[[0712.3466] Interpretation of quantum theory - An overview](https://arxiv.org/abs/0712.3466)
https://twitter.com/yacineMTB/status/1781299113243369691
https://twitter.com/Teknium1/status/1781345814633390579
Or this one is very oldschool [[0712.3329] Universal Intelligence: A Definition of Machine Intelligence](https://arxiv.org/abs/0712.3329)
And put that into math [[1911.01547] On the Measure of Intelligence](https://arxiv.org/abs/1911.01547)
[[2403.01267] Dissecting Language Models: Machine Unlearning via Selective Pruning](https://arxiv.org/abs/2403.01267)
AlphaLLM [[2404.12253] Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing](https://arxiv.org/abs/2404.12253)
https://twitter.com/QuintinPope5/status/1780907564601106546
Deep Differentiable Logic Gate Networks: [[2210.08277] Deep Differentiable Logic Gate Networks](https://arxiv.org/abs/2210.08277)
The Hydra Effect: Emergent Self-repair in Language Model Computations: [[2307.15771] The Hydra Effect: Emergent Self-repair in Language Model Computations](https://arxiv.org/abs/2307.15771)
Deep learning generalizes because the parameter-function map is biased towards simple functions: [[1805.08522] Deep learning generalizes because the parameter-function map is biased towards simple functions](https://arxiv.org/abs/1805.08522)
Bridging RL Theory and Practice with the Effective Horizon: [[2304.09853] Bridging RL Theory and Practice with the Effective Horizon](https://arxiv.org/abs/2304.09853)
here's one of the papers trying to explain why neural networks don't severely overfit as predicted by classical learning theory, it says that the parameter-function map of many deep neural nets should be exponentially biased towards simple functions by applying a very general probability-complexity bound recently derived from algorithmic information theory [[1805.08522] Deep learning generalizes because the parameter-function map is biased towards simple functions](https://arxiv.org/abs/1805.08522)
Maybe when we start getting more autonomous OOD agents in practice then some of the inherent AI risks will start making more sense to me as current AI systems have very weak generalization capabilities and are pretty easily shaped by training data even tho in quite alchemist ways https://twitter.com/__HMYS__/status/1781408253756236249 "
https://twitter.com/liron/status/1675724309745246208
[[2210.05492] Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning](https://arxiv.org/abs/2210.05492)
https://twitter.com/Liv_Boeree/status/1781485570835058853?t=kmFOLCTWYsAy8J6r7B8C6Q&s=19
[[1806.07366] Neural Ordinary Differential Equations](https://arxiv.org/abs/1806.07366)
[Quanta Magazine](https://www.quantamagazine.org/insects-and-other-animals-have-consciousness-experts-declare-20240419/)
Map of milky way https://twitter.com/burny_tech/status/1781675087143301617?t=sh2IVKA-Klb0AUv7NDQLqQ&s=19
https://twitter.com/anthrupad/status/1781579406420721955?t=9dtRv3djmKIqZPujkwVVaw&s=19
https://twitter.com/jam3scampbell/status/1781518399174062214?t=tqjQot79FTZ1MA14z3Oafg&s=19
