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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.

We developed Significant Latent Factor Interaction Discovery and Exploration (SLIDE), an interpretable machine learning approach that can infer hidden states (latent factors) underlying biological outcomes. These states capture the complex interplay between factors derived from multiscale, multiomic datasets across biological contexts and scales of resolution.

We can’t tell much about their nature, but now we know early lifeforms included different types of organisms.

In this first article in a series on philosophy and science, we take a look at materialism and why it is fundamental to science.

A short disclaimer before we read further: I’m a materialist. Materialism is a branch of philosophy to which the sciences, particularly the physical and life sciences, owe a lot. Materialism posits that the material world — matter — exists, and everything in the Universe, including consciousness, is made from or is a product of matter. An objective reality exists and we can understand it. Without materialism, physics, chemistry, and biology as we know it wouldn’t exist.

Another branch of philosophy, idealism, is in direct contradiction to materialism. Idealism states that, instead of matter, the mind and consciousness are fundamental to reality; that they are immaterial and therefore independent of the material world.

New models predict biological age more robustly by pinpointing different age-related changes and distinguishing between cause and effect.

A collaborative project has made a breakthrough in enhancing the speed and resolution of widefield quantum sensing, leading to new opportunities in scientific research and practical applications.

By collaborating with scientists from Mainland China and Germany, the team has successfully developed a quantum sensing technology using a neuromorphic vision sensor, which is designed to mimic the human vision system. This sensor is capable of encoding changes in fluorescence intensity into spikes during optically detected magnetic resonance (ODMR) measurements.

The key advantage of this approach is that it results in highly compressed data volumes and reduced latency, making the system more efficient than traditional methods. This breakthrough in quantum sensing holds potential for various applications in fields such as monitoring dynamic processes in biological systems.