How are AI and Machine Learning (ML) different?

AI and Machine Learning (ML) are buzzwords often heard in the technology industry. which are often misunderstood and used interchangeably Both of which do not have the same functionality. We must first understand what AI and Machine Learning (ML) are and how they work.

What is AI?

AI is an abbreviation for artificial intelligence, or in Thai, it is artificial intelligence. The broad picture is machines that can work and make decisions on their own, such as making decisions, translating languages, and learning to recognize voices. And create things. Many submodel sets and filters of AI can solve specific problems and provide different guidelines for each model. The 5 most popular AI model sets are:

1. Natural Language Processing (NLP) is natural language processing that focuses on AI being able to understand, interpret, and create human language, mostly used in chatbots. Voice command system and language translation etc.

2. Computer Vision is enabling machines to process, interpret, and understand their surroundings with computer systems, such as object detection. or facial recognition Autonomous driving cars, etc.

3. Robotics are machines that can physically and autonomously interact with the world around them to perform tasks such as assembly line work or rescue operations. Boston Dynamics focuses on robotics.

4. Expert Systems are systems designed to imitate the reasoning-based decision-making abilities of experts in a specific field, such as medical diagnosis or financial analysis.

5. Machine Learning involves feeding data into a machine learning algorithm and allowing it to learn from that data to predict or classify new data accurately.

Therefore, Machine Learning is one of the AI ​​models. That is the first big difference to note. While AI is a term that covers a wide range of technologies and techniques, Machine Learning is a specific approach. in creating AI systems

What is Machine Learning?

From the above information, Machine Learning is one of the AI ​​model categories, which is an important step in the robot creation process. Using statistical algorithms, machines can learn from data and improve their performance on specific tasks over time. ML algorithms analyze large amounts of data to identify patterns. which is used to make predictions or make decisions about new information Like humans, ML is a process that requires "teaching" a machine by exposing it to data.

Therefore, when understanding AI and Machine Learning (ML), you can identify their differences and their ability to perform different tasks. Machine Learning (ML) is one of the subtypes of AI models, which has many other forms. There are many sub-model types.