[[2404.11794] Automated Social Science: Language Models as Scientist and Subjects](https://arxiv.org/abs/2404.11794?fbclid=IwZXh0bgNhZW0CMTEAAR3tHLXnVqq6ySpLvHrmKqoL5pxXQKuZ282Ml5RZhUcjNHYtbDEuFQGPEzQ_aem_AR5j0vWukrY_Il2GTfxv8eynoxG5AoAaRGN68jDFHYUCzzaTLaZRl6SYKKft_UsQXR1cc-Cdg46PcLnmhO3emCyZ)
https://a16z.com/the-techno-optimist-manifesto/
Tons of S-curves composed into an exponential https://twitter.com/BasedBeffJezos/status/1781486311922372906?t=y9eDUB3bf47ALUHQA7ANnw&s=19
https://twitter.com/QuintinPope5/status/1780907564601106546
Bacteria have a small motor that makes them move: https://twitter.com/slava__bobrov/status/1631289659366993924
[[2112.07544] Modeling Strong and Human-Like Gameplay with KL-Regularized Search](https://arxiv.org/abs/2112.07544)
Here is an even more detailed gigantic map of astrophysics with a focus on equations and mathematics:
Astrophysics
|-- Cosmology
| |-- Big Bang Theory
| | |-- Friedmann Equations
| | | |-- Friedmann–Lemaître–Robertson–Walker (FLRW) metric
| | | |-- Hubble's Law: v = H₀D
| | |
| | |-- Cosmic Microwave Background (CMB)
| | | |-- Blackbody Radiation: Planck's Law
| | | |-- CMB Temperature: T = 2.725 K
| | | |-- CMB Anisotropies: ΔT/T ≈ 10⁻⁵
| | |
| | |-- Nucleosynthesis
| | |-- Abundances of Light Elements: ⁴He, D, ³He, ⁷Li
| |-- Dark Matter
| | |-- Virial Theorem
| | |-- Rotation Curves of Galaxies
| | |-- Gravitational Lensing
| | |-- Lens Equation: β = θ - α
| |-- Dark Energy
| | |-- Cosmological Constant: Λ
| | |-- Equation of State: w = P/(ρc²)
| | |-- Accelerating Universe: ä > 0
| |-- Inflation Theory
| | |-- Scalar Fields: φ
| | |-- Slow-roll Conditions: ε ≪ 1, |η| ≪ 1
| |-- Large-scale Structure of the Universe
| | |-- Power Spectrum: P(k) ∝ k^n
| | |-- Correlation Function: ξ(r)
| | |-- Baryonic Acoustic Oscillations (BAO)
| |-- Cosmological Models
| |-- ΛCDM Model
| |-- Quintessence Models
| |-- Modified Gravity Models
|-- Stellar Astrophysics
| |-- Stellar Evolution
| | |-- Equations of Stellar Structure
| | | |-- Hydrostatic Equilibrium: dP/dr = -ρg
| | | |-- Mass Continuity: dM/dr = 4πr²ρ
| | | |-- Energy Transport: dL/dr = 4πr²ρε
| | | |-- Energy Conservation: dT/dr = -(3κρ/4acT³)(L/4πr²)
| | |
| | |-- Jeans Instability: λ_J = (πc_s²/(Gρ))^(1/2)
| | |-- Main Sequence Stars
| | | |-- Mass-Luminosity Relation: L ∝ M^α
| | | |-- Hertzsprung-Russell (HR) Diagram
| | |
| | |-- Post-Main Sequence Evolution
| | |-- Red Giants
| | |-- Asymptotic Giant Branch (AGB) Stars
| | |-- Planetary Nebulae
| |-- Stellar Atmospheres
| | |-- Radiative Transfer Equation: dI_ν/dτ_ν = -I_ν + S_ν
| | |-- Opacity: κ_ν
| | |-- Line Formation: Voigt Profile, Equivalent Width
| |-- Stellar Interiors
| | |-- Nuclear Reaction Rates: r ∝ ρT^n
| | |-- Energy Generation: pp-chain, CNO cycle
| | |-- Equation of State: P = P(ρ, T)
| | |-- Convection: Schwarzschild Criterion, Mixing Length Theory
| |-- Stellar Pulsations
| |-- Cepheid Variables: Period-Luminosity Relation
| |-- RR Lyrae Stars
| |-- Asteroseismology: Oscillation Frequencies
|-- Galactic Astrophysics
| |-- Galactic Structure
| | |-- Density Profiles: ρ(r) ∝ r^-α
| | |-- Rotation Curves: v(r) = (GM(r)/r)^(1/2)
