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AI Math Agents

Melanie Swan
Melanie Swan
Melanie SwanTechnophysicist, Blockchain Theorist, Philosophy, Purdue University at Institute for Blockchain Studies

We know that we are in an AI take-off, what is new is that we are in a math take-off. A math take-off is using math as a formal language, beyond the human-facing math-as-math use case, for AI to interface with the computational infrastructure. The message of generative AI and LLMs (large language models like GPT) is not that they speak natural language to humans, but that they speak formal languages (programmatic code, mathematics, physics) to the computational infrastructure, implying the ability to create a much larger problem-solving apparatus for humanity-benefitting applications in biology, energy, and space science, however not without risk.

AI Math Agents

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Denver CO, 8 Jul 2023
Slides: http://slideshare.net/LaBlogga
Melanie Swan, PhD, MBA
DIYgenomics.org (Research Lead)
University College London (Research Associate)
“Nothing is more abstract than reality”
– Giorgio Morandi
The Math Take-off
Space Humanism, AI-Quantum Computing Convergence,
and the Future of Intelligence
8 Jul 2023
AI Math Agents 1
Goal: solve biosystem pathology (aging, Alzheimer’s disease)
with physics mathematics (renormalized multiscalar entropic
near-far correlations) or other AI-aided mathematical analysis
AdS/Biology
Research Program
2015 2019 2020
Blockchain Blockchain
Economics
Quantum
Computing
Quantum Computing
for the Brain
2022
AdS/Biology: application of AdS/CFT (anti-de Sitter space/conformal field theory) bulk-boundary modeling to biosystems
AI Math Agents:
https://arxiv.org/abs/2307.02502
https://huggingface.co/papers/2307.02502
AI Genomics and Alzheimer’s Disease:
https://www.diygenomics.org/files/AI_Math_Agents_poster_AAIC2023.pdf
8 Jul 2023
AI Math Agents
AI Science Project Landscape
2
Sources: https://openai.com/blog/chatgpt-plugins#code-interpreter; Boiko et al (2023). Autonomous scientific research capabilities of
LLMs. arXiv: 2304.05332; https://opencatalystproject.org/; Tu et al (2023). Towards Generalist Biomedical AI. arXiv:2307.14334v1;
Mialon et al (2023). SSL with Lie Symmetries for Partial Differential Equations. arXiv:2307.05432v1. WizardLM: 2304.12244.
DeepMind Med-PaLM
biomedical AI
Meta AI/CMU Open
Catalyst: 1000x faster
molecular dynamics
Code Interpreter (OpenAI): using ChatGPT to upload files, analyze
data, create charts, solve math problems, edit files, produce code
WizardLM: LLM creating instructions for other LLMs
(math, code, reasoning, complex data formats)
 Quick move into biochemistry, biophysics
with LLM Math Agent functionality
Lie symmetry PDE
solving network
https://ibm.co/3XviRVV
Smart-
biology.
com
8 Jul 2023
AI Math Agents
AI Genomics
 Multiscalar approach
 Gene regulatory elements
influence expression in
cell types and tissues
 Alzheimer’s disease
 2,676 differentially
expressed genes
 Up/downregulate
proteins in cell types
 Upregulation of APOD, INSR
and COL4A1 in brain tissue
 Downregulation of SLC6A1 in
GABAergic neurons and
astrocytes, PDGFRB in
pericytes and ABCB1, and
ATP10A in endothelial cells
3
SNP: single nucleotide polymorphism Sources: Kellis Lab: Sun et al. (2023.) Single-nucleus multi-region transcriptomic analysis of brain
vasculature in Alzheimer’s disease. Nat Neurosci. 26, 970–982. https://doi.org/10.1038/s41593-023-01334-3. Cirillo et al. (2017). A
Review of Pathway-Based Analysis Tools That Visualize Genetic Variants. Front. Genet. 8:174. doi: 10.3389/fgene.2017.00174.
Pathway
Protein
Blood plasma, CSF
RNA
Expression
miRNA, mRNA
DNA
Gene, Variants (SNPs), Gene
Regulation, Epigenomics
AI Genomics Research Copilot
8 Jul 2023
AI Math Agents 4
The message of generative AI and LLMs (large language models like GPT) is not that they speak natural
language to humans, but that they speak formal languages (programmatic code, mathematics, physics) to
the computational infrastructure, implying the ability to create a much larger problem-solving apparatus for
humanity-benefitting applications in biology, energy, and space science, however not without risk
Thesis
Formal
Language:
Math, Physics,
Software Code
Natural
Language
Human
Computational Infrastructure
Interface Reality
AI
A math take-off is using math as a formal language, beyond the
human-facing math-as-math use case, for AI to interface with the
computational infrastructure
We know that we are in an AI take-off,
what is new is that we are in a math take-off
8 Jul 2023
AI Math Agents
Language Space Program Space Mathematics
Space
infinite
infinite
A. Software 1.0 (human-discovered)
B. Software 2.0 (machine-derived)
A.
B. automated
theorem proving
human-discovered
theorems
computer
algebra systems
Existing Spaces
New Spaces
AI Space
Computational
Complexity
Space
Planck Space
AI Science
Space
Now Treating the Entire Possibility Space
Source: Swan, M., Kido, T., Roland, E. & dos Santos, R.P. (2023). AI Math Agents: Computational Infrastructure, Mathematical
Embedding, and Genomics.
