There are two wolves inside me: - we must create the most predictive scientific model of neurophenomenology to effectively engineer it using neurotechnologies and psychotechnologies - all language and knowing and assumptions is inherently empty and all problems are confusions so lets all just dissolve into ineffable void beyond all including all [x.com](https://twitter.com/algekalipso/status/1778931075332399553) https://arxiv.org/abs/2206.10445 https://arxiv.org/abs/2403.01643 How much competition is healthy driver of progress and how much competition starts becoming unhealthy and mutually selfdestructive? all models are wrong but some are usefully predicting the world better than others Ignorance might be bliss shortterm, but longterm it backfires Growth of sentience across the whole universe with amazing experiences is all that matters Enter the "if enemies stopped existing there would be peace therefore destroy then" mindset or try to push peace with healthy competition? https://arxiv.org/abs/2307.07515 https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2817087 [x.com](https://twitter.com/NTFabiano/status/1778755493747638419?t=1HM3bAsw_R1gJEelvUShSg&s=19) I'm aware of rogue superintelligent AI risk when given agency etc., but I don't think systems like https://arxiv.org/abs/2310.10553v2 will go rogue The very mechanisms that makes people intelligently adaptive simultaneously make them vulnerable to self-deceptive, self-destructive behavior. Illusionism is valid position in philosophy of mind if you're open to philosophy What is the sweet spot between acceleration and caution to maximize all positive benefits and minimize all downsides to maximize the chances of sentient life becoming intergalactic civilization? [How Do Machines ‘Grok’ Data? | Quanta Magazine](https://www.quantamagazine.org/how-do-machines-grok-data-20240412/) Relativize everything that isn't empirically verifiable predictive math where you relativize according to predictive strength Accelerate AI-accelerated preparedness for pandemics https://arxiv.org/abs/2402.08210 " AI transforming life science - a new antifibrotic where the target was discovered using AI and the molecule was designed using generative AI " [A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models | Nature Biotechnology](https://www.nature.com/articles/s41587-024-02143-0) [Where Are All the New Semiconductor Fabs in North America & Europe? - Z2Data](https://www.z2data.com/insights/new-semiconductor-fabs-in-north-america-europe) [Tulsi Gabbard: War, Politics, and the Military Industrial Complex | Lex Fridman Podcast #423 - YouTube](https://youtu.be/_El9riy9Zjw?si=2F2EjtJn61JXg5n2) https://arxiv.org/abs/2404.04125 [This snake robot could hunt life on moons like Saturn's Enceladus | Space](https://www.space.com/enceladus-snake-robot-alien-life-hunter) [In memoriam: Murray Gell-Mann | Santa Fe Institute](https://www.santafe.edu/news-center/news/murray-gell-mann-passes-away-89) AI systems to help people in war (chatbot helping people in Ukraine find help by giving contacts) TacticsAI for war Drones in war Social engineering optimistic unifying memeplex Political movement effective omni [ICLR 2021 Keynote - "Geometric Deep Learning: The Erlangen Programme of ML" - M Bronstein - YouTube](https://www.youtube.com/watch?v=w6Pw4MOzMuo) [The Fast Track – Sheafification](https://sheafification.com/the-fast-track/) physics books https://arxiv.org/abs/2402.05120 https://imgur.com/96CUCHi [Astrophysicists Put All Objects in Universe into One Pedagogical Plot | Sci.News](https://www.sci.news/astronomy/all-objects-universe-pedagogical-plot-12372.html) https://pubs.aip.org/aapt/ajp/article-abstract/91/10/819/2911822/All-objects-and-some-questions WW3 will maybe incentivize solving quantum gravity [A three-node Turing gene circuit forms periodic spatial patterns in bacteria | bioRxiv](https://www.biorxiv.org/content/10.1101/2023.10.19.563112v1) [John Mearsheimer: Israel-Palestine, Russia-Ukraine, China, NATO, and WW3 | Lex Fridman Podcast #401 - YouTube](https://youtu.be/r4wLXNydzeY?si=qL7wT2HOOS7hKSuC) I am nobody nowhere never I am everyone everywhere all the time Superpositions of all nerdisms "Realism and liberalism are two major theories in international relations that offer contrasting perspectives on how the world works and what drives state behavior. Realism posits that the international system is anarchic and that