Two of the most used buzzwords in the tech industry today are artificial intelligence (AI) and machine learning (ML). Despite the fact that they are frequently used synonymously, they are not the same. The distinctions between AI and ML will be discussed in this blog post.
What exactly are AI and ML?
The creation of intelligent machines that can carry out tasks that typically require human-like intelligence falls under the broad umbrella of artificial intelligence (AI), a branch of computer science. Natural language processing, image recognition, decision-making, and numerous other processes can be included in these tasks.
Contrarily, machine learning is a branch of artificial intelligence that focuses on creating algorithms that let computers learn from data without explicit programming. In other words, ML makes it possible for computers to gain knowledge from experience and develop over time.
What are the differences between AI and ML?
Natural language processing, computer vision, robotics, and machine learning are all subfields of the larger field of artificial intelligence (AI). On the other hand, machine learning (ML) is a subfield of artificial intelligence that focuses on creating algorithms that let computers learn from data.
Artificial intelligence (AI) is created to mimic human intelligence and carry out tasks that call for cognitive processes like decision-making, problem-solving, and reasoning. By allowing machines to learn from data, ML, on the other hand, aims to enhance machine performance.
- Data Dependency
While ML algorithms are made to learn from data, AI algorithms are made to make decisions based on rules and logic. In other words, while ML algorithms are data-driven, AI algorithms are rule-based.
As it involves more subfields and necessitates a thorough knowledge of cognitive science, computer science, and mathematics, AI is a more complicated field than ML. On the other hand, ML is a more specialized area and is simpler to comprehend and use.
AI has many different uses, including decision-making, computer vision, robotics, and natural language processing. On the other hand, ML is primarily utilized for tasks like fraud detection, image recognition, and predictive analytics.
Author – Shrey Mehra