Quantum Intelligence: Responsible Human-AI Entities

Melanie Swan, MBA, PhD
Research Associate
University College London
Quantum Intelligence
Responsible Human-AI Entities
Socially Responsible AI for Well-Being
AAAI, San Francisco CA, 27 Mar 2023
Slides: http://slideshare.net/LaBlogga

27 Mar 2023
Bioinspired Reality
1

27 Mar 2023
Quantum Intelligence 2
Information Systems Biology Research Program
2015 2019 2020
Blockchain Blockchain
Economics
Quantum
Computing
Quantum
Computing
for the Brain
2022
Image: Thomasian, 2021, Nat
Rev Endocrinol. 18:81-95, p. 12

27 Mar 2023
Thesis
Introducing “quantum intelligence” as a concept of intelligence for operating in the
quantum realm may help in a potential AI-Quantum Computing convergence (~2030e),
and towards the realization of SRAI for well-being (economics, health, energy, space).
“Scale-free intelligence” is formulated as a generic capacity for learning.
AI did not spring onto the scene with chatGPT, but is in an ongoing multi-year adoption.
A transition may be underway from an information society to a knowledge society
(one tempered and specifically using knowledge to improve the human condition).
AI is a dual-use technology with both significant risk and upleveling possibilities.
SRAI for well-being is a social objective, and also a technological objective. SRAI is part
of AI development and within the technological trajectory of harnessing all scales of
physical reality ranging from quantum materials to space exploration.
Conceptually, thinking in quantum and relativistic terms expands the physical worldview,
and likewise the social worldview of entities inhabiting the larger world.
Practically, SRAI may be realized in phases: short-term regulation and registries, medium-
term agents learning to implement human values with internal reward functions, and long-
term responsible human-AI entities acting in partnership in a future of SRAI for well-being
SRAI: Socially-Responsible Artificial Intelligence
Artificial intelligence: technology with capabilities traditionally considered to be human

27 Mar 2023
Agenda
 AI Research Copilot Technologies for Science
 AI Engines, AI Chips, Software 2.0
 Potential AI and Quantum Computing convergence
 Quantum Intelligence
 Scale-free Intelligence
 Socially-Responsible AI for Well-being
 Responsible Human-AI Entities
 Conclusion, Risks, and Use Cases
 Research Copilot for Biology
4

27 Mar 2023
Rapid AI Technology Adoption
5
Bad
actors
emerge
Bad Actors
Newtech
Takeoff
1.0
Discovery
Corporate
Enterprise
Mainstream
Crossing the
Chasm
Accelerated
Newtech
Takeoff
2.0
Humans worldwide explore AI use cases, virtual agents
Early adopters
Years (internet, twitter, blockchains, smartphones)
Months (AI, tablets, 5G, 2/3 global population now online)
Discovery
Bad actors Dual-use expected (misinformation, manipulation, fraud)
Corporate Microsoft 365 Copilot, Dynamics 365 Copilot CRM ERP
Mainstream
 Data-intense connected world: quicker adoption cycles
Bad
actors
mitigated
Time
Time
Sources: Stanford Global AI Vibrancy Tool: US and China lead AI innovation https://aiindex.stanford.edu/vibrancy/
McKinsey (Jun 2022): China may add $600 billion to economy with AI in transportation, automotive, logistics, manufacturing, enterprise
software, healthcare, and life sciences https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-next-frontier-for-ai-in-china-
could-add-600-billion-to-its-economy

27 Mar 2023
Potential for Al-Facilitated Science
6
Formulate
research
question…
Design
polygenic study
protocol…
Suggest
theoretical
underpinning…
Analyze
study
results…
Draft
paper
skeleton…
Generate
slides for
colloquium…
Analyze
pathway…
Oncology
Immune
System
Update
research
agenda…
 Research Copilot: “Microsoft 365 Copilot” for Science
 Integrated AI learning of multi-modal health data streams
Research Copilot
Public Private
Select Data Corpus
Mockup only

27 Mar 2023
Knowledge Society
 Knowledge platforms
 Wikipedia: interface for knowledge access
 Coursera (MOOCs): interface for knowledge learning
 Research Copilot: interface for knowledge generation
7
Wikipedia:
Knowledge Access
Coursera (MOOCs):
Knowledge Learning
Research Copilot:
Knowledge Production
Science Knowledge Graph
Space
Research Copilot
Biology Energy
Copilot: active interface on a data corpus

27 Mar 2023
AI Engines
 Synthesized search, multimodal creation
 GPT-3, LaMDA (large language models); DALL-E, Midjourney, Lensa
(image); Stable Diffusion (video), Codex (code)
 Initial societal response
 Universities reminding of plagiarism policies
 Faculty crash course re: AI engine plagiarism detection
 Arms race: re: anti-plagiarism detection software
 Call for watermarking AI-generated text
 Early adopter response: investigate and deploy
 Memorization -> Information more dramatic than Information -> Ideas
8
Midjourney (CO
state fair image)
Knowledge Era Currency Basis of Knowledge Valorization Incentive
1 Renaissance Scribes Closed Information Rote memorization Control access to information
2 Information Society Open Information Find and synthesize information Share information, collaborate
3 Post-plagiarism Society Open Ideas Apply ideas to problems Large-scale problem resolution
What is knowledge?
Ability to memorize -> to synthesize information -> to deploy ideas
GPT-3: 175 billion parameters
GPT-4: 170 trillion parameters
1,000x bigger
Parameter: learned system weight