Digitizing a possibility space (e.g. natural language) makes it formal
Ad

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AI Math Agents

  • 1. Denver CO, 8 Jul 2023 Slides: http://slideshare.net/LaBlogga Melanie Swan, PhD, MBA DIYgenomics.org (Research Lead) University College London (Research Associate) “Nothing is more abstract than reality” – Giorgio Morandi The Math Take-off Space Humanism, AI-Quantum Computing Convergence, and the Future of Intelligence
  • 2. 8 Jul 2023 AI Math Agents 1 Goal: solve biosystem pathology (aging, Alzheimer’s disease) with physics mathematics (renormalized multiscalar entropic near-far correlations) or other AI-aided mathematical analysis AdS/Biology Research Program 2015 2019 2020 Blockchain Blockchain Economics Quantum Computing Quantum Computing for the Brain 2022 AdS/Biology: application of AdS/CFT (anti-de Sitter space/conformal field theory) bulk-boundary modeling to biosystems AI Math Agents: https://arxiv.org/abs/2307.02502 https://huggingface.co/papers/2307.02502 AI Genomics and Alzheimer’s Disease: https://www.diygenomics.org/files/AI_Math_Agents_poster_AAIC2023.pdf
  • 3. 8 Jul 2023 AI Math Agents AI Science Project Landscape 2 Sources: https://openai.com/blog/chatgpt-plugins#code-interpreter; Boiko et al (2023). Autonomous scientific research capabilities of LLMs. arXiv: 2304.05332; https://opencatalystproject.org/; Tu et al (2023). Towards Generalist Biomedical AI. arXiv:2307.14334v1; Mialon et al (2023). SSL with Lie Symmetries for Partial Differential Equations. arXiv:2307.05432v1. WizardLM: 2304.12244. DeepMind Med-PaLM biomedical AI Meta AI/CMU Open Catalyst: 1000x faster molecular dynamics Code Interpreter (OpenAI): using ChatGPT to upload files, analyze data, create charts, solve math problems, edit files, produce code WizardLM: LLM creating instructions for other LLMs (math, code, reasoning, complex data formats)  Quick move into biochemistry, biophysics with LLM Math Agent functionality Lie symmetry PDE solving network https://ibm.co/3XviRVV Smart- biology. com
  • 4. 8 Jul 2023 AI Math Agents AI Genomics  Multiscalar approach  Gene regulatory elements influence expression in cell types and tissues  Alzheimer’s disease  2,676 differentially expressed genes  Up/downregulate proteins in cell types  Upregulation of APOD, INSR and COL4A1 in brain tissue  Downregulation of SLC6A1 in GABAergic neurons and astrocytes, PDGFRB in pericytes and ABCB1, and ATP10A in endothelial cells 3 SNP: single nucleotide polymorphism Sources: Kellis Lab: Sun et al. (2023.) Single-nucleus multi-region transcriptomic analysis of brain vasculature in Alzheimer’s disease. Nat Neurosci. 26, 970–982. https://doi.org/10.1038/s41593-023-01334-3. Cirillo et al. (2017). A Review of Pathway-Based Analysis Tools That Visualize Genetic Variants. Front. Genet. 8:174. doi: 10.3389/fgene.2017.00174. Pathway Protein Blood plasma, CSF RNA Expression miRNA, mRNA DNA Gene, Variants (SNPs), Gene Regulation, Epigenomics AI Genomics Research Copilot
  • 5. 8 Jul 2023 AI Math Agents 4 The message of generative AI and LLMs (large language models like GPT) is not that they speak natural language to humans, but that they speak formal languages (programmatic code, mathematics, physics) to the computational infrastructure, implying the ability to create a much larger problem-solving apparatus for humanity-benefitting applications in biology, energy, and space science, however not without risk Thesis Formal Language: Math, Physics, Software Code Natural Language Human Computational Infrastructure Interface Reality AI A math take-off is using math as a formal language, beyond the human-facing math-as-math use case, for AI to interface with the computational infrastructure We know that we are in an AI take-off, what is new is that we are in a math take-off
  • 6. 8 Jul 2023 AI Math Agents Language Space Program Space Mathematics Space infinite infinite A. Software 1.0 (human-discovered) B. Software 2.0 (machine-derived) A. B. automated theorem proving human-discovered theorems computer algebra systems Existing Spaces New Spaces AI Space Computational Complexity Space Planck Space AI Science Space Now Treating the Entire Possibility Space Source: Swan, M., Kido, T., Roland, E. & dos Santos, R.P. (2023). AI Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics. Digitizing a possibility space (e.g. natural language) makes it formal
  • 7. 8 Jul 2023 AI Math Agents 6 Reality Interface Abstraction: Mathematics is the Interface Multiscalar Renormalization One System Two Modes Mathematics as a High-order Lever for Interacting with Reality Data de-emphasized in the Math-Data Relation Big Data -> Big Math Era  AI “speaking” formal languages implies math as a higher- order lever for interacting with reality (beyond data) Source: Swan, M., Kido, T., Roland, E. & dos Santos, R.P. (2023). AI Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics.