states are primarily motivated by self-interest and the pursuit of power[1][2][3][4]. Key tenets of realism include: - States are the main actors in international politics and operate in an anarchic system with no overarching authority[1][2] - Power, especially military power, is the currency of international politics. States seek to maximize their relative power[1][3][4] - Conflict and competition between states is inevitable as they pursue their national interests[1][2][4] - Moral concerns are secondary to power politics and national security[4] In contrast, liberalism offers a more optimistic view, emphasizing the potential for cooperation, peace and progress[2][4][5]. Core liberal ideas include: - International institutions, law, and norms can mitigate the effects of anarchy and facilitate cooperation[2][4] - Economic interdependence makes war less likely as states have mutual interests in trade and growth[2][4] - The spread of democracy and human rights can contribute to international peace[4][5] - Individuals and non-state actors also matter in world politics, not just states[3] However, both theories have limitations. Realism struggles to explain instances of cooperation and the changing nature of power in an globalized world[3][4]. Liberalism can seem overly idealistic and has difficulty accounting for the persistence of conflict[1][4]. Many scholars argue the world is too complex to be explained by one paradigm[2][3]. Some posit a synthesis of realist and liberal ideas is needed[3][4]. Ultimately, both theories offer valuable insights and remain influential in explaining state behavior and international relations[4][5]. Citations: [1] [Student Spotlight: Is Liberalism more persuasive to you than Realism? or are both theories flawed? by Anna McCracken – City Politics Blog](https://blogs.city.ac.uk/cityintpolitics/2021/02/18/student-spotlight-is-liberalism-more-persuasive-to-you-than-realism-or-are-both-theories-flawed-by-anna-mccracken/) [2] [Realism or Liberalism; what explains international relations today](https://timesofindia.indiatimes.com/readersblog/cosmopolitan/realism-or-liberalism-what-explains-international-relations-today-49696/) [3] [Discover thousands of collaborative articles on 2500+ skills](https://www.linkedin.com/pulse/realism-liberalism-learning-live-together-nathan-colvin) [4] [Realism and Liberalism in International Relations](https://www.e-ir.info/2011/07/02/realism-and-liberalism-in-modern-international-relations/) [5] [Realism, Liberalism and War](https://www.e-ir.info/2023/11/22/realism-liberalism-and-war/) " "The free energy principle, proposed by neuroscientist Karl Friston, is a unifying theory of how biological systems maintain their order and resist entropy. The key ideas are: - Any self-organizing system that appears to resist the natural tendency to disorder must minimize its free energy[1][2][4]. Free energy can be thought of as surprise or prediction error - the difference between the system's model of the world and its sensory inputs[2]. - Biological systems like the brain accomplish this by: 1) Updating their internal model to infer the hidden causes of sensory data (perception/learning)[1][2][4]. This makes the internal model a good predictor of sensory inputs, minimizing surprise. 2) Acting on the environment to sample data that conforms to the current model (action)[1][2][4]. This also minimizes surprise by making sensory data more predictable. - Minimizing free energy mathematically corresponds to maximizing Bayesian model evidence - the probability of sensory data given the system's model[1][3]. So self-organizing systems can be seen as continuously optimizing their models of the world. - The free energy principle applies to any biological system, from single cells to complex organisms and societies[1][3]. It provides a unified account of perception, learning, action, homeostasis, and evolutionary adaptation[1][2][4]. In summary, the free energy principle proposes that living systems maintain their integrity and adaptively respond to their environment by minimizing the mismatch between their internal models and sensory reality. This elegant theory connects ideas from thermodynamics, information theory, and Bayesian inference to explain the behavior of biological systems across scales. Citations: [1] https://arxiv.org/pdf/2201.06387.pdf [2] https://www.fil.ion.ucl.ac.uk/~karl/The%20free-energy%20principle%20-%20a%20rough%20guide%20to%20the%20brain.pdf [3] [Free Energy Principle — Karl Friston - YouTube](https://www.youtube.com/watch?v=NIu_dJGyIQI) [4] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2660582/ [5] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9345684/ " "Geometric deep learning is an emerging field that aims to extend the success of deep learning to more complex, non-Euclidean datasets by incorporating geometric priors and invariances into neural network architectures[1][4]. The key idea is to exploit the known symmetries and regularities arising from the underlying structure of the data, such as grids, graphs, or manifolds[4]. Some key aspects of geometric deep learning include: - Designing linear and non-linear equivariant layers that respect the symmetries of the data, such as convolutions on graphs or spheres[1] - Incorporating local averaging and pooling to impose scale invariance and hierarchical representations[1] - Using global averaging to achieve invariance to transformations like rotations and permutations[1] - Unifying popular architectures like CNNs, GNNs, and Transformers under a common mathematical framework based on symmetry and invariance principles[4] Geometric deep learning provides a principled way to incorporate prior knowledge into neural networks and generalize them to new domains[4]. It has already led to state-of-the-art results in areas like computer vision, graphics, and computational chemistry, with applications to protein structure prediction, particle physics, and 3D shape analysis[4][5]. However, the field is still rapidly evolving with many open challenges around scalability, stability, and extending the theory to handle approximate symmetries and deformable domains[3]. Overall, geometric deep learning offers an exciting direction to expand the frontiers of AI to more complex data while maintaining strong geometric priors and guarantees. Citations: [1] https://towardsdatascience.com/a-brief-introduction-to-geometric-deep-learning-dae114923ddb [2] [Geometric Deep Learning - Grids, Groups, Graphs, Geodesics, and Gauges](https://geometricdeeplearning.com) [3] [Towards Geometric Deep Learning](https://thegradient.pub/towards-geometric-deep-learning/) [4] https://arxiv.org/abs/2104.13478 [5] [GDL Course](https://geometricdeeplearning.com/lectures/) " "The Standard Model of particle physics is a mathematical theory that describes the properties and interactions of elementary particles. It is formulated as a quantum field theory based on gauge symmetries and the Lagrangian formalism. The key aspects of the mathematical formulation are: 1. The Standard Model is based on the gauge symmetry group SU(3) × SU(2) × U(1), which corresponds to the strong, weak, and electromagnetic interactions respectively[4]. 2. Elementary particles are represented by quantum fields that pervade spacetime. Each type of particle obeys a wave equation such as the Dirac, Maxwell, Yang-Mills, or Klein-Gordon equations[2]. 3. Particle interactions are described by potential terms in the Lagrangian. The Lagrangian is a mathematical function that determines the dynamics and kinematics of the theory[1][4]. 4. A significant portion of the Standard Model Lagrangian is dedicated to describing the interactions between gauge bosons (force carriers) like gluons, W and Z bosons[3]. 5. The Lagrangian also contains terms describing how matter particles (quarks and leptons) interact via the weak force and gain mass through the Higgs mechanism[3]. 6. Mathematical artifacts called ghosts are introduced in the Lagrangian to cancel out redundancies that appear due to the quantum mechanical nature of the theory[3]. The Standard Model has been incredibly successful in explaining and predicting a wide range of phenomena, but it is not a complete theory. Challenges remain in areas such as neutrino masses, matter-antimatter asymmetry, unification with gravity, and the nature of dark matter and dark energy[1][4]. Citations: [1] [Standard Model - Wikipedia](https://en.wikipedia.org/wiki/Standard_Model) [2] https://indico.cern.ch/event/528094/contributions/2171249/attachments/1319109/1977592/ASPStandardModelSmall.pdf [3] [The deconstructed Standard Model equation | symmetry magazine](https://www.symmetrymagazine.org/article/the-deconstructed-standard-model-equation?language_content_entity=und) [4] [Mathematical formulation of the Standard Model - Wikipedia](https://en.wikipedia.org/wiki/Mathematical_formulation_of_the_Standard_Model) " "Intelligence refers to the mental capability to learn from experience, adapt to new situations, understand complex concepts, and use knowledge to manipulate one's environment. Key aspects of intelligence include reasoning, problem-solving, planning, abstract thinking, and learning[1]. There are several major theories of intelligence in psychology: 1. General Intelligence (g factor) - Proposed by Charles Spearman, this theory suggests there is a single general intelligence factor underlying all cognitive abilities[4]. 