![]() Considering uncertainty represented within our models, our result is robust: reverting the conclusion required simultaneously changing the 3-5 most important parameters to the pessimistic ends. Another version of the model based on one of the authors produced ∼93% and ∼99% confidence, respectively. ![]() ![]() One version of the model populated partly by a survey of global catastrophic risk researchers finds the confidence that resilient foods is more cost effective than artificial general intelligence safety is ∼84% and ∼98% for the 100 millionth dollar spent on resilient foods and at the margin now, respectively. Using Monte Carlo (probabilistic) models, we analyze the long-term cost-effectiveness of resilient foods (alternative foods) - roughly those independent of sunlight such as mushrooms. Global agricultural catastrophes, which include nuclear winter and abrupt climate change, could have long-term consequences on humanity such as the collapse and nonrecovery of civilization. The extent of this list illustrates that there is no one simple solution to the problem of AI safety, and that AI safety theory is complex and must be customized for each AI development level. Overall, we identified around several dozen scenarios of AI-driven global catastrophe. AI could also halt at later stages of its development either due to technical glitches or ontological problems. Global catastrophic failure could happen at various levels of AI development, namely, (1) before it starts self-improvement, (2) during its takeoff, when it uses various instruments to escape its initial confinement, or (3) after it successfully takes over the world and starts to implement its goal system, which could be plainly unaligned, or feature-flawed friendliness. Our classification demonstrates that the field of AI risks is diverse, and includes many scenarios beyond the commonly discussed cases of a paperclip maximizer or robot-caused unemployment. We show that at each level of AI’s intelligence power, separate types of possible catastrophes dominate. This classification allows the identification of several new risks. AIAIĮnables spectral data of SNe to be quantitatively analyzed under theoreticalįrameworks based on well-defined physical assumptions.A classification of the global catastrophic risks of AI is presented, along with a comprehensive list of previously identified risks. SNe Ia with broader light curves showing larger 56Ni mass in the envelope. The massĭeduced from AIAI is correlated to the light-curve shapes of SNe Ia, with the The AIAI reveals a spectral signature near 3890 Åwhich can be identified asīeing produced by multiple Ni II lines between 39 Å. Input for the sample is found to agree with the theoretical 56Ni decay rate. The 56Ni mass derived from AIAI using the observed spectra as Observations for which the decay of the radioactive 56Ni can be tested Photosphere for a sample of 153 well-observed SNe Ia. The neural networks areĪpplied to derive the mass of 56Ni in velocity ranges well above the (SNe Ia) between 10 and 40 days after the explosion. (Kerzendorf & Sim 2014) to simulate the optical spectra of Type Ia supernovae Networks based on the one-dimensional radiative transfer code TARDIS (AIAI) of supernova analyses (Chen et al. Download a PDF of the paper titled Artificial Intelligence Assisted Inversion (AIAI): Quantifying the Spectral Features of $^$Ni of Type Ia Supernovae, by Xingzhuo Chen and 3 other authors Download PDF Abstract: Following our previous study of Artificial Intelligence Assisted Inversion
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