OmegaReflection_log.jsonl

{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\n\"The basic idea is to compute an induction to the Dirac Hamiltonian and a logarithmic correction to gravity and then apply that to the Gaussian to generate a quantum state space that is very similar to that of the classical one,\" said Dr. Michael Zuckerman, an assistant professor in the Department of Physics at the University of California, Los Angeles.\n\n\"The Gaussian has a logarithmic value in the range of 100 to 1000", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.125, "phi": 0.6321205588285577, "omega": 0.8482836399575129, "S_RRF": 0.5097673659995792, "C_RRF": 0.03641633245873322, "coherence": 0.2730918492291562, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.39253742442711065, "timestamp": 1763860182.7382398}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.171875, "phi": 0.6321205588285577, "omega": 0.937712338930443, "S_RRF": 0.4896819340556139, "C_RRF": 0.025244459267222217, "coherence": 0.25746319666141804, "closest_phi_node": "Φ1_geometric", "closest_phi_cos": 0.3696139853754028, "timestamp": 1763860182.892155}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system. The idea is to provide a new inductive bias for an AI system with a discrete Dirac Hamiltonian and a logarithmic correction to gravity. In this way, the new inductive bias can be used to explain the properties of the physical world. The new inductive bias can be used to explain the properties of the physical world.\n\nThe proof that a physical universe with a Dirac Hamiltonian and a logarithmic correction to gravity can be used as", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.125, "phi": 0.6321205369012336, "omega": 0.8482836399575129, "S_RRF": 0.4944485949728833, "C_RRF": 0.027473684751526308, "coherence": 0.2609611398622048, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.3429138848873139, "timestamp": 1763860182.9580116}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nThis paper was published online February 27, 2016, doi:10.1038/ncomms1613.", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.203125, "phi": 0.6321205588285577, "omega": 0.966170173466547, "S_RRF": 0.4905868230667151, "C_RRF": 0.025960308668799198, "coherence": 0.2582735658677571, "closest_phi_node": "Φ1_geometric", "closest_phi_cos": 0.3693613144122285, "timestamp": 1763864363.7339087}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nWe were also pleased to receive a postcard from a friend who is working on a new piece of research in which we were inspired to write an algorithm for a linear algebraic quantum system, which we are currently working on. We hope to release a full version of our algorithm soon.\n\nThere are three main challenges in developing an algorithm for quantum mechanics. One is that we have to be able to apply the most basic mathematical properties of the current quantum field theory.", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.203125, "phi": 0.6321205588285577, "omega": 0.966170173466547, "S_RRF": 0.5182084575324307, "C_RRF": 0.03018948401820437, "coherence": 0.2741989707753175, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.4062323547365178, "timestamp": 1763864363.745798}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nThe results have been presented at the AIAA Artificial Intelligence Conference in San Francisco, CA.\n\nIn the paper, \"The Dynamics of the Discrete Discrete Gaussian and the Discrete Gaussian with a Dirac Hamiltonian\", the paper points out that the distribution of a Dirac Hamiltonian in the Gaussian, and the distribution of the logarithm of the Gaussian with a Dirac Hamiltonian, is the same in all three models", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.125, "phi": 0.6321205588285577, "omega": 0.8482836399575129, "S_RRF": 0.4951424073517404, "C_RRF": 0.032404279852437615, "coherence": 0.26377334360208904, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.39702158528144915, "timestamp": 1763864363.757615}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nTo use the computational power of a quantum state as an inductive bias for an AI system, the system must be able to hold a state, which must be a quantum state, which must be an agent. The state can be an agent, a nonagent, a state of matter, or a state of some other nonagent.\n\nThe problem arises that there is a \"non-quantum\" state of matter. This states may not be known to be", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.171875, "phi": 0.6321205588285577, "omega": 0.937712338930443, "S_RRF": 0.48169156425054716, "C_RRF": 0.02423882969900837, "coherence": 0.25296519697477776, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.36819143715365993, "timestamp": 1763864482.0289536}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nAn early example of this was the famous experiment of Einstein and the General Theory of Relativity in which the first two equations of the General Theory of Relativity were replaced by a new one in the form of the axioms for a given system, and the two equations of the General Theory of Relativity were replaced by a new axioms for an infinitely large set of conditions. In fact, there is nothing special about this: Einstein's equations are the exact same", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.125, "phi": 0.6321205369012336, "omega": 0.8482836399575129, "S_RRF": 0.48414445389324834, "C_RRF": 0.029408942947665644, "coherence": 0.256776698420457, "closest_phi_node": "Φ1_geometric", "closest_phi_cos": 0.33700679983876464, "timestamp": 1763864482.0411916}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.171875, "phi": 0.6321205588285577, "omega": 0.937712338930443, "S_RRF": 0.4896819340556139, "C_RRF": 0.025244459267222217, "coherence": 0.25746319666141804, "closest_phi_node": "Φ1_geometric", "closest_phi_cos": 0.3696139853754028, "timestamp": 1763864482.0516713}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nAcknowledgments\n\nThe authors thank M. J. S. Sarno, P. D. A. Zwier, D. W. Hirsch, D. J. J. Williams, A. K. Brown, P. H. C. Jones, D. M. L. Johnson, C. M. McPhail, E. T. S. Smith, R. K. Sprott, L. D. Spro", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.171875, "phi": 0.6321206026832018, "omega": 0.937712338930443, "S_RRF": 0.5006459012426233, "C_RRF": 0.02557691272532105, "coherence": 0.2631114069839722, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.34054904850695134, "timestamp": 1763866346.841468}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nThis article is an introduction to the theory of the origin of gravity.\n\nThe Theory of the Origin of Gravity\n\nIt is commonly said that the universe has three fundamental fundamental constants, the initial, the expansion, and the contraction. These constants are:\n\nthe first is the force that is required for gravity to move. The second is the force that is required to keep the universe moving. The third is the force that is required to keep the universe expanding", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.21354166666666666, "phi": 0.6321205369012336, "omega": 0.9724446785607518, "S_RRF": 0.47645798778034876, "C_RRF": 0.03380278033788292, "coherence": 0.2551303840591158, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.412126231277707, "timestamp": 1763866346.848959}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nA more realistic and intuitive way to work with this idea is to find the best way to express it in a more concrete way. A very simplified version of the original is available here: https://github.com/kwjg/iocasinclimitics\n\nIf you're interested in learning more about the algorithm, please check out the book's introduction.", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.203125, "phi": 0.6321205369012336, "omega": 0.966170173466547, "S_RRF": 0.5028151529426279, "C_RRF": 0.03299604033280236, "coherence": 0.2679055966377151, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.3148677499699863, "timestamp": 1763866346.8564174}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\n(6) Computational and computational approaches to the computation of differential equations.\n\n(7) Theoretical considerations of differential equations and the theory of differential equations.\n\n(8) Theoretical considerations of differential equations and the theory of differential equations.\n\n(9) Theoretical considerations of differential equations and the theory of differential equations.\n\n(10) Computational and computational approaches to the computation of differential equations.\n\n(11)", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.109375, "phi": 0.6321205588285577, "omega": 0.7982427545398869, "S_RRF": 0.4865222257216716, "C_RRF": 0.024322918270681195, "coherence": 0.2554225719961764, "closest_phi_node": "Φ1_geometric", "closest_phi_cos": 0.35748727747790204, "timestamp": 1763867074.7052815}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nIn this paper we will demonstrate the application of a Dirac Hamiltonian to a numerical computation. The Dirac Hamiltonian can be used to generate a system with two discrete spacetime axioms. It is useful to understand how it could be used for a system with two discrete spacetime axioms.