Are biological hardwares even possible ?
And the Answer is Yes, beacuse the concept of biological hardware dates back to ancient times when humans used natural materials such as bones, shells, and rocks to create tools and weapons. The use of biological materials continued through the Middle Ages, where animal bones and hides were used for various purposes, including armor, tools, and musical instruments.
In the following decades, advances in biotechnology and materials science led to the development of a wide range of biological hardware, including implantable medical devices, biosensors, and bioelectronic interfaces.
But what purpose does it serve in the computing power, lets discuss this below;
Neural chips and Computers
With potential to revolutionize many aspects of computing, from artificial intelligence and robotics to medical devices and prosthetics. Neural chips are a type of computer chip that is designed to emulate the structure and function of the human brain. They are used in computers to perform tasks that require parallel processing and machine learning.
Just like our Brains the Neural chips can also learn and adapt over time which is important for applications such as artificial intelligence and robotics. By training neural networks on large datasets, these systems can learn to recognize patterns and make predictions with a high degree of accuracy.
Researchers at Northwestern University and the University of Hong Kong have developed a device modeled after the human brain that simulates human learning.
The big down side to these Chips are that millions of them are needed to mimic even a single nerve response of the brain.
Even a Giant like the “Frontier, the supercomputer in Kentucky, a $600 million, 6,800-square-feet installation. Only in June of last year, it exceeded for the first time the computational capacity of a single human brain — but using a million times more energy.”
Brain Still the MVP
Brains are analog. The brain’s billions of neurons behave very differently from the digital switches and logic gates in a digital computer.
“We’ve known since the 1920s that neurons don’t just turn on and off,” says biologist Matthew Cobb of the University of Manchester in the UK.
Some neuroscientists have argued that even individual neurons can perform certain kinds of computations, comparable to what computer scientists call an XOR, or “exclusive or,” function.
Artificial Networks
All of the biggest breakthroughs in artificial intelligence today have involved artificial neural networks.
These systems employ a type of machine learning called deep learning. Their algorithms learn by processing massive amounts of data through hidden layers of interconnected nodes, referred to as deep neural networks. As their name suggests, deep neural networks were inspired by the real neural networks in the brain, with the nodes modeled after real neurons.
Timothy Lillicrap, who designs decision-making algorithms at the Google-owned AI company DeepMind, said the new result suggests that it might be necessary to rethink the old tradition of loosely comparing a neuron in the brain to a neuron in the context of machine learning. “This paper really helps force the issue of thinking about that more carefully and grappling with to what extent you can make those analogies,” he said.
Analogy between artificial and real neuron
The most basic analogy between artificial and real neurons involves how they handle incoming information. Both kinds of neurons receive incoming signals and, based on that information, decide whether to send their own signal to other neurons. While artificial neurons rely on a simple calculation to make this decision, decades of research have shown that the process is far more complicated in biological neurons.
Energy Consumption
Research suggests that the human brain consumes only about 20 watts of power, which is barely enough to run a dim light bulb, whereas the world’s fastest supercomputer, Tianhe-2 in China consumes about 17.8 megawatts of power, which is enough to run about 900,000 such light bulbs.
Two years ago, a team at the Allen Institute for Brain Science in Seattle, US, mapped the 3D structure of all the neurons (brain cells) comprised in one cubic millimetre of the brain of a mouse — a milestone considered extraordinary.
A question of space
Two years ago, a team at the Allen Institute for Brain Science in Seattle, US, mapped the 3D structure of all the neurons (brain cells) comprised in one cubic millimetre of the brain of a mouse — a milestone considered extraordinary.
Within this minuscule cube of brain tissue, the size of a grain of sand, the researchers counted more than 100,000 neurons and more than a billion connections between them. They managed to record the corresponding information on computers, including the shape and configuration of each neuron and connection, which required two petabytes, or two million gigabytes of storage. And to do this, their automated microscopes had to collect 100 million images of 25,000 slices of the minuscule sample continuously over several months.
The human brain contains about 100 billion neurons (as many stars as could be counted in the Milky way) — one million times those contained in our cubic millimetre of mouse brain. And the estimated number of connections is a staggering ten to the power of 15. That is ten followed by 15 zeroes — a number comparable to the individual grains contained in a two meter thick layer of sand on a 1km-long beach.
Now collection of this information from the human brain is not going to be a walk in the park.
Conclusion
Bio-computers could soon become a reality with the emerging field organoid intelligence (OI) where researchers are developing biological computing using 3D cultures of human brain cells (brain organoids) and brain-machine interface technologies, but it won’t be easy and may take more time then we anticipate.
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