2. Primary Mental Abilities - Louis Thurstone proposed that intelligence is composed of seven distinct primary abilities rather than a single general ability[2]. 3. Multiple Intelligences - Howard Gardner's theory proposes eight distinct types of intelligence, such as linguistic, logical-mathematical, spatial, musical, and interpersonal intelligence[1][2]. 4. Triarchic Theory - Robert Sternberg's theory divides intelligence into three aspects: analytical, creative, and practical abilities[4]. 5. Cattell-Horn-Carroll (CHC) Theory - A prominent theory that divides cognitive abilities into fluid intelligence (reasoning and problem-solving) and crystallized intelligence (knowledge)[2][4]. IQ tests aim to measure intelligence, but only assess certain aspects like analytical and reasoning skills. They do not fully capture the broad scope of human intelligence[3][5]. Factors like genetics, environment, nutrition, and education all likely play a role in intellectual development[4][5]. Citations: [1] [Intelligence - Wikipedia](https://en.wikipedia.org/wiki/Intelligence) [2] https://www.verywellmind.com/theories-of-intelligence-2795035 [3] https://www.apa.org/topics/intelligence [4] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3341646/ [5] [Intelligence quotient - Wikipedia](https://en.wikipedia.org/wiki/Intelligence_quotient) " "Quantum chemistry is the application of quantum mechanics to chemical systems and the study of how quantum physics affects chemical behavior[1][3]. It is a fundamental field that has led to many advancements across various disciplines. Key points about quantum chemistry: - It describes the behavior of matter at the atomic and subatomic scale, where classical physics breaks down and quantum effects become significant[3]. - Quantum chemistry enables the modeling and simulation of chemical systems and reactions with high precision, providing insights not possible with classical models[1]. - It forms the theoretical foundation for understanding chemical bonding, molecular structure, spectroscopy, reaction dynamics, and many material properties[3][4]. - The development of quantum mechanics in the 1920s by pioneers like Schrödinger, Heisenberg, and Dirac gave birth to modern quantum chemistry[3]. - Quantum chemistry is essential to many modern technologies, including semiconductors, lasers, MRI, electron microscopy, and the understanding of biochemical systems like DNA[3]. - The field continues to advance, and quantum computing promises to revolutionize the simulation of complex chemical systems in the future[1]. Quantum chemistry is also enabling the development of novel qubits and materials for quantum technologies[1]. In summary, quantum chemistry is the essential bridge between quantum physics and chemistry. It provides the fundamental theory and computational tools to understand and engineer chemical systems at the deepest level. Citations: [1] [Quantum Chemistry | Chicago Quantum Exchange](https://chicagoquantum.org/research-areas/quantum-chemistry) [2] [Quantum Chemistry 0.1 - Introduction - YouTube](https://www.youtube.com/watch?v=HC81oYe43DI) [3] [Quantum mechanics - Wikipedia](https://en.wikipedia.org/wiki/Quantum_mechanics) [4] [Unit 1. Quantum Chemistry - Chemistry LibreTexts](https://chem.libretexts.org/Courses/Mount_Royal_University/Chem_1201/Unit_1:_Quantum_Chemistry) [5] [Quantum Mechanics – First Year General Chemistry](https://ecampusontario.pressbooks.pub/queenschem1/chapter/chapter-6-quantum-mechanics/) " "Consciousness refers to the state of being aware of one's own existence, sensations, thoughts, surroundings, and emotions[1][3]. At its simplest, it is the awareness of internal and external existence[1]. Key points about consciousness: - It is a complex and multifaceted concept that has been debated by philosophers