| | |-- Velocity Dispersion: σ_v
| |-- Interstellar Medium
| | |-- Radiative Transfer: Emission, Absorption, Scattering
| | |-- Ionization Equilibrium: Saha Equation
| | |-- Molecular Clouds: Jeans Mass, Virial Theorem
| | |-- Dust Extinction: Av ∝ λ^-β
| |-- Chemical Evolution
| |-- Stellar Yields: y_i
| |-- Initial Mass Function (IMF): ξ(M) ∝ M^-α
| |-- Gas Infall and Outflow
|-- High-Energy Astrophysics
| |-- Accretion Disks
| | |-- Shakura-Sunyaev α-disk Model
| | |-- Novikov-Thorne Model
| | |-- Eddington Luminosity: L_Edd = 4πGMm_pc/σ_T
| |-- Compact Objects
| | |-- White Dwarfs
| | | |-- Chandrasekhar Mass: M_Ch ≈ 1.4 M_☉
| | | |-- Mass-Radius Relation: R ∝ M^(-1/3)
| | |
| | |-- Neutron Stars
| | | |-- Tolman–Oppenheimer–Volkoff (TOV) Equation
| | | |-- Equation of State: P = P(ρ)
| | |
| | |-- Black Holes
| | |-- Schwarzschild Metric
| | |-- Kerr Metric
| | |-- Hawking Radiation: T_H = hc³/(8πGMk_B)
| |-- Radiative Processes
| |-- Synchrotron Radiation
| |-- Inverse Compton Scattering
| |-- Bremsstrahlung
| |-- Pair Production and Annihilation
|-- Astrometry and Celestial Mechanics
| |-- Positions and Motions of Celestial Bodies
| | |-- Spherical Trigonometry
| | |-- Celestial Coordinate Systems: RA, Dec, l, b
| | |-- Proper Motion: μ_α, μ_δ
| |-- Parallaxes and Distances
| | |-- Trigonometric Parallax: π = 1/d
| | |-- Distance Modulus: m - M = 5 log₁₀(d/10)
| |-- Orbital Dynamics
| |-- Kepler's Laws
| |-- Two-body Problem
| |-- Orbital Elements: a, e, i, Ω, ω, ν
| |-- Virial Theorem: 2⟨T⟩ = -⟨V⟩
|-- Astronomical Instrumentation and Techniques
| |-- Telescopes
| | |-- Angular Resolution: θ ≈ λ/D
| | |-- Collecting Area: A ∝ D²
| | |-- Adaptive Optics: Zernike Polynomials
| |-- Detectors
| | |-- CCD Equation: S = ηΦAtQλ/hc
| | |-- Noise Sources: Read Noise, Dark Current, Photon Noise
| | |-- Signal-to-Noise Ratio (SNR): SNR ∝ √N
| |-- Interferometry
| |-- Young's Double Slit Experiment
| |-- Visibility: V = (I_max - I_min)/(I_max + I_min)
| |-- Aperture Synthesis: uv-plane
|-- Astrobiology
| |-- Habitable Zones
| | |-- Liquid Water: 273 K < T < 373 K
| | |-- Circumstellar Habitable Zone: d = (L/L_☉)^(1/2) AU
| |-- Exoplanets
| |-- Radial Velocity Method: ΔV = (2πG/P)^(1/3) M_p sin(i)/M_*^(2/3)
| |-- Transit Method: ΔF/F ≈ (R_p/R_*)²
| |-- Microlensing: Einstein Radius, Magnification
|-- Computational Astrophysics
|-- Hydrodynamics
| |-- Euler Equations
| |-- Navier-Stokes Equations
| |-- Godunov's Method, Riemann Solvers
|-- Radiative Transfer
| |-- Monte Carlo Methods
| |-- Ray Tracing
| |-- Discrete Ordinates Method
|-- Magnetohydrodynamics (MHD)
| |-- Ideal MHD Equations
| |-- Alfvén Waves: v_A = B/(4πρ)^(1/2)
| |-- Magnetic Reconnection
|-- N-body Simulations
| |-- Barnes-Hut Algorithm
| |-- Particle-Mesh (PM) Method
| |-- Smoothed Particle Hydrodynamics (SPH)
|-- Cosmological Simulations
|-- Initial Conditions: Gaussian Random Fields
|-- Gravity Solvers: Tree Codes, Particle-Mesh
|-- Subgrid Physics: Star Formation, Feedback
[The Meta-Problem of Consciousness with David Chalmers - YouTube]([The Meta-Problem of Consciousness with David Chalmers - YouTube](https://www.youtube.com/watch?v=yHTiQrrUhUA))
