Artificial Intelligence (AI) historically has aimed at developing machines which can simulate human thought and creativity. Such systems can analyze data, learn from it, make decisions and adapt to different situations. Even though such machines currently are considered not completely matching human brain reasoning level, however, at the speed field is evolving it appears likely such systems will soon not only mimic but surpass human cognitive abilities, which were shaped over millions of years of evolution. These machines will increasingly find their way in various industories, and despite their enormous potential advantages, it also has ethical dimension of breaking the existing socio-economic order. There is also possibility, that in future once they can't be kept completely under human control with their level of thought more superior, they can then pose risk to even very existence of humans and even for other animals over the planet. Such scenarios though can be visualized but for machines once ahead of human intellect, they can't be comprehended completely, that how can potentially be implemented by such near future robotic creatures! However, such fears are of no hinder to rapid ongoing advances in field which seems a race to keep ahead of others, and with currently there is lack of regulations globally for charting the direction and progress of field. Not against R & D in this amazing field but as a side note, if this techonolgy not stricly checked and regulated globally and completely in its development scope, then tomorrow's robots based on AI once don't need human dependence to keep them functional, to upgrade and ruplicate etc can not only give themselves unimaginable forms (more then just digital machines depending upon and controlled by humans!) but can eliminate humans from planet, a probable occurrence which can only be compaired in scale to complete extinction of Dinosaurs, who ruled on this planet for many folds more duration then 50 millions plus years of early primates evolution giving rise to humans!

AI models are typically trained on large and diverse dataset which is often beneficial for improving the performance of the model. However, AI models don't necessarily have to be trained only on large datasets. The appropriateness of the dataset size depends on the specific task, the complexity of the model, and the goals of the project. The quality of the data is often more important than the quantity. No amount of data can compensate for poor data quality. Clean, labeled, and representative data is crucial for model performance.

Training a model for artifical intelligence means teaching a mathematical algorithm, typically a neural network or another machine learning model, to make predictions or decisions based on data. It essentially involves optimizing its internal parameters to make accurate predictions or classifications based on the input data. The goal is for the model to learn patterns and relationships within the data so that it can generalize and make correct predictions on new, unseen data.

AI encompasses many subfields, but here only most prominent areas of Machine Learning (ML) and Deep Learning (DL) are included. Basic idea is that machines (computers) are trained to accomplish specific tasks by processing of input data and recognizing patterns in it. AI achives accuracy through neural networks (subfield of ML), which are like interconnected neurons in the brain processes and learn from data and recognize complex patterns. While Artificial neural neworks (ANNs) are inspired by the brain's neural structure, they are highly simplified and abstract representations of biological neurons.

ML is a subset of AI that involves the development of algorithms and statistical models of Data Science that enable computers to learn and improve their performance on a specific task through experience. Algorithms are trained to make classifications. These insights subsequently drive decision making. In ML, focus is on creating algorithms and models once learn from data then make predictions or decisions on new, unseen data, without being explicitly programmed following rules of traditional software development.

Deep Learning is a subfield of ML that specifically focuses on neural networks with many layers (deep neural networks). These multiple layers help to optimize and refine predictions accuracy. These networks get inspiration by the structure and function of the human brain. DL has been particularly successful in tasks like image and speech recognition due to its ability to automatically learn hierarchical features from data. DL is at core of many AI everyday products such as digital assistants, voice enabled TV remotes, credit card fraud detection and self-driving cars.

Neural networks, are a fundamental component of deep learning. They consist of interconnected nodes (neurons) organized into layers. They are intricate networks of interconnected nodes, or neurons, that collaborate to tackle complicated problems. Each neuron processes input data and passes it to the next layer. Neural networks learn by adjusting the weights and biases of connections between neurons during training, to recognize complex data patterns. Neural networks has been widely used in a variety of applications, including image recognition, predictive modeling and natural language processing (NLP), handwriting recognition for check processing, speech-to-text transcription, oil exploration data analysis, weather prediction and facial recognition.

Artificial Intelligence (AI) has a wide range of applications across various industries and sectors to enhance efficiency, accuracy, and decision-making in many different domains such as health care, education, manufacturing, finance, retail etc. Unlike humans there is no fatigue associated with such systems. The accuracy of AI systems depend upon many factors such as the quality of data, the complexity of the task, the training of the algorithms, and the specific domain where they are applied. Currently AI systems are used as decision support tools, and final decisions involve human oversight.

AI algorithms allows machines to process information, reason, learn, and make informed decisions. The choice of algorithm depends on the specific task, the type of data available, and the desired outcome. New algorithms and techniques are developed to address increasingly complex challenges.

Some of the common algorithm types include:

• Supervised Learning: Algorithms learn from labeled training data to make predictions or classify new data.
• Unsupervised Learning: Algorithms identify patterns or structures in data without labeled examples.
• Reinforcement Learning: Algorithms learn through trial and error based on rewards and penalties.
• Convolutional Neural Networks (CNNs): Used for image and video analysis, capturing spatial relationships.
• Recurrent Neural Networks (RNNs): Suitable for sequence data and time series analysis due to their memory of past inputs.
• Tokenization: Splits text into smaller units (tokens) like words or subwords.
• Evolutionary Algorithms: Inspired by natural selection, they optimize solutions by iteratively evolving a population of potential solutions.
• Decision Trees: Model decisions and outcomes in a tree-like structure for classification and regression.
• Random Forests: Ensemble of decision trees that improves prediction accuracy.