Machine Learning, Artificial Intelligence, and Deep Learning. How Are They Related?
Let’s dive into how these fields leading the 4th industrial revolution are closely related
In an age where we are trying to make machines intelligent as humans, Machine Learning, Artificial Intelligence, and Deep learning have become very common words that you can’t ignore from your social media feed.
So how are they related?
In this article, I will demystify each term, providing clear and concise explanations to ensure that they no longer remain unfamiliar concepts. By breaking down each term into easily understandable forms, you will gain a comprehensive understanding of their meanings and their relationships to each other as they are used often used synonymously nowadays.
What is Artificial Intelligence
In its simplest form, AI is getting machines to perform tasks normally done by humans. The ability of machines to mimic human intelligence.
In this regard, any machine that can perform a human-related task can be regarded as an AI whether we gave it instruction on how to do it (Symbolic AI) or it simply learned it by observing(Machine Learning).
Symbolic AI involves using knowledge representation and explicit embedding of human knowledge and behavior rules in computer programs. The knowledge is hard-coded into the machine using logic programming. Expert Systems fall into this category. Expert Systems are designed to emulate the problem-solving skills and knowledge of human experts in specific domains.
A Doctor who is a domain expert can help a programmer design or code the process of diagnosing a particular disease into a computer. The computer then acts like a Doctor(domain expert) in the diagnosis of that particular disease.
What Is Machine Learning
Machine learning focuses on developing algorithms(programs) and models that enable computer systems to learn from data and make accurate predictions without explicitly being programmed. It is a subset of Artificial Intelligence, whereas AI involves machines that can learn and others that do not learn. Machine learning emphasizes giving machines the ability to learn from data.
Machine learning can be classified into three categories:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Deep Learning, How does It Fit In?
Deep learning is a subfield of machine learning that focuses on using artificial neural networks to learn from data. A neural network is inspired by the human brain( the interconnection of neurons in the brain) though it works very differently for the human brain.
Deep learning takes the learning process of machine learning further through more complex algorithms that can effectively learn from data and detect complex patterns unlike Classical Machine Learning algorithms. Deep learning has also shown exceptional performance on unstructured data types, such as images, audio, and text.
It has achieved state-of-the-art results in computer vision, natural language processing, and speech recognition tasks. Classical machine learning methods may struggle to capture complex patterns and dependencies in unstructured data without extensive feature engineering
Conclusion
Collectively, these disciplines hold immense promise as catalysts for transforming numerous industries. From the realms of Agriculture, Architecture, and Cinematography to Cybersecurity and Healthcare, among various others, they have the potential to revolutionize and reshape traditional practices and methodologies.
Artificial Intelligence, Machine Learning, and Deep Learning are all closely interconnected and build upon each other. Out of AI, comes Machine Learning, and out of Machine Learning comes deep Learning.
Now, we know that all Machine Learning and Deep Learning Applications can be termed as Artificial Intelligence but not All Artificial Intelligence applications are Machine Learning or Deep Learning Applications.
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