[Quantum Turing machine - Wikipedia]([Quantum Turing machine - Wikipedia]([Quantum - Wikipedia](https://en.wikipedia.org/wiki/Quantum)_Turing_machine))?wprov=sfla1
[Quantum - Wikipedia](https://en.wikipedia.org/wiki/Quantum)_finite_automaton?wprov=sfla1
[Quantum machine learning - Wikipedia]([Quantum - Wikipedia](https://en.wikipedia.org/wiki/Quantum)_machine_learning)?wprov=sfla1
Intuitive explanation of FEP [A Gentle Introduction to the Free Energy Principle | by Arthur Juliani | Medium](https://awjuliani.medium.com/a-gentle-introduction-to-the-free-energy-principle-03f219853177)
Scott Alexander pauseAI [Pause For Thought: The AI Pause Debate - by Scott Alexander]([Pause For Thought: The AI Pause Debate - by Scott Alexander]([Pause For Thought: The AI Pause Debate - by Scott Alexander](https://www.astralcodexten.com/p/pause-for-thought-the-ai-pause-debate)))
Interpretability and AI wireheading [Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency](https://www.ai-transparency.org/)))))))) https://twitter.com/mezaoptimizer/status/1709292930416910499?t=9UKfocv_wJSVxhixqIKgxQ&s=19 https://twitter.com/ch402/status/1709998674087227859?t=WIXXcRrnAN4Hn4n0zv7GuQ&s=19 https://twitter.com/DanHendrycks/status/1709227490592612671?t=dwVl1oLHgakPlDRTD42JRw&s=19
[Chris Timpson - "QBism, Ontology, and Explanation" - YouTube]([Chris Timpson - "QBism, Ontology, and Explanation" - YouTube](https://youtu.be/6g96Yly67mg?si=tSKuR3-YHIfemaBq)) Chris Timpson - "QBism, Ontology, and Explanation"
Edward Frenkel: Langlands problem,, ToE, Infinity, Ai, String Theory, Death, The Self [Edward Frenkel: Infinity, Ai, String Theory, Death, The Self - YouTube]([Edward Frenkel: Infinity, Ai, String Theory, Death, The Self - YouTube](https://www.youtube.com/watch?v=n_oPMcvHbAc))
[My predictions about Artificial Super Intelligence (ASI) - YouTube]([My predictions about Artificial Super Intelligence (ASI) - YouTube](https://www.youtube.com/watch?v=skozI33D3t4)) My predictions about Artificial Super Intelligence (ASI) David Shapiro
stochastic thermodynamics and algirhtmic information theory of observers David Wolpert [David Wolpert: Monotheism Theorem, Uncaused Causation - YouTube](https://www.youtube.com/watch?v=WkVq0p2WBmQ)
[Gregory Chaitin: Complexity, Metabiology, Gödel, Cold Fusion - YouTube]([Gregory Chaitin: Complexity, Metabiology, Gödel, Cold Fusion - YouTube](https://www.youtube.com/watch?v=zMPnrNL3zsE))
algorithmic information dynamics [Algorithmic Information Dynamics - YouTube]([Algorithmic Information Dynamics - YouTube](https://www.youtube.com/playlist?list=PLbvAAv4mbxdgp3qZRr34zUTAdaq9Qycuu))
Max tegmark AI safety paper [Steve Omohundro on Provably Safe AGI - YouTube]([Steve Omohundro on Provably Safe AGI - YouTube](https://youtu.be/YhMwkk6uOK8?si=n8uPIYsuadhWbGpN))
Utok [Now UTOKing | Learning the Language - YouTube]([Now UTOKing | Learning the Language - YouTube](https://youtube.com/playlist?list=PLTJe1xFfoxrDi19ew-Zbo9oDOwv_c-Ysz))
