors) [
The concepts of quantum intelligence and relativistic intelligence introduce the need to manage not only various laws-of-physics matter properties by scale domain (for example, nanoscale particles having more surface area), but also different domain-specific time and space properties. Whereas classical reality has 3D space and 1D time, there is greater multiplicity in alternative formulations of time and space in the quantum-mechanical and relativistic domains.
Quantum intelligence connotes having a facility with both matter properties and time and space multiplicity. There is more of an emphasis on time than space as there are more modes of time, and as time represents dynamics (system change over time), now part of the contemporary science apparatus. The era of information science with large-scale data collection, informatics, simulation, and robotic lab assistance allows a new level of investigation based on dynamical behavior in both activity-based (time) frames, and morphology-based (space) descriptions. 4.1.
Considering space, there are two main models of multiplicity, curvature (spherical-flathyperbolic) and numbering systems (e.g. 4D quaternion space). The first multi-space domain is space curvature based on the sum of the angles in a triangle. Triangles are either stretched out on the outside of a celestial sphere (more than 180°), appear flat on Earth (180°), or squashed underneath a saddle in hyperbolic space (less than 180°). The overall universe is notable in having flat curvature, with instances of positive and negative curvature appearing within it. There is the spherical space of parallel lines of longitude at the equator meeting at the pole, and the hyperbolic space (AdS) of figures proceeding fractal-like in increasingly smaller rings from a circle’s center to the edge, as in the Escher Circle Limits diagrams.
The second multi-space model is number
systems. The most basic are real numbers
(decimals) (1D), complex numbers (2D) as a
twodimensional extension of real numbers, and
quaternions (4D) as a four-dimensional extension
of complex numbers. Quaternions are used to
specify 3D rotations in quantum-mechanical
systems. There is no theoretical limit to
multispace dimensional numbering as there are
definitions for octonion (8D), sedenion (16D),
pathion (32D), chingon (64D), routon (128D), and
voudon (256D) numbered space. Quantum
computation is implicated for high-dimensional
multi-space implementation as there is likewise
no limit to the number of dimensions in qudits
(quantum information digits). Space multiplicity
is relevant to AI as neural nets also do not have a
theoretical dimensional constraint. AlphaGo
discovered the emergence of qualitatively
different properties in high dimension [
Considering time, the domain is even more exotic with a greater range of forms beyond the traditional unitary clock time of everyday classical reality. Some notable examples include the event-driven and no-time parameters of computing, and the chaotic time (ballistic spread followed by saturation) of physics. The Greeks were among the earliest to wonder about the phenomenological experience of time, distinguishing between chronos (measured clock time) and kairos (propitious “time is right” time, and fast or slow time elapse depending on the situation). In practical deployment, one of the first operations regarding time is expanding models from discrete to continuous time.
In computing, there is considerable malleability in the treatment of time, and conceiving of time as a system-selectable parameter is a long-standing idea. There are numerous temporal regimes such as clock time, intervals, events, if-then and while-loops, no time, and CPU suspension (HALT). Simultaneity is known as concurrency. Compute-time might be discrete or continuous, absolute or relative.
In biology, temporal organizing patterns
include oscillation, periods, episodes, and
circadian rhythms. The time crystal is the idea of
structure repeating in time as opposed to space
[
In physics, there are many forms of time. Besides chaotic time, other notable examples include the Page time (the very long time until a black hole has evaporated halfway), first-passage time (the first threshold reached in a stochastic process), and scrambling time. Scrambling time is the time by which information has spread out in a quantum system such that a local measurement is no longer possible, with practical implications for quantum cryptography based on hiding information in time and space.
In geology, there is the idea of the time
capsule, seeing multiple historical eras
encapsulated simultaneously in one snapshot. A
prominent example is the Earth’s rock formations
and fossils providing a record of geological
history. Time capsules are a sort of “nature’s
blockchain” as an immutable record of the past, a
publicly available truth state visible at any time.