27 Mar 2023
 Human-AI entities as the competitive unit
 Digital knowledge prosthesis (phone: external; BCI: internal)
 Conducting science
 Executing experiments
 Publishing results
 Founding startups
 Collaborating with others
Human-AI Entity
Human AI
Ishiguro, 2021
Literature Examples:
Personal AI agents
Humans and other objects
equipped with WAIs (weak AIs)
Divya, 2021 Schroeder, 2005
Dystopian Realistic Utopian
The big merge
Human-AI Entities
BCI: brain-computer interface: direct communication pathway between an enhanced or wired brain and an external device

27 Mar 2023
AI Chips
 Platform progression
 Mainframe, mini, workstation, PC, smartphone, AI chips, quantum computing,
topological materials (quantum spin liquids, quasiparticles, topological insulators)
 CPU-GPU-TPU(NPU)-QPU at 1014 IPS (compare: human ~1018-1020 IPS)
 TPU: tensor processing unit (deep learning: matrix multiplications)
 NPU: neural processing unit (machine learning: training, inference)
 QPU: quantum processing unit (quantum computing chip with error correction)
10
Example:
Hardware
Accelerators
Traditional AI chips:
accelerator cards on
attached to server
Tenstorrent AI chips:
a single chip that is
an edge server (faster
and more integrated)
IPS: instructions per second; Source: https://www.aiacceleratorinstitute.com/top-20-chips-choice-2022
Tenstorrent AI Chip Roadmap
CPU GPU
TPU
NPU
QPU
Chip Progression

27 Mar 2023
Source: Karpathy, A. (2017). Software 2.0. Medium. 11 November 2017. https://karpathy.medium.com/software-2-0-a64152b37c35.
 Software 2.0: machine designed & programmed code
 Machine coding: AlphaCode, Codex API (Github Copilot)
 Search interface for internet-available code
 Ability to seed code for new applications
 New software development paradigm
 Human specifies
 Data, objective, framework, problem space
 Machine learning optimizes
 Node weights, network architecture
 Algorithm for compilation and transfer
 Algorithms more effective at code-writing than humans
 Theorem-proving, code security audit, bug fixing
Software 2.0
Software 2.0
Algorithms can explore a
larger possible program space

27 Mar 2023
New Kantian Goggles: Written by AI
12
Software
Physical Reality
Humans
 AI-written software is a new interface on reality
 Have always had an interface on reality (Kantian goggles)
AI learns
human values,
writes classical
and quantum
algorithms,
augments the
interface with
reality: Kantian
Goggles 2.0
Classical
Relativistic
Quantum

27 Mar 2023
The big offload
The AI Stack: Moore’s Law Curve of AI
13
Knowledge
Generation
disease cure,
theorem-proving
Cognitive Labor-
outsourcing, Virtual Labor
Computational Contracts
Moore’s Law Curve of AI
Data Analytics
Physical Labor-outsourcing
The AI Stack
Precision Tasks
LASIK eye surgery,
system control, atomic
manufacturing,
autonomous driving,
software programming
Data analytics, informatics
Physical labor-outsourcing
Cognitive labor outsourcing
Virtual labor
Computational contracts
Precision tasks
Software programming
Knowledge generation
Large-scale problems: space, biology, energy
 Outsourcing classes of tasks to technology

27 Mar 2023
Science as Information Science
 Impact of digitization
 Information science overlay
 Science, corporate enterprise, government, personal life
 Information science
 Study of information as a topic (computing, math, physics)
 Study of fields as information content using information methods
 Computational neuroscience
 Computational chemistry
 Digital humanities
14
Domain Activity
3 Theoretical Study biology as an information creation and transfer endeavor
2 Practical Study biology with information (machine learning)
1 Practical Study biology as information (DNA code, chirality)
Information Systems Biology

27 Mar 2023
Agenda
15

27 Mar 2023
Quantum Computing
Sources: IBM Quantum, https://quantumai.google/qecmilestone
Dequantization: certain purported quantum speed-ups reclassed to the Dequantization Zone per sufficiency of classical methods
Google Quantum Computing Roadmap
 Hardware needs: error correction
 2023e: 1000 qubits (Google, IBM)
 2030e: million-qubits (general-purpose) (IBM)
 Software needs: algorithms
 Software 2.0 implication
 AI discovers new quantum algorithms
 AI writes code for quantum computing
 Dequantization trend (classical sufficiency)
Cybersecurity: million qubits
needed to break RSA
US NIST quantum-safe
algorithms hacked but
“in progress”
IBM Quantum
Computing Roadmap

