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AI is one of those things that does not have a concrete definition. Still, most simply, AI is a phenomenon where a machine tries to mimic human thinking to a certain degree, helping to solve the problems that are faced by humankind as a whole. Today AI is widely used and can be a great help to humans.
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This article is a guide to knowing what Artificial Intelligence is. This is a complex field, so here are the steps that one should take to learn AI.
Introduction • AI is one of those things that does not have a concrete definition. Still, most simply, AI is a phenomenon where a machine tries to mimic human thinking to a certain degree.
Helping to solve the problems that are faced by humankind as a whole. Today AI is widely used and can be a great help to humans.
There is not a tried, tested, or straightforward path to learning Artificial Intelligence.
But broadly, one must know statistics, mathematics, and computer/programming. Knowing about Data Science, Machine Learning, and Predictive Modeling can be a great help through the process.
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How to Learn AI? • Learning here requires a lot of hard work and dedication. So to learn AI, one must focus on the following concepts-
Develop a good knowledge about data because everything comes back to it. • Understand Machine Learning and its concepts.
Build a good foundation in Python as it helps in data exploration, manipulation, reporting, predictive modeling, machine learning, and deep learning.
Explore different resources like books, online courses and gather every information you can • Join a community, get involved in it
Concepts to learn in AI • AI is a heterogeneous field it involves many different fields. Some of the main concepts are-
Big Data is an important topic here. It manages high velocity, variety, and volume of data, architectures, and methodologies. • Machine Learning is relative to AI, ML is a sub-category of AI.
Deep Learning and Neural networks- here multiple neurons are stacked together to mimic the human brain • Back Propagation helps the AI systems to ‘learn’
Reinforcement Learning Setup- Most of the ML algorithms are supervised learning. Reinforcement learning works in the concept of rewards. If the network predicts the label, it is rewarded. And if wrong, it is punished.
Types of AI • Reactive Machines- This type of machine does not have a memory of its own. They are hard-coded AI systems.
One such example is IBM’s Deep Blue. It was able to play chess and even predict plausible moves but couldn’t learn from its past wins or losses.
Theory of Mind- These systems can comprehend other AI systems just like humans. This includes understanding complex human emotions, intentions, requirements, thoughts, complex speech patterns, etc.
Limited Memory- These systems have limited memory, utilize it, and get better over a period of time. Deep learning Architectures generally use this concept
Self Awareness- This is the highest level of AI systems. It is self-aware to such an extent that it is capable of independent thinking.
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