Roon most important about future? [Why the Culture Wins: An Appreciation of Iain M. Banks - Sci Phi Journal]([Why the Culture Wins: An Appreciation of Iain M. Banks - Sci Phi Journal](https://www.sciphijournal.org/index.php/2017/11/12/why-the-culture-wins-an-appreciation-of-iain-m-banks/))
[[Lagrangian and Hamiltonian Mechanics in Under 20 Minutes: Physics Mini Lesson - YouTube]([Lagrangian and Hamiltonian Mechanics in Under 20 Minutes: Physics Mini Lesson - YouTube]([Lagrangian and Hamiltonian Mechanics in Under 20 Minutes: Physics Mini Lesson - YouTube]([Lagrangian and Hamiltonian Mechanics in Under 20 Minutes: Physics Mini Lesson - YouTube](https://www.youtube.com/watch?v=0DHNGtsmmH8)))) Lagrangian and Hamiltonian Mechanics in Under 20 Minutes: Physics Mini Lesson]
[[The Hydrogen Atom, Part 1 of 3: Intro to Quantum Physics - YouTube]([The Hydrogen Atom, Part 1 of 3: Intro to Quantum Physics - YouTube]([The Hydrogen Atom, Part 1 of 3: Intro to Quantum Physics - YouTube]([The Hydrogen Atom, Part 1 of 3: Intro to Quantum Physics - YouTube]([The Hydrogen Atom, Part 1 of 3: Intro to Quantum Physics - YouTube](https://www.youtube.com/watch?v=-Y0XL-K0jy0))))) The Hydrogen Atom, Part 1 of 3: Intro to Quantum Physics]
[[The Maths of General Relativity - YouTube]([The Maths of General Relativity - YouTube]([The Maths of General Relativity - YouTube](https://www.youtube.com/playlist?list=PLu7cY2CPiRjVY-VaUZ69bXHZr5QslKbzo))) The Maths of General Relativity]
Wolfram x Friston [STEPHEN WOLFRAM + KARL FRISTON - OBSERVERS [SPECIAL EDITION] - YouTube](https://youtu.be/6iaT-0Dvhnc?si=x0IvnegaORrK3FAV)
[Neel Nanda](https://www.neelnanda.io/)blog/41-helplessness
MULTI AGENT LEARNING - LANCELOT DA COSTA [MULTI AGENT LEARNING - LANCELOT DA COSTA - YouTube]([MULTI AGENT LEARNING - LANCELOT DA COSTA - YouTube]([MULTI AGENT LEARNING - LANCELOT DA COSTA - YouTube](https://www.youtube.com/watch?v=qxEfcrmTWO4)))
[Mathematical physics - Wikipedia]([Mathematical physics - Wikipedia]([Mathematical physics - Wikipedia](https://en.wikipedia.org/wiki/Mathematical_physics)))
[AGI Can Be Safe]([AGI Can Be Safe]([AGI Can Be Safe]([AGI Can Be Safe](https://dataskeptic.com/blog/episodes/2023/agi-can-be-safe))))
[Meta-learning (computer science) - Wikipedia]([Meta-learning (computer science) - Wikipedia]([Meta-learning (computer science) - Wikipedia]([Meta-learning (computer science) - Wikipedia]([Meta-learning (computer science) - Wikipedia](https://en.wikipedia.org/wiki/Meta-learning_(computer_science))))))
[From Language to Consciousness (Guest: Joscha Bach) - YouTube]([From Language to Consciousness (Guest: Joscha Bach) - YouTube]([From Language to Consciousness (Guest: Joscha Bach) - YouTube]([From Language to Consciousness (Guest: Joscha Bach) - YouTube](https://www.youtube.com/watch?v=ApHnqHfFWBk)))) from language to consciousness presentation by joscha bach
[Explainable artificial intelligence - Wikipedia]([Explainable artificial intelligence - Wikipedia]([Explainable artificial intelligence - Wikipedia](https://en.wikipedia.org/wiki/Explainable_artificial_intelligence)))