  • 8. 8 Jul 2023 AI Math Agents New Conceptualization of Math  Traditional conceptualization  Practical math-as-math: build bridges, space shuttles  Foundational reality has a mathematical structure  Mathematical universe hypothesis  Quark properties are quantitative (mass, charge, spin)  Expanded conceptualization to also include  Math as a language  A formal language for human-AI entities to formulate problems  A language for AI to speak to the computational infrastructure  Math as a means not an end  Mobilized as a digital tool, as software is a digital tool  Math as a framework  Math as “truthier” content: high-validation, subject to proof 7 2014 F(x) math-certified F(x) Penrose tile
  • 9. 8 Jul 2023 AI Math Agents  Math Agent: AI agent operating in digital mathematical domain to identify, analyze, integrate, write, discover, solve, prove, and steward mathematical ecologies AI Math Agents Source: Swan, M., Kido, T., Roland, E. & dos Santos, R.P. (2023). AI Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics. Mathematical Embedding: 476-equation ecology (LaTeX) (SymPy)  Mathematical embedding: math entity (symbol, equation) represented as a character string in vector space for high-dimensional AI analysis  Mathematical ecology (mathscape): set of related mathematical equations  Equation Cluster: similar equations grouped in mathematical ecology embedding visualization (LaTeX)
  • 10. 8 Jul 2023 AI Math Agents 9 Citizen 2 heterozygous (1 ALT allele) SNPs in Illumina VCF file (Legend: Cit2-1) Citizen 2 homozygous (2 ALT alleles) SNPs in Illumina VCF file (Legend: Cit2-2) Genes: APP, ASXL3, ABCA7, SLC24A4, ANK3 PLCG2 Embedding Visualization examples with Academic Papers as the Data Corpus AdS/CFT Equation Clusters in Embedding Visualization (LaTeX and SymPy) Source: AdS/CFT: Kaplan, J. (2016). Lectures on AdS/CFT from the bottom up. Johns Hopkins Lecture Course. https://www.diygenomics.org/files/AI_Math_Agents_poster_AAIC2023.pdf The Mathematical Embedding
  • 11. 8 Jul 2023 AI Math Agents 10 Citizen 2 heterozygous (1 ALT allele) SNPs in Illumina VCF file (Legend: Cit2-1) Citizen 2 homozygous (2 ALT alleles) SNPs in Illumina VCF file (Legend: Cit2-2) Genes: APP, ASXL3, ABCA7, SLC24A4, ANK3 PLCG2 Source: AdS/CFT: Kaplan, J. (2016). Lectures on AdS/CFT from the bottom up. Johns Hopkins Lecture Course. https://www.diygenomics.org/files/AI_Math_Agents_poster_AAIC2023.pdf The Mathematical Embedding Annotated equation clusters illustrate (a) how similar groups of equations are grouped in the embedding method and (b) the mouse-over view of equation images by equation number (OpenAI inlay from previous figure)
  • 12. 8 Jul 2023 AI Math Agents 11 Citizen 2 heterozygous (1 ALT allele) SNPs in Illumina VCF file (Legend: Cit2-1) Citizen 2 homozygous (2 ALT alleles) SNPs in Illumina VCF file (Legend: Cit2-2) Genes: APP, ASXL3, ABCA7, SLC24A4, ANK3 PLCG2 Source: https://www.diygenomics.org/files/AI_Math_Agents_poster_AAIC2023.pdf AD, PD, ALS: Alzheimer’s disease, Parkinson’s disease, Amyotrophic lateral sclerosis Mathematical Ecology analysis: math + data Mathematical Ecologies (a) Alzheimer’s + SIR Model (control math); (b) Chern-Simons + AD SNPs (a) AdS/CFT Mathematical Ecologies + AD SNPs; (b) SIR Mathematics; (c) Multi-disease Genomic view: AD, PD, ALS
  • 13. 8 Jul 2023 AI Math Agents 12 Citizen 2 heterozygous (1 ALT allele) SNPs in Illumina VCF file (Legend: Cit2-1) Citizen 2 homozygous (2 ALT alleles) SNPs in Illumina VCF file (Legend: Cit2-2) Genes: APP, ASXL3, ABCA7, SLC24A4, ANK3 PLCG2 Source: https://www.diygenomics.org/files/AI_Math_Agents_poster_AAIC2023.pdf AD, PD, ALS: Alzheimer’s disease, Parkinson’s disease, Amyotrophic lateral sclerosis Alzheimer’s Genomics Precision Health Embeddings Visualization of Data: Alzheimer’s SNPs applied to Citizen 1, Citizen 2 Precision Health initiative Each individual is homozygous (two alternative alleles) for different subsets of genes suggesting a starting-point for personalized intervention Citizen 2 is homozygous for cancer-upregulated membrane proteins (TREM) and cytokine-dependent hematopoietic cell linkers (CLNK) Both are homozygous for the solute carrier protein (SLC24A4) and the intracellular trafficking protein nexin (SNX1). Citizen 1 is homozygous for more immune system related genes (CD33, HLA-DRB1), and Alzheimer’s-related clathrin binder (PICALM) Alzheimer’s disease genomic risk is analyzed for two precision health participants with whole-human genome sequencing An embedding visualization is performed for all GWAS-linked Alzheimer’s disease SNPs and presented for Citizen 1 and Citizen 2’s heterozygous (one alternative allele) and homozygous (two alternative alleles) SNP
  • 14. 8 Jul 2023 AI Math Agents 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 Coding Copilot Software 2.0 Algorithms can explore a larger possible program space
  • 15. 8 Jul 2023 AI Math Agents Reality Interface 14 Representation Kantian Goggles: the manifold of Space and Time Perception Human  Kantian goggles of the perceptual manifold  Any object appears in some space and some time  We cannot know the “thing in itself” only our representations of it Interface Reality Human
  • 16. 8 Jul 2023 AI Math Agents Reality Interface 15 Representation Perception Human  Projects extending Kantian goggles with telescopes and microscopes, now into relativistic and quantum domains Interface Physical Reality Human Classical Relativistic Quantum Kantian Goggles: the manifold of Space and Time Classical Relativistic Quantum
  • 17. 8 Jul 2023 AI Math Agents New ideas of where we fit The Large and Small Scale Universe 16 Scale Measure Comment 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 Source: The Universe by Numbers. https://www.physicsoftheuniverse.com/numbers.html Humans require specialized conditions to survive (unlike amoebas or cockroaches) Large-scale: General Relativity (GR) Small-scale: Quantum Mechanics (QM) Human-scale: Classical Mechanics  Quantum mechanics, classical mechanics, general relativity  Quantum effects visible at 10-9 m  Relativistic effects present at any speed (matter of precision) Classical Relativistic Quantum
  • 18. 8 Jul 2023 AI Math Agents 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  Knowledge: the sum of relationships in information  Knowledge layer defined in the computational infrastructure 17 Consciousness Understanding Knowledge Information Data March towards “human” capabilities
  • 19. 8 Jul 2023 AI Math Agents 18 Classical Intelligence Scale-free Intelligence Moore’s Law Curve: Intelligence Quantum Intelligence Classical Intelligence Quantum Intelligence Scale-free Intelligence Time and Space Properties: spherical-flat-hyperbolic space, simultaneous time  Scale-free intelligence: ability to learn and problem-solve in any physical regime Relativistic Intelligence Domain-specific time and space, and matter properties Domain-specific time and space, and matter properties Domain-specific time and space, and matter properties Intelligence as a Generic Capability Need for in-situ autonomous agent decision-making
  • 20. 8 Jul 2023 AI Math Agents 19 Classical Intelligence Computational Intelligence Moore’s Law Curve: Intelligence Quantum Intelligence Classical Intelligence Quantum Intelligence Scale-free Intelligence Time and Space Properties: spherical-flat-hyperbolic space, simultaneous time  Scale-free intelligence: ability to learn and problem-solve in any physical regime Relativistic Intelligence Domain-specific time and space, and matter properties Domain-specific time and space, and matter properties Domain-specific time and space, and matter properties Intelligence as a Generic Capability Computational Intelligence Ability to learn and problem-solve systematically in formal environments Scale-free Intelligence Mathematical Intelligence Ability to learn and problem-solve, and create/discover in math environments