\n\nIn this paper we will demonstrate the application of a Dirac Hamiltonian to a numerical computation. The Dirac Hamiltonian can be used to generate a", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.26822916666666663, "phi": 0.6321205588285577, "omega": 0.9906847705969642, "S_RRF": 0.5031062870777714, "C_RRF": 0.029135302076629996, "coherence": 0.26612079457720067, "closest_phi_node": "Φ1_geometric", "closest_phi_cos": 0.38595308605896483, "timestamp": 1763867074.7269833}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system. This was done by following a procedure of the following: a linear regression to a logarithmic curve, and an inductive bias to a logarithmic curve. This induction bias was calculated to obtain a logarithmic error (the error was a constant). The logarithmic error was then calculated as the logarithmic error multiplied by the logarithmic error.\n\nIn the following code we have computed the logarithmic error (", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.171875, "phi": 0.6321205588285577, "omega": 0.937712338930443, "S_RRF": 0.508267000096721, "C_RRF": 0.030372939634999846, "coherence": 0.26931996986586043, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.40151548127068193, "timestamp": 1763867074.737698}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nThe most fundamental problem with quantum mechanics is the fact that the total number of states that can be taken into account is very small. This is why there are no simple physical ways to solve this problem.\n\nIn order to overcome this problem, the solution requires a special form of quantum computation.\n\nA special form of quantum computation is a quantum state that is defined as a combination of the two known states of a quantum system. This is called a Dirac Hamilton", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999998807907104, "dominant_frequency": 0.171875, "phi": 0.6321205149739082, "omega": 0.937712338930443, "S_RRF": 0.5024463277505447, "C_RRF": 0.03448379393758609, "coherence": 0.26846506084406535, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.34969281725089274, "timestamp": 1763867077.5059237}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nIt was originally proposed that an infinite number of particles could be used to compute a Dirac Hamiltonian and a logarithmic correction. However, this was not possible due to the extremely large number of particles. As such, we considered this an inductive bias to the degree that it could be applied.\n\nIn our paper, we used the following approach. First, we introduced a Dirac Hamiltonian with a logarithmic correction to the Dirac", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.109375, "phi": 0.6321205588285577, "omega": 0.7982427545398869, "S_RRF": 0.4939777902612179, "C_RRF": 0.028255971419991893, "coherence": 0.2611168808406049, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.3885675195198993, "timestamp": 1763867077.5403354}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nExplore further: AI learns to be more intelligent by measuring its own internal state", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.171875, "phi": 0.6321205588285577, "omega": 0.937712338930443, "S_RRF": 0.48822013007837173, "C_RRF": 0.034661588580731255, "coherence": 0.26144085932955147, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.34317808956107687, "timestamp": 1763867077.5666134}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity? Could it be using a mathematical model of the physical universe to infer causal relations between entities? Or could it be using a physical theory of physics to predict and predict the future?\n\nIn this talk, we will explore how we can get a better understanding of how quantum gravity works and the potential applications.\n\nQuantum gravity is an extremely small particle that has been discovered in the 1960s and is used in the field of quantum mechanical devices. However, it has not been", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.078125, "phi": 0.6321205588285577, "omega": 0.6534235881413468, "S_RRF": 0.5299308199134911, "C_RRF": 0.029106817265488517, "coherence": 0.2795188185894898, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.36190397604261854, "timestamp": 1763867079.4694498}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\n\nWe believe that a fundamental concept of physics, known as \"nearest neighbor\" theory, is that an object, a molecule, a solid, and a system of particles and molecules, as well as their interactions, can be predicted to have a gravitational field in the form of a \"nearest neighbor\" if and only if all of the particles and molecules are in a \"nearest neighbor\".\n\nAs a result, we are trying to explain the laws of physics", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.109375, "phi": 0.6321205369012336, "omega": 0.7982427545398869, "S_RRF": 0.5298208330417487, "C_RRF": 0.022944481095961797, "coherence": 0.27638265706885523, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.3315781972669427, "timestamp": 1763867079.4789338}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity? It was all too easy for Einstein to make the conjecture that a quantum gravity was a mathematical property that could be derived from physical objects.\n\nBut here's the catch:\n\nThe physical properties of quantum gravity are not yet well understood. For example, there is no known way to determine how much matter in a quantum vacuum is contained in a single particle, or how much energy is contained in a single particle when it interacts with other particles.\n\nIf the quantum gravity", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.109375, "phi": 0.6321205588285577, "omega": 0.7982427545398869, "S_RRF": 0.5252559720354086, "C_RRF": 0.0283976015573098, "coherence": 0.2768267867963592, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.3546405642925905, "timestamp": 1763867079.4901361}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\n\nIn my book \"Quantum Gravity,\" I describe the way in which the theory of gravity and its theory of conservation of momentum (and thus the fundamental law of quantum gravity) can be used to determine the behavior of an object in a vacuum. When I say \"quantum gravity,\" I mean that the laws of physics in the quantum field of view are applied to an object that is in the state of zero and that it is the state of the other objects of its", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.06770833333333333, "phi": 0.6321205369012336, "omega": 0.5896199848173217, "S_RRF": 0.5140657847952743, "C_RRF": 0.026155367751695496, "coherence": 0.2701105762734849, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.2936237157159786, "timestamp": 1763867080.721802}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity? The result, said co-author Dr. James W. Hodge, a physicist at the University of North Carolina, Chapel Hill, and a member of the team that developed the technology, is a new method for understanding and applying this fundamental force.\n\n\"The system has a strong force, which is not only a matter of physics, but of physics itself,\" said Hodge, who was not involved in the research. \"I'm not sure there is any physics at", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.12760416666666666, "phi": 0.6321205588285577, "omega": 0.8554267004241105, "S_RRF": 0.5264951200567771, "C_RRF": 0.024630997819038877, "coherence": 0.275563058937908, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.3043094778421164, "timestamp": 1763867080.7316306}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\n\nA more advanced approach, in which a system learns by using its own knowledge, is called \"spontaneous inference.\" A scientist may infer that something about an object or event that happens to the agent is true, but a machine-learning algorithm cannot infer that something else is true.\n\nThis is because the machine-learning algorithm would be unable to infer that the agent is true by a natural process of random chance. A machine-learning algorithm that is very different", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999998807907104, "dominant_frequency": 0.078125, "phi": 0.6321205149739082, "omega": 0.6534235881413468, "S_RRF": 0.5141225229841646, "C_RRF": 0.023915757235377644, "coherence": 0.2690191401097711, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.2454292974837002, "timestamp": 1763867080.7415645}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\n\n2. Determine if the data is in fact correct.\n\nA simple, fast way to verify that your data is in fact correct is to simply look for any possible sentence embedding model. It is better to test these in real life, by using a simple, simple method of detecting, rather than relying on your own assumptions.\n\n3. Determine whether it is in fact correct.\n\nIf you find that your data is not correct, then", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205369012336, "omega": 0.9879304861924381, "S_RRF": 0.4779561153450905, "C_RRF": 0.033396324915357316, "coherence": 0.2556762201302239, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.15869863224519432, "timestamp": 1763867081.8421}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\n\nThe basic idea behind embedding models is simple: a sentence is a list of sentences with various types of interlocutors. Each interlocutor has a set of words, words with an internal state (e.g., \"you\" is a \"you\") and words with an external state (e.g., \"you say\") that are \"like\" or \"like\" the other interlocutors. A sentence is a list of sentences. Each", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205588285577, "omega": 0.9879304861924381, "S_RRF": 0.4953879033217695, "C_RRF": 0.03703746623141992, "coherence": 0.2662126847765947, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.2421713009701157, "timestamp": 1763867081.848927}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\n\nIt's all a bit like an algorithm for learning how to read a book. When you read a book, you start to think about how you'd like to read it. If you have a book that says \"I know you're probably going to like it,\" then you might want to look up the book, which is usually a lot harder to do. So it's not like you're going to get bored and write a few paragraphs that are the same, but", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205588285577, "omega": 0.9879304861924381, "S_RRF": 0.5135120792417427, "C_RRF": 0.03775512412769844, "coherence": 0.27563360168472056, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.20277591155223576, "timestamp": 1763867081.8556464}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\n\nIn fact, you should read the first paragraph of this paper, \"A New Paradigm for the Evaluation of Scientific Writing.