David Deutsch (beggining of infinity) [David Deutsch - AI, America, Fun, & Bayes - YouTube]([David Deutsch - AI, America, Fun, & Bayes - YouTube]([David Deutsch - AI, America, Fun, & Bayes - YouTube](https://www.youtube.com/watch?v=EVwjofV5TgU)))
[Nick Bostrom: How AI will lead to tyranny - YouTube]([Nick Bostrom: How AI will lead to tyranny - YouTube]([Nick Bostrom: How AI will lead to tyranny - YouTube]([Nick Bostrom: How AI will lead to tyranny - YouTube]([Nick Bostrom: How AI will lead to tyranny - YouTube]([Nick Bostrom: How AI will lead to tyranny - YouTube](https://www.youtube.com/watch?v=_Oo-m893-xA)))))) Nick Bostrom: How AI will lead to tyranny
[Our AI Future: Hopes and Hurdles Ahead - YouTube]([Our AI Future: Hopes and Hurdles Ahead - YouTube](https://www.youtube.com/watch?v=IeVY_Ag8JI8)) Our AI Future: Hopes and Hurdles Ahead recursive selfimporvement
Yann lecun práce conference [Navigating the AI Revolution: Shaping a Decade of Promise and Peril - YouTube]([Navigating the AI Revolution: Shaping a Decade of Promise and Peril - YouTube](https://www.youtube.com/live/pMvQIUhKcZ4?si=5N7vqxTBdVksZFbW))
AI safety math podcast [AGI Can Be Safe](https://dataskeptic.com/blog/episodes/2023/agi-can-be-safe)
[MEGATHREAT: Why AI Is So Dangerous & How It Could Destroy Humanity | Mo Gawdat - YouTube](https://www.youtube.com/watch?v=itY6VWpdECc) MEGATHREAT: Why AI Is So Dangerous & How It Could Destroy Humanity | Mo Gawdat
[AGI Can Be Safe](https://dataskeptic.com/blog/episodes/2023/agi-can-be-safe) Koen Holtman independent ai safety
Robin Hanson [The Foresight Institute Podcast - Stuart Armstrong & Robin Hanson: Iterate The Game | Gaming the Future Chapter 10](https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5hY2FzdC5jb20vcHVibGljL3Nob3dzLzY1MjdlNGYxZDQwYzk3MDAxMjVmNDJkZQ/episode/QnV6enNwcm91dC0xMTAzMzIzNw?ep=14)
[Clearer Thinking with Spencer Greenberg: Simulacra levels, moral mazes, and low-hanging fruit (with Zvi Mowshowitz) on Apple Podcasts](https://podcasts.apple.com/us/podcast/clearer-thinking-with-spencer-greenberg/id1535406429?i=1000639301977)
AGI people [Reddit - Dive into anything](https://www.reddit.com/r/singularity/comments/18vawje/singularity_predictions_2024/kfpntso/)
https://www.piratewires.com/p/techno-industrialist-manifesto
LLM survey [[2402.06196] Large Language Models: A Survey](https://arxiv.org/abs/2402.06196)
[[2402.18563] Approaching Human-Level Forecasting with Language Models](https://arxiv.org/abs/2402.18563)
tolearn [[1911.01547] On the Measure of Intelligence](https://arxiv.org/abs/1911.01547)
[Streamlit](https://fmcheatsheet.org/) Foundation Model Development Cheatsheet
[[1911.01547] On the Measure of Intelligence](https://arxiv.org/abs/1911.01547) francoiz intelligence
[To de-risk AI, the government must accelerate knowledge production | by Greg Fodor | Medium](https://gfodor.medium.com/to-de-risk-ai-the-government-must-accelerate-knowledge-production-49c4f3c26aa0)
Bayesian program synthesis [[2006.08381] DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning](https://arxiv.org/abs/2006.08381)
[NeurIPS 2023](https://nips.cc/virtual/2023/84295) Topological Deep Learning: Going Beyond Graph Data