Lifeboat Foundation

Fiber sensors conform to skin:

In another scientific marvel inspired by the wonder that is spider silk, researchers have developed an innovative method to create adaptive and eco-friendly sensors that can be seamlessly and invisibly printed onto various biological surfaces, such as a finger or a flower petal.

This breakthrough in high-performance bioelectronics allows for the customization of sensors on a wide range of surfaces, from fingertips to the delicate seedheads of dandelions, by printing them directly onto them.

University of Southern California Dornsife molecular biologist John Tower suggests that while living things generally prefer stability to conserve energy and resources, instability may also play a crucial role.

A molecular biologist at the USC Dornsife College of Letters, Arts and Sciences may have found a new “rule of biology.”

A rule of biology, sometimes called a biological law, describes a recognized pattern or truism among living organisms. Allen’s rule, for example, states that among warm-blooded animals, those found in colder areas have shorter, thicker limbs (to conserve body heat) than those in hotter regions, which need more body surface area to dissipate heat.

Joints are an essential part in robotics, especially those that try to emulate the motion of (human) animals. Unlike the average automaton, animals are not outfitted with bearings and similar types of joints, but rather rely sometimes on ball joints and a lot on rolling contact joints (RCJs). These RCJs have the advantage of being part of the skeletal structure, making them ideal for compact and small joints. This is the conclusion that [Breaking Taps] came to as well while designing the legs for a bird-like automaton.

These RCJs do not just have the surfaces which contact each other while rotating, but also provide the constraints for how far a particular joint is allowed to move, both in the forward and backward directions as well as sideways. In the case of the biological version these contact surfaces are also coated with a constantly renewing surface to prevent direct bone-on-bone contact. The use of RCJs is rather common in robotics, with the humanoid DRACO 3 platform as detailed in a 2023 research article by [Seung Hyeon Bang] and colleagues in Frontiers in Robotics and AI.

Continue reading “Emulating Biology For Robots With Rolling Contact Joints” »

Year 2020 This holds promise for near infinite lifespans for humans still in the first stages but still very promising for immortality and so much more.

The microbes had survived on trace amounts of oxygen and were able to feed and multiply once revived in the lab.

In the vast and ever-evolving landscape of technology, neuromorphic computing emerges as a groundbreaking frontier, reminiscent of uncharted territories awaiting exploration. This novel approach to computation, inspired by the intricate workings of the human brain, offers a path to traverse the complex terrains of artificial intelligence (AI) and advanced data processing with unprecedented efficiency and agility.

Neuromorphic computing, at its core, is an endeavor to mirror the human brain’s architecture and functionality within the realm of computer engineering. It represents a significant shift from traditional computing methods, charting a course towards a future where machines not only compute but also learn and adapt in ways that are strikingly similar to the human brain. This technology deploys artificial neurons and synapses, creating networks that process information in a manner akin to our cognitive processes. The ultimate objective is to develop systems capable of sophisticated tasks, with the agility and energy efficiency that our brain exemplifies.

The genesis of neuromorphic computing can be traced back to the late 20th century, rooted in the pioneering work of researchers who sought to bridge the gap between biological brain functions and electronic computing. The concept gained momentum in the 1980s, driven by the vision of Carver Mead, a physicist who proposed the use of analog circuits to mimic neural processes. Since then, the field has evolved, fueled by advancements in neuroscience and technology, growing from a theoretical concept to a tangible reality with vast potential.

To be clear, humans are not the pinnacle of evolution. We are confronted with difficult choices and cannot sustain our current trajectory. No rational person can expect the human population to continue its parabolic growth of the last 200 years, along with an ever-increasing rate of natural resource extraction. This is socio-economically unsustainable. While space colonization might offer temporary relief, it won’t resolve the underlying issues. If we are to preserve our blue planet and ensure the survival and flourishing of our human-machine civilization, humans must merge with synthetic intelligence, transcend our biological limitations, and eventually evolve into superintelligent beings, independent of material substrates—advanced informational beings, or ‘infomorphs.’ In time, we will shed the human condition and upload humanity into a meticulously engineered inner cosmos of our own creation.

Much like the origin of the Universe, the nature of consciousness may appear to be a philosophical enigma that remains perpetually elusive within the current scientific paradigm. However, I emphasize the term “current.” These issues are not beyond the reach of alternative investigative methods, ones that the next scientific paradigm will inevitably incorporate with the arrival of Artificial Superintelligence.

The era of traditional, human-centric theoretical modeling and problem-solving—developing hypotheses, uncovering principles, and validating them through deduction, logic, and repeatable experimentation—may be nearing the end. A confluence of factors—Big Data, algorithms, and computational resources—are steering us towards a new type of discovery, one that transcends the limitations of human-like logic and decision-making— the one driven solely by AI superintelligence, nestled in quantum neo-empiricism and a fluidity of solutions. These novel scientific methodologies may encompass, but are not limited to, computing supercomplex abstractions, creating simulated realities, and manipulating matter-energy and the space-time continuum itself.

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A molecular biologist at the USC Dornsife College of Letters, Arts and Sciences may have found a new “rule of biology.”

Artificial neural networks (ANNs) show a remarkable pattern when trained on natural data irrespective of exact initialization, dataset, or training objective; models trained on the same data domain converge to similar learned patterns. For example, for different image models, the initial layer weights tend to converge to Gabor filters and color-contrast detectors. Many such features suggest global representation that goes beyond biological and artificial systems, and these features are observed in the visual cortex. These findings are practical and well-established in the field of machines that can interpret literature but lack theoretical explanations.

Localized versions of canonical 2D Fourier basis functions are the most observed universal features in image models, e.g. Gabor filters or wavelets. When vision models are trained on tasks like efficient coding, classification, temporal coherence, and next-step prediction goals, these Fourier features pop up in the model’s initial layers. Apart from this, Non-localized Fourier features have been observed in networks trained to solve tasks where cyclic wraparound is allowed, for example, modular arithmetic, more general group compositions, or invariance to the group of cyclic translations.

Researchers from KTH, Redwood Center for Theoretical Neuroscience, and UC Santa Barbara introduced a mathematical explanation for the rise of Fourier features in learning systems like neural networks. This rise is due to the downstream invariance of the learner that becomes insensitive to certain transformations, e.g., planar translation or rotation. The team has derived theoretical guarantees regarding Fourier features in invariant learners that can be used in different machine-learning models. This derivation is based on the concept that invariance is a fundamental bias that can be injected implicitly and sometimes explicitly into learning systems due to the symmetries in natural data.