27 Mar 2023
Potential AI-QC Convergence
17
AI
Artificial Intelligence
QC
Quantum Computing
QML: Quantum Machine Learning
QML

27 Mar 2023
Quantum Versions of AI Tools
18
Quantum Machine Learning:
quantum algorithms applied to machine
learning methods
Classical Machine Learning:
computer systems learning without explicit
instructions, modeling statistical patterns in data
Quantum Monte Carlo (quadratically faster)
BioPharma multi-genic biomarker discovery
Quantum Transformers (quantum attention
using Clifford algebra)
Quantum Natural Language Processing
Transformers: attention-based neural network
Natural Language Processing
Monte Carlo methods: repeated random sampling
Machine
Learning
Monte
Carlo
Methods
Transformer
NN
NLP
Copilot
Classical Copilot Quantum Copilot
Classical Intelligence
Classical includes dequantization demonstrations of sufficiency of classical methods “dequantizing” claims of quantum speedup
Existing
Proposed

27 Mar 2023
Quantum Research Copilot for Biology
19
AI
Artificial
Intelligence
QC
Quantum
Computing
Biology
 “Gato Cell” for Biology concept
 Reinforcement learning agent for data-intense quantum
environment of multi-modal health data stream aggregation
 Genomics, epigenetic methylations, imaging, biomarker
Problem Implicated Area
1 85% drug targets ineffective Quantum chemistry
2 Early cancer detection Computational biology
3 Alzheimer’s disease Dx no Rx Computational neuroscience
4 Heart disease event prediction Computational biology
Disease resolution might be facilitated
with scalable quantum computing
Top Killers
Prevention Cure
450,000 users
Disease Solver Copilot
Cell
Suggested
concept only

27 Mar 2023
AI raises the Definition of Intelligence
 Intelligence: ability to learn, understand, and think (OED)
 Artificial Intelligence (AI): technology with capabilities
traditionally considered to be human
 Quantum Intelligence: ability to think in quantum terms
 Quantum Artificial Intelligence: AI algorithms
running on quantum platforms
 Scale-free Intelligence: ability to think
in terms of any scale of physical reality
 Classical, quantum, relativistic
20

27 Mar 2023
What is Quantum?
 “Quantum” = atoms & subatomic particles
 Working at all scale levels of physical reality
 Quantum effects visible at 10-9 m
 Relativistic effects present at any scale (matter of precision)
21
Scale Measure Comments
1 5.1 x 1096 Planck density Kg/Meter3 Density of the universe immediately after the Big Bang
2 1 x 1080 Particles Total particles in the observable universe (est.)
3 1 x 1014 Cells Cells in the human body (9 out of 10 are bacteria)
4 8 x 1010 Stars Number of stars in the Milky Way galaxy (est.)
5 1 x 102 Meter Earth Earth’s atmosphere: 10,000 ft life support, 62 mi to space
6 1 x 101 Meter Human Human-scale: Classical Mechanics
7 1 x 10-9 Nanometer Atoms Quantum mechanics (nanotechnology)
8 1 x 10-12 Picometer Ions, photons Optics, photonics
9 1 x 10-15 Femtometer Subatomic Gauge theories
10 1 x 10-35 Planck scale Meters Smallest known length scale
11 5.4 x 10-44 Planck time Seconds Shortest meaningful interval of time
Theory
Large-scale:
General
Relativity
Small-scale:
Quantum
Mechanics
Human-scale:
Classical
Mechanics
Scale Domains of Physical Reality: Classical, Quantum, Relativistic
Quantum
Computing
Space
10-9 to 10-15 m

27 Mar 2023
Scale-free
Intelligence
Moore’s Law Curve:
Intelligence
Quantum
Intelligence
Learning and solving
problems in classical terms
Learning and solving
problems in quantum terms
Matter Properties:
Classical Mechanics
Scale-free Intelligence
Intelligence as a generic capacity, the automated
computational overlay for operating in all physical
scale domains (classical, quantum, relativistic)
Autonomous scale-free learning, problem-solving,
knowledge generation
Matter Properties:
Quantum Mechanics
Time and Space Properties:
3D space and 1D time
(Minkowski spacetime)
Time and Space Properties:
spherical-flat-hyperbolic space,
simultaneous time
 Intelligence is intertwined with physical properties
 A coordinate system is a time and space map

27 Mar 2023
 Generic concept of intelligence needed
 Scale-free, substrate-agnostic, universally-deployable
 Operational concept of learning and problem solving for
working in non-classical domains due to different properties
(time-space regimes)
 Already implementing the capability of “intelligence”
at multiple scales and in various platforms
 Space: need autonomous intelligence that can think in time
dilation and spherical-flat-hyperbolic space
 Biology: atomic-microscopy, need intelligence that can think in
superposition (treat entangled particles and wave-lengths)
 Quantum copilot for epigenetic medicine: think in >3D space
topological knotting, quantum tunneling behavior in enzymes
 Energy: many-body problem: need particle prediction system
23

