Artificial Neural Networks – Before we embark on this high-level journey, let’s understand and take the facts for granted. Artificial neural networks do not work like a human brain, besides being limited to a simple simulation. Can neural coding simulate the human brain? If yes, then 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? This nuanced comparison highlights the need for a balanced assessment of the strengths and limitations of both artificial neural networks and the human brain in various computational tasks.
Artificial Neural Network – Outlook
The artificial intelligence hype and too much usage of buzzwords have created a “misinformation epidemic,” which is entirely centred around artificial neural networks working like the human brain and machine learning. In reality, it’s quite far from how the human brain works. ANN simply works like a mathematical model, where its structure receives a signal from one side, either informing the pattern or a vector (for an image). The output is calculated within hidden layers with corresponding weights for the 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 in the human brain. Sadly, many researchers are too quick and pivot the “Human Brain and ANNs” under one pin tip, which is causing and creating huge confusion for newcomers. Their point in terms of 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 and 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 “undoubtedly a mind-blowing synchronization technique applied on the basis of three 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, please).
Deep learning is used with too much ease to predict the unpredictable. In our opinion, “We are all so busy creating artificial intelligence by using a combination of non-biological 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, requires 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 the 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 is it so unique? Such questions always arise when we start comparing ourselves with other species. It is a special and critical point of discussion for everyone and a puzzling game of all times as well. The human brain has cognitive ability and capacity, which makes it a great 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 cannot 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 directives, knowledge, decisions, and critical thinking processes and consumes lots of energy as compared to its size and weight. Brain intelligence is not because of size or weight but because of the number of neurons. Why does the human brain have the highest number of neurons (approximately 86 billion)? Well, that question still doesn’t have a concrete answer yet.
Artificial Neural Networks vs Biological Neural Networks
Artificial Neural Networks (ANNs) and Biological Neural Networks (BNNs) are two distinct systems that serve different functions, each with unique features and limitations. ANNs, a key component of machine learning, are designed to simulate the functions of the human brain in processing information and recognizing patterns. While they excel at tasks like pattern recognition and data processing, they lack the complexity and adaptability of BNNs.
On the other hand, BNNs are the intricate networks of neurons in the human brain responsible for cognition, sensory perception, and motor control. They exhibit a level of adaptability, learning, and memory retention that current ANNs cannot fully replicate. While ANNs have proven efficient for specific computational tasks, they still fall short of capturing the nuanced capabilities of the human brain. As research progresses, the aim is to further bridge the gap between ANNs and BNNs, fostering advancements in both artificial intelligence and our understanding of biological cognition.
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.
- The number of neurons relative to the human brain size and weight in other primates may be consistent.
- 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)
- Brain size myth: the bigger brain is not better; otherwise, cows, blue whales, or elephants would have better cognitive abilities.
- The human brain is the largest in terms of body mass.
- The higher the brain weighs, the higher the cognitive ability, which is sadly not true again. The human brain weighs between 1.2kg and 1.5 kg, and the elephant weighs 4.5 kg, 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 cannot achieve this in any practical time limit.
Quantum NNs would be able to process a million times more data in much less time. Still, all this 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 in the cloud.
When it comes to the human brain, it needs much less time and much less data to recognize some of the pictures and objects. In reality, we cannot compare the human brain and neural networks; each of them has its own unique features and capabilities. Quantum computers open up a floodgate of opportunities. ways for the empirical implementation of quantum artificial neural networks (in case it’s going to happen).
The artificial neural network can create calculations at a speed that the human brain cannot, 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, the 6G network, super AI, and machine consciousness, what else can we dream of for our next or younger generations? So two thoughts come to 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 or ML cannot make mistakes, it cannot create or discover new ideas or inventions. AI and 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 an idea, though you can google the information”.
It has revolutionized today’s industries by demonstrating near-human-level accuracy in certain tasks. Tasks 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.
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.
—
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 on the problem at hand. One needs to be patient and experienced enough to have the correct answer. All credits, if any, remain with 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.
============================ About the Author =======================
Read about Author at : About Me
Thank you all, for spending your time reading this post. Please share your opinion / comments / critics / agreements or disagreement. Remark for more details about posts, subjects and relevance please read the disclaimer.
FacebookPage ContactMe Twitter
====================================================================
