Neural networks or Artificial Neural Networks, also known as synthetic synapses or robot brains, are like the turbo boosters of machine learning and keep the engine of deep learning chugging along.
Their name and composition are based on the human brain, kind of like a computer-age recreation of biology’s most impressive organ. 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. Can we say this any better than the human mind for speedy mathematics? This is the first post in the “Neural Networks – Plain Text” series
Understanding Deep Learning For Neural Networks
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. tasks like pattern recognition, image classification, voice or text decoding, and many more. Self-driving cars are one of the best examples and biggest achievements so far.
The hype and optimism surrounding Artificial Intelligence have led to a widespread real “fake news disease” and misbelief about neural networks, resembling a prevalent issue of misinformation, as people mistakenly assume they function similarly to the human brain.
AILabPage Define Artificial Neural Networks as
Deep learning which is a subset of machine learning utilizes interconnected nodes to create a layered structure that emulates (try to) the operation of the human brain through connected neurons. Artificial neural networks strive to achieve precision in intricate duties such as identifying faces and condensing texts.
Artificial Neural Network – Outlook
Neural networks were intentionally crafted to emulate biological neural networks and serve as algorithms dedicated to this specific objective. The basic concept of neural networks relies on connecting neurons according to the unique arrangement of the network. Initially, the aim was to create an artificial system with the ability to function like the human brain sadly its far from the reality.
To be honest, the functioning of our brains is significantly more intricate than that of an artificial neural network. Put simply, an ANN is a mathematical tool that processes inputs (such as patterns or images) and uses specialised hidden layers to determine the appropriate output based on the weights of those inputs.
Yikes, researchers have gone off the deep end by trying to compare human brains to ANNs! They’re like two different galaxies, and studying them together has caused a lot of chaos and confusion for people who don’t know much about the topic. These scientists (no offense to anyone) need to chill out, slow down, and figure out why they’re researching this in the first place.
In brief, Artificial Neural Networks (ANNs) are mathematical entities that were initially formulated to mimic biological neurons, although the degree of approximation remains open for further inquiry. Researchers are endeavoring to unravel the potential of a brain-computer interface. The task of simulating the human brain with AI is a formidable undertaking and is unlikely to be achieved within the next half-century or so.
What Makes Human Brain So Special
The question of what sets the human brain apart from that of other species and renders it unique frequently arises in comparative analysis. The topic under consideration has been the subject of profound scrutiny and engenders enigmatic intrigue, rendering it a perplexing puzzle of enduring interest.
The human brain possesses a remarkable cognitive aptitude that renders it highly suited for the generation of novel ideas and the identification of new concepts. We have successfully fabricated a wheel that surpasses the capabilities of both artificial intelligence and other animal species. The human brain represents a significant milestone in the progression of biological organisms as it is the most prominent accomplishment in evolution.
The human brain is like a supercomputer, constantly churning out directives, knowledge, and decisions – all while using an astounding amount of energy for such a small device. Suzana Herculano-Houzel, a neuroscientist (video) found a novel way to count a number of neurons with the help of a “homogenous mixture” what 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 brain (human) size/weighs with 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 found in other mammals as well. (From link)
- Brain size – the Bigger brain is not a better brain, otherwise cow, blue whale or elephant would have better cognitive abilities.
- The human brain is largest to the proportion of body mass – Not true.
- 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 roughly got around 4.5kg.
The human brain grows in size as it starts consuming more calories and most likely credit goes to the cooked food we eat. The enormous complexity of the human brain, with billions of neurons creating sophisticated networks, allows for unprecedented cognitive capacities such as critical thinking, language, and emotion management. Its versatility and plasticity aid in learning, memory, and inventiveness. These qualities, coupled with self-awareness and consciousness, distinguish the human brain as a remarkable and one-of-a-kind organ.
