This article argues that consciousness has a logically sound, explanatory framework, different from typical accounts that suffer from hidden mysticism. The article has three main parts. The first describes background principles concerning information processing in the brain, from which one can deduce a general, rational framework for explaining consciousness. The second part describes a specific theory that embodies those background principles, the Attention Schema Theory. In the past several years, a growing body of experimental evidence—behavioral evidence, brain imaging evidence, and computational modeling—has addressed aspects of the theory. The final part discusses the evolution of consciousness. By emphasizing the specific role of consciousness in cognition and behavior, the present approach leads to a proposed account of how consciousness may have evolved over millions of years, from fish to humans. The goal of this article is to present a comprehensive, overarching framework in which we can understand scientifically what consciousness is and what key adaptive roles it plays in brain function.
A vulnerability in the domain name system (DNS) component of a popular C standard library that is present in a wide range of IoT products may put millions of devices at DNS poisoning attack risk.
A threat actor can use DNS poisoning or DNS spoofing to redirect the victim to a malicious website hosted at an IP address on a server controlled by the attacker instead of the legitimate location.
The library uClibc and its fork from the OpenWRT team, uClibc-ng. Both variants are widely used by major vendors like Netgear, Axis, and Linksys, as well as Linux distributions suitable for embedded applications.
Japanese researcher Makoto Kasu, at Saga University, and a precision diamond jewellery manufacturer have built a 2-inch diamond-coated wafer that can store, they claim, 25 exabytes of data using quantum memory.
Binary data is stored in quantum superpositions using nitrogen vacancies in the diamond material. Currently binary stored is stored as bits, with a value of one or zero, represented by magnetic polarity (north or south), charge in flash (current flows or not) or resistance in ReRAM (high or low). Quantum memory is different in that it stores qubits (quantum bits).
As we understand it, a qubit can have a value of ⎢0⟩ or⎢1⟩ (pronounced “ket 0” and “ket 1”) or a linear combination of both states in any proportion – it does not have a single value. It has a certain probability of being a ⎢0⟩ and another probability of being a ⎢1⟩. This property of a qubit is called superposition and is used in quantum computing, which can use other quantum phenomena such as entanglement and interference.
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.