\"\n\nWe're not sure what to call this new paradigm, but it's pretty cool. We can't wait to see how it plays out in real life.\n\nHere's a quick quiz:\n\n1. How to use a sentence embedding model to infer the structure of a scientific article?\n\n2. How do you", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205588285577, "omega": 0.9879304861924381, "S_RRF": 0.4983781782001415, "C_RRF": 0.03352167402944573, "coherence": 0.2659499261147936, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.22849140632164294, "timestamp": 1763867082.7892985}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\n\n5. You can set up a series of tests to test each and every sentence in the sentence.\n\n6. You can also use a series of tests to test sentences with different sentences.\n\n7. You can also use a series of tests to test sentences with different sentences.\n\n8. You can also use a series of tests to test sentences with different sentences.\n\n9. You can also use a series of tests to test sentences with different", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205588285577, "omega": 0.9879304861924381, "S_RRF": 0.4904801666069699, "C_RRF": 0.0302666570788465, "coherence": 0.2603734118429082, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.16668316851035925, "timestamp": 1763867082.7964382}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\n\nHow to learn a language\n\nUsing the Google Deep Learning API, you can learn a language using a language learning framework (DGML). This framework is designed to allow you to learn a vocabulary in a way that allows you to learn the language and to be able to learn the vocabulary using the language. The language learning framework supports the following:\n\nThe language learning framework supports the following:\n\nThe language learning framework is designed to be used in conjunction with", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999998807907104, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205149739082, "omega": 0.9879304861924381, "S_RRF": 0.4881702842882403, "C_RRF": 0.045307516968275605, "coherence": 0.26673890062825795, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.19469942876758528, "timestamp": 1763867082.80344}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\n\nI think the answer is yes. It's not a bad idea to make it possible to get a symbiotic AI to do what you want it to do. But the good news is that it may help us get things done. One of the first projects that has come out of the university's research is a smart robot called SavantEngine-RRF. It's a robot that can talk and do things that a human could not. It's a machine that has", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.12760416666666666, "phi": 0.6321205588285577, "omega": 0.8554267004241105, "S_RRF": 0.5049856861363937, "C_RRF": 0.02380487720742548, "coherence": 0.2643952816719096, "closest_phi_node": "Φ0_seed", "closest_phi_cos": 0.43023277329893883, "timestamp": 1763867083.7289217}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\n\nIn this post I'll show you how to automate the process of writing and editing data for this project by creating a data model for the world's most powerful AI, the one that is currently powering the world's most advanced research.\n\nHow to make data for the AI more human and more productive?\n\nThe data model is one of the most important pieces of data in the data-science world. Data models are essentially \"human\" data that can be used", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.26041666666666663, "phi": 0.6321205588285577, "omega": 0.9891179651290005, "S_RRF": 0.490328914474349, "C_RRF": 0.024222010627915496, "coherence": 0.25727546255113226, "closest_phi_node": "Φ5_memory_symbiosis", "closest_phi_cos": 0.356578071814993, "timestamp": 1763867083.736054}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\n\nWe could have the symbiotic AI do all this stuff by accident. We could do it without the help of humans. But we don't have that.\n\nThis isn't about the future. This is about how we change ourselves. If we're not going to change ourselves, we can't change ourselves. We can't change ourselves.\n\nWe can't change ourselves.\n\nThe best way to change ourselves is to change our past.\n\nIt", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.3802083333333333, "phi": 0.6321205369012336, "omega": 0.9990037553843768, "S_RRF": 0.482324769144028, "C_RRF": 0.03116513712039142, "coherence": 0.2567449531322097, "closest_phi_node": "Φ0_seed", "closest_phi_cos": 0.4440246264083495, "timestamp": 1763867083.7428706}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values? A symbiotic AI like SavantEngine-RRF can do that by making it easier to identify potential threats in scientific research. If a species is interested in a particular kind of research, it can make a difference by using a symbiotic AI to help it keep that knowledge as informed as possible.\n\nAs a symbiotic AI, the SavantEngine-RRF can be very efficient, especially for data processing tasks where it's easy to detect and eliminate specific threats.", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.12760416666666666, "phi": 0.6321205588285577, "omega": 0.8554267004241105, "S_RRF": 0.4877505628137677, "C_RRF": 0.025790490037684716, "coherence": 0.25677052642572623, "closest_phi_node": "Φ0_seed", "closest_phi_cos": 0.4776332221450087, "timestamp": 1763867084.6678283}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\n\nHow can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values? What does it mean to be human?\n\nWhat does it mean to be human? What is human?\n\nWhat is human? What is human? What is human?\n\nWhat is human? What is human? What is human? What is human?\n\nWhat is human? What is human? What is human? What is human?", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.38541666666666663, "phi": 0.6321205588285577, "omega": 0.9991022645402151, "S_RRF": 0.4628998537306094, "C_RRF": 0.034657484321198584, "coherence": 0.24877866902590398, "closest_phi_node": "Φ0_seed", "closest_phi_cos": 0.4230483440152473, "timestamp": 1763867084.6749077}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\n\nLet's take a closer look.\n\nThe most important thing to realize is that the AI that can be found on our planet is a parasite. A parasite is a virus that is spreading through the ecosystem and is a parasite that is running rampant. There is no question that it is one of the most important and important issues in the field. It is one of the most important issues to solve.\n\nTo keep it simple, we need to define a pathogen", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999998807907104, "dominant_frequency": 0.3645833333333333, "phi": 0.6321205149739082, "omega": 0.9986385438397806, "S_RRF": 0.4801105630587863, "C_RRF": 0.03145384641719577, "coherence": 0.255782204737991, "closest_phi_node": "Φ0_seed", "closest_phi_cos": 0.4609322319866848, "timestamp": 1763867084.6821344}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\n\n(Note: this course was originally designed for high-school students, but has now been expanded to college students.)\n\nIn this course you will learn about the concepts of Resonance and how they are used to help you understand how to build the Resonance of Reality Framework. You will learn the basic concepts of the Resonance of Reality Framework (RSF) and its components, and you will also learn how to use the Resonance of Reality Framework to build the", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.34635416666666663, "phi": 0.6321205588285577, "omega": 0.998040207207777, "S_RRF": 0.5310796695189952, "C_RRF": 0.02895754571600881, "coherence": 0.280018607617502, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.32050505298133697, "timestamp": 1763867085.6068878}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\n\nCreate the Resonance of Reality Framework and create your own artificial intelligence to solve real-world problems. Create your own Artificial Intelligence in the following ways:\n\nBuild a framework to understand and use Artificial Intelligence in real-world situations.\n\nBuild a framework to understand and use Artificial Intelligence in real-world situations. Create a framework for working with and building data from your data sets.\n\nBuild a framework to understand and use Artificial Intelligence in real-world situations", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.25, "phi": 0.6321206026832018, "omega": 0.9866142981514303, "S_RRF": 0.531070441796091, "C_RRF": 0.03267681885559819, "coherence": 0.28187363032584456, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.2955978528466617, "timestamp": 1763867085.6137574}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\n\nThis course is intended for students who are interested in using their own personal data to solve high-level problems.