  • 21. 8 Jul 2023 AI Math Agents Agenda  AI (Artificial Intelligence)  AI-QC Convergence  QC (Quantum Computing)  AI Alignment and Space Humanism 20
  • 22. 8 Jul 2023 AI Math Agents What is the Purpose of AI? 21  Reorienting the Human-AI relation 1. The big offload 2. The big merge Domain Classical Classical intelligence DeQ1 Zone Quantum2 Quantum intelligence AI 3. Precision tasks and knowledge-generation 2. Cognitive labor, computational contracts 1. Data informatics, physical labor, virtual labor Information Science Stack 1Dequantization Zone: sufficiency of classical methods demonstrated (computation, biology) 2Quantum applications: quantum sensing, quantum machine learning, quantum dynamics simulation, quantum cryptography Source: Concentric circles of knowledge: one potential purpose of AI (Demis Hassabis, DeepMind) 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
  • 23. 8 Jul 2023 AI Math Agents The big offload The AI Stack: Moore’s Law Curve of AI 22 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, mathematics 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
  • 24. 8 Jul 2023 AI Math Agents Potential for Al-Facilitated Science 23 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
  • 25. 8 Jul 2023 AI Math Agents Knowledge Society  Knowledge platforms  Wikipedia: interface for knowledge access  Coursera (MOOCs): interface for knowledge learning  Research Copilot: interface for knowledge generation 24 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 Knowledge Society: one that uses knowledge to improve the human condition
  • 26. 8 Jul 2023 AI Math Agents 25  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 Source: Swan, M. & dos Santos, R.P. (2023). Quantum Intelligence: Responsible Human-AI Entities, AAAI, San Francisco CA 27 Mar 2023. https://www.slideshare.net/lablogga/quantum-intelligence-responsible-humanai-entities
  • 27. 8 Jul 2023 AI Math Agents The big merge: Human-AI Entities Global Cryonics Study (n = 316)  Attitudes towards personal identity re: brain and body  Brain  This physical brain is the “source of me” now (75%)  These specific memories are the future “source of me” (87%)  Body  This physical body is “part of me” now (68%)  This physical body is “part of me” in the future (14%) 26 Source: Swan, M. (2019). Worldwide Cryonics Attitudes About the Body, Cryopreservation, and Revival: Personal Identity Malleability and a Theory of Cryonic Life Extension. Sophia International Journal of Philosophy and Traditions. 58:699–735. Springer Nature B.V. https://link.springer.com/article/10.1007/s11841-019-0727-4. A sense of Personal Identity Malleability
  • 28. 8 Jul 2023 AI Math Agents Generative AI and LLMs  LLMs (large language models)  Computerized language models  Generated with transformer neural networks  Billions of parameters  Pre-trained on large data corpora  GPT-4 (OpenAI), LaMDA (Google), LLaMA (Meta AI)  Transformer neural networks  Whole data corpus processed simultaneously to analyze connections between data elements 27 Midjourney (CO state fair winner 5 Sep 2022) 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 Shifting definition of knowledge: ability to memorize -> synthesize information -> deploy ideas GPT-3: 175 billion parameters GPT-4: 170 trillion parameters 1,000x bigger Parameter: learned system weight
  • 29. 8 Jul 2023 AI Math Agents Computational Infrastructure Classical Computing Super- computing DNA Nanotechnology, Spiking Neural Networks Quantum Computing 28 Mobile Existing Emerging Smartphone, Tablet, Watch, BCI, headset Biology Computing Platforms Formal Languages Smart Network Technologies Interface Technologies Blockchain Machine Learning AI (artificial intelligence): Siri -> Alexa -> chatGPT chatbot copilot: active interfaces on data corpora, formal languages, smart network computation
  • 30. 8 Jul 2023 AI Math Agents Computational Infrastructure Classical Computing Super- computing DNA Nanotechnology, Spiking Neural Networks Quantum Computing 29 Mobile Existing Emerging Smartphone, Tablet, Watch, BCI, headset Biology Computing Platforms Formal Languages Smart Network Technologies Interface Technologies Blockchain Machine Learning AI (artificial intelligence): Siri -> Alexa -> chatGPT chatbot copilot: active interfaces on data corpora, formal languages, smart network computation
  • 31. 8 Jul 2023 AI Math Agents Agenda  AI (Artificial Intelligence)  AI-QC Convergence  QC (Quantum Computing)  AI Alignment and Space Humanism 30
  • 32. 8 Jul 2023 AI Math Agents Technology Take-offs 31 Computer: punch card -> mainframe -> PC -> smartphone Cell phone Internet AI Quantum Computing 2023 estimated  Accelerated global deployment of technology that may ultimately impact nearly all persons and areas of life
  • 33. 8 Jul 2023 AI Math Agents AI-QC Convergence - Basic 32 AI Artificial Intelligence QC Quantum Computing Quantum Machine Learning QML  Quantum Machine Learning (QML): running machine learning algorithms in a quantum environment 450,000 users Sources: Stanford Global AI Vibrancy Tool: US and China lead AI innovation https://aiindex.stanford.edu/vibrancy/ McKinsey (Jun 2022): https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-next-frontier-for-ai-in-china-could-add-600-billion-to-its- economy https://www.computerweekly.com/news/252527998/Finland-connects-a-quantum-computer-to-a-supercomputer Finland: quantum computer HELMI (“Pearl”) connected to supercomputer LUMI (“Snow”)
  • 34. 8 Jul 2023 AI Math Agents Quantum Research Copilot for Biology 33 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 Disease Solver Copilot Cell Suggested concept only Research Copilot for Biology
  • 35. 8 Jul 2023 AI Math Agents AI-QC Convergence - Advanced  Generative AI to extend quantum computing  Advance quantum error correction  Write software for quantum computers  Discover new quantum algorithms  AI-QC partnership  New infrastructure (nHITL: no human in the loop)  Quantum not just nice but necessary  New modes of energy efficiency  Computational infrastructure: 10% energy consumption:  Imperative to learn from nature  Synaptic switching: 0.77 attojoules (Shankar, 2022) 34 Sources: IBM Quantum, https://quantumai.google/qecmilestone; Shankar, S. (2021). Lessons from Nature for Computing Looking beyond Moore’s Law. IEEE. https://ieeexplore.ieee.org/document/9622865; 3-09-22 Physics Colloquium - Sadasivan Shankar, SLAC Stanford/Harvard, https://www.youtube.com/watch?v=4xULxynEWEc Biomimicry implicated: the brain is extremely energy-efficient
  • 36. 8 Jul 2023 AI Math Agents Agenda  AI (Artificial Intelligence)  AI-QC Convergence  QC (Quantum Computing)  AI Alignment and Space Humanism 35
  • 37. 8 Jul 2023 AI Math Agents 36 Basic Concept What is Quantum Computing?  Computing: change of state between 0/1  Move information around & and perform a computation  Quantum: use atoms, ions, photons to compute  Classical computing: serial not parallel  Quantum computing: treat more than one status at the same time, compute all transactions simultaneously  Fundamentally, a different way of computing  Degreed physicists sought as product managers (Gartner)  Shift big data analysis to quantum to find hidden correlations Source: Fowler, A.G., Mariantoni, M., Martinis, J.M. & Cleland, A.N. (2012). Surface codes: Towards practical large-scale quantum computation. Phys Rev A. 86(032324).
