Artificial intelligence and machine learning are two fields of computer science that are related. These two technologies are the most popular for developing intelligent systems. Therefore, we can regard both of them as futuristic technologies. 

Although these are two related technologies that are sometimes used as synonyms for each other, they are still distinct terms in numerous cases. Here are the key distinctions between them. 

Differences because Artificial Intelligence and Machine Learning

• Artificial intelligence is a branch of computer science that develops computer systems that can simulate human intelligence. It is made up of the words “artificial” and “intelligence,” and it means “man-made thinking power.” Artificial intelligence systems do not need to be pre-programmed; instead, they use algorithms that work with their intelligence. 

• Machine learning algorithms such as reinforcement learning algorithms and deep learning neural networks are used. Machine learning, on the other hand, allows a computer system to make predictions or make decisions based on historical data without being explicitly programmed. Machine learning makes extensive use of structured and semi-structured data for a machine-learning model to produce accurate results or make predictions based on that data. Machine learning is based on an algorithm that learns on its own using historical data. It only works for specific domains, for example, if we create a machine-learning model to detect pictures of dogs, it will only return results for dog images, but if we provide new data, such as a cat image, it will become unresponsive. Machine learning is used in a variety of applications, including online recommender systems, Google search algorithms, email spam filters, Facebook Auto friend tagging suggestions, and so on.

• The term “artificial intelligence” is poorly defined, which adds to the confusion between it and machine learning. Artificial intelligence is essentially a smart-appearing system. However, that is not a very good definition because it is akin to saying something is ‘healthy.’ Problem-solving, learning, and planning are examples of these behaviours, which are accomplished by analysing data and identifying patterns within it to replicate those behaviours. 

• Machine learning, on the other hand, is a subset of artificial intelligence. Whereas artificial intelligence is the overall appearance of intelligence, machine learning is the process by which machines collect data and learn things about the world that humans would find difficult. Machine Learning has the potential to surpass human intelligence. Machine Learning is primarily used to process large amounts of data quickly using algorithms that change over time and improve at what they are supposed to do. A manufacturing plant’s network may collect data from machines and sensors in quantities far exceeding what any human is capable of processing. Machine Learning is then used to detect patterns and identify anomalies, which may indicate a problem that humans can then address. Machine learning is a technique that enables machines to obtain information that humans cannot. We do not understand how our vision or language systems work—it’s difficult to express ourselves simply. As a result, we rely on data and feed it to computers, which simulate what we think we are doing. That is what machine learning accomplishes. 

• Artificial intelligence (AI) is a technology that allows machines to mimic human behaviour. Machine learning is a subset of AI that allows a machine to learn from past data without explicitly programming it. 

• The goal of AI is to create a smart computer system that can solve complex problems like humans. The goal of Machine Learning is to enable machines to learn from data to produce accurate results. In AI, we create intelligent systems that can perform any task that a human can. The two main subsets of AI are machine learning and deep learning. The main subset of machine learning is deep learning. 

• AI has a very broad application. Machine learning has a limited application. AI is working to develop an intelligent system capable of performing a variety of complex tasks. Machine learning is working to create machines that can only perform the tasks for which they have been trained. The AI system is concerned with increasing its chances of success. 

• Machine learning is primarily concerned with accuracy and pattern recognition. Siri, customer service via catboats, expert systems, online gaming, intelligent humanoid robots, and other applications are examples of AI applications. Machine learning is most commonly used in online recommender systems, Google search algorithms, Facebook auto-friend tagging suggestions, and so on.

Also published on Medium.