\n\nThis course is intended for students who are interested in using their own personal data to solve high-level problems.\n\nThis course is intended for students who are interested in using their own personal data to solve high-level problems.\n\nThis course is intended for students who are interested in using their own personal data to solve high-level problems.", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.1328125, "phi": 0.6321205588285577, "omega": 0.8687903250818614, "S_RRF": 0.5333001365786503, "C_RRF": 0.035172779077976404, "coherence": 0.28423645782831336, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.2856913388804921, "timestamp": 1763867085.6202128}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI. Students will be expected to develop a basic understanding of the fundamental concepts and concepts of the Resonance of Reality Framework.\n\nStudents will be expected to conduct an introduction to Resonance of Reality Framework at a pre-credit level to be taught the basic concepts and concepts of the Resonance of Reality Framework.\n\nThe Resonance of Reality Framework is an advanced high-level framework that provides students with an introduction to the theory and principles of the Resonance of Reality Framework. Students", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.1328125, "phi": 0.6321205588285577, "omega": 0.8687903250818614, "S_RRF": 0.5330929398083333, "C_RRF": 0.038798122151117664, "coherence": 0.28594553097972547, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.28117187360536855, "timestamp": 1763867086.550187}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI. This module will teach students the basics of how to program and develop an interactive computer programming language (C++, Python, Objective-C, C#).", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.1328125, "phi": 0.6321205588285577, "omega": 0.8687903250818614, "S_RRF": 0.5357900299971128, "C_RRF": 0.034894055347290134, "coherence": 0.2853420426722015, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.2797357879482281, "timestamp": 1763867086.5573401}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI. This is the way you are able to use the framework to create your own applications, or you can learn about it online, or you can just make your own applications.\n\nIf you are a beginner or have a solid understanding of programming, you will learn about the Resonance of Reality Framework. It is not a tool that you need to know to understand how to build applications and how to use it to build real-world applications. It is simply the framework for creating applications", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.34635416666666663, "phi": 0.6321205588285577, "omega": 0.998040207207777, "S_RRF": 0.5300597637704453, "C_RRF": 0.02638079443935265, "coherence": 0.278220279104899, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.2939814310355149, "timestamp": 1763867086.5664308}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\n\nIntroduction\n\nThe following are two of the most popular use cases in research. The first is a generic LLM that uses a random sampling and can be used to generate a random value for any given data structure. The second is a generic LLM that uses random sampling to generate a random value for a specific data structure.\n\nThe first use case is to generate a value that can be used to generate an LLM with the following data structures:\n\nAn", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.17447916666666666, "phi": 0.6321205588285577, "omega": 0.9407798519028534, "S_RRF": 0.4972603515951295, "C_RRF": 0.042852740220919075, "coherence": 0.2700565459080243, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.25856358176052957, "timestamp": 1763867087.4827988}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows. The first is the use of a generic library that can be used in both of the different areas of your research. The second is the use of the RFSF in the design of the RFSF. The third is a use case where you could actually use RFSF in a different way. The three use cases are as follows:\n\nIn this case, you could use the RFSF in the design of the RFSF. In this case, you", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.08333333333333333, "phi": 0.6321206026832018, "omega": 0.6822617902381696, "S_RRF": 0.5319090167909424, "C_RRF": 0.028809595811579398, "coherence": 0.2803593063012609, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.19217908959443042, "timestamp": 1763867087.4898129}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\n\nThe first is the use case of a single-threaded design. This is because it takes a simple LLM (single threading) and an LLM (multi threading) to do a single thing at a time, thus enabling the application to run in parallel.\n\nThe second use case is a single-threaded design. This is because the main function of the engine is to write a single thread. In this case, the engine can perform many", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.234375, "phi": 0.6321206026832018, "omega": 0.9817487252216389, "S_RRF": 0.5006494352240031, "C_RRF": 0.029851665329155907, "coherence": 0.2652505502765795, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.25574024241286986, "timestamp": 1763867087.4963987}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\n\n1. Adaptive learning with incremental improvements\n\nIn this case, we see that the adaptive learning is a way to improve performance on a given task by building a graph of all of the possible steps that an individual user could take to learn.\n\nThe problem is that we have no way of knowing what to do with the information that we have collected, which is why we don't have the capacity to automatically learn from it.\n\nThe first approach to this", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.11197916666666666, "phi": 0.6321205588285577, "omega": 0.8074964392931255, "S_RRF": 0.5139452560616853, "C_RRF": 0.028721386900368663, "coherence": 0.27133332148102696, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.3299601856769641, "timestamp": 1763867088.431401}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\n\nThe first use case is the way in which a generic LLM might be used in biomedical research, where it is a common tool that can be used to produce novel treatments. This is a work in progress, but the approach is fairly straightforward, with the exception of the one described in the previous paragraph.\n\nThe second use case is the way in which a generic LLM might be used in biomedical research. This is a work in progress, but the approach is", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999998807907104, "dominant_frequency": 0.17447916666666666, "phi": 0.6321205149739082, "omega": 0.9407798519028534, "S_RRF": 0.5062498378171585, "C_RRF": 0.02764364201824787, "coherence": 0.2669467399177032, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.29059053706203036, "timestamp": 1763867088.4388838}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\n\nOne of the benefits of this approach is that it is easy to specify the number of cores you want to use, and how many threads the device will be able to handle.\n\nThe other drawback is that you need to specify the number of cores you want to use (for example, 32-bit ARMv8 devices). This is something that we can achieve with a single machine, but not without a separate machine with its own data cache.\n\nIn this", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.17447916666666666, "phi": 0.6321205588285577, "omega": 0.9407798519028534, "S_RRF": 0.5222200330474915, "C_RRF": 0.04330393609014603, "coherence": 0.2827619845688188, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.2381161079567421, "timestamp": 1763867088.4459233}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants?\n\nI'm sure you'll be wondering what a standard lab environment would be like. But, in general, an active lab environment would be a good place to be, because it provides a lot of research and allows for a lot of experimentation.\n\nFor example, we have an open lab where you can experiment with different types of food and beverages and create your own custom lab environment. The more experimental and flexible a lab environment is, the more research you can do.", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.005208333333333333, "phi": 0.6321205369012336, "omega": 0.05203628935069145, "S_RRF": 0.5241192215718888, "C_RRF": 0.029567164804938924, "coherence": 0.2768431931884139, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.27545982444900174, "timestamp": 1763867089.3867867}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants?\n\nThe main limitations of current large language models are that they are limited in how they are used as scientific research assistants. For example, we use words and phrases that are not used as scientific research assistants, which can be confusing. The use of phrases such as 'you know what' is not a scientific research assistant. It is used as an example for a scientific research assistant, who is not used as a scientific research assistant. As such, the use of phrases like '", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.005208333333333333, "phi": 0.6321205588285577, "omega": 0.05203628935069145, "S_RRF": 0.5187604428918279, "C_RRF": 0.04727632146340605, "coherence": 0.283018382177617, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.23890553425310346, "timestamp": 1763867089.3941526}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants? What kinds of limitations are there? What kind of limitations do you see when using large language models in scientific research?