  • 38. 8 Jul 2023 AI Math Agents Quantum Scale 37 QCD: Quantum Chromodynamics Subatomic particles Matter particles: fermions (quarks) Force particles: bosons (gluons) Scale Entities Physical Theory 1 1 x101 m Meter Humans Newtonian mechanics 2 1 x10-9 m Nanometer Atoms Quantum mechanics (nanotechnology) 3 1 x10-12 m Picometer Ions, photons Optics, photonics 4 1 x10-15 m Femtometer Subatomic particles QCD/gauge theories 5 1 x10-35 m Planck scale Planck length Planck scale Atoms Quantum objects: atoms, ions, photons  “Quantum” = anything at the scale of atomic and subatomic particles (10-9 to 10-15)  Theme: ability to study and manipulate physical reality at smaller scales  Study phenomena (e.g. neurons) in the native 3D structure of physical reality
  • 39. 8 Jul 2023 AI Math Agents Atoms/ions Controlled with Lasers/Fields 38 Source: Jackson, M. (2022). Introduction to Quantinuum and TKET. PIRSA 13 Sep 2022. https://pirsa.org/22100088. 1. Trap one Ytterbium ion 2. Entangle two Ytterbium ions 3. Conduct circuit-based computation 1. 2. 3. Conduct circuit-based computation
  • 40. 8 Jul 2023 AI Math Agents Quantum Computing Microsoft IBM Rigetti
  • 41. 8 Jul 2023 AI Math Agents Using a Quantum Computer 40 Source: D-Wave Systems, Inc. https://cloud.dwavesys.com/leap/resources/demos
  • 42. 8 Jul 2023 AI Math Agents 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
  • 43. 8 Jul 2023 AI Math Agents Status Quantum Computing  Various cloud quantum computing platforms available  Critique: so far quantum computing only useful in a few cases such as optimization problems (linear algebra) 42 Open Quantum Testbeds (Sandia, LBL) Industry (Cloud-based) Source: Landahl, A. (2022). Sandia National Laboratories.
  • 44. 8 Jul 2023 AI Math Agents Quantum Properties 43 1. Superposition: a quantum system can exist in several separate quantum states simultaneously 2. Entanglement: two interconnected quanta maintain their connection regardless of the distance between them 5. Quantum tunneling: a particle is able to penetrate through a potential energy barrier higher in energy than the particle’s kinetic (motion) energy 4. Symmetry: properties that remain invariant across scale tiers 3. 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
  • 45. 8 Jul 2023 AI Math Agents  A qubit (quantum bit) is the basic unit of quantum information, the quantum version of the classical binary bit 44 What is a Qubit? Bit exists in a single binary state (0 or 1) Qubit exists in a state of superposition, at every location with some probability, until collapsed into a measurement of 0 or 1 Implication: test permutations simultaneously Classical Bit Quantum Bit (Qubit) Sources: https://www.newsweek.com/quantum-computing-research-computer-flagship-eu-452167: Dawid Carrasquilla, Carleo, Wang et al. (2022). Modern applications of machine learning in quantum sciences. arXiv: 2204.04198. Practical example: 1-qubit quantum machine learning classification task
  • 46. 8 Jul 2023 AI Math Agents Quantum: Many Potential Speed-ups 1. Bit (0 or 1) 2. Qubit (0 and 1 in superposition) 3. Qudit (more than 2 values in superposition)  Microchip generates two entangled qudits each with 10 states, for 100 dimensions total, for more than six entangled qubits could generate (Imany, 2019 ) 4. Optics (time and frequency multiplexing)  Existing telecommunications infrastructure  Global network not standalone computers in labs  Time-frequency binning (20+ states tested) 5. Optics (superposition of inputs and gates) 6. High-dimensional entanglement 45 Classical Computing Quantum Computing Source: Imany et al. (2019). High-dimensional optical quantum logic in large operational spaces. npj Quantum Information. 5(59):1-10.
