{"title": "Ellimist", "logoImg": "bafkreickchssq6o33zuw23vtdsglratqvygavl6mhonnjybpgx7exftigi", "website": "https://ellimist.ai", "bannerImg": "bafkreiewtirvf2jqiyvf5gi4wu7fjwggc36iktmwhcznuuxrm7tf2cfwnq", "createdAt": 1682121510603, "credentials": {}, "description": "At a global scale, humanity needs to learn to create more life. This requires that leaders and builders innovate from their hearts, and it requires that we address specific pivotal risks and opportunities.\n\nEllimist integrates transdisciplinary expertise in service of a wiser global human civilization, one that reaches deeply into the past, present, and future to empower life and creativity, rather than control. We build the technical, conceptual, and qualitative foundations necessary to avoid catastrophic and existential risks and advance alignment between tech, humans, and nature at a global scale.\n\nWe are particularly focused on research, training, and innovation for  wisdom cultivation, AI alignment, bio/tech mutualism at all scales, catastrophic risk surveillance, and cause discovery.\n\nOur current projects focus on increasing the empirical tractability of wisdom, approaching population ethics with complex moral pluralism, making generative patterns in nature more machine readable, and gaining insight into causal reasoning in large language models.\n\n### Project 1: Maximizing Relevance\nOne approach we are taking to defining wisdom with greater empirical tractability is to build on the work of cognitive scientist John Vervaeke on relevance realization and wisdom. Relevance realization is the cognitive process in organisms that leads them to detect and attend to what is most meaningful to them relative to their goals. Vervaeke defines wisdom as “rationality transcending itself rationally so as to greatly enhance one’s ability to realize relevance.” Relevance realization may be equivalent to the experience of insight, as defined by neuroscientist Ruben Laukkonen. Though relevance realization is mostly unconscious, it is possible it can be improved through deliberate practice. If both of these are true, then the success of such practice may be measurable using Laukkonen’s methodology for measuring the frequency and intensity of insight experiences. This would contribute to empirically verifiable methods for training wisdom.\n\n### Project 2: A Wise Approach to the Repugnant Conclusion\nParfit’s Repugnant Conclusion takes seriously the core ethical motivation of utilitarianism but leads to discomforting results. The same structural features of the argument underlie other, more immediate, concerns that utilitarianism encourages elitism, racism, and eugenics. This project raises problems with relatively simple approaches to the problem and builds a more nuanced, dynamic, and useful view that integrates both justice and utilitarian motivations simultaneously. These issues include the ontological status of consciousness, the relationships between identity across scales, practical measurement challenges, methodological questions about how to correctly aggregate valence, long-term discounting, fundamental uncertainty, and the ethics of measurement.\n\n### Project 3: More Natural than Nature\nEvolutionary processes result in complex resilience and opportunities for thriving across very diverse participants. Of course, such processes also result in species and ecosystem collapse. We may be able to formalize the difference and use this to design more resilient infrastructure. Our previous work built on resilience metrics in network ecology to suggest three candidate scale-free design objectives - fractality, coherence, and autopoiesis - and a prototype application for monitoring supply chain resilience. This project will assess the relatively value of different definitions of these terms across multiple industries and applications. We’re especially interested in applying this to AI safety to formalize more generative patterns on which to train AI in order to generate outputs that may at least support greater complexity, even if not yet specifically human values.\n\n### Project 4: Detecting Causal Reasoning in Large Language Models \nPublicly available large language models respond to original questions about causal situations with accurate (and inaccurate) explanations of the probable causal relationships involved, including predicting future outcomes in hypothetical circumstances. Understanding whether causal reasoning is already an emergent capability of LLMs is extremely important given that some AI scientists and engineers, including Judea Perl, believe we will not have built transformative AI until it can reason causally. This project advances emerging work in mechanistic interpretability assessing the ability of LLMs to reason causally.\n\n### Team\nEllimist is managed by Zach Schlosser (https://www.linkedin.com/in/zacharyschlosser). Project teams are composed of researchers with domain expertise in fields relevant to each project.\n\n### Contact\nFor more information contact zacharyschlosser@gmail.com.", "logoImgData": {}, "bannerImgData": {}}