Lifeboat Foundation

Alex Rosenberg is professor of Philosophy at Duke University and has made several important contributions to the philosophy of science, biology, and social science.

0:00 intro.
2:53 scientism.
5:09 naturalism and the manifest image.
7:25 pragmatism.
10:40 intentionality.
12:38 objections to eliminativism and truth.
14:35 consciousness.
16:50 biological functions, purposes, and the selected effects theory.
22:28 reductionism.
28:05 causality.
31:02 multiple realizability.
35:13 math.
39:45 morality.
44:51 humanism, art, and history.

Continue reading “Thing in itself” »

Alex Rosenberg is the R. Taylor Cole Professor of Philosophy at Duke University. His research focuses on the philosophy of biology and science more generally, mind, and economics.

/ friction.
/ discord.
/ frictionphilo.

Continue reading “Alex Rosenberg | Intentionality, Evolution, and More” »

Eyes with lower pigment (blue or grey eyes) don’t need to absorb as much light as brown or dark eyes before this information reaches the retinal cells. This might provide light-eyed people with some resilience to SAD.

Other theories propose it happens due to an imbalance in serotonin and melatonin in the body. Serotonin makes us feel energetic, while the release of melatonin makes us feel sleepy. Since melatonin is made from serotonin, people with SAD may potentially produce too much melatonin during the winter months, leaving them feeling lethargic or down.

All these studies are inconsistent and, in some cases, contradictory. But because SAD is likely due to a combination of many biological and physiological factors working together, these different explanations for what causes SAD may well be interconnected.

Continue reading “How your eye color might increase your risk of seasonal affective disorder” »

An international team of researchers, led by Swinburne University of Technology, demonstrated what it claimed is the world’s fastest and most powerful optical neuromorphic processor for artificial intelligence (AI). It operates faster than 10 trillion operations per second (TeraOPs/s) and is capable of processing ultra-large scale data.

The researchers said this breakthrough represents an enormous leap forward for neural networks and neuromorphic processing in general. It could benefit autonomous vehicles and data-intensive machine learning tasks such as computer vision.

Artificial neural networks can ‘learn’ and perform complex operations with wide applications. Inspired by the biological structure of the brain’s visual cortex system, artificial neural networks extract key features of raw data to predict properties and behaviour with unprecedented accuracy and simplicity.

Do the impacts of deforestation go beyond the environment? What about human health, specifically the health of children? This is what a recent study published in Economics & Human Biology hopes to address as Dr. Gabriel Fuentes Cordoba, who is an associate professor of economics from Sophia University in Japan, investigated how deforestation in Cambodia effects the health of children around the time of their birth. This study holds the potential to help scientists, conservationists, and the public better understand the health effects of deforestation, specifically with the increasing effects of climate change around the world.

For the study, Dr. Fuentes Cordoba analyzed data obtained from the Cambodian Demographic Health Surveys and forest loss to ascertain the health impacts for pregnant women and children under five years of age who reside in areas of deforestation. In the end, Dr. Fuentes Cordoba discover alarming results that suggest deforestation exposure to women less than one year before pregnancy could lead to development of anemia, which is a precursor to malaria. This could result in significant health impacts on children being born, specifically reductions in birth weight, along with overall height and weight as they age.

“This research shows a negative impact of deforestation on child health,” Dr. Fuentes Cordoba said in a statement. “This negative impact may persist into adulthood and affect other aspects of wellbeing such as education acquisition and even wages. My findings indicate that future research should explore this aspect further.”

https://www.youtube.com/watch?v=tFx_UNW9I1U&si=QxOgeE59dOkGDFck

This definitely is a Lifeboat post embodying what Lifeboat is about, and it’s only about AI. They did a really good job explaining the 10 stages.

This video explores the 10 stages of AI, including God-Like AI. Watch this next video about the Technological Singularity: • Technological Singularity: 15 Ways It…
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Continue reading “The 10 Stages of Artificial Intelligence” »

While Artificial Intelligence (AI) focuses on simulating and surpassing human intelligence, Artificial Life (A-Life) takes a different approach. Instead of replicating cognitive abilities, A-Life seeks to understand and model fundamental biological processes through software, hardware, and even… wetware.

Forget Turing tests and chess games. A-Life scientists don’t care if their creations are “smart” in the traditional sense. Instead, they’re fascinated by the underlying rules that govern life itself. Think of it as rewinding the movie of evolution, watching it unfold again in a digital petri dish.

Explaining isolated steps on the road from simple chemicals to complex living organisms is not enough. Looking at the big picture could help to bridge rifts in this fractured research field.

Summary: A new study combines deep learning with neural activity data from mice to unlock the mystery of how they navigate their environment.

By analyzing the firing patterns of “head direction” neurons and “grid cells,” researchers can now accurately predict a mouse’s location and orientation, shedding light on the complex brain functions involved in navigation. This method, developed in collaboration with the US Army Research Laboratory, represents a significant leap forward in understanding spatial awareness and could revolutionize autonomous navigation in AI systems.

The findings highlight the potential for integrating biological insights into artificial intelligence to enhance machine navigation without relying on GPS technology.