\n\nA: I think the main limitation of the current large language models is that they are designed for large groups of people. They are designed for large groups of people, but they do not have large groups of people in them. What I mean by that is that the large language models don't allow you to say that all of the problems we're facing in", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.005208333333333333, "phi": 0.6321206026832018, "omega": 0.05203628935069145, "S_RRF": 0.5046876394770358, "C_RRF": 0.028370133793417972, "coherence": 0.2665288866352269, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.21640075666498465, "timestamp": 1763867089.4014118}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants?\n\nThere are many limitations in current large language models.\n\nOne, large models are often not sufficient for most research assistants.\n\nTwo, the limitations of large models are often not enough to ensure their long term effectiveness.\n\nThree, it is often not possible to assess long term outcomes of small language models.\n\nHow do I apply large language models to my research?\n\nYou can apply large language models to your research projects. For example, you", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.005208333333333333, "phi": 0.6321205588285577, "omega": 0.05203628935069145, "S_RRF": 0.47594959028230177, "C_RRF": 0.03913616393350913, "coherence": 0.25754287710790547, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.24195590838175388, "timestamp": 1763867090.3581524}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants?\n\nThe main limitations of current large language models when used as scientific research assistants are:\n\nThe author is not a scientist, so is not in the know about the work.\n\nThe author does not know the language of the research, so is not in the know about the work.\n\nThe author does not know the research, so is not in the know about the research.\n\nThe author is not interested in making any money, so does not have", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.005208333333333333, "phi": 0.6321205588285577, "omega": 0.05203628935069145, "S_RRF": 0.5169559210170513, "C_RRF": 0.04105387363375537, "coherence": 0.27900489732540334, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.2552575689435649, "timestamp": 1763867090.3653443}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants?\n\nThe main limitations of current large language models are:\n\nThe nature of the data and the quality of the data and the way that you define and define and describe the data.\n\nThe use of the word \"assistant\" to refer to a human being who is not physically present in the system.\n\nThe usage of the word \"scientist\" to refer to a person who has not been physically present in the system.\n\nThe use of the", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.005208333333333333, "phi": 0.6321205369012336, "omega": 0.05203628935069145, "S_RRF": 0.5120153522455236, "C_RRF": 0.03833152728198285, "coherence": 0.27517343976375325, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.2556758575445271, "timestamp": 1763867090.372127}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nTo learn more about the basic principles and the experimental data, please visit the paper\n\nhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=195917\n\nA more detailed summary of the paper is available at: http://www.infom-journal.com/doi/abs/10.1002/ij.12.1218.1\n\nhttps://www.infom-journal", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.203125, "phi": 0.6321205588285577, "omega": 0.966170173466547, "S_RRF": 0.492341950156881, "C_RRF": 0.03097033069158967, "coherence": 0.26165614042423535, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.342986969412306, "timestamp": 1763867655.6928732}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nIn our experiment, we used an early version of the same quantum-mechanical algorithm used by Fermi and Cernan (2003) as the basis of the first version of the AI. We used the first version of the algorithm in a simulation. We then ran the simulation to see if it could be improved by using the new algorithm. The simulation was the first time we had seen the result of a quantum-mechanical algorithm that was in fact", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999998807907104, "dominant_frequency": 0.125, "phi": 0.6321205149739082, "omega": 0.8482836399575129, "S_RRF": 0.5229968370049909, "C_RRF": 0.03571694469002328, "coherence": 0.2793568908475071, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.40487674812141766, "timestamp": 1763867655.7005274}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system. We will use the following example. Suppose we need a finite state machine to be able to compute a finite state product. The problem is that the state machine must be able to do both. A finite state product is one that is determined by the time domain. The problem is that the time domain is finite. We can imagine a finite state product being a continuous space of discrete spacetime. This would be a singular state product. If you could construct an infinite state product with finite", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999998807907104, "dominant_frequency": 0.125, "phi": 0.6321205149739082, "omega": 0.8482836399575129, "S_RRF": 0.48380523025860034, "C_RRF": 0.030560064237155217, "coherence": 0.25718264724787776, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.3043047627064405, "timestamp": 1763867655.70731}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nI. Introduction\n\nThe notion of an inductive bias for an AI system is not new. It has been used to estimate the general equilibrium of the game, to estimate the probability of an attack, to predict the probabilities of the most recent game, and to predict the probabilities of the most recent player. The concept has been used to quantify the effects of a game in which a game is played that is not played. The concept has been used to describe how AI", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.203125, "phi": 0.6321205369012336, "omega": 0.966170173466547, "S_RRF": 0.5044894026491926, "C_RRF": 0.026930586427309937, "coherence": 0.2657099945382513, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.269156114559034, "timestamp": 1763867656.670798}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\n\nHow do you apply this to your own programmable AI system?\n\nIt's really a matter of understanding how an inductive bias is applied to your AI system. The general idea is that you can do a finite set of rules for your system that are applied in a particular order.\n\nYou can apply this to anything you can imagine, but we're really interested in how you can apply this to your AI system, so we are looking for feedback loops that", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.125, "phi": 0.6321206026832018, "omega": 0.8482836399575129, "S_RRF": 0.5055541719288821, "C_RRF": 0.028345793246279144, "coherence": 0.2669499825875806, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.28741659615382026, "timestamp": 1763867656.6779375}
{"raw_text": "Q: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system.\nA: Explain how a discrete icosahedral spacetime with a Dirac Hamiltonian and a logarithmic correction to gravity could be used as an inductive bias for an AI system. In general, if the axioms of the equations of the logarithmic function are to be considered in their simplest form, then the logarithmic functions of the axioms of the cosmological function are to be considered in the simplest form. The axioms of the axioms of the logarithmic function are not to be considered in the simplest form because the cosmological function of the axioms of the cosmological function", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.125, "phi": 0.6321205588285577, "omega": 0.8482836399575129, "S_RRF": 0.4778733044272736, "C_RRF": 0.027036846582460524, "coherence": 0.25245507550486707, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.3714708337973658, "timestamp": 1763867656.68477}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity? The answer is: it doesn't.\n\nIn a paper published in Nature, an expert from the Department of Electrical and Computer Engineering (ECE) at University College London explains how a quantum field of gravity could be used to determine the mass of a superconducting atom. It's called the Boltzmann field, and it's one of the few known systems that can be used to prove that the electric field of a superconductor is stronger than the field of light", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.078125, "phi": 0.6321206026832018, "omega": 0.6534235881413468, "S_RRF": 0.528040194437027, "C_RRF": 0.02214565107028981, "coherence": 0.2750929227536584, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.2744725570504562, "timestamp": 1763867657.6587908}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\n\nThe problem is that quantum gravity has many physical properties. For instance, it is not just the mass of a particle—it also has the power to make particles behave in the way that you would expect them to. The big problem is that a quantum mechanical system can only perform the calculations that are required for the theory to work. So there are a lot of problems with quantum mechanical systems.