Lifeboat Foundation

Decentralized finance is built on blockchain technology, an immutable system that organizes data into blocks that are chained together and stored in hundreds of thousands of nodes or computers belonging to other members of the network.

These nodes communicate with one another (peer-to-peer), exchanging information to ensure that they’re all up-to-date and validating transactions, usually through proof-of-work or proof-of-stake. The first term is used when a member of the network is required to solve an arbitrary mathematical puzzle to add a block to the blockchain, while proof-of-stake is when users set aside some cryptocurrency as collateral, giving them a chance to be selected at random as a validator.

To encourage people to help keep the system running, those who are selected to be validators are given cryptocurrency as a reward for verifying transactions. This process is popularly known as mining and has not only helped remove central entities like banks from the equation, but it also has allowed DeFi to open more opportunities. In traditional finance, are only offered to large organizations, for members of the network to make a profit. And by using network validators, DeFi has also been able to cut down the costs that intermediaries charge so that management fees don’t eat away a significant part of investors’ returns.

For years, the brain has been thought of as a biological computer that processes information through traditional circuits, whereby data zips straight from one cell to another. While that model is still accurate, a new study led by Salk Professor Thomas Albright and Staff Scientist Sergei Gepshtein shows that there’s also a second, very different way that the brain parses information: through the interactions of waves of neural activity. The findings, published in Science Advances on April 22, 2022, help researchers better understand how the brain processes information.

“We now have a new understanding of how the computational machinery of the brain is working,” says Albright, the Conrad T. Prebys Chair in Vision Research and director of Salk’s Vision Center Laboratory. “The model helps explain how the brain’s underlying state can change, affecting people’s attention, focus, or ability to process information.”

Researchers have long known that waves of electrical activity exist in the brain, both during sleep and wakefulness. But the underlying theories as to how the brain processes information—particularly sensory information, like the sight of a light or the sound of a bell—have revolved around information being detected by specialized brain cells and then shuttled from one neuron to the next like a relay.

Engineers have developed a way to mass-produce diamond wafers that could store the equivalent of a billion Blu-ray disks on a single device.

The yin-yang codec transcoding algorithm is proposed to improve the practicality and robustness of DNA data storage.

Given these results, YYC offers the opportunity to generate DNA sequences that are highly amenable to both the ‘writing’ (synthesis) and ‘reading’ (sequencing) processes while maintaining a relatively high information density. This is crucially important for improving the practicality and robustness of DNA data storage. The DNA Fountain and YYC algorithms are the only two known coding schemes that combine transcoding rules and screening into a single process to ensure that the generated DNA sequences meet the biochemical constraints. The comparison hereinafter thus focuses on the YYC and DNA Fountain algorithms because of the similarity in their coding strategies.

The robustness of data storage in DNA is primarily affected by errors introduced during ‘writing’ and ‘reading’. There are two main types of errors: random and systematic errors. Random errors are often introduced by synthesis or sequencing errors in a few DNA molecules and can be redressed by mutual correction using an increased sequencing depth. System atic errors refer to mutations observed in all DNA molecules, including insertions, deletions and substitutions, which are introduced during synthesis and PCR amplification (referred to as common errors), or the loss of partial DNA molecules. In contrast to substitutions (single-nucleotide variations, SNVs), insertions and deletions (indels) change the length of the DNA sequence encoding the data and thus introduce challenges regarding the decoding process. In general, it is difficult to correct systematic errors, and thus they will lead to the loss of stored binary information to varying degrees.

Continue reading “Towards practical and robust DNA-based data archiving using the yin–yang codec system” »

Flow is a desired but elusive state characterized by the subjective experience of immersion and engagement in an activity. Here, the authors develop and empirically validate a formal model that specifies variables and computations involved in the subjective experience of flow.

Researchers in Japan have developed a new method for making 5-cm (2-in) wafers of diamond that could be used for quantum memory. The ultra-high purity of the diamond allows it to store a staggering amount of data – the equivalent of one billion Blu-Ray discs.

Diamond is one of the most promising materials for practical quantum computing systems, including memory. A particular defect in the crystal, known as a nitrogen-vacancy center, can be used to store data in the form of superconducting quantum bits (qubits), but too much nitrogen in the diamond disrupts its quantum storage capabilities.

That meant there was a trade-off to make – scientists had to create either large diamond wafers with too much nitrogen, or ultra-pure diamond wafers that are too small to be of much use for data storage. But now, researchers at Saga University and Adamant Namiki Precision Jewelery Co. in Japan have developed a new method for manufacturing ultra-high purity diamond wafers that are big enough for practical use.

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As computers have improved at an accelerating rate for generations now, fears of some emergent super intelligent computer mind have grown, but is such a Technological Singularity Inevitable, and can we survive it?

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Continue reading “Is a Technological Singularity Inevitable?” »

Another key insight of Cybernetic Theory can be referred to as “Mind Over Substrates”: Phenomenal “local” mind is “cybernetically” emergent from the underlying functional organization, whereas holistic “non-local” consciousness is transcendentally imminent. Material worlds come and go, but fundamental consciousness is ever-present, as the multiverse ontology is shown to be testable. From a new science of consciousness to simulation metaphysics, from evolutionary cybernetics to computational physics, from physics of time and information to quantum cosmology, this novel explanatory theory for a deeper understanding of reality is combined into one elegant theory of everything (ToE).

If you’re eager to familiarize with probably the most advanced ontological framework to date or if you’re already familiar with the Syntellect Hypothesis which, with this newly-released series, is now presented to you as the full-fledged Cybernetic Theory of Mind, then this 5-book set will surely present to you some newly-introduced and updated material if compared with the originally published version and can be read as a stand-alone work just like any book of the series:

Continue reading “Cybernetic Theory: Interpretive Model of Everything We Call the Universe” »