Exploring Diverse Neural Networks
There are several kinds of Neural Networks used in deep learning or machine learning and artificial intelligence applications. Neural networks consist of input and output layers and at least one hidden layer. Some commonly encountered types and kinds of neural networks include:
- Feedforward Neural Networks: These neural networks consist of an input layer, several middle layers, and an ending layer. They take information in and give it out without going back or repeating anything. The two types of algorithms in this are as below
- Multilayer Perceptrons (MLPs)
- Convolutional Neural Networks. (CNNs) – Primarily used for image and video analysis. They are designed to automatically learn and extract hierarchical features from visual data using convolutional layers. These are highly successful in tasks like image classification, object detection, and image generation.
- Recurrent Neural Networks (RNNs): RNNs have feedback connections that allow information to flow in loops. They are suitable for processing sequential data, such as time series or natural language, due to their ability to retain the memory of past information. Some popular variations of RNNs. are
- Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, which are pitted against each other in a competition. The generator tries to generate synthetic data that resembles the real data, while the discriminator aims to distinguish between real and fake data. GANs are widely used for tasks like image
- Radial Basis Function Network: (RBFNs): RBFNs are characterized by radial basis functions that measure the similarity between input data and prototypes. They are commonly used for pattern recognition, function approximation, and time series prediction.
- Reinforcement Learning Networks: These networks learn through interactions with an environment. They aim to maximize a reward signal by selecting actions based on observed states. Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) are popular algorithms used in reinforcement learning.
- Self-Organizing Maps (SOMs): SOMs are a type of unsupervised learning neural network used for clustering and visualizing high-dimensional data. They organize data into a low-dimensional grid structure, preserving topological relationships between the input data points.
Neural Network Algorithms are based on radial basis functions with can be used for strategic reasons. There are several other models of the neural network including what we have mentioned above.
By adopting an empirical approach, ANNs analyze vast quantities of data to extract meaningful patterns and insights, ultimately providing us with the optimal and highly accurate outcomes we seek. While ANNs contribute significant value in processing complex datasets, the creative ingenuity and innate inventiveness of the human brain remain unparalleled.
Example – Chess Game (Between me and my son)
The integration of an Artificial Neural Network (ANN) into my chess game with my son adds a new layer of strategic analysis and move suggestions. Let’s assume a scenario where an Artificial Neural Network (ANN) is used to assist in analyzing my chess game with my son.
- Me and my son are playing a chess game on a digital platform.
- An Artificial Neural Network (ANN) is integrated into the platform.
- The ANN analyzes the current state of the chessboard, piece positions, and move history.
- It predicts potential outcomes and suggests moves based on historical patterns and strategies.
- The ANN enhances my gameplay by providing insights, move suggestions, and strategic analysis.
This modern technology enhances the gameplay experience by leveraging historical data and patterns to provide valuable insights. As I continue to engage in this interactive and dynamic chess match, the ANN’s contributions showcase the potential of AI-driven assistance in recreational activities, paving the way for innovative and engaging experiences.
Conclusion – It is unquestionable that artificial neural networks (ANNs) and the human brain exhibit notable distinctions in their characteristics, operations, and functionalities. As highlighted in the previous discussion, ANNs lack the ability to generate or conceive novel information or facts, a capability exclusive to the human brain. Rather, ANNs play a crucial role in aiding our comprehension and interpretation of existing information that may be concealed from our immediate perception. Each network type has its own strengths and is suited for different applications, providing researchers and practitioners with a rich toolbox for solving various machine learning and AI problems.
Feedback & Further Questions
Do you have any burning questions about Big Data, “AI & ML“, Blockchain, FinTech,Theoretical PhysicsPhotography or Fujifilm(SLRs or Lenses)? Please feel free to ask your question either by leaving a comment or by sending me an email. I will do my best to quench your curiosity.
Points to Note:
It’s time to figure out when to use which “deep learning algorithm”—a tricky decision that can really only be tackled with a combination of experience and the type of problem in hand. So if you think you’ve got the right answer, take a bow and collect your credits! And don’t worry if you don’t get it right in the first attempt.
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.
- Self-Learning through Live Webinars, Conferences, Lectures, and Seminars, and AI Talkshows
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