Lifeboat Foundation

About six years ago, scientists discovered a new type of more powerful neural network model known as a transformer. These models can achieve unprecedented performance, such as by generating text from prompts with near-human-like accuracy. A transformer underlies AI systems such as ChatGPT and Bard, for example. While incredibly effective, transformers are also mysterious: Unlike with other brain-inspired neural network models, it hasn’t been clear how to build them using biological components.

Now, researchers from MIT, the MIT-IBM Watson AI Lab, and Harvard Medical School have produced a hypothesis that may explain how a transformer could be built using biological elements in the brain. They suggest that a biological network composed of neurons and other brain cells called astrocytes could perform the same core computation as a transformer.

This video covers digital immortality, its required technologies, processes of uploading a mind, its potential impact on society, and more. Watch this next video about the world in 2200: https://bit.ly/3htaWEr.
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CHAPTERS
00:00 Required Technologies.
01:42 The Processes of Uploading a Mind.
03:32 Positive Impacts On Society.
05:34 When Will It Become Possible?
05:53 Is Digital Immortality Potentially Dangerous?

Continue reading “The Future of Digital Immortality [Documentary]” »

Philosophers have wrestled with the question of whether we are truly free to decide on our actions for centuries. Now, insights from genetics, neuroscience and evolutionary biology are shedding fresh light on the issue.

By Clare Wilson

Engineers and chemists at Lawrence Livermore National Laboratory (LLNL) and Meta have developed a new kind of 3D-printed material capable of replicating characteristics of biological tissue, an advancement that could impact the future of “augmented humanity.”

In a paper recently published in the journal Matter, LLNL and Meta researchers describe a framework for creating a “one-pot” 3D-printable resin in which light is used to pattern smooth gradients in stiffness to approximate gradients found in biology, such as where bone meets muscle.

The framework addresses a key challenge in developing more lifelike wearables: “mechanical mismatch.” Whereas natural tissues are soft, electronic devices are usually made of rigid materials and it can be difficult and time-consuming to assemble such devices using traditional means.

Continue reading “LLNL and Meta engineers develop 3D-printed material with potential for more lifelike wearables” »

Current Biology. They trained Caribbean box jellyfish (Tripedalia cystophora) to learn to spot and dodge obstacles. The study challenges previous notions that advanced learning requires a centralized brain and sheds light on the evolutionary roots of learning and memory.

No bigger than a fingernail, these seemingly simple jellies have a complex visual system with 24 eyes embedded in their bell-like body. Living in mangrove swamps, the animal uses its vision to steer through murky waters and swerve around underwater tree roots to snare prey. Scientists demonstrated that the jellies could acquire the ability to avoid obstacles through associative learning, a process through which organisms form mental connections between sensory stimulations and behaviors.

It lets researchers extract pixel-by-pixel information from nanoscale.

The nanoscale refers to a length scale that is extremely small, typically on the order of nanometers (nm), which is one billionth of a meter. At this scale, materials and systems exhibit unique properties and behaviors that are different from those observed at larger length scales. The prefix “nano-” is derived from the Greek word “nanos,” which means “dwarf” or “very small.” Nanoscale phenomena are relevant to many fields, including materials science, chemistry, biology, and physics.

The newly upgraded Linac Coherent Light Source (LCLS) X-ray free-electron laser (XFEL) at the Department of Energy’s SLAC National Accelerator Laboratory successfully produced its first X-rays, and researchers around the world are already lined up to kick off an ambitious science program.

The upgrade, called LCLS-II, creates unparalleled capabilities that will usher in a new era in research with X-rays.

Scientists will be able to examine the details of quantum materials with unprecedented resolution to drive new forms of computing and communications; reveal unpredictable and fleeting chemical events to teach us how to create more sustainable industries and clean energy technologies; study how biological molecules carry out life’s functions to develop new types of pharmaceuticals; and study the world on the fastest timescales to open up entirely new fields of scientific investigation.

By stepping outside the box of our usual way of thinking about numbers, my colleagues and I have recently shown that arithmetic has biological roots and is a natural consequence of how perception of the world around us is organized.

Our results explain why arithmetic is true and suggest that mathematics is a realization in symbols of the fundamental nature and creativity of the mind.

Thus, the miraculous correspondence between mathematics and physical reality that has been a source of wonder from the ancient Greeks to the present—as explored in astrophysicist Mario Livio’s book Is God a Mathematician?—suggests the mind and world are part of a common unity.

Many biological processes depend on chemical reactions that are localized in space and time and therefore require catalytic components that self-organize. The collective behavior of these active particles depends on their chemotactic movement—how they sense and respond to chemical gradients in the environment. Mixtures of such active catalysts generate complex reaction networks, and the process by which self-organization emerges in these networks presents a puzzle. Jaime Agudo-Canalejo of the Max Planck Institute for Dynamics and Self-Organization, Germany, and his colleagues now show that the phenomenon of self-organization depends strongly on the network topology [1]. The finding provides new insights for understanding microbiological systems and for engineering synthetic catalytic colloids.

In a biological metabolic network, catalysts convert substrates into products. The product of one catalyst species acts as the substrate for another species—and so on. Agudo-Canalejo and his team modeled a three-species system. First, building on a well-established continuum theory for catalytically active species that diffuse along chemical gradients, they showed that systems where each species responds chemotactically only to its own substrate cannot self-organize unless one species is self-attracting. Next, they developed a model that allowed species to respond to both their substrates and their products. Pair interactions between different species in this more complex model drove an instability that spread throughout the three-species system, causing the catalysts to clump together. Surprisingly, this self-organization process occurred even among particles that were individually self-repelling.

The researchers say that their discovery of the importance of network topology—which catalyst species affect and are affected by which substrates and products—could open new directions in studies of active matter, informing both origin-of-life research and the design of shape-shifting functional structures.