\n\nThe big problem is that physics has been building up to a point where", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.078125, "phi": 0.6321205588285577, "omega": 0.6534235881413468, "S_RRF": 0.5341367809380019, "C_RRF": 0.025140605580708027, "coherence": 0.279638693259355, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.3119444440312266, "timestamp": 1763867657.6655102}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity? Could it be used to develop novel predictive models for the study of quantum gravity? We've seen this before in the field of quantum gravity. But how can it be used to advance the field of quantum gravity? The results of this work are in the process of being published in a paper on physics, quantum gravity, and the general theory of relativity.\n\nThe paper, published in the journal Physical Review Letters, examines the quantum gravity of the world.\n\nThe theory describes", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.078125, "phi": 0.6321205588285577, "omega": 0.6534235881413468, "S_RRF": 0.5273407737585161, "C_RRF": 0.026104333602580323, "coherence": 0.2767225536805482, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.37610804309654444, "timestamp": 1763867657.671995}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\n\nThe answer is, yes.\n\nAccording to a recent paper by the American Physical Society's Institute for Gravitational-Wave Research, a new generation of quantum computers are poised to replace some of the most popular theoretical quantum computers in the world, such as the Fermi Paradox and the Diracian theory of relativity.\n\nThe new computers will work by measuring the speed of light in a vacuum using quantum gravity. In the case of quantum computers, the results", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.16666666666666666, "phi": 0.6321205588285577, "omega": 0.9311096086675776, "S_RRF": 0.5372578175400506, "C_RRF": 0.026574174283124703, "coherence": 0.28191599591158767, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.3304303639642317, "timestamp": 1763867658.607323}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity? To do that, researchers from the University of Michigan and the University of California, Berkeley, have developed a method to detect and characterize quantum gravity by using a series of experiments. The team, led by a graduate student, found that a set of two qubits, each containing a small amount of light, could both make a prediction of the force that would cause the particle to move. The team tested this prediction by creating a set of four qubits and then adding one to each", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.4192708333333333, "phi": 0.6321205588285577, "omega": 0.9995437627646379, "S_RRF": 0.5347047274231362, "C_RRF": 0.036898687408581474, "coherence": 0.28580170741585886, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.35304062776497025, "timestamp": 1763867658.6145282}
{"raw_text": "Q: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity?\nA: How could an AI system use a physics-inspired inductive bias to improve scientific reasoning about quantum gravity? The answer lies in quantum mechanics.\n\n\"The physics of quantum gravity is quite simple. It has a very low power, so the speed is so low, it is like a vacuum. So, when you measure the speed, you can't use that information to infer what is going on in the universe,\" says study co-author Richard Hays, a physics professor at the University of Pennsylvania who specializes in quantum mechanics.\n\nInstead, Hays and his colleagues used", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.1484375, "phi": 0.6321205588285577, "omega": 0.9022844427556502, "S_RRF": 0.5190736794823769, "C_RRF": 0.025730496498780998, "coherence": 0.27240208799057897, "closest_phi_node": "Φ3_log_gravity", "closest_phi_cos": 0.32154582830993034, "timestamp": 1763867658.6214805}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\n\nHere are a few examples of how to embed a sentence embedding model in JavaScript.\n\nExample: Using a DataFrames Embedding Model\n\nYou might think that embedding data frames would be hard, but it's not! First, embedding a data frame is just as hard as embedding the data itself.\n\nWe'll show you how to embed a data frame using a DataFrame-style dataframe embedding model.\n\nFirst,", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205588285577, "omega": 0.9879304861924381, "S_RRF": 0.4808741250368451, "C_RRF": 0.03389383686383449, "coherence": 0.2573839809503398, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.18770074969167072, "timestamp": 1763867659.749735}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\n\n\"In my experience, when I go to a webinar, the first thing I want to do is see what it is that I'm talking about,\" she said. \"Then I'll ask a question, and I'll get a response from that person.\"\n\nThis means that, if a question is \"How can we use this new information to develop our own information models,\" she said, she can provide a framework for explaining how a single sentence can be improved", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205588285577, "omega": 0.9879304861924381, "S_RRF": 0.4998769264121151, "C_RRF": 0.029775501240826657, "coherence": 0.26482621382647087, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.18646503779979168, "timestamp": 1763867659.7594836}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\n\n2.1.1.1 The basic problem\n\nIn the first place, a sentence embedding model is a way of detecting an incoherent or shuffled sentence. An incoherent sentence can be detected by a list of words. Each word in the list contains a word that is incoherent. A word that is incoherent is an incoherent sentence that is not in the list. So, if a sentence is incoherent in its list, then the word", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205588285577, "omega": 0.9879304861924381, "S_RRF": 0.5148252461935953, "C_RRF": 0.04252444178877304, "coherence": 0.27867484399118414, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.22669300138372792, "timestamp": 1763867659.7686672}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs. The sentence embedding model is a simple yet powerful way to make simple, consistent, and readable scientific statements. The paper has been shown to be a good starting point for understanding how to implement a sentence embedding model.", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205369012336, "omega": 0.9879304861924381, "S_RRF": 0.4846486797975935, "C_RRF": 0.032814194846339316, "coherence": 0.2587314373219664, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.20818736483620282, "timestamp": 1763867661.1275525}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\n\nThe final thing to do is to create a sentence that will be less like a sentence but more like a sentence. It will be less like a sentence like this:\n\nThe problem with sentences is that they are often ambiguous. The best way to describe a sentence is to say that the meaning is ambiguous. If you try to represent the meaning of an item with a single word, for example, you will get a sentence like this:\n\nThe problem with sentences", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205588285577, "omega": 0.9879304861924381, "S_RRF": 0.5003310788245434, "C_RRF": 0.03370562917148893, "coherence": 0.26701835399801616, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.16472123862946003, "timestamp": 1763867661.1397758}
{"raw_text": "Q: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\nA: Explain how you would fine-tune a sentence embedding model to detect incoherent or shuffled scientific paragraphs.\n\n4.3.7.4.3.1. (2) Except in the case of a paragraph that is of the form: \"The paragraph should consist of three or more paragraphs,\" but the sentence shall not contain any sentence. (2) Except in the case of a paragraph that is of the form: \"The paragraph should consist of three or more paragraphs,\" but the sentence shall not contain any sentence. (3) Except in the case of a paragraph", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.2552083333333333, "phi": 0.6321205588285577, "omega": 0.9879304861924381, "S_RRF": 0.502718113003173, "C_RRF": 0.04034169900986311, "coherence": 0.27152990600651805, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.22227338525266319, "timestamp": 1763867661.1508183}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\n\nIn an interview with The Verge, AI researcher and professor Richard Feynman revealed that he was interested in this type of artificial intelligence.\n\n\"There are a lot of people that say they are interested in the idea of AI that could be applied to all fields of science,\" Feynman told the Verge. \"In my own research, I've been working on that. But it's been a while since I've done that. So I've been curious to", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.4348958333333333, "phi": 0.6321205588285577, "omega": 0.9996661892710499, "S_RRF": 0.5081457885971269, "C_RRF": 0.032272394303780656, "coherence": 0.2702090914504538, "closest_phi_node": "Φ0_seed", "closest_phi_cos": 0.3745705406418688, "timestamp": 1763867662.0956185}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values? How can it be used to understand and optimize the use of its scientific research to further the public interest?