This document summarizes a presentation on quantum intelligence and socially responsible artificial intelligence. The key points are:
- Quantum intelligence refers to intelligence operating in the quantum realm, which may help with potential convergence of AI and quantum computing around 2030. Scale-free intelligence is formulated as a generic capacity for learning.
- AI adoption is happening rapidly through technologies like research copilots, AI engines, chips, and potential for software 2.0 with machine-written code. This may facilitate knowledge generation and problem solving.
- Socially responsible AI (SRAI) for well-being is proposed as a social and technological objective, to be realized through short-term regulation, medium-term value learning, and
1. 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
3. 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
4. 27 Mar 2023
Quantum Intelligence 3
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
5. 27 Mar 2023
Quantum Intelligence
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
6. 27 Mar 2023
Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence 9
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
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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
15
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Quantum Intelligence
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
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Quantum Intelligence
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
Quantum Intelligence
Classical Intelligence
Classical includes dequantization demonstrations of sufficiency of classical methods “dequantizing” claims of quantum speedup
Existing
Proposed
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence 22
Classical Intelligence
Scale-free
Intelligence
Moore’s Law Curve:
Intelligence
Quantum
Intelligence
Classical Intelligence
Learning and solving
problems in classical terms
Quantum Intelligence
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
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Quantum Intelligence
Scale-free Intelligence
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
25. 27 Mar 2023
Quantum Intelligence
Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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.
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Quantum Intelligence
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
30. 27 Mar 2023
Quantum Intelligence 29
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
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Quantum Intelligence
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.
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Quantum Intelligence
Quantum Copilot
31
Quantum Copilot
Quantum Intelligence
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
Quantum Intelligence
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
Copilot: active interface on a data corpus
Mockup only
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Quantum Intelligence
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
34. 27 Mar 2023
Quantum Intelligence
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
33
35. 27 Mar 2023
Quantum Intelligence 34
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)
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Quantum Intelligence 35
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
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Quantum Intelligence
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
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Quantum Intelligence 37
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
Moore’s Law Curve:
AI Ethics
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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
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Quantum Intelligence
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)
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44. 27 Mar 2023
Quantum Intelligence
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
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Quantum Intelligence
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”
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Quantum Intelligence
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.
47. 27 Mar 2023
Quantum Intelligence 46
Socially-Responsible AI for Well-being
Classical Intelligence
Socially-Responsible
Human-AI Entities
Scale-free
Intelligence
Moore’s Law Curve:
Responsible Intelligence
Quantum
Intelligence
Humans
Non-socially
responsible AI
Responsible Human-AI Entities:
intelligent agents interacting with
competence and empathy
48. 27 Mar 2023
Quantum Intelligence
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
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49. 27 Mar 2023
Quantum Intelligence
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
Scale-free Intelligence
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
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Quantum Intelligence 49
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
Quantum Intelligence
Responsible Human-AI Entities
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
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Quantum Intelligence
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
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52. 27 Mar 2023
Quantum Intelligence
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
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Quantum Intelligence
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.
54. 27 Mar 2023
Quantum Intelligence
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%)
Copilot: active interface on a data corpus
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Quantum Intelligence
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
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Quantum Intelligence 55
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
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Quantum Intelligence
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
Quantum Intelligence
58. 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
Quantum Intelligence
Responsible Human-AI Entities
Thank you!
Questions?