27 Mar 2023
 Quantum Intelligence: ability to think in quantum terms
 Matter properties
 Superposition, entanglement, interference, symmetry, topology
 Time and space properties
 Simultaneity, oscillation, events; spherical-flat-hyperbolic space
 Quantum properties incorporated into the foundations of
thinking itself, as a mode of cognition which
 Holds ideas simultaneously in superposition
 Sees near and far correlations in a landscape
 Identifies how patterns reinforce or decohere one other
 Distinguishes invariant properties across scales
 Apprehends the shape of a thought landscape
24
Superposition
Entanglement
Topology
Symmetry
Interference

27 Mar 2023
Quantum Properties
25
Superposition: a quantum system can exist in
several separate quantum states simultaneously
Entanglement: two interconnected quanta
maintain their connection regardless of
the distance between them
Quantum tunneling: a particle is able to penetrate
through a potential energy barrier higher in energy
than the particle’s kinetic (motion) energy
Symmetry: properties that remain
invariant across scale tiers
Interference (coherence): an object’s wave
property is split in two, and the two waves
cohere (reinforce) or interfere with each other
Source: Mazzoccoli, G. (2022). Chronobiology Meets Quantum Biology: A New Paradigm Overlooking the Horizon? Front. Physiol.
13:892582. doi: 10.3389/fphys.2022.892582.
 Schrödinger Cat State: quantum state comprised of
two opposed conditions at the same time

27 Mar 2023
Wavefunction
 The wavefunction (Ψ) (psi “sigh”)
 The fundamental object in
quantum physics
 Complex-valued probability
amplitude (with real and
imaginary wave-shaped
components) [intractable]
 Contains all the information of
a quantum state
 For single particle, complex
molecule, or many-body
system (multiple entities)
26
Source: Carleo, G. & Troyer, M. (2017). Solving the Quantum Many-Body Problem with Artificial Neural Networks. Science.
355(6325):602-26.
Ψ = the wavefunction that
describes a specific wave
EΨ(r) = -ћ2/2m ∇2 Ψ(r) + V(r)Ψ(r)
Total Energy = Kinetic Energy + Potential Energy
Schrödinger wave equation
 Schrödinger equation
 Measures positions or speeds (momenta)
of complete system configurations
Wavefunction: description of
the quantum state of a system

27 Mar 2023
Multi-Space Regimes
27
Dimensional Number Systems:
Real (1D), Complex (2D), quaternion (4D)
Space Curvature:
Spherical, Flat, Hyperbolic
Mercator Projection
Real (1D), Complex
(2D), Quaternion (4D),
Octonion (8D), Sedenion
(16D), Pathion (32D),
Chingon (64D), Routon
(128D), Voudon (256D)
 AI is operating in high dimensional space
Alternative Space Regimes
Source: Sejnowski, T. 2018. The Deep Learning Revolution. Cambridge MA: The MIT Press.

27 Mar 2023
Multi-Time Regimes
28
Time Capsule (nature’s
blockchain): multiple
eras in one snapshot
(time simultaneity)
Domain Examples Description Simultaneity
1 Time Crystal Structure repeating in time, not space X
2 Biology Oscillation, periodicity, episodic, circadian rhythm X
3 Physics
Chaos (ballistic spread followed by saturation)
Page time (black hole evaporated halfway)
First-passage (stochastic process first reaches threshold)
X
4 Geology Time capsule: snapshot of multiple historical regimes X
5 Quantum Mechanics
Superposition, entanglement, interference, symmetry
-Time-reversal symmetry: time direction (running the system backward or
forward) does not change the physics of the system
-Time-translation symmetry: placement in time (running the system in the
past, present, or future) does not change the physics of the system
X
6 Topological Materials Time engineering: periodic (Floquet), quasiperiodic X
7 Computing Clock time, event time, episodic, interval, no time X
8 Mathematics Time series, Fibonacci time, high dimensional time X
9 Phenomenology Chronos vs Kairos (clock time vs experienced time) X
 Computing time capsules: superposition,
concurrency, multitasking, simultaneity
 Oscillation, periods, event-based time
Source: Barbour, J.; Koslowski, T.; and Mercati, F. 2013. The solution to the problem of time in Shape Dynamics. Classical and
Quantum Gravity 31(15): 155001. doi.org/10.1088/0264-9381/31/15/155001

27 Mar 2023
Sources: Winfree, A. T. 1980. The Geometry of Biological Time. Berlin, DE: Springer-Verlag;
London Centre for Nanotechnology, University College London
 Time Crystal: structure repeating in time
 Hatching behavior plotted along three temporal axes
 Time (phase) of stimulatory light pulse
 Duration (energy) of the pulse
 Hatching time
Biotime Crystal
Winfree, 1980; Wilczek, 2012

27 Mar 2023
Time Engineering (2D Time)
 Quantum materials: Engineer temporal behavior
 Apply external fields (laser or microwave) to atoms, ions,
photons on a time-periodic (Floquet) or quasiperiodic basis
 Periodic (Floquet discrete time crystals)
 Solvable version of time-dependent Schrödinger equation
 Used to shape quantum system energy bands on demand
 Quasiperiodic (ordered but not regular)
 Two offsetting lasers effectively create second time dimension
 Produce error-resistant materials
 Fibonacci time laser pulses
 2-circuit layer recursion relation
 Quasiperiodic system evolution
30
Fibonacci sequence: each number is the sum of the last two numbers
Sources: Dumitrescu et al., 2022, Dynamical topological phase realized in a trapped-ion quantum simulator. Nature 607: 463–467.
Merali, Z. 2022. New Phase of Matter Opens Portal to Extra Time Dimension. Sci. Am. July 26.