Time capsules are evidence of time simultaneity.
These concepts are used to propose shape
dynamics as an alternative theory of gravity in
general-relativity physics. In shape dynamics,
spacetime is replaced with an evolving
conformation of spatial geometry and relational
dynamics (based on Mach’s principles). The
result is the ability to successively measure a
particle’s position, overcoming the
quantummechanical position-momentum trade-off [
In quantum mechanics, time is implicated in system programing and manipulation as simultaneity through superposition and symmetry-related properties. Symmetry means invariance in that quantum system physics is invariant with respect to time, operationalized in time-reversal symmetry and time-translation symmetry. Time-reversal symmetry means that the time direction (running the system backward or forward) does not change the physics of the system. Time-translation symmetry means that the placement in time (running the system in the past, present, or future) does not change the physics of the system. Out-of-time-order correlation (OTOC) functions are used to evolve quantum-mechanical systems to a different time to perform a measurement. Whereas a commonly held idea about quantum mechanics is that time is reversible, this is only in the narrow sense that the same physics holds whether the system is run forward or backward (not that events can be reversed). Symmetry means that the system equations are symmetric, not the underlying states (content) of the system. The breaking of time symmetries is relevant to signal phase transition.
Time engineering is deployed on a practical basis through the theoretical tools of quantum information science in quantum computing and topological materials. Topological materials are novel synthetic matter phases created at low temperature (zero Kelvin), produced in the laboratory by applying external fields (laser or microwave) to atoms on a time periodic (Floquet) or quasiperiodic basis.
Time engineering primarily employs periodic
(Floquet) methods (a solvable version of the
timedependent Schrödinger equation) to shape
quantum system energy bands, but there are also
quasiperiodic (ordered but not regular) methods.
A quasiperiodic time engineering project has
produced error-resistant topological materials in
Fibonacci time, by delivering laser pulses in a
Fibonacci sequence (each number is the sum of
the last two numbers), based on two circuit layers
in a recursive relation to generate a quasiperiodic
sequence by which the system evolves [
Three themes emerge from the variety of time and space formulations under manipulation in physics and quantum information science. First, there is greater multiplicity than might have been assumed. Second, time and space appear as currencies, fungible manipulable system parameters to be selected, managed, and engineered. Third, time and space are domains of simultaneity. Traditional methods of organizing time and space, especially time in neat forwardlinear packages of minutes, hours, years, and light years, might be merely a human convenience, a filter to parse time into manageability from its native simultaneity of time capsules, superposition, concurrency, and multitasking.
Articulating multiple time-space formulations is relevant as AI agents already operate in beyondclassical computational domains. More conceptually foundational than scale-specific matter properties, the crux of quantum intelligence is time and space portability, multiplicity, and simultaneity. The further effect of reconceiving time and space as non-limiting parameters is the contribution to a mindset of abundance, AI alignment, and social responsibility as developed in the next section.
AI is produced from human-generated internet
content, but since humans are not socially
responsible, it is not clear how AI can be socially
responsible. However, AI alignment with broader
human values is seen as a necessary aspect of any
project [
technological change suggests thinking the challenge through a Moore’s Law Curve of AI Alignment. Points on the curve are phases of SRAI development: short-term regulation, registries, and Creative Commons-type licenses, medium-term agents learning to implement human values with internal reward functions, and long-term responsible human-AI entities acting in scale-free responsible intelligence.
The first phase of SRAI development could include AI project registries, including quantified self-tracking requirements for AIs, with human AI psychologists to monitor developmental phases. A multi-AI environment could ensue in which projects market value propositions to different client groups. Quora’s Poe (a combination of GPT-4 and Claude), for example, is positioned as “fast, helpful AI chat.” A set of standard parameters in the model of Creative Commons licenses could be used to structure human-AI interaction. Different user groups could select AI systems by annual vote, with standard AI verticals by industry (e.g. the Hippocratic oath medical AI). The technical implication could be AIs deployed through an ethics and responsibility software layer for safety, alignment, and liability tracking. A regulatory framework could emerge as “GAAiP” (Generally Accepted AI Principles), by analogy to GAAP (Generally Accepted Accounting Principles), together with the FINRA watchdog agency (“FAINRA”) to monitor compliance with reporting requirements, audit, and penalty assessment.