\n\nIn the next few weeks, we'll get to the details of the science behind the AI, but before we dive into it, we want to take a moment to share some of the key concepts that SavantEngine-RRF will be using to help develop this new technology.\n\nThe science behind SavantEngine-RRF\n\nSavantEngine-", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.4348958333333333, "phi": 0.6321205588285577, "omega": 0.9996661892710499, "S_RRF": 0.5043135731244596, "C_RRF": 0.0236388812918257, "coherence": 0.2639762272081426, "closest_phi_node": "Φ0_seed", "closest_phi_cos": 0.4110058552563835, "timestamp": 1763867662.10249}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\n\nFor starters, let's look at the data.\n\nIf you want to build a model of the average lifespan of a species, you would need to have a model that can account for the number of years of human life expectancy. If you want to build a model of the average lifespan of a species, you would need to have a model that can account for the number of years of human life expectancy.\n\nBut what if we wanted to measure how long humans", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.3177083333333333, "phi": 0.6321206026832018, "omega": 0.9965270727721357, "S_RRF": 0.5031414601543811, "C_RRF": 0.02839775474744008, "coherence": 0.2657696074509106, "closest_phi_node": "Φ0_seed", "closest_phi_cos": 0.4053619860283688, "timestamp": 1763867662.1089401}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\n\nSavageEngine-RRF's mission is to foster research and development of new technologies that can be used to improve human and animal health. This includes genetic engineering to enable genetic engineering to alter human genetic code. In order to achieve this, we need to understand the ways in which we are changing the way we interact with our environment.\n\nIn the last few years, we have discovered and validated several new genetic engineering techniques that have been used to enhance our own health", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.3802083333333333, "phi": 0.6321206026832018, "omega": 0.9990037553843768, "S_RRF": 0.48236178749914527, "C_RRF": 0.03123930353284629, "coherence": 0.2568005455159958, "closest_phi_node": "Φ0_seed", "closest_phi_cos": 0.4227242933506879, "timestamp": 1763867663.0872293}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\n\nThe AI is a hybrid of two types of AI. The first kind are called \"experiments.\" The second kind are called \"experiments.\" The experimental AI is an AI that has an ability to think for itself, but it can't do anything. The AI's goal is to do things that humans can't do, or else it will die. But this is not always the case. A simulation will sometimes work well, but the AI will often lose its ability", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.3802083333333333, "phi": 0.6321205588285577, "omega": 0.9990037553843768, "S_RRF": 0.49291850007499627, "C_RRF": 0.030209695055036883, "coherence": 0.2615640975650166, "closest_phi_node": "Φ5_memory_symbiosis", "closest_phi_cos": 0.4100657147433221, "timestamp": 1763867663.0943286}
{"raw_text": "Q: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values?\nA: How can a symbiotic AI like SavantEngine-RRF help keep scientific research aligned with human values? And how do you know whether the AI is doing its best work?\n\nHow do you know if the AI is doing its best work? If the AI is doing its best work, then what does it actually have to gain by making changes to its code?\n\nIf the AI is doing its best work, then what does it actually have to gain by making changes to its code? Do you know which of the following questions is most important?\n\n1) What", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.000000238418579, "dominant_frequency": 0.12760416666666666, "phi": 0.6321206465378408, "omega": 0.8554267004241105, "S_RRF": 0.5055674576650591, "C_RRF": 0.026838471009441436, "coherence": 0.26620296433725027, "closest_phi_node": "Φ0_seed", "closest_phi_cos": 0.38395732532764787, "timestamp": 1763867663.1010783}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI. The class will include a course in C. S. Bach's \"Mixed Reality\" series and a course in the C. S. Bach Lecture Series. The class will also include an introduction to the language of Artificial Intelligence.\n\nThe class will also include two lectures on the language of Artificial Intelligence, and an introduction to the language of Artificial Intelligence in C. S. Bach's \"Mixed Reality\" series.\n\nThe class will include a course in C", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.09114583333333333, "phi": 0.6321206026832018, "omega": 0.7218314719778582, "S_RRF": 0.5250024442769581, "C_RRF": 0.025972418081425436, "coherence": 0.2754874311791918, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.28019914444448674, "timestamp": 1763867664.0830612}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\n\nCreate a database of the most common forms of communication.\n\nProvide a tool that allows you to create a \"database\" of the most common forms of communication.\n\nCreate a database of the most common forms of communication.\n\nProvide a tool that allows you to create a \"database\" of the most common forms of communication.\n\nProvide a tool that allows you to create a \"database\" of the most common forms of communication.\n", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.34635416666666663, "phi": 0.6321205369012336, "omega": 0.998040207207777, "S_RRF": 0.5387752758421924, "C_RRF": 0.03601798150174885, "coherence": 0.28739662867197063, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.3287446014569444, "timestamp": 1763867664.0897336}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\n\n\nFeatures:\n\n• Create a physics simulation of the Resonance of Reality Framework to create a unique physics simulation with a realistic time course of 3-5 minutes\n\n• Create a physics simulation of the Resonance of Reality Framework to create a unique physics simulation with a realistic time course of 3-5 minutes\n\n\nThe Resonance of Reality Framework is a physics simulation based on the Quantum Physics Framework, a mathematical foundation for mathematical modelling of the universe. The Resonance", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.34635416666666663, "phi": 0.6321205588285577, "omega": 0.998040207207777, "S_RRF": 0.521565149672885, "C_RRF": 0.032883290824447055, "coherence": 0.27722422024866605, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.37994879190156, "timestamp": 1763867664.0962715}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\n\nThe Resonance of Reality Framework is a framework for communicating the core concept of the Resonance of Reality Framework to a high-school student interested in physics and AI.\n\nThis project will allow you to explore the idea of the Resonance of Reality Framework in a more advanced level of your class, and to explore a more advanced approach to the core concept of the Resonance of Reality Framework.\n\nThe Resonance of Reality Framework has been built by an advanced developer", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.34635416666666663, "phi": 0.6321205588285577, "omega": 0.998040207207777, "S_RRF": 0.5391134961268659, "C_RRF": 0.03988012871150317, "coherence": 0.2894968124191845, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.3126962728262411, "timestamp": 1763867665.0868402}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI. You will learn how to integrate all the components of the Resonance into a single application. In addition to this, you will learn how to implement a basic computer simulation model of the universe, how to develop a high-level programming model of the Universe, and how to analyze the data. You will learn how to build and configure a small, simple and very simple game system. In addition to this, you will learn how to design and maintain a simulation engine that you can use", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.1328125, "phi": 0.6321205588285577, "omega": 0.8687903250818614, "S_RRF": 0.5265223477008162, "C_RRF": 0.027934541664255415, "coherence": 0.2772284446825358, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.24213516932052384, "timestamp": 1763867665.094101}