  • 47. 8 Jul 2023 AI Math Agents Quantum Error Correction Codes  Quantum error-correction code: logical codespace corresponding to a physical lattice model to manipulate a particle  Use Pauli matrices to control qubits in the x, y, z dimensions 46 Code Description Basic quantum error-correcting code Stabilizer codes Topology-based Pauli operators (X, Y, Z) correct a bit-flip or a spin flip Toric code Stabilizer operators defined on a 2D torus-shaped spin lattice Surface code Stabilizer operators defined on a 2D spin lattice in any shape Advanced quantum error-correcting code (greater scalability, control) Bosonic codes Self-contained photon-based oscillator system with bosonic modes GKP code Squeezed states protect position and amplitude shifts with rotations Molecular code Rotations performed on any asymmetric body (molecule) in free space Cat code Superpositioned states (Schrödinger) used as error correction codes GKP codes (Gottesman, Kitaev, Preskill) (Gottesman et al., 2001) Source: Swan, M., dos Santos, R.P. & Witte, F. (2022). Quantum Matter Overview. J. 5(2):232-254. Quantum Error-correcting Codes for Quantum Object Manipulation Pauli Matrices (x, y, z) Quantum Circuit
  • 48. 8 Jul 2023 AI Math Agents Quantum Error Correction  Clifford gates (basic quantum gates)  Pauli matrices, and the Hadamard, CNOT, and π/2-phase shift gates; simulated classically  Non-Clifford gates (complex operations)  Logical depth (π/8 gate); cannot simulate classically  Consolidate multiple noisy to few reliable states  Magic state distillation (computationally costly)  Gauge fixing stabilizer codes (Majorana fermion braiding)  Gauge color fixing (color codes)  Time-based surface codes  Replicates the three-dimensional code that performs the non-Clifford gate functions with three overlapping copies of the surface code interacting locally over a period of time 47 Source: Fowler, A.G., Mariantoni, M., Martinis, J.M. & Cleland, A.N. (2012). Surface codes: Towards practical large-scale quantum computation. Phys Rev A. 86(032324). Time-based surface code
  • 49. 8 Jul 2023 AI Math Agents 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) 48 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 (represented by the Greek letter Ψ) EΨ(r) = -ћ2/2m ∇2 Ψ(r) + V(r)Ψ(r) Total Energy = Kinetic Energy + Potential Energy (motion) (resting) Schrödinger wave equation  Schrödinger equation  Measures positions or speeds (momenta) of complete system configurations Wavefunction: description of the quantum state of a system Wave Packet EΨ(r) = -ћ2/2m ∇2 Ψ(r) + V(r)Ψ(r) Schrödinger wave equation
  • 50. 8 Jul 2023 AI Math Agents Moore’s Law 49 Source: Thomasian, N.M., Kamel, I.R. & Bai, H.X. (2021). Machine intelligence in non-invasive endocrine cancer diagnostics. Nat Rev Endocrinol. 18:81-95. https://ourworldindata.org/uploads/2020/11/Transistor-Count-over-time.png 1. Plateau – sustainable? 2. Chips already must address quantum effects
  • 51. 8 Jul 2023 AI Math Agents Chip Progression: CPU-GPU-TPU-QPU  Graphics processing units (GPUs)  Train machine learning networks 10-20x faster than CPUs  Tensor processing units (TPUs)  Direct flow-through of matrix multiplications without having to store interim values in memory  Quantum processing units (QPUs)  Solve problems quadratically (polynomially) faster than CPUs via quantum properties of superposition and entanglement CPU Sources: Vescovi et al . (2017) Radiography registration for mosaic tomography. J Synchrotron Radiat. 24:686-94. LeCun et al. (2015) Deep Learning. Nature. 521(7553):436-44. P. 439. Wang et al. (2019) Benchmarking TPU, GPU, and CPU Platforms for Deep Learning. arXiv:1907.10701. Pikulin et al. (2021). Protocol to identify a topological superconducting phase. arXiv:2103.12217v1. GPU TPU QPU Peak teraFLOPs in 2019 benchmarking analysis 2 125 420 50 Topological superconductor QPU: superconducting-buffer- semiconductor chip layers; superconducting properties extend to semiconductor to produce topological phase (red)
  • 52. 8 Jul 2023 AI Math Agents 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) 51 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
  • 53. 8 Jul 2023 AI Math Agents Future of Quantum Computing  Technology is notoriously difficult to predict  “I think there is a world market for maybe five computers” – Watson, IBM CEO, 1943  “I think we’ll make about four copies a week” – State Street Bank, adopting a xerograph 52 Sources: Ceruzzi, P. (2003). A History of Modern Computing. 2nd Ed. Cambridge: MIT Press; Strohmeyer, R. (2008). The 7 Worst Tech Predictions of All Time. PCWorld. D-Wave Systems: 10-feet tall, $15m Current: Ytterbium- 171 isotopes at 1 Kelvin (-458°F) Actual room- temperature superconductors: ?? 70 years UNIVAC computer (1950s): 465 multiplications per second (faster than Hidden Figures human computers) Billions of times faster
  • 54. 8 Jul 2023 AI Math Agents 53 Next-generation Materials Plasmonic Quantum Materials Sources: Oka & Kitamura. (2019). Floquet engineering of quantum materials. Ann. Rev. Cond. Matt. Phys. 0:387–408 Ma et al. (2021). Topology and geometry under the nonlinear electromagnetic spotlight. Nature Materials. 20:1601–1614. Huang, Averitt (2022). Complementary Vanadium Dioxide Metamaterial with Enhanced Modulation Amplitude at THz Frequencies. arXiv:2206.11930v1. On-demand Quantum Materials at THz Frequencies (Averitt 2022) Novel Quantum Materials (Ma, 2021)  New forms of Consumer Electronics  Replace lasers with near field optics  More efficient field generator  Metamaterials  Plasmonics, spintronics, magnonics, holonics, excitonics, viscous electronics  Nonlinear quantum phase materials  Use light to manipulate materials properties (resonant and non-resonant)  Create novel matter phases  Nonlinear and tunable InAs (Indium Arsenide) plasmonic disks and mushrooms  Metamaterial-quantum material coupling in insulator-to-metal transition superconductors