and scientists for centuries[1][4]. There are around 40 different meanings attributed to consciousness based on functions and experiences[1]. - Consciousness involves an awareness of oneself and the world[3]. It enables abilities like thinking, reasoning, perceiving, feeling emotions, making choices and decisions[3][4]. - Different states of consciousness include waking consciousness, sleep, dreams, meditation, hypnosis, and drug-induced states[4]. Higher states like transcendence and "flow" are associated with spiritual experiences[4]. - Theories proposed to explain consciousness include integrated information theory, which focuses on the physical processes underlying consciousness, and global workspace theory, which proposes consciousness draws on a memory bank of information[4]. - Changes in consciousness can occur naturally or due to factors like brain damage and drugs[4]. Sudden changes may indicate a serious medical issue[4]. In summary, while consciousness is a universal human experience, its exact nature and mechanisms are still not fully understood. Research spanning neuroscience, psychology, philosophy and other fields aims to expand our understanding of this complex phenomenon that is central to the human mind and experience[1][3][4]. Citations: [1] [Consciousness - Wikipedia](https://en.wikipedia.org/wiki/Consciousness) [3] [Consciousness Definition & Meaning - Merriam-Webster](https://www.merriam-webster.com/dictionary/consciousness) [4] https://www.verywellmind.com/what-is-consciousness-2795922 [5] [Consciousness (Stanford Encyclopedia of Philosophy)](https://plato.stanford.edu/entries/consciousness/) " "Paraconsistent logic is a type of logic that allows for inconsistent or contradictory premises without leading to trivial or absurd conclusions. In other words, a paraconsistent logic rejects the classical principle of explosion, which states that from a contradiction, anything follows (*ex contradictione quodlibet*)[1][2][3]. The key features and motivations of paraconsistent logic include: 1. Allowing for inconsistent but non-trivial theories: There are apparently inconsistent theories in areas like the history of science that are nevertheless non-trivial. Paraconsistent logic provides a framework to reason about such theories without collapsing into absurdity[1]. 2. Invalidity of certain classical principles: Paraconsistent logics typically invalidate principles like *ex falso quodlibet* (from falsehood, anything follows), the law of non-contradiction, or disjunctive syllogism, while possibly retaining other principles like modus ponens[2][4]. 3. Philosophical and practical applications: Paraconsistent logic has drawn interest from philosophers, mathematicians, and computer scientists. It has potential applications in areas like modeling cognition, machine reasoning, and artificial intelligence[4][5]. There are many specific paraconsistent logics that have been developed, differing in their strength and the principles they satisfy or invalidate. Some major schools of thought include relevant logics, adaptive logics, and preservationism[1]. However, formulating suitable conditionals and dealing with paradoxes like the liar paradox remain ongoing challenges in the field[5]. Citations: [1] [Paraconsistent Logic (Stanford Encyclopedia of Philosophy)](https://plato.stanford.edu/entries/logic-paraconsistent/) [2] [paraconsistent logic in nLab](https://ncatlab.org/nlab/show/paraconsistent%2Blogic) [3] https://philarchive.org/archive/SZMWIA [4] [Paraconsistent Logic | SpringerLink](https://link.springer.com/chapter/10.1007/978-94-017-0460-1_4) [5] [Paraconsistent Logic | Internet Encyclopedia of Philosophy](https://iep.utm.edu/para-log/) " Researchers at Tsinghua University in China have developed a revolutionary new AI chip that uses light instead of electricity to process data. Dubbed “Taichi,” the chip is reportedly over 1,000 times more energy-efficient than Nvidia’s high performance H100 GPU chip [Light-based chip: China's Taichi could power artificial general intelligence](https://interestingengineering.com/science/taichi-light-based-chip) [Reddit - Dive into anything](https://www.reddit.com/r/singularity/comments/1c3pkv8/abundance_is_coming/) https://finance.yahoo.com/news/amazon-grows-over-750-000-153000967.html?guccounter=1 Solve everything Invariances are basis of everything Divergence so divergent that it converges to everything