Lifeboat Foundation

The idea of the brain as a computer is everywhere. So much so we have forgotten it is a model and not the reality. It’s a metaphor that has lead some to believe that in the future they’ll be uploaded to the digital ether and thereby achieve immortality. It’s also a metaphor that garners billions of dollars in research funding every year. Yet researchers argue that when we dig down into our grey matter our biology is anything but algorithmic. And increasingly, critics contend that the model of the brain as computer is sending scientists (and their resources) nowhere fast. Is our attraction to the idea of the brain as computer an accident of current human technology? Can we find a better metaphor that might lead to a new paradigm?

Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we develop Brainoware, living AI hardware that harnesses the computation power of 3D biological neural networks in a brain organoid. Brain-like 3D in vitro cultures compute by receiving and sending information via a multielectrode array. Applying spatiotemporal electrical stimulation, this approach not only exhibits nonlinear dynamics and fading memory properties but also learns from training data. Further experiments demonstrate real-world applications in solving non-linear equations. This approach may provide new insights into AI hardware.

Artificial intelligence (AI) is reshaping the future of human life across various real-world fields such as industry, medicine, society, and education¹. The remarkable success of AI has been largely driven by the rise of artificial neural networks (ANNs), which process vast numbers of real-world datasets (big data) using silicon computing chips ^{2, 3}. However, current AI hardware keeps AI from reaching its full potential since training ANNs on current computing hardware produces massive heat and is heavily time-consuming and energy-consuming ^{4– 6}, significantly limiting the scale, speed, and efficiency of ANNs. Moreover, current AI hardware is approaching its theoretical limit and dramatically decreasing its development no longer following ‘Moore’s law’^{7, 8}, and facing challenges stemming from the physical separation of data from data-processing units known as the ‘von Neumann bottleneck’^{9, 10}. Thus, AI needs a hardware revolution^{8, 11}.

A breakthrough in AI hardware may be inspired by the structure and function of a human brain, which has a remarkably efficient ability, known as natural intelligence (NI), to process and learn from spatiotemporal information. For example, a human brain forms a 3D living complex biological network of about 200 billion cells linked to one another via hundreds of trillions of nanometer-sized synapses^{12, 13}. Their high efficiency renders a human brain to be ideal hardware for AI. Indeed, a typical human brain expands a power of about 20 watts, while current AI hardware consumes about 8 million watts to drive a comparative ANN⁶. Moreover, the human brain could effectively process and learn information from noisy data with minimal training cost by neuronal plasticity and neurogenesis,^{14, 15} avoiding the huge energy consumption in doing the same job by current high precision computing approaches^{12, 13}.

Starting with the direct detection of gravitational waves in 2015, scientists have relied on a bit of a kludge: they can only detect those waves that match theoretical predictions, which is rather the opposite way that science is usually done.

Over the past decades, computer scientists have developed various computing tools that could help to solve challenges in quantum physics. These include large-scale deep neural networks that can be trained to predict the ground states of quantum systems. This method is now referred to as neural quantum states (NQSs).

Machine learning influences numerous aspects of modern society, empowers new technologies, from Alphago to ChatGPT, and increasingly materializes in consumer products such as smartphones and self-driving cars. Despite the vital role and broad applications of artificial neural networks, we lack systematic approaches, such as network science, to understand their underlying mechanism. The difficulty is rooted in many possible model configurations, each with different hyper-parameters and weighted architectures determined by noisy data. We bridge the gap by developing a mathematical framework that maps the neural network’s performance to the network characters of the line graph governed by the edge dynamics of stochastic gradient descent differential equations. This framework enables us to derive a neural capacitance metric to universally capture a model’s generalization capability on a downstream task and predict model performance using only early training results. The numerical results on 17 pre-trained ImageNet models across five benchmark datasets and one NAS benchmark indicate that our neural capacitance metric is a powerful indicator for model selection based only on early training results and is more efficient than state-of-the-art methods.

