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Understanding these terms will lay a solid foundation for your journey into the world of AI.
Artificial Intelligence (AI):
The overarching goal of AI is to create machines capable of thinking and acting like humans — learning from experience, making decisions, and solving complex problems. It is a broad field