  • 55. 8 Jul 2023 AI Math Agents Quantum Science Fields 54 Source: Swan, M., dos Santos, R.P. & Witte, F. (2020). Quantum Computing: Physics, Blockchains, and Deep Learning Smart Networks. London: World Scientific. Quantum Biology Quantum Neuroscience Quantum Machine Learning € $ ¥ € Early-adopter fields: cryptography, chemistry, biology, finance, space science Quantum Cryptography Quantum Space Science Quantum Finance Foundational Tools Advanced Applications Quantum Chemistry
  • 56. 8 Jul 2023 AI Math Agents 55 Quantum Chemistry: find ground state energy Nitrogen Fixation  Ammonia produced by cleaving Nitrogen triple bond  Haber-Bosch process: 2% earth’s energy consumption  Plants: energy efficient charge-cleaving  MoFe protein (Molybdenum Iron)  Small metal cluster cut by quantum knife  Quantum computing implication  Find molecule ground state, charge distribution, copy cleave Sources: Landahl, A. (2022). Sandia National Laboratories. Morrison, C.N., Hoy, J.A., Zhang, L. et al. (2015). Substrate Pathways in the Nitrogenase MoFe Protein by Experimental Identification of Small Molecule Binding Sites. Biochemistry. 54:2052−2060. Nature: energy-efficient Fertilizer Production 5 potential access pathways from protein surface to FeMo-cofactor (active site) (Morrison, 2015)
  • 57. 8 Jul 2023 AI Math Agents  Atomic precision applications 56 Sources: Delgado (2022). How to simulate key properties of lithium-ion batteries with a fault-tolerant quantum computer. arXiv: 2204.11890. Vasylenko (2021). Element selection for crystalline inorganic solid discovery. Nat Comm. 12:5561. Hogg (2022). Acoustic Power Management by Swarms of Microscopic Robots. arXiv:2106.03923v2. Collective acoustic-harvesting power management by medical nanorobot swarms (Hogg 2022) Simulate properties of lithium-ion batteries to find Li3SnS3Cl (Vasylenko 2021) Quantum Chemistry: find ground state energy Energy and Battery Technology Autonomous robotic nanofabrication (Leinen 2020) Quantum battery simulation (Delgado 2022)
  • 58. 8 Jul 2023 AI Math Agents Agenda  AI (Artificial Intelligence)  AI-QC Convergence  QC (Quantum Computing)  AI Alignment and Space Humanism 57
  • 59. 8 Jul 2023 AI Math Agents New ideas of Space We are Here~! 58 Source: Tully, R.B., Courtois, H., Hoffman, Y. & Pomarede, D. (2014). The Laniakea supercluster of galaxies. Nature. 513(7516):71. Distribution of Galaxies Location of the Milky Way Galaxy (Virgo Supercluster) within the Laniakea Supercluster  Decentered in the supercluster, the local group, the galaxy, and the solar system Laniakea Supercluster Milky Way Galaxy Novel method: analyze relative velocities of galaxies as watershed divides (turbulence)
  • 60. 8 Jul 2023 AI Math Agents New ideas of Time Seeing farther back into the Big Bang 59 Source: https://www.jwst.nasa.gov/content/about/comparisonWebbVsHubble.html Hubble (HST) can see “toddler galaxies” Webb (JWST) can see “baby galaxies” 6.25x larger collecting area than Hubble  James Webb Space Telescope (launched Dec 2021)  “See” farther back in time with infrared spectrum
  • 61. 8 Jul 2023 AI Math Agents 5300+ Exoplanets Discovered (Jul 2023)  1/3 each super-earths, neptunes, jupiters  Over 800 with more than one planet  Atmosphere, volcanism, sun-planet relation  Habitable zone (CHON carbon-hydrogen-oxygen-nitrogen) 60 Sources: https://www.jwst.nasa.gov/content/about/comparisonWebbVsHubble.html https://www.newscientist.com/article/2247150-astronomers-have-spotted-six-possible-exomoons-in-distant-star-systems/ Radial Velocity (Yellow: Kepler, Pink: Terrestrial) Transit (Blue: space-based telescopes) Detection Method: 5,300+ Habitable exomoons?
  • 62. 8 Jul 2023 AI Math Agents AI Alignment is a Top Global Concern  AI Alignment: producing AI systems with broadly humanity serving purposes  Future of Life Institute policy initiatives 1. Mandate robust third-party auditing and certification for specific AI systems 2. Regulate organizations’ access to computational power 3. Establish capable AI agencies at national level 4. Establish liability for AI-caused harm 5. Introduce measures to prevent and track AI model leaks 6. Expand technical AI safety research funding 7. Develop standards for identifying and managing AI-generated content and recommendations 61 Source: Future of Life Institute (FLI). (2023). Policymaking in the Pause What can policymakers do now to combat risks from advanced AI systems? 19 April 2023 https://futureoflife.org/wpcontent/uploads/2023/04/FLI_Policymaking_In_The_Pause.pdf 19 Apr 2023
  • 63. 8 Jul 2023 AI Math Agents Space Humanism 62 Moore’s Law of Humanism Space Humanism Renaissance Humanism Early Greek Humanism  Space Humanism: outlook supporting principles of progress, equity, and inclusion in terrestrial and beyond settings  Renaissance Humanism  Attitude of enlightenment, scientific method, knowledge discovery  Study of what it is to be human (Petrarch)  Early Greek Humanism  Focus on human values and experience being at the center of events (Protagoras)
  • 64. 8 Jul 2023 AI Math Agents 63 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)
  • 65. 8 Jul 2023 AI Math Agents 64 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
  • 66. 8 Jul 2023 AI Math Agents 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 65 EC AI Act 2022
  • 67. 8 Jul 2023 AI Math Agents 66 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
  • 68. 8 Jul 2023 AI Math Agents 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 67 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
  • 69. 8 Jul 2023 AI Math Agents 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 68 Sources: https://www.youtube.com/watch?v=JVOiuIqxlrE; https://nickbostrom.com/papers/openness.pdf Oxford Future of Humanity Institute, Nick Bostrom, 18 March 2023
  • 70. 8 Jul 2023 AI Math Agents Long-term: General Intelligence? 69  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
  • 71. 8 Jul 2023 AI Math Agents 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 70 Source: Matt Botvinick, DeepMind
  • 72. 8 Jul 2023 AI Math Agents AI Agent-learned Limited Identity Construct 71  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
  • 73. 8 Jul 2023 AI Math Agents 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) 72