The Cartesian model of mind-body dualism concurs with religious traditions. However, science has supplanted this idea with an energy-matter theory of consciousness, where matter is equivalent to the body and energy replaces the mind or soul. This equivalency is analogous to the concept of the interchange of mass and energy as expressed by Einstein’s famous equation [Formula: see text]. Immanuel Kant, in his Critique of Pure Reason, provided the intellectual and theoretical framework for a theory of mind or consciousness. Any theory of consciousness must include the fact that a conscious entity, as far as is known, is a wet biological medium (the brain), of stupendously high entropy. This organ or entity generates a field that must account for the “binding problem”, which we will define. This proposed field, the conscious electro-magnetic information (CEMI) field, also has physical properties, which we will outline. We will also demonstrate the seamless transition of the Kantian philosophy of the a priori conception of space and time, the organs of perception and conception, into the CEMI field of consciousness. We will explore the concept of the CEMI field and its neurophysiological correlates, and in particular, synchronous and coherent gamma oscillations of various neuronal ensembles, as in William J Freeman’s experiments in the early 1970s with olfactory perception in rabbits. The expansion of the temporo-parietal-occipital (TPO) cortex in hominid evolution epitomizes metaphorical and abstract thinking. This area of the cortex, with synchronous thalamo-cortical oscillations has the best fit for a minimal neural correlate of consciousness. Our field theory shifts consciousness from an abstract idea to a tangible energy with defined properties and a mathematical framework. Even further, it is not a coincidence that the cerebral cortex is very thin with respect to the diameter of the brain. This is in keeping with its fantastically high entropy, as we see in the event horizon of a black hole and the conformal field theory/anti-de Sitter (CFT/ADS) holographic model of the universe. We adumbrate the uniqueness of consciousness of an advanced biological system such as the human brain and draw insight from Avicenna’s gendanken, floating man thought experiment. The multi-system high volume afferentation of a biological wet system honed after millions of years of evolution, its high entropy, and the CEMI field variation inducing currents in motor output pathways are proposed to spark the seeds of consciousness. We will also review Karl Friston’s free energy principle, the concept of belief-update in a Bayesian inference framework, the minimization of the divergence of prior and posterior probability distributions, and the entropy of the brain. We will streamline these highly technical papers, which view consciousness as a minimization principle akin to Hilbert’s action in deriving Einstein’s field equation or Feynman’s sum of histories in quantum mechanics. Consciousness here is interpreted as flow of probability densities on a Riemmanian manifold, where the gradient of ascent on this manifold across contour lines determines the magnitude of perception or the degree of update of the belief-system in a Bayesian inference model. Finally, the science of consciousness has transcended metaphysics and its study is now rooted in the latest advances of neurophysiology, neuro-radiology under the aegis of mathematics.

Keywords: anatomy & physiology; brain anatomy; disorders of consciousness; philosophy.

Copyright © 2020, Kesserwani et al.

“Compared with other traditional methods, the proposed has lower computational complexity, faster operation speed, weak influence of light, and strong ability to locate dirt,” the research group said. “The improved path planning algorithm used in this study greatly improves the efficiency of UAV inspection, saves time and resources, reduces operation and maintenance costs, and improves the corresponding operation and maintenance level of photovoltaic power generation.”

The novel approach uses mathematical morphologies for image processing, such as image enhancement, sharpening, filtering, and closing operations. It also uses image histogram equalization and edge detection, among other methods, to find the dusted spot. For path optimization, it uses an improved version of the A (A-star) algorithm.

The quantum advantage, a key goal in quantum computation, is achieved when a quantum computer’s computational capability surpasses classical means. A recent study introduced a type of Instantaneous Quantum Polynomial-Time (IQP) computation, which was challenged by IBM Quantum and IonQ researchers who developed a faster classical simulation algorithm. IQP circuits are beneficial due to their simplicity and moderate hardware requirements, but they also allow for classical simulation. The IQP circuit, known as the HarvardQuEra circuit, is built over n 3m 32k inputs. There are two types of simulation for quantum computations: noiseless weak/direct and noisy.

The quantum advantage is a key goal for the quantum computation community. It is achieved when a quantum computer’s computational capability becomes so complex that it cannot be reproduced by classical means. This ongoing negotiation between classical simulations and quantum computational experiments is a significant focus in the field.

A recent publication by Bluvstein et al. introduced a type of Instantaneous Quantum Polynomial-Time (IQP) computation, complemented by a 48-qubit logical experimental demonstration using quantum hardware. The authors projected the simulation time to grow rapidly with the number of CNOT layers added. However, researchers from IBM Quantum and IonQ reported a classical simulation algorithm that computes an amplitude for the 48-qubit computation in only 0.00257947 seconds, which is roughly 10³ times faster than that reported by the original authors. This algorithm is not subject to a significant decline in performance due to the additional CNOT layers.

Quantum field theory (QFT) was a crucial step in our understanding of the fundamental nature of the Universe. In its current form, however, it is poorly suited for describing composite particles, made up of multiple interacting elementary particles. Today, QFT for hadrons has been largely replaced with quantum chromodynamics, but this new framework still leaves many gaps in our understanding, particularly surrounding the nature of strong nuclear force and the origins of dark matter and dark energy. Through a new algebraic formulation of QFT, Dr Abdulaziz Alhaidari at the Saudi Center for Theoretical Physics hopes that these issues could finally be addressed.

The emergence of quantum field theory (QFT) was one of the most important developments in modern physics. By combining the theories of special relativity, quantum mechanics, and the interaction of matter via classical field equations, it provides robust explanations for many fundamental phenomena, including interactions between charged particles via the exchange of photons.

Still, QFT in its current form is far from flawless. Among its limitations is its inability to produce a precise description of composite particles such as hadrons, which are made up of multiple interacting elementary particles that are confined (cannot be observed in isolation). Since these particles possess an internal structure, the nature of these interactions becomes far more difficult to define mathematically, stretching the descriptive abilities of QFT beyond its limits.