Lifeboat Foundation

Researchers from North Carolina State University and Johns Hopkins University have demonstrated a technology capable of a suite of data storage and computing functions – repeatedly storing, retrieving, computing, erasing or rewriting data – that uses DNA rather than conventional electronics. Previous DNA data storage and computing technologies could complete some but not all of these tasks.

“In conventional computing technologies, we take for granted that the ways data are stored and the way data are processed are compatible with each other,” says project leader Albert Keung, co-corresponding author of a paper on the work (Nature Nanotechnology, “A Primordial DNA Store and Compute Engine”). “But in reality, data storage and data processing are done in separate parts of the computer, and modern computers are a network of complex technologies,” Keung is an associate professor of chemical and biomolecular engineering and a Goodnight Distinguished Scholar at NC State.

“DNA computing has been grappling with the challenge of how to store, retrieve and compute when the data is being stored in the form of nucleic acids,” Keung says. “For electronic computing, the fact that all of a device’s components are compatible is one reason those technologies are attractive. But, to date, it’s been thought that while DNA data storage may be useful for long-term data storage, it would be difficult or impossible to develop a DNA technology that encompassed the full range of operations found in traditional electronic devices: storing and moving data; the ability to read, erase, rewrite, reload or compute specific data files; and doing all of these things in programmable and repeatable ways.

Measurement in quantum mechanics presents unique challenges. Observing one particle in an entangled pair determines the states of both, leading to critical inquiries: What constitutes a ‘measurement,’ and how does it influence our understanding of reality?

The complex mathematics underpinning quantum mechanics — incorporating concepts like Hilbert spaces, wave functions, and operators — can be intimidating, rendering entanglement less accessible to many.

Simply put, quantum entanglement is just too complicated for most people to fully understand. It defies classical intuitions, involves sophisticated mathematics, and urges us to reevaluate our understanding of reality.

https://www.youtube.com/watch?app=desktop&v=2dK3ABl-KWQ

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Timestamps:
00:00 — Breakthrough in Quantum Computing.
10:45 — Quantum Teleportation achieved.
15:38 — New Quantum Devices.
20:00 — Explaining my absence.

Continue reading “Big News For Quantum Computing: Scalable Qubits and Quantum Teleportation Achieved” »

Why did the experience of consciousness evolve from our underlying brain physiology? Despite being a vibrant area of neuroscience, current research on consciousness is characterised by disagreement and controversy – with several rival theories in contention.

A recent scoping review of over 1,000 articles identified over 20 different theoretical accounts. Philosophers like David Chalmers argue that no single scientific theory can truly explain consciousness.

We define consciousness as embodied subjective awareness, including self awareness. In a recent article published in Interalia (which is not peer reviewed), we argue that one reason for this predicament is the powerful role played by intuition.

Understand the difference between self-awareness and consciousness and learn key ways of how to raise your self-awareness and consciousness.

Neuromorphic computing memristors are attractive to construct low-power-consumption electronic textiles. Here, authors report an ultralow-power textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics and firing energy consumption of 1.9 fJ/spike.

Recently, computation in memory becomes very hot due to the urgent needs of high computing efficiency in artificial intelligence applications. In contrast to von-neumann architecture, computation in memory technology avoids the data movement between CPU/GPU and memory which could greatly reduce the power consumption. Memristor is one ideal device which could not only store information with multi-bits, but also conduct computing using ohm’s law. To make the best use of the memristor in neuromorphic systems, a memristor-friendly architecture and the software-hardware collaborative design methods are essential, and the key problem is how to utilize the memristor’s analog behavior. We have designed a generic memristor crossbar based architecture for convolutional neural networks and perceptrons, which take full consideration of the analog characteristics of memristors. Furthermore, we have proposed an online learning algorithm for memristor based neuromorphic systems which overcomes the varation of memristor cells and endue the system the ability of reinforcement learning based on memristor’s analog behavior.

Full abstract and speaker details can be found here: https://nus.edu/3cSFD3e.

Continue reading “Neuromorphic computing with memristors: from device to system — Professor Huaqiang Wu” »

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