Lifeboat Foundation

Joe Henrich is Professor of Human Evolutionary Biology at Harvard University and an author. Humans like to think that we’re sovereign individuals with agency over our preferences and actions. But we are also a part of our social environment and Joe has teased apart some fascinating trends which explain how our location and culture have huge impacts on the way we behave, our preferences on everything from dating to work and family life to religion. Expect to learn why the things we consider to be human nature could just be cultural conditioning, the dangerous future if there’s lots of sexless men, how the choice between growing rice and wheat impacts family life, what diplomatic immunity to parking tickets tells us about human nature, how Joe’s lab can use language to archaeologically tell us about social trends from history…

Joyce Benenson is a lecturer of Human Evolutionary Biology at Harvard University who’s research focuses on human social structures and sex differences in competition and cooperation. We’re often told that men are more competitive, more status-driven and more ruthless with rivals for potential mates. In reality doesn’t seem to be true, the difference is that women’s competition takes a more subtle, cynical and sophisticated route to drive away their competitors. Expect to learn how women compete for status, why women exclude more than men, why women who promote an egalitarian world are less charitable than you might think, how you can interfere with a rivals’ relationship without getting caught, the usefulness of gossip as an enforcement mechanism and much more…

Recently, economists and behavioral scientists have studied the pattern of human well-being over the lifespan. In dozens of countries, and for a large range of well-being measures, including happiness and mental health, well-being is high in youth, falls to a nadir in midlife, and rises again in old age. The reasons for this U-shape are still unclear. Present theories emphasize sociological and economic forces. In this study we show that a similar U-shape exists in 508 great apes (two samples of chimpanzees and one sample of orangutans) whose well-being was assessed by raters familiar with the individual apes. This U-shaped pattern or “midlife crisis” emerges with or without use of parametric methods. Our results imply that human well-being’s curved shape is not uniquely human and that, although it may be partly explained by aspects of human life and society, its origins may lie partly in the biology we share with great apes. These findings have implications across scientific and social-scientific disciplines, and may help to identify ways of enhancing human and ape well-being.

Recently, economists and behavioral scientists have studied the pattern of human well-being over the lifespan. In dozens of countries, and for a large range of well-being measures, including happiness and mental health, well-being is high in youth, falls to a nadir in midlife, and rises again in old age. The reasons for this U-shape are still unclear. Present theories emphasize sociological and economic forces. In this study we show that a similar U-shape exists in 508 great apes (two samples of chimpanzees and one sample of orangutans) whose well-being was assessed by raters familiar with the individual apes. This U-shaped pattern or “midlife crisis” emerges with or without use of parametric methods. Our results imply that human well-being’s curved shape is not uniquely human and that, although it may be partly explained by aspects of human life and society, its origins may lie partly in the biology we share with great apes. These findings have implications across scientific and social-scientific disciplines, and may help to identify ways of enhancing human and ape well-being.

Imagine brain scanning technology improves greatly in the coming decades, to the point that we can observe how each individual neuron talks to other neurons.

Then, imagine we can record all this information to create a simulation of someone’s brain on a computer.

This is the concept behind mind uploading – the idea that we may one day be able to transition a person from their biological body to a synthetic hardware.

This is the concept behind mind uploading – the idea that we may one day be able to transition a person from their biological body to a synthetic hardware. The idea originated in an intellectual movement called transhumanism and has several key advocates including computer scientist Ray Kurzweil, philosopher Nick Bostrom and neuroscientist Randal Koene.

The transhumanists’ central hope is to transcend the human condition through scientific and technological progress. They believe mind uploading may allow us to live as long as we want (but not necessarily forever). It might even let us improve ourselves, such as by having simulated brains that run faster and more efficiently than biological ones. It’s a techno-optimist’s dream for the future. But does it have any substance?

The feasibility of mind uploading rests on three core assumptions.

A research group has made new insights into how locomotion occurs in bacteria. The group identified the FliG molecule in the flagellar layer, the ‘motor’ of bacteria, and revealed its role in the organism. These findings suggest ways in which future engineers could build nanomachines with full control over their movements.

The researchers, who were led by Professor Emeritus Michio Homma and Professor Seiji Kojima of the Graduate School of Science at Nagoya University, in collaboration with Osaka University and Nagahama Institute of Bio-Science and Technology, published the study in iScience.

UCLA department of integrative biology and physiology.

Luskin Endowment for Leadership Symposium.

Continue reading “27 Michael Levin — Memory and intelligent problem-solving by unconventional collective intelligences” »

“In your machine-learning project, how much time will you typically spend on data preparation and transformation?” asks a 2022 Google course on the Foundations of Machine Learning (ML). The two choices offered are either “Less than half the project time” or “More than half the project time.” If you guessed the latter, you would be correct; Google states that it takes over 80 percent of project time to format the data, and that’s not even taking into account the time needed to frame the problem in machine-learning terms.

“It would take many weeks of effort to figure out the appropriate model for our dataset, and this is a really prohibitive step for a lot of folks that want to use machine learning or biology,” says Jacqueline Valeri, a fifth-year PhD student of biological engineering in Collins’s lab who is first co-author of the paper.

BioAutoMATED is an automated machine-learning system that can select and build an appropriate model for a given dataset and even take care of the laborious task of data preprocessing, whittling down a months-long process to just a few hours. Automated machine-learning (AutoML) systems are still in a relatively nascent stage of development, with current usage primarily focused on image and text recognition, but largely unused in subfields of biology, points out first co-author and Jameel Clinic postdoc Luis Soenksen PhD ‘20.