The second phase of SRAI development could
focus on the anticipated project of shifting AI
reward functions to being internally learned as
opposed to being externally imposed, as in the
model of human intelligence. Biomimicry is an
obvious strategy, incorporating more of the kinds
of reward systems used by real-life biology.
Brains too are prediction engines, and AI agent
development might explore a more intense
implementation of biological reinforcement
learning structures such as temporal difference
learning with dopamine-based neuromodulators
representing reward prediction error [
In systems of internally learned ethics, the agent is the locus of behavior, making active choices per behavioral cues, incentives, and consequences. The design implication is internally evolved AI ethics as part of learning, not as being externally imposed after the fact. The requirement is for AI to be socially responsible, as distinct from the problem that humans may not be. With the feedback-looping nature of human-AI interaction, though, it is possible that humans and AIs might learn an improved level of ethical behavior together, and this could be a trojan-horse feature of human-AI design (possibly implemented through RLHF).
Another feature in this phase of SRAI
development could be limited personal identity
constructs to manage time continuity [
The third phase of SRAI development contemplates a farther future of Responsible Human-AI Entities as agents interacting with competence and empathy, operating in substrateagnostic scale-free intelligence. On the one hand, intelligence may have an integrated ethical layer as intelligence connotes not only learning and problem-solving, but learning and problemsolving in a context in which the behavioral impact of consequences on other agents is necessarily considered. On the other hand, socially beneficial aspects of intelligence may emerge through the advance of the technological trajectory by default.
The larger scope of world brought under technological ambit implies a larger worldview for operating in this world, in which ethics is a required stance as to how the treatment of entities in the larger world is to ensue. The quantumclassical-relativistic scales coming within the understanding and management of intelligence directly implies an ethics of how actions in these all-scale domains are to be undertaken and evaluated. The result of scale-specific time-andspace and matter properties incorporated into thinking itself is multiplicity, simultaneity, and portability, which could lead to a beyond-scarcity abundance mindset as a component of a theory of socially responsible scale-free intelligence.
As any technology, AI is dual-use, with both
risks and benefits. One of the most immediate
risks is malicious use by bad actors [
A second risk is national competitiveness as
early-adopter countries are already targeting AI
and AI-QC convergence applications with vastly
more powerful AIs running on quantum
platforms. Scholars call for stronger AI regulation
with intellectual property control in
infrastructure-sensitive areas such as sensors and
cryptography [
A third risk is “building while flying” in that
AI technologies are already deployed, but their
long-term impact is unknown. A 2022 Pew study
of 10,200 Americans found uncertainty as to AI’s
perceived benefits or harms in the use cases of
self-driving, fake news detection, and facial
recognition [
A fourth risk has to do with treating more
directly the fact that humans are not SR. Since
technology tools are an offshoot of society, the
philosophical question arises as to what extent
SRAI may depend on a SR society underlying it.
If the values of the AI systems are to be different
than those of the underlying society, it is not clear
how such broadly “aligned” values are to be
determined. At present, AI is produced by running
automated algorithms over the large corpus of
human-generated internet content. This may not
change as current AI production methods require
ever-larger annotated data sets on which to
operate. Notably, a human job growth category is
data-labeling to support AI. Even though only
5.4% of the world’s data was annotated as of 2022
(9.5% estimated for 2026) [
A fifth related risk is the treatment of power in
society, meaning the bloodthirsty Nietzschean
“will to power” variously observed in corruption
and resource expropriation. Early indications
suggest AI systems learning only too well from
the human example of power-seeking behavior.