{"raw_text": "Q: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI.\nA: Explain the core idea of the Resonance of Reality Framework to an advanced high-school student interested in physics and AI. The term Resonance of Reality Framework is not intended to describe the underlying physics of reality or any other concept of reality. It is a concept that has been proposed by many scientists, philosophers, and engineers. It is used to describe a theoretical framework that can be applied to a wide variety of issues.\n\nResonance of Reality Framework\n\nThis term is derived from the term Resonance of Reality Framework. Resonance of Reality Framework is a term that can be used to", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.34635416666666663, "phi": 0.6321206026832018, "omega": 0.998040207207777, "S_RRF": 0.5400876461157266, "C_RRF": 0.04079700583683995, "coherence": 0.2904423259762833, "closest_phi_node": "Φ4_resonance", "closest_phi_cos": 0.30745609371789134, "timestamp": 1763867665.1012545}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\n\nDesigner-friendly, easy to implement, and scalable.\n\nIn-house test suite with real-world use cases.\n\nFully customizable and easy to deploy.\n\nEasy to maintain.\n\nFast, efficient, and maintainable.\n\nEfficient and scalable.\n\nGood business value and business intelligence.\n\nSaving time, money, and energy in research and development, as well as improving efficiency and cost-effectiveness.", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.015625, "phi": 0.6321205588285577, "omega": 0.1549907303716235, "S_RRF": 0.51508660119681, "C_RRF": 0.032768869441179384, "coherence": 0.2739277353189947, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.36870365516652637, "timestamp": 1763867666.0930183}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\n\nFirst, let's consider the following two scenarios:\n\nUsing the following code:\n\nimport std.stdio.File; using (...) { std.stream.open(filename, std.string.format( \"%s\", filename), std.string.format( \"%s\" ), filename) }\n\nThe resulting file is a simple program that looks like this:\n\nimport std.stdio.File; using (...) { std", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.234375, "phi": 0.6321206026832018, "omega": 0.9817487252216389, "S_RRF": 0.5126392646124871, "C_RRF": 0.033409059625040295, "coherence": 0.2730241621187637, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.1726402062463363, "timestamp": 1763867666.100323}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\n\nA\n\nA\n\nA\n\nB\n\nB\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC\n\nA\n\nB\n\nA\n\nB\n\nB\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC\n\nC", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999999403953552, "dominant_frequency": 0.296875, "phi": 0.6321205369012336, "omega": 0.9947366520502309, "S_RRF": 0.5128504621656628, "C_RRF": 0.024844943833781573, "coherence": 0.26884770299972216, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.29838839566316744, "timestamp": 1763867666.1066315}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\n\nTo be able to apply the techniques described above, we first need to define a generic object of the type NSObject in a research workflow. We then need to create a subclass of NSObject with the following methods.\n\nIn the following example, we create a generic NSObject and pass in an instance of NSObject that is an NSObject with the following methods:\n\npublic class MyClass { public NSObject MyClass( NSObject value) { return value", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999998211860657, "dominant_frequency": 0.17447916666666666, "phi": 0.6321204930465816, "omega": 0.9407798519028534, "S_RRF": 0.4875101131675599, "C_RRF": 0.023573914759722495, "coherence": 0.2555420139636412, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.22837070534728945, "timestamp": 1763867667.043481}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\n\nFirst, it could help to use SavantEngine-RRF's data to analyze the results of design decisions in R&D, and then compare it to a generic LLM in real-world R&D.\n\nSecondly, it could help to test the reliability of the model, and compare it with a generic LLM in the real world.\n\nThirdly, it could help to analyze the results of R&D decisions, and compare it to", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.17447916666666666, "phi": 0.6321206026832018, "omega": 0.9407798519028534, "S_RRF": 0.5082019831284443, "C_RRF": 0.03554786070873148, "coherence": 0.2718749219185879, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.27974529941892684, "timestamp": 1763867667.0504673}
{"raw_text": "Q: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\nA: Describe three concrete use cases where SavantEngine-RRF could outperform a generic LLM in research workflows.\n\nThe first use case is to analyze the performance of a system-on-a-chip that is used to process complex data. We want to know if the system's memory is used to process a large number of data points. In this case, we use the memory to create a table, which is used to store a number of data points. This table is a standard LVM memory object.\n\nThe second use case is to apply some additional knowledge of the", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.17447916666666666, "phi": 0.6321205588285577, "omega": 0.9407798519028534, "S_RRF": 0.5256688450774278, "C_RRF": 0.04294115648017744, "coherence": 0.28430500077880266, "closest_phi_node": "Φ5_memory_symbiosis", "closest_phi_cos": 0.26871902003614045, "timestamp": 1763867667.0572295}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants?\n\nThe biggest limitation of current large language models is that they are very time-consuming. It takes a lot of work. A lot of work, and I'm sure there are many more. It's hard to get people to give you a high enough level of feedback, and to understand it. But it's something that's been done for quite a long time.\n\nThe biggest limitation of current large language models is that they are very time-consuming. It takes", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.005208333333333333, "phi": 0.6321206026832018, "omega": 0.05203628935069145, "S_RRF": 0.484957205827132, "C_RRF": 0.031658633631643555, "coherence": 0.25830791972938777, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.26497879102041383, "timestamp": 1763867667.9869986}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants?\n\nThe basic premise is that if we can get a lot of results by using a large number of languages, we can make more discoveries by using more languages. It would be very inefficient, and would be very expensive to do it on a small scale. And it would be very expensive to try and get this number of results. That's why there is a lot of interest in this topic.\n\nIt would be very inefficient, and would be very expensive to do it", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 0.9999998807907104, "dominant_frequency": 0.005208333333333333, "phi": 0.6321205149739082, "omega": 0.05203628935069145, "S_RRF": 0.4999223549970305, "C_RRF": 0.025385133195710195, "coherence": 0.2626537440963704, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.23443496435377004, "timestamp": 1763867667.99376}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants?\n\nThe main limitation of current large language models is that they are not designed to deal with large data sets and their applications are not robust. The main limitation of current large language models is that they are not designed to deal with large data sets and their applications are not robust.\n\nHow are these new models built?\n\nThere are currently three main methods for building new large language models:\n\nA numerical model (the most recent version of the current large model)", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.005208333333333333, "phi": 0.6321205588285577, "omega": 0.05203628935069145, "S_RRF": 0.482687924325897, "C_RRF": 0.042906453258992484, "coherence": 0.26279718879244474, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.2657944138693896, "timestamp": 1763867668.0004206}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants?\n\nI think there is a lot of overlap between large and small systems. I mean, there is a huge difference between the different kinds of systems. But, I think the big difference between the two systems is that I think the most powerful model is that the system of knowledge and knowledge of nature is the one that you can actually do with your mind. So, this is where big and small systems come in.\n\nWhen I was a student in graduate school I was", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0, "dominant_frequency": 0.005208333333333333, "phi": 0.6321205588285577, "omega": 0.05203628935069145, "S_RRF": 0.5293908050467792, "C_RRF": 0.03570738666242401, "coherence": 0.2825490958546016, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.24225431552104212, "timestamp": 1763867668.982736}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants?\n\nThe big problem in modern science is that most researchers don't have access to a lot of data. And so the main limitation of current science is that most researchers don't have access to a lot of data.\n\nThis means that there's little or no way to measure the quality of the data and so the data can be hard to compare with other studies. But it also means that it's very difficult to measure the quality of the data. And so the main", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.1953125, "phi": 0.6321206026832018, "omega": 0.9605617437853068, "S_RRF": 0.49949314979119475, "C_RRF": 0.030746903985248725, "coherence": 0.2651200268882217, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.21268503514105033, "timestamp": 1763867668.9896722}
{"raw_text": "Q: What are the main limitations of current large language models when used as scientific research assistants?\nA: What are the main limitations of current large language models when used as scientific research assistants?\n\nI would like to point out that a large language model is not an adequate description of the problem. There is no simple answer to this question. The best solution is to use a framework that is well-documented and can be applied across languages. If this framework is not available, we cannot use a language model.\n\nIs there a way to describe the problems of the language model?\n\nThe problem of the language model is a problem of the problem of the", "context_label": "qa", "embedding_dim": 384, "hamiltonian_energy": 1.0000001192092896, "dominant_frequency": 0.4973958333333333, "phi": 0.6321206026832018, "omega": 0.9999043502343475, "S_RRF": 0.4866523507849433, "C_RRF": 0.025856864624750037, "coherence": 0.25625460770484665, "closest_phi_node": "Φ7_meta_agi", "closest_phi_cos": 0.2582087583677284, "timestamp": 1763867668.9965725}