27 Mar 2023
Quantum Copilot
31
Quantum Copilot
Minimal Claim: Need quantum intelligence for operating (as
human, AI, hybrid) in the quantum environment
Maximal Claim: Need quantum intelligence as an improved
version of classical intelligence for thinking more generally
AI Track
Quantum Intelligence for AI
Human Track
for Humans
AI knowledge assist: solving problems
 Molecular dynamics modeling of novel drug discovery small molecules
 High-dimensional topological modeling of DNA, RNA, protein knotting, compaction
 Cancer tumor growth dynamics: chaotic spread unadhered to substrate
 Produces knowledge
Quantum AI learns its own concept of “quantum
intelligence” by operating in the domain
 Produces knowledge
 Produces code to produce knowledge
Mockup only

27 Mar 2023
Structure of Cognition
 Intelligence may be completely computational
 March towards “human” capabilities
 Knowledge layer defined for AI neural network graph
 Knowledge: the sum of relationships in information
32
Consciousness
Understanding
Knowledge
Information
Data
Sources: Price. (2018). The Evolution of Cognitive Models: From Neuropsychology to Neuroimaging and back. Cortex. 107: 37–49.
doi:10.1016/j.cortex.2017.12.020; Shouval et al. (2010). Spike timing dependent plasticity: a consequence of more fundamental
learning rules. Frontiers in Computational Neuroscience. 4(19):1-13. doi: 10.3389/fncom.2010.00019.
Spike timing
dependent plasticity:
input received before
output strengthens
the brain’s learning
rules and synaptic
plasticity
Moving up the Intelligence Stack

27 Mar 2023
Agenda
33

27 Mar 2023
Socially-Responsible AI for Well-being
Source: Debate at the Harvard Museum of Natural History, Cambridge MA, 9 September 2009,
https://www.oxfordreference.com/display/10.1093/acref/9780191826719.001.0001/q-oro-ed4-00016553
 “The problem of humanity is Paleolithic
emotions, medieval institutions and
godlike technology” – naturalist E.O.
Wilson, 2009 (paraphrase)

27 Mar 2023
Biological Intelligence
 Evolved multiple times
in separate pathways
on Earth, but is not
“socially responsible”
Sources: Godfrey-Smith, P. 2016. Other minds: the octopus, the sea, and the deep origins of consciousness. NY: Farrar-Strauss
and Giroux. https://www.tessamontague.com/cuttlecam
Memory Storage in the Honey Bee
via Synapsin Promoter
(Carcaud, 2023, PLOS Biology)
Cuttlefish neurons
(Montague, 2022, Brain
Atlas of the Cuttlefish)
Neurons Synapses Ratio Volume Complete
Worm 302 7,500 25 5 x 104 1992
Fly 100,000 10,000,000 100 5 x 107 2018
Mouse 71,000,000 100,000,000,000 1,408 5 x 1011 NA
Human 86,000,000,000 242,000,000,000,000 2,814 5 x 1014 NA
Connectome: map of
synaptic connections
between neurons
(wiring diagram), but
structure is not function
Biological Organisms and Connectome Completion Status

27 Mar 2023
Aim: Socially-responsible AI
 AI technologies are not socially responsible
 AI is produced from human-generated internet content
 Humans are not socially responsible
 Therefore AI is not socially responsible
 No precise definition of “socially responsible”
 Current solution
 Censor AI-produced content after the fact
 Regulation: EC AI Act 2022
 AI ethicists: consulted before technology is released
 Delayed release, freemium, source-code not released
 Situation: non-SR AI, rapid technological change
 Suggests a Moore’s Law curve to think the problem
36
EC AI Act 2022

27 Mar 2023
Moore’s Law of AI Ethics
Source:
 Rapid technological change automatically
contributes to socially-responsible AI
 Short-term: regulation and registries
 Medium-term: internally-learned morality
 Long-term: responsible human-AI entities
 Larger-scope responsible behavior
 Post-scarcity economic entities
GAAP/FINRA regulation and audit
principles for AI entities
Incentive system
produces ethical
behavior by
default (AI peers)
Larger scope of
concern
Human-Agent
Interaction Design
Bad actors expected as early
adopters of any new technology
(internet, blockchain)
AI ethics via
internal rewards,
morality functions
1. Regulation, Registries, Bad Actors
2. AI Alignment
3. Reputational
Ethics
Verified identity AI registries
Long-term
Medium-term
Short-term
AI Ethics

