Lifeboat Foundation

Quantum computing began in the early 1980s. It operates on principles of quantum physics rather than the limitations of circuits and electricity which is why it is capable of processing highly complex mathematical problems so efficiently. Quantum computing could one day achieve things that classical computing simply cannot. The evolution of quantum computers has been slow, but things are accelerating, thanks to the efforts of academic institutions such as Oxford, MIT, and the University of Waterloo, as well as companies like IBM, Microsoft, Google, and Honeywell.

IBM has held a leadership role in this innovation push and has named optimization as the most likely application for consumers and organizations alike.

Honeywell expects to release what it calls the “world’s most powerful quantum computer” for applications like fraud detection, optimization for trading strategies, security, machine learning, and chemistry and materials science.

These groups of brain cells are called “assemblies,” which Papadimitriou describes as “a highly connected, stable set of neurons which represent something: a word, an idea, an object, etc.”

Award-winning neuroscientist György Buzsáki describes assemblies as “the alphabet of the brain.”

The US Department of Energy on Thursday is officially dedicating Perlmutter, a next-generation supercomputer that will deliver nearly four exaflops of AI performance. The system, based at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory, is the world’s fastest on the 16-bit and 32-bit mixed-precision math used for AI.

The HPE Cray system is being installed in two phases. Each of Phase 1’s GPU-accelerated nodes has four Nvidia A100 Tensor Core GPUs, for a total of 6159 Nvidia A100 Tensor Core GPUs. Each Phase 1 node also has a single AMD Milan CPU.

## MATHEMATICS • MAY 24, 2021

# *Noise is commonly discarded, but identifying patterns in noise can be very useful.*

*Generalize the Hearst exponent by adding more coefficients in order to get a more complete description of the changing data. This makes it possible to find patterns in the data that are usually considered noise and were previously impossible to analyze.*

Continue reading “Scientists recognize intruders in noise” »

From a purely scientific frame of reference, many quantum phenomena like non-local correlations between distant entities and wave-particle duality, the wave function collapse and consistent histories, quantum entanglement and teleportation, the uncertainty principle and overall observer-dependence of reality pin down our conscious mind being intrinsic to reality. And this is the one thing the current physicalist paradigm fails to account for. Critical-mass anomalies will ultimately lead to the full paradigm shift in physics. It’s just a matter of time.

With consciousness as primary, everything remains the same and everything changes. Mathematics, physics, chemistry, biology are unchanged. What changes is our interpretation as to what they are describing. They are not describing the unfolding of an objective physical world, but transdimensional evolution of one’s conscious mind. There’s nothing “physical” about our physical reality except that we perceive it that way. By playing the “Game of Life” we evolved to survive not to see quantum mechanical reality. At our classical level of experiential reality we perceive ourselves as physical, at the quantum level we are a probabilistic wave function, which is pure information.

Continue reading “The Mental Universe Hypothesis: Reconnecting to Your Cosmic Self” »

Engineering A Safer World For Humans With Self Driving Cars, Drones, and Robots — Dr. Missy Cummings PhD, Professor, Duke University, Director, Humans and Autonomy Laboratory, Duke Engineering.

Dr. Mary “Missy” Cummings, is a Professor in the Department of Electrical and Computer Engineering, at the Pratt School of Engineering, at Duke University, the Duke Institute of Brain Sciences, and is the Director of the Humans and Autonomy Laboratory and Duke Robotics.

Continue reading “Dr. Missy Cummings, Ph.D — Professor, Duke University — Director, Humans and Autonomy Laboratory” »

Protocol to reverse engineer Hamiltonian models advances automation of quantum devices.

Scientists from the University of Bristol ’s Quantum Engineering Technology Labs (QETLabs) have developed an algorithm that provides valuable insights into the physics underlying quantum systems — paving the way for significant advances in quantum computation and sensing, and potentially turning a new page in scientific investigation.

In physics, systems of particles and their evolution are described by mathematical models, requiring the successful interplay of theoretical arguments and experimental verification. Even more complex is the description of systems of particles interacting with each other at the quantum mechanical level, which is often done using a Hamiltonian model. The process of formulating Hamiltonian models from observations is made even harder by the nature of quantum states, which collapse when attempts are made to inspect them.

Artificial womb technology for extremely preterm infants — jasmijn kok, juno perinatal healthcare.

Every year, 800000 babies are born extremely preterm (defined as less than 28 weeks of age) worldwide. These infants are usually transferred to an air-based neonatal intensive care unit to support their heart and lung development. Exposure to air, however, leads to many complications, because the lungs are not fully developed yet.

Continue reading “Jasmijn Kok — Juno Perinatal Healthcare — Artificial Womb Technology For Extremely Preterm Infants” »

Training neural networks to perform tasks, such as recognizing images or navigating self-driving cars, could one day require less computing power and hardware thanks to a new artificial neuron device developed by researchers at the University of California San Diego. The device can run neural network computations using 100 to 1000 times less energy and area than existing CMOS-based hardware.

Researchers report their work in a paper published recently in Nature Nanotechnology.

Neural networks are a series of connected layers of artificial neurons, where the output of one layer provides the input to the next. Generating that input is done by applying a mathematical calculation called a non-linear activation function. This is a critical part of running a neural network. But applying this function requires a lot of computing power and circuitry because it involves transferring data back and forth between two separate units – the memory and an external processor.