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Human beings routinely help strangers at costs to themselves. Sometimes the help offered is generous—offering more than the other expects. The proximate mechanisms supporting generosity are not well-understood, but several lines of research suggest ...

neral role in behavioral adjustments. We hypothesized, therefore, that the anterior cingulate cortex is also involved in the ‘framing effect’. Our hypothesis was tested by using a binary attractiveness judgment task (‘liking’ versus ‘nonliking’) during functional magnetic resonance imaging. We found that the framing-related anterior cingulate cortex activity predicted how strongly susceptible an individual was to a biased response. Our results support the hypothesis that paralimbic processes are crucial for predicting an individual's susceptibility to framing....

By combining the models and tasks of Game Theory with modern psychological and neuroscientific methods, the neuroeconomic approach to the study of social decision-making has the potential to extend our knowledge of brain mechanisms involved in ...

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This episode is sponsored by Thryve. Get 50% off your at home gut health test when you go to https://trythryve.com/impacttheory Scientific break-throughs are happening all around you. As technology advances, biologists such as the Chief Science Officer of the SENS Research Foundation, Aubrey de Grey, is leading the way to pioneering tech that will allow you to choose how long you want to live. For years scientists have been trying to find a way to slow down the aging process, but Aubrey introduces the idea of repairing the damage that aging does on the body to theoretically restore the body’s biological age to maybe 30 years younger. If you are familiar with the obsession I’ve had with living forever, you already know how much this excites me. SHOW NOTES: Quality of Life | Aubrey on why a long life depends on the quality and being invested in choice [0:58] Reverse Aging | Why damage repair could be easier than slowing aging & the push back met [4:40] Indefinite Life | Aubrey on why the result of expected life for reversed aging could be indefinite [8:08] Structure Repair | Landing on the idea of repairing structure to restore cellular level function [9:32] Pushback | Aubrey on pushback among scientists about reverse aging in biology [11:17] Body Damage | Aubrey on self-inflicted damage being reversed to same level 30 years prior [14:13] Longevity Escape Velocity | Aubrey on his theory how to reverse biological age 30 years [16:26] Types of Damage | 7 categories of damage that correspond to therapeutic methods of repair [21:18] Stem Cell Therapy | Aubrey explains how stem cells could treat loss of cell problems [22:17] Cancer Treatments | Aubrey gives category of damage due to too many cells [23:26] Senescent Cells | Aubrey explains how these cells can promote cancer [26:53] Mitochondrial Mutations | Aubrey explains problems at the molecular level inside cells [28:53] Cellular Waste | Aubrey breaks down how cellular waste over years impacts old age [33:35] Macular Degeneration | Aubrey explains specific enzymes that could prevent blindness [35:24] Excretion | Aubrey explains diseases that could be resolved by breaking down waste [37:50] Alzheimer’s | Breaking down amyloid as extracellular waste and modest benefit [39:22] Advancing Therapies | Aubrey gives sobering guess how close effective therapies are [44:07] QUOTES: “I want to make sure that my choice about how long to live, and, of course, how high quality that life will be, is not progressively taken away from me by aging.” [2:24] “Old age is something that evolution doesn't care about at all. Evolution only cares about the propagation of genetic information.” [34:41] “you've got to fix them all. You haven't got to fix any of them perfectly. But you've got to fix them all pretty well.” [43:28] “what excites me is typically the breakthroughs that would take me half an hour background to describe why it's even important.” [46:16] Guest Bio: Dr. de Grey is the biomedical gerontologist who devised the SENS platform and established SENS Research Foundation to implement it. He received his BA in Computer Science and Ph.D. in Biology from the University of Cambridge in 1985 and 2000, respectively. Dr. de Grey is Editor-in-Chief of Rejuvenation Research, is a Fellow of both the Gerontological Society of America and the American Aging Association Follow Aubrey De Grey: Website: https://www.sens.org/ Twitter: https://twitter.com/aubreydegrey LinkedIn: https://www.linkedin.com/in/aubrey-de-grey-24260b/ Dive Deeper On Related Episodes: Reset Your Age with David Sinclair https://youtu.be/IEz1P4i1P7s Lifestyle For Longevity with Kellyann Petrucci https://youtu.be/l9QO0JlnU8w Secrets To Longevity https://youtu.be/Ulm01gzU8rU

Alexis’s research proposes to investigate computational design rules to rationally install biochemical energy driven dynamic and mechanical behavior within de novo protein nanostructures, by tailoring the energy landscape to capture favorable thermal fluctuations allowing to perform work (i.e. rationally designing a Brownian ratchet mechanism using energy from the catalysis of a biologically orthogonal small molecule to break symmetry). As a proof of concept, he is focusing on the de novo design of protein rotary motors, in which symmetric energy minima along an interface between multiple components couples rotation to a catalytic event, thereby converting the biochemical energy of a fuel molecule into work.

We implement a bigram character-level language model, which we will further complexify in followup videos into a modern Transformer language model, like GPT....

Artificial agents have achieved great success in individual challenging simulated environments, mastering the particular tasks they were trained for, with their behaviour even generalising to maps and opponents that were never encountered in training. In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges. We define a universe of tasks within an environment domain and demonstrate the ability to train agents that are generally capable across this vast space and beyond. The environment is natively multi-agent, spanning the continuum of competitive, cooperative, and independent games, which are situated within procedurally generated physical 3D worlds. The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem. We propose an iterative notion of improvement between successive generations of agents, rather than seeking to maximise a singular objective, allowing us to quantify progress despite tasks being incomparable in terms of achievable rewards. Training an agent that is performant across such a vast space of tasks is a central challenge, one we find that pure reinforcement learning on a fixed distribution of training tasks does not succeed in. We show that through constructing an open-ended learning process, which dyna