27 Mar 2023
Short-term: Regulation and Registries
 Regulatory registration, principles, audit
 AI Registries with verified identity, accountability
 Engineers sign bridges, bioengineers sign DNA
 Website certifications: (~CC-Licenses) certified AI
 “GAAiP” (GAAP analog)
 GAAP: Generally-accepted accounting principles
 GAAiP: Generally-accepted AI principles
 Annual audit by “FINRA” of AI
 Dual-use technology: bad actors expected
 Early new technology adopters (internet, blockchain)
 Legal framework for assigning responsibility
 At-fault: platform, content-creator, consumer
 Difficulty of policing virtual behavior
38
FINRA: Financial Industry Regulatory Authority
History of Technology: in the
long-term, good uses can
outweigh bad (internet, cell
phones, Minitel, blockchains)
Blockchain Case Study:
Public: high-profile bad
actors, negative public view
Private: implementation of
computational contracts in
global infrastructure, drug IP
registries, supply chain

27 Mar 2023
Medium-term: AI Alignment
 AI Alignment: AI goals for positive impact on humanity
 AI able to learn and appreciate human values and desires
 Ideal if AI learns human goals as difficult to specify
 Immediate situational awareness
 Figure out what one/multiple humans want
 Have motivation to pursue these values
 Longer-term strategic planning
 Deliberative future goal attainment
 Overall message
 AI systems may become very smart and powerful learners
 AI may influence any area in which human intelligence is used,
having an essentially unlimited impact
39
Sources: https://www.youtube.com/watch?v=JVOiuIqxlrE; https://nickbostrom.com/papers/openness.pdf
Oxford Future of Humanity Institute,
Nick Bostrom, 18 March 2023

27 Mar 2023
Future-of-work job growth category
Human-Agent Interaction Design
 Building learning systems, not out of the box systems
 Specify framework within which agents can learn internal
reward functions to implement human values
 Human values difficult to specify, agents learn directly
 AI game-play agents already symbolically representing
other agents and possibly themselves
 Human-agent interaction design
 How can agents learn human values
 What do humans want agents to want
 Agents that improve humans experience
 Facilitate making choices in a democratic way
 Maximize human autonomy
 Increase the quality and depth of a conversation
40
Source: Matt Botvinick, DeepMind

27 Mar 2023
AI Agent-learned Limited Identity Construct
41
 AI personal identity construct
 Limited non-sentient framework
 Agents are embedded in
environments
 Thinking is not exogenous
 Limited personal identity construct as
mechanism of continuity and morality
 What does AI look like?
 How does AI self-represent?
 Symbols, equations, graphs, code base
 How does AI self-represent to humans?
 A graph entity
Source: Price, C.J. (2018). The Evolution of Cognitive Models: From Neuropsychology to Neuroimaging and back. Cortex. 107: 37–
49. doi:10.1016/j.cortex.2017.12.020.
Limited AI Identity Construct
Internal
Rewards Function
Internal
Morality Function

27 Mar 2023
Philosophy of Personal Identity
 “Self” concept as catchall for experience continuity
 View: There is no self
 Personal identity is not required for survival, only a
relational link between past/future experience (Parfit)
 Self is a flux of unconnected perceptions (Hume)
 Individuation is a dynamic process (Simondon)
 Living being capacity spectrum for individuation
 The subject is an effect not a cause
 View: There is a self
 The self is a thinking intelligent being, that has reason
and reflection, and can consider itself as itself (Locke)
42

27 Mar 2023
Long-term: General Intelligence?
43
 AGI: Artificial General Intelligence: general-purpose
problem solving in any context
 Internally-learned reward and morality functions
Kurzweil:
AGI 2045e
DeepMind generalist agent, Gato, a transformer neural network which can perform
hundreds of tasks such as playing Atari games, captioning images, chatting, and
stacking blocks with a real-life robot arm, hardcoded reward function
Source: Reed, S., Zolna, P., Parisotto, E., et al., 2022. A Generalist Agent. Transactions on Machine Learning Research (11/2022).
https://www.deepmind.com/publications/a-generalist-agent.
 Generalist Agent
 Reinforcement
learning agent
 Agent taking actions
in an environment to
maximize
cumulative rewards
per a value policy

27 Mar 2023
Long-term: Bigger Scope of World
44
500 BCE: The Mediterranean 2023: The Time and Space of the Universe
 Larger scope of concern
 Beyond the immediate self-entity and community others
 A larger sense of individual and collective identity, new peers
 Broader notion of rights and responsibilities
 Others: human, animal, machine, hybrid, environment
 Prosperity, stewarding, survival: even selfish acts cannot help but
benefit others in a larger worldview system
Source: Fatehi, K., Priestley, J.L., & Taasoobshirazi, G. (2020). The expanded view of individualism and collectivism: One, two, or
four dimensions? International Journal of Cross Cultural Management. 20(1) 7–24. DOI: 10.1177/1470595820913077.
Sphere of Concern: “The World”

27 Mar 2023
Abundance Economy
 Mindset of prosperity
 Practical
 Basic income floors, GBIs
 Wide-ranging economic benefit of AI
 Conceptual
 Larger scope of concern
 Open individualism: help others realize their goals
 Positive disintegration, reintegration (Dabrowski)
45
Source: Fatehi, K., Priestley, J.L., & Taasoobshirazi, G. (2020). The expanded view of individualism and collectivism: One, two, or
four dimensions? International Journal of Cross Cultural Management. 20(1) 7–24. DOI: 10.1177/1470595820913077.

