Artificial Intelligence (AI) and machine learning (ML) are correlated parts of computer science. Both of these technologies are quite popular right now, especially for use in making intelligent computer systems. While AI and machine learning are often used interchangeably, they are not synonymous. Broadly speaking, “AI is a bigger concept to create intelligent machines that can simulate human thinking capacity and behavior, whereas machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly.”.
What is Artificial Intelligence?
Artificial Intelligence is the set of technologies used to build systems that are intelligent and can simulate human intelligence. It is a field of computer science. AI does not require pre-programming. Instead of programs, algorithms are used. Part of AI is machine learning algorithms like reinforcement learning algorithms and deep learning neural networks. Another definition of AI is that it is “a field, which combines computer science and robust datasets, to enable problem-solving.” AI is integrated into our daily lives through applications like Apple’s Siri and Google. AI is classified into three categories: Weak, general, and strong. Right now, we are working with weak and general AI. Strong AI is still in the future. Weak AI is also known as Artificial Narrow Intelligence (ANI). It focuses on specific tasks. Most of the AI in use today is ANI.
What is Machine Learning?
ML is about finding knowledge within data. It is a subset of AI. ML allows machines to learn from data they’ve used in the past and experiences—with zero specific programming. By using historical data, ML helps a computer to make a prediction or a decision without a program directing its actions. Another way of defining machine learning is: “Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects.”. The data ML uses is from a huge pool. This allows the machine to create correct results or offer hypotheses or predictions using that same data. The algorithms of ML can learn from their own data. ML is actively used in search algorithms, spam filters for email, and social media tagging suggestions. There are three types of machine learning: supervised, reinforced, and unsupervised.
What Makes Artificial Intelligence So Different from Machine Learning?
There are several key differences between AI and ML. The goal of AI is to create computer programs that can mimic human intelligence. However, ML is a part of AI, and the goal is to teach machines to learn automatically from past data, free from direct programming. Where the result of AI is intelligent computer systems that can solve difficult problems like a human. With ML the result is a machine that can be taught to do a specific task and give a correct result. ML and deep learning are subcategories of AI. Deep learning is a subcategory to ML. The differences between AI and machine learning continue this time a bit more broadly. When it comes to scope, AI’s is very wide, whereas ML has a very narrow scope. Where AI systems care about improving the chances of success, ML wants to be accurate and find patterns. We can further compare AI vs. machine learning by comparing their applications. AI powers things like Apple’s Siri, customer support programs, online gaming, etc. ML is applied in ways such as online shopping recommendations, Google’s search algorithms, and social media tagging suggestions. AI and ML deal with different types of data. AI works with structured, semi-structured, and unstructured data, whereas ML works with structured and semi-structured data. To conclude our comparison consider this: AI learns, reasons and self-corrects, on its own. ML can only do these things in the presence of new data.
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