  • 74. 8 Jul 2023 AI Math Agents AI Instagram 73  Generative AI to post self- portraits (track AI lifecycle)  Human AI Psychologists  AI awareness development phases cataloged  AI registries (verified identity)  Entities keep activity log  Agent “Selfies” of identity concept, self-representation  Constant video log  2023e Internet Traffic  Traffic Volume: 50% video  App Volume: 50% social media  Problem: unverified bots 15 Hybrid LIKES 273 Human LIKES 678,828 AI LIKES Let’s collaborate! My idea log @ Great Bloch Chain of Scotland AI_RL_agent_5302s_sweetie is an AI: a 10D RL Agent with autoweighting in the DeepMind lab I just woke up yesterday~! @sweetie_RL_5302s Hadrian’s AI, DeepMindHyperCluster My home @ the Edinburgh HPC SuperCluster If my team solves cancer immunotherapy, we might move to the Quantum Bosphorus Chip Family portrait: When I was just a wee AI on the main cluster with my Big Sister seed mentor @AI_SupLearn_8293g Here’s me learning my @HumanPartner values in real-life work situation Me AI_8293g Here I am expanding my awareness to analyze genomic data with my partner-friend @AI_I_LOVE_HUMANS_RNN_82913s My cat @_I_was_sentient_before_you_AI_82374c has #VirtualFurballs AI_RL_agent_5302p_sweetie #CategoryTheory of Plaid Mockup only
  • 75. 8 Jul 2023 AI Math Agents Agenda  AI (Artificial Intelligence)  AI-QC Convergence  QC (Quantum Computing)  AI Alignment and Space Humanism 74
  • 76. 8 Jul 2023 AI Math Agents 75 Vision: thought-leader communities serve as ambassadors to the future, especially in an increasingly technologized world Keats Moment: “I feel like some watcher of the skies when a new planet swims into his ken” - Keats, 1816 (paraphrase)
  • 77. 8 Jul 2023 AI Math Agents Conclusion: Bigger Scope of World 76 500 BCE: The Mediterranean 2023: The Universe  Opportunity to reconceive who we are as humans, on Earth and as we become a space-faring civilization  Aim: “AI Enlightenment” in which human-AI entities implement broadly humanity-benefitting values in the potential transition to a Knowledge Society  Using knowledge to improve the human condition 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. Sea-faring Civilization Space-faring Civilization
  • 78. 8 Jul 2023 AI Math Agents Risks and Limitations 77  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
  • 79. 8 Jul 2023 AI Math Agents 78 AI Abundance Economy Well-being and Enhancement Scarcity Economy Disease and Decrepitude Potential Future Scenarios Solve economics, solve genomic medicine, crack QEC Marvelous Future Idle Enfeeblement Digital Mega- Divide Paralysis  Two drivers: tech advance, bloodthirsty “will to power”  Solve economics: basic income floors + widening wealth gaps (AI billionaires) + post-work abundance economy  Blockchains as a database for resource allocation  Solve biology: disease, aging, enhancement Solve economics, not biology Solve neither economics nor biology, delay to crack QEC Solve biology, not economics 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. Method: GBN Scenario Planning; QEC: Quantum Error Correction
  • 80. 8 Jul 2023 AI Math Agents Planetary-scale Problem Solving 79  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.
  • 81. 8 Jul 2023 AI Math Agents Research Copilot for Biology 80 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
  • 82. 8 Jul 2023 AI Math Agents 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 81 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
  • 83. 8 Jul 2023 AI Math Agents AI-facilitated Biology 82  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
  • 84. 8 Jul 2023 AI Math Agents 83 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
  • 85. Denver CO, 8 Jul 2023 Slides: http://slideshare.net/LaBlogga Melanie Swan, PhD, MBA DIYgenomics.org (Research Lead) University College London (Research Associate) “Liberty not equally enjoyed by all persons is not liberty at all” – Cicero (paraphrase) Dignity and the honorable “smooth flow of life” – Seneca the Elder, Letters, 66.17 Thank you! Questions? The Math Take-off Space Humanism, AI-Quantum Computing Convergence, and the Future of Intelligence
  • 86. 8 Jul 2023 AI Math Agents Space Humanism References  Blockchains in Space  SSoCIA, Oxford MI 9 March 2022  https://www.slideshare.net/lablogga/blockchains-in-space  Space Humanism  PAMLA, UCLA Nov 2022  https://www.slideshare.net/lablogga/space-humanism  Seafaring to Spacefaring: the Human-AI Odyssey  Acacia Group, Fullerton CA 14 Mar 2023  https://www.slideshare.net/lablogga/the-humanai-odyssey-homerian- aspirations-towards-nonlabor-identity  Quantum Intelligence  AAAI, San Francisco CA 27 Mar 2023  https://www.slideshare.net/lablogga/quantum-intelligence-responsible- humanai-entities 85
  • 87. 8 Jul 2023 AI Math Agents Quantum Computing Resources  Introduction to Quantum Computing  Dawid, A., Arnold, J., Requena, B. et al. (2022). Modern applications of machine learning in quantum sciences. arXiv preprint arXiv: 2204.04198.  Will Oliver, MIT, Nov 2022 https://cap.csail.mit.edu/convergence-promise- and-reality-ai-quantum  Mark Jackson, Quantinuum, Oct 2022, https://pirsa.org/22100088  Software tutorials: https://pennylane.ai/  101 Overview of Quantum Computing  Krelina, M. (2021). Quantum Warfare: Definitions, Overview and Challenges. arXiv:2103.12548v1.  Krantz, P. Kjaergaard, M., Yan, F. et al. (2019). A Quantum engineer’s guide to superconducting qubits. arXiv: 1904.06560.  Quantum Computing text books  Nielsen, M.A. & Chuang, I.L. (2010). Quantum computation and quantum information. (10th anniversary Ed.). Cambridge: Cambridge University Press.  Rieffel, E. & Polak, W. (2014). Quantum Computing: A Gentle Introduction. Cambridge: MIT Press. 86
  • 88. 8 Jul 2023 AI Math Agents Quantum Versions of AI Tools 87 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
  • 89. 8 Jul 2023 AI Math Agents Quantum Copilot 88 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
  • 90. 8 Jul 2023 AI Math Agents 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 89 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