27 Mar 2023
Socially-Responsible AI for Well-being
Socially-Responsible
Human-AI Entities
Scale-free
Intelligence
Responsible Intelligence
Quantum
Intelligence
Humans
Non-socially
responsible AI
 Responsible Human-AI Entities:
intelligent agents interacting with
competence and empathy

27 Mar 2023
Agenda
47

27 Mar 2023
Al-facilitated Transition to a Knowledge Society
48
Classical
Relativistic
Quantum
The Knowledge Stack
Physical Reality
Socially-responsible Society
The Social Stack The AI Stack
Larger scope of concern
Open individualism, GBI
Planetary-scale Problems
Space 2.0
Biology 2.0
Classical
Quantum
Energy 2.0
Software 2.0
AI is the API
AI learns
human values,
writes classical
and quantum
algorithms,
augments the
interface with
reality: Kantian
Goggles 2.0
Source: Concentric circles of knowledge (Demis Hassabis, DeepMind)
Knowledge Society: Society concerned with generating and sharing knowledge to improve the human condition
The totality of all knowledge
Knowledge that can be understood by the human mind
Knowledge that is currently understood by the human mind
Concentric Circles of Knowledge
->
Humans AI Knowledge
->
Reality Copilot

27 Mar 2023
Conclusion
Maximal Claim: A Research Copilot concept might be deployed as an active
interface on public and private scientific knowledge to hasten fields such as
Information Systems Biology towards disease prevention and cure
Minimal Claim: We are doing more work in the quantum realm (quantum computing
and quantum materials) with automation technology (AI engines), and a definition of
“quantum intelligence” as the capacity to think and operate according to quantum
properties (mechanics and space-time) might improve the accuracy, safety, human-
AI alignment, and success of these activities
Thesis: Expanding the definition of intelligence to include “quantum intelligence” and
“scale-free intelligence” could facilitate a successful potential future of responsible
human-AI entities operating in various multi-time and multi-space physical environments
Responsible human-AI entity interaction
Quantum Mechanics properties: superposition, entanglement, interference, symmetry, and topology

27 Mar 2023
Summary of Key Points
 SRAI is not a one-problem fix, but a systemic objective
 Challenge: architect AI to learn human values
 Internal reward-morality function and personal identity construct
 We may be transitioning from an information society to
knowledge society (knowledge to improve the human condition)
 AI-facilitated scientific knowledge discovery (Research Copilot)
 Early adopters are adopting AI technologies
 Other envisioned technologies possibly higher
magnitude in impact: BCI, AI-QC convergence
 Intelligence of the future
 Computational scale-free, domain-agnostic capability
 Learn, create knowledge, solve problems
 Multi-time multi-space classical-quantum-relativistic
50

27 Mar 2023
Risks and Limitations
51
 AI-generated content assumed accurate
 AI Ethics lags technology development
 Disorienting pace of rapid automation
 Lack of diverse society-benefiting applications
 Monopoly control in Human-AI relation
 Widening digital divide (cost, accessibility)
 Overwhelm and alienation
 No right to non-adoption in technologized world
 Lack of empowering relation with technology
 Humans willingly self-enframing as mindless
standing reserve (doom-scrolling, game addicts),
versus technology as a background enabler
 Regulation of AI technologies: EC AI Act 2022
Heidegger, The Question
Concerning Technology
+
-
Source: Wadhwa, V. (2022). Quantum Computing Is Even More Dangerous than Artificial Intelligence. Foreign Policy. 21 Aug 2022.
https://foreignpolicy.com/2022/08/21/quantum-computing-artificial-intelligence-ai-technology-regulation/.
Panopticon
Surveillance

27 Mar 2023
Planetary-scale Problem Solving
52
 AI Knowledge Society
 Large-scale problem
resolution domains
Domain Space Health and Biology Energy
Identity Space-faring civilization Health-faring civilization Energy-marshalling
civilization
Vision Exploration, settlement,
mining, exoplanets: solved
Obesity, cancer, disease,
aging, death: solved
High-availability clean
global energy: solved
Field Space Humanism BioHumanism Energy Humanism
of Study The Space Humanities The BioHumanities The Energy Humanities
European Extremely Large
Telescope, Chile
European Extremely Large Telescope (E-ELT) under construction in Chile. Size comparison of the E-ELT (left) with the four 8-meter
telescopes of the European Very Large Telescope (center) and the Colosseum in Rome (right). E-ELT: 39-meter diameter mirror, p. 985.