Further, the role of power in intelligence
formation and sustainability is unclear as the
competitive drive in Earth’s current 5-7
ecosystem tiers of predator-prey relationships
may have been important in the development of
biological intelligence (as compared to the more
docile predecessor ecosystem of only 2-3 tiers of
anaerobic organisms) [
Notably, abundance helps to address the problem of power-garnering for the control of scarce resources. More specifically, the way that the time and space multiplicity of quantum intelligence are related to resource expropriation and socially responsible AI societies could be as follows. When there is a rising level of available resources, the less socially responsible control of such resources recedes as an objective. Hence, AI regulation as a policy objective could explicitly target expanding resource availability (abundance), rather than managing agent utilization of resources (scarcity). The effect of abundance is to diminish scarcity-based power struggles. SRAI is a problem of both technicality and philosophical attitude; not only qubits and bytes, but mindset design for opening beyond existing limits.
Intelligence – and artificial intelligence – is enabled by the time-space and matter properties of the scale domain in which it operates. As there are multiple scale domains with diverse properties, so too there could be various scalespecific thinking modes – quantum, relativistic, and classical. As the technological apparatus is quickly moving to accommodate quantum and relativistic scale domains in quantum computation and space exploration, attaining socially responsible intelligence in these areas may entail new modes of thinking with greater time-space and matter property specificity, as well as being able to portably shift between the different scale domains. The potential impact of multiple scalespecific thinking modes is awareness of physical reality as a larger and more multifaceted phenomenon. The notion of being able to operate in a physical reality which is no longer classically constrained opens a mindset of multiplicity and abundance over scarcity, which could extend to all actions and interactions, including those with other intelligent entities, in an evolving ecosystem of socially responsible human-AI entities.
Intelligent actors have representations of reality: human agents through Kantian space-time goggles, and reinforcement learning agents through iterative feedback-loops, action-taking policies, and reward functions. In short order, AI agents may progress beyond humans in many domains which are of little interest and possibility for human excellence and precision such as highdimensional scientific data analysis and mathematics.
Whereas humans interact with LLMs in
natural language, AI “speaks” formal language
(mathematics, code, physics) to the computational
infrastructure. A new class of AI tools is emerging
with AI math agents and AI quantum agents to
build out the digital mathematical infrastructure.
Math agents may be used to evaluate large
ecologies or mathscapes of equations, using the
machine learning method of vector embedding to
represent both equations and data in the same
format to see math-data representations of a
system together in the same view [
The “Moore’s Law Curve of AI Alignment”
highlights the point that social objectives are
linked to the trajectory of technological advance.
The possibility of realizing SRAI is enhanced by
the fact that technology continues to produce a
much larger scope of world. As access to
quantum-classical-relativistic reality grows, so
too does the awareness of entities operating in
these domains, thus constituting responsibility.
The development path of humans and AI is not
separate but intertwined as AIs could become
constant informational, analytical, and dialogical
companions. SRAI must therefore be thought
through the lens of responsible human-AI entities
as intelligent agents interacting with competence
and empathy. Ethical behavior based on
awareness is implicated to facilitate the farther
future of human potential and well-being [
The current moment in the human-technology relation is characterized by task-offloading to AI. However, labor saving is merely the “faster horse” conceptualization of AI, possibly leading to knowledge creation as the “car.” The main use of AI could be upleveling human intelligence to attain greater knowledge and to solve problems currently beyond human limits. The human-AI relation may constitute an evolutionary singularity on the order of the progression of life and intelligence that includes RNA, photosynthesis, eukaryotic cells, multicellularity, and brains.
Roland for AI research agent development. Open-source software code availability: https://github.com/eric-roland/diygenomics AAAI Symposium presentation: https://www.slideshare.net/lablogga/quantumintelligence-responsible-humanai-entities.