Debunking The Myth – Before we embark on this high-level journey, let’s understand and take the fact for granted. Artificial neural networks do not work like a human brain besides being limited to a simple simulation only. Can neural coding simulate the human brain if yes then up to what extent? Artificial neural networks are extremely powerful for doing excellent and super-fast computation but can we say this any better than the human mind for speedy mathematics.

Artificial Neural Network – Outlook

The Artificial Intelligence hype and too much usage of buzzwords have created a “misinformation epidemic” which is entirely centered around artificial neural networks working like the human brain and machine learning. In reality, it’s quite far away from the human brain work. ANN simply works like a mathematical model where its structure receives a signal from one side either informing of the pattern or vector (for image). The output is calculated within hidden layers with corresponding weights to inputs.

Deep Learning – Introduction to Artificial Neural Networks

How Neural Network Algorithms Works: An Overview

ANNs are an emerging discipline and they are the subject of research, study, and emulation for the information-processing capabilities of neurons of the human brain. Sadly many researchers are too quick and pivot the “Human Brain and ANNs” under one pin tip which is causing & creating huge confusion for newcomers. Their point in forms of discovery is entirely different though.

In short Artificial neural networks (ANNs) are mathematical constructs, originally designed to approximate biological neurons (not a bad attempt though). Scientists/researchers are trying to unlock the possibility of a brain-computer interface. Possibilities simulating the human brain is not an easy task and may remain like this for the next 50 years or so.

What is Deep Learning?

AILabPage defines Deep learning as “Undeniably a mind-blowing synchronisation technique applied on the bases of 3 foundation pillars large data, computing power, skills (enriched algorithms) and experience which practically has no limits“.

Deep Learning is a subfield of the machine learning domain. In deep learning, the computer trains itself to process and learn from data. It is entirely concerned with algorithms inspired by the structure and function of artificial neural networks which are inspired by the human brain (inspired only pls).

Deep learning is used with too much ease to predict the unpredictable. In our opinion “We all are so busy in creating artificial intelligence by using a combination of non-bio neural networks and natural intelligence rather than exploring what we have in hand.

“I think people need to understand that deep learning is making a lot of things, behind the scenes, much-better” – Sir Geoffrey Hinton

Deep learning, also called a subset of machine learning is a specialist with an extremely complex skillset in order to achieve far better results from the same data set. It is purely on the basis of NI (Natural Intelligence) mechanics of the biological neuron system. It has a complex skill set because of the methods it uses for training i.e. learning in deep learning is based on “learning data representations” rather than “task-specific algorithms.” which is the case for other methods

What Makes Human Brain So Special – Debunking The Myth

Human Brain  – What makes the human brain so special? why it is so unique? such questions always arise when we start comparing with other species. It is a special/critical point of discussion for everyone and a puzzling game of all times as well.  The human brain has cognitive ability/capacity which is super fit for inventions and discoveries. We created a wheel that Artificial intelligence or other animals can’t do. The human brain is the biggest achievement in the evolution of living organisms.

How the human brain is designed and how it functions can not be covered in this post as I do not have any expertise in neuroscience. Out of curiosity, I am tempted to compare Artificial Neural networks with the human brain (With the help of talk shows on such topics).

It’s fascinating to me to know, how the human brain can decode technologies, numbers, puzzles, handle entertainment, understand science, set body mode into pleasure, aggression, art, etc. How does the brain train itself to name a certain object by just looking at 2-3 images when ANNs need millions of those.

The human brain is a kind of supreme intelligence that gives directive, knowledge, decisions, critical thinking process and consumes lots of energy as compared to its size and weight.

Brain intelligence is not because of size or weight but the number of neurons. Why the human brain has the highest number of neurons (86 billion approx), well that question still doesn’t have any concrete answer yet.

Suzana Herculano-Houzel, a neuroscientist (video) found a novel way to count several neurons with the help of a “homogenous mixture” which she referred to as brain soup by dissolving the brain in it. In the video, you will find an interesting argument she made with the help of brain soup.