27 Mar 2023
Research Copilot for Biology
53
Genomics
Pathway Conservation
Advanced Research Copilot
Information Systems Biology
Evolution
 Science Knowledge Graph
DIY Drug Discovery
Knowledge Composer
Knowledge Finder
Literature Search (BioRxiv Sanity)
Mar 27
Jan Feb Mar 20
Missing Knowledge Tableau
GACU
Origins of Life
DNA, RNA,
protein synthesis
Epigenetic Mthyl.
Protein
Structure
OneView Knowledge Computation
Multi-scalar Multi-organism Integrated-math
Neuron
Network
AdS/Brain
Synapse
Molecule
Whale
Krill
Phytoplankton
Light gradient
Organism
Yeast, Worm, Fly, Mouse, Human, Plant
Human cohorts (healthy, gender, ethnicity)
Operation
System Tier
Biology, Physics,
Chemistry, Math
Cell, Tissue, Organ,
Organism, Ecosystem
Extending “Copilot for Science” efforts such as paper summarization (https://typeset.io/)
Mockup
only
1) Disease Solver
Prevention (80%)-Cure (20%)

27 Mar 2023
Century of AI-facilitated Biology
54
 Learn grand theories and organizing principles
 Darwin: evolutionary survival of the fittest adaptation
 Chaitin: biology too efficient for evolution alone per required
adaptation time cycles, other natural mechanisms implicated
 Davies: “Maxwell’s demon of biology” efficient sorting
 Integrate multi-scalar math: 4-tier ecosystem (neural signaling)
1900s: Physics
(Geometry is the math)
2000s: Biology
Information Systems Biology (Topology is the math)
1905 Special Relativity
(time dilation)
1915 General Relativity
(gravity = spacetime
curvature = geometry)
1927 Quantum Mechanics
(behavior of particles)
1) DeepMind: math:physics as AI:biology; biology too dynamic/emergent for grand theories
2) DeepMind: humans build AI systems to access human-inaccessible knowledge
3) Albada: all 5-6 neocortical levels in constant comparison of perception and prediction
4) Sejnowski: too early for theories, but notable brain-wide encoding of asynchronous
traveling waves, harmonic oscillation in elliptical geometry of dendritic spines
5) LeCun: hierarchical prediction model of perception, reasoning, planning
6) Wolfram: building block “atoms” + computational layer + “general relativity” of biology
7) Taleb: clinical empiricism over statistical averaging to avoid medical error (n=1)
8) Goldenfeld: universality in biology: life is a consequence of the laws of physics, matter
self-organizes out of equilibrium and evolves in open-ended complexity, phase transition
9) Silburzan: mesoscale organizing principles of biology-inspired physics
10) Levin: multiscalar competency, generic baseline capability of cells, bioelectricity
Scientific Method: Hypothesis -> Theory -> Law
2) Biomath Integration of
Multi-scalar Theory
Landscape

27 Mar 2023
Application Description Property
1 Magneto-navigation* Magnetically sensitive pairs in retinal cryptochrome protein Entanglement (a)
2 Tunneling in enzymes* Electron, proton, hydrogen atom tunneling in enzyme reactions Tunneling (b)
3 DNA mutation Proton exchange in DNA double hydrogen bond between bases Coherence
4 Photosynthesis Oscillatory signals in light harvesting (but are not quantum) Coherence
5 Olfaction (vibrational) Olfactory sensory neurons detect odorous molecule vibrations Vibration
6 Oil and gas exploration Chiral probe electron transport sensing of cellular temperature Chirality
 Quantum Biology
a) study of the functional role of quantum effects (superposition,
entanglement, tunneling, coherence) in living cells
b) study of biology with quantum (computational) methods
Quantum Biology Applications with Purported Description and Quantum Property
*Quantum effects empirically confirmed
Classical /
DeQ Zone
Quantum
Effects
Demonstrated
Quantum Biology
Sources: (a) Hore, P.J., Mouritsen, H. 2016. The Radical-Pair Mechanism of Magnetoreception. Ann. Rev. Biophys. 45, 299–
344. (b) Cha, Y., Murray, C.J., Klinman, J.P. (1989). Hydrogen tunneling in enzyme reactions. Science 243 (4896), 1325-1330.
3) Classical-Quantum
Effect Investigation
DeQ: Dequantization Zone: sufficiency demonstration of classical methods “dequantizing” claims of quantum speedup

27 Mar 2023
Further Implications
 Philosophy-aided physics
 Responsible Human-AI Entities in time and space
 Kant: transcendental idealism and empirical realism
 Hegel: self-knowing time series
 Consciousness progression to beyond-individual sociality
 Applies to all forms of intelligence, individual and collective
human, machine (algorithm, robot), hybrid entities
56
Source: https://www.slideshare.net/lablogga/critical-theory-of-silence
Kant, Hegel, and the non-unitary time of events: intelligent entity
subjectivation as the self-knowing time series
Research Copilot

AAAI Spring Symposium: Socially Responsible AI
for Well-Being, San Francisco CA, 27 Mar 2023
Slides: http://slideshare.net/LaBlogga
Melanie Swan, MBA, PhD
Research Associate
University College London
Thank you!
Questions?

Quantum Intelligence: Responsible Human-AI Entities

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