Anatomy of computers and human physiology

Anatomy of computers

The science behind the computer and anatomy of the human physiology is similar. Many experts have been talking about the human brain to have more capabilities than the supercomputer, and it works five times faster than that of the Summit, a supercomputer that was used at Oak Ridge National Laboratory.

A human brain includes a neural network far more powerful than a computer. There are 100 billion neurons in the human mind, which are attached to near about 10,000 others, forming a neural network of incredible potential. If we compare the human body with the AI-powered technology available these days, we can see the human body as the most advanced supercomputer available in the world right now.

Let’s have an in-depth comparison between technology and the anatomy of human physiology:

Human Vision and model of visual conception:

Human memory is a combination of many pictures that have been captured by the human eyes and giving the name or recognizing each image with a person, area, or things. A network of million neurons that are connected, which each other sends a signal to the human brain which processes the image and tells us what we are seeing.

With the help of deep learning and artificial intelligence, the machines gained the ability to understand a picture or visual image, and they can now even describe what all they see in the image. Real-time detector or Convolutional Neural Networks used the simulation of the neural networks following the concept of hierarchical processing similar to the human brain.

Edge Computing and Processing:

The increase of the Internet of Things (IoT) and the progress of abundant cloud services have advanced the extent of a modern computing standard, edge computing, which is for processing the data as compared to distant data centers.

Experts are utilizing these system-defined process to build more optimized computational or machine models which are more and more inspired by the human brain functioning. They considered how the human brain neural network works simultaneously using algorithms circuit designs and more to build more optimized machines.

Phagocytes and Continuous Development:

Phagocytes are a variety of white blood cell that protects the body. These cells can phagocytize unknown substances, bacteria, and dying cells. Macrophages defend the human body from a broad collection of pathogens and xenobiotic and play a primary role in the human response to tissue injury.

The job Phagocyte is to understand how they are curing the human body of particular bacteria and save the result in the repository of data, to use it again in the future.

Machine learning works on a similar concept, with the help of the right ML model, machines analysis the data do the segmentation accordingly and stimulate the behavior in the future for better results.

Any machine that exists in the present world or ever made is somewhere the stimulation of the human physiology. 

“If petascale computers like the K computer are capable of representing one percent of the network of a human brain today, then we know that stimulating the whole brain at the level of the individual nerve cell and its synapses will be possible with exascale computers – hopefully, available within the next decade,” one of the scientists, -Markus Diesmann researcher: Neuroscience, Computer Science, Simulation

The human brain is, without a doubt, the most complicated arrangement in the world. Still an enigma, scientists all over the world are trying to map and simulate portions of the human brains.

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All Comments

  • Avatar Jon Wright

    It is beyond amazing to even try to contemplate what onpassive is going to accomplish in the next few years. What they do in years to come really is beyond anything I can think of. It is expanding out dreams in ways yet known.

    9 months ago | 4 February, 2020 2:10 pm Reply
  • Avatar Gerald D Greger

    The evolution of this revolutionary concept gives me hope like I have never felt before!

    9 months ago | 3 February, 2020 8:18 pm Reply

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