  1. The number of neurons relative to the brain (human) size/weight with other primates may be consistent.
  2. Cerebral cortex –  The outer layer of the neural tissue of the cerebrum of the brain is the area responsible for higher cognition. It only holds around 20% of all our brain’s neurons. Interestingly a similar proportion was found in other mammals as well. (From link)
  3. Brain size myth- the Bigger brain is not better, otherwise cow, blue whale, or elephant would have better cognitive abilities.
  4. The human brain is the largest in the proportion of body mass – Not true.
  5. The higher the brain weighs, the higher the cognitive ability sadly not true again. The human brain weighs between 1.2kg to 1.5kg and the elephant got 4.5kg approx

The human brain grows in size as it starts consuming more calories and most likely credit goes to the cooked food we eat.

Quantum Computing and Neural Networks – Debunking The Myth

The role of quantum computing in AI and machine learning is now a hot discussion topic for every researcher in this field. Quantum neural networks would be able to learn much faster as compared to conventional computers or simply conventional computers can not achieve in any practical time limit.

Quantum NNs would be able to process a million times more data in much less time. Still, all these pattern learning and detection would happen based on the huge amount of data. Interestingly we do have ways to implement quantum artificial neural networks on IBM’s quantum computers on the cloud.

When it comes to the human brain it needs much less time and much less data to recognize some of the pictures/objects. In reality, we can not compare the human brain and neural networks, each of them have its unique feature and capabilities. Quantum computers open up a floodgate of opportunities. The ways for the empirical implementation of quantum artificial neural networks (in case it’s gonna happen).

The artificial neural network can create calculations at a speed that the human brain can not but here the reference for learning is only based on the amount of data available. In this age when we are talking about quantum physics, quantum computing, 6G network, Super AI, and machine consciousness what else we can dream of for our next/young generations. So two thoughts come to my mind now

  • AI or ML of today is ZERO without data.
  • AI/ML cannot make mistakes so no mistake means it a cant rule because intelligence is not enough.

If AI/ML cannot make mistakes mean it can not create or discover new ideas or inventions.  AI/ML can only apply one information pack to one set of data but the human brain can do infinite. I was watching some interesting ted talk shows and picked the best line which says “You can’t google idea though you can google the information”.

Points to Note:

When to use artificial neural networks as opposed to traditional machine learning algorithms is a complex one to answer. It entirely depends upon the problem at hand to solve. One needs to be patient and experienced enough to have the correct answer. All credits if any remain on the original contributor only. The next upcoming post will talk about Recurrent Neural Networks in detail.

Books Referred & Other material referred

  • Open Internet research, news portals and white papers reading
  • Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.
  • Learning through
    • Live webinars
    • Conferences, Lectures & Seminars
    • AI Talkshows

Feedback & Further Question

Do you have any questions about Deep Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.

Machine Learning (ML) - Everything You Need To Know

Conclusion –  Undeniably, ANN and Human Brain are not the same and their function/working is also very different. We have seen in the post above that ANNs don’t create or invent any new information or facts but the human brain does. ANN helps us to make sense of what’s already available in the hidden format. ANN takes an empirical approach to a massive amount of data to give the best and near-accurate results.

Deep Learning, in short, is going much beyond machine learning and its algorithms that are either supervised or unsupervised. In DL it uses many layers of nonlinear processing units for feature extraction and transformation. It has revolutionized today’s industries by demonstrating near-human-level accuracy in certain tasks. The task like pattern recognition, image classification, voice/text decoding, and many more. Self-driving CAR is one of the best examples and biggest achievements so far.

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Posted by V Sharma

A Technology Specialist boasting 22+ years of exposure to Fintech, Insuretech, and Investtech with proficiency in Data Science, Advanced Analytics, AI (Machine Learning, Neural Networks, Deep Learning), and Blockchain (Trust Assessment, Tokenization, Digital Assets). Demonstrated effectiveness in Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Payments), IT Service Management, Software Engineering, and Mobile Telecom (Mobile Data, Billing, Prepaid Charging Services). Proven success in launching start-ups and new business units - domestically and internationally - with hands-on exposure to engineering and business strategy. "A fervent Physics enthusiast with a self-proclaimed avocation for photography" in my spare time.

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