Lifeboat Foundation

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.

Elon Musk is hyping the imminent release of his ChatGPT competitor Grok, yet another example of how his entire personality is just itself a biological LLM made by ingesting all of Reddit and 4chan. Grok already seems patterned in many ways off of the worst of Elon’s indulgences, with the sense of humor of a desperately unfunny and regressive internet troll, and biases informed by a man whose horrible, dangerous biases are fully invisible to himself.

There are all kinds of reasons to be wary of Grok, including the standard reasons to be wary of any current LLM-based AI technology, like hallucinations and inaccuracies. Layer on Elon Musk’s recent track record for disastrous social sensitivity and generally harmful approach to world-shaping issues, and we’re already looking at even more reason for concern. But the real risk probably isn’t yet so easy to grok, just because we have little understanding yet of the extent of the impact that widespread use of LLMs across our daily and online lives will have.

One key area where they’re already having and are bound to have much more of an impact is user-generated content. We’ve seen companies already deploying first-party integrations that start to embrace some of these uses, like Artifact with its AI-generated thumbnails for shared posts, and Meta adding chatbots to basically everything. Musk is debuting Grok on X as a feature reserved for Premium+ subscribers initially, with a rollout supposedly beginning this week.

Fio Omenetto, Silklab, Tufts University.

Transistors were a pivotal innovation in the evolution of electronic technology, and they have played a critical part in the miniaturization and advancement of electronic equipment. However, they could be even better.

According to the team, the main advantage of Brainnetome is that synthesizes various repositories of information taken from various imaging techniques and experimental methodologies, allowing it to better represent brain connectivity at different scales and modalities across the brain’s 246 subregions, including structural and functional patterns between them.

Brain atlases “can help us understand how brain regions are interconnected and how they interact at various levels of granularity,” explained the team. “Such insights are crucial for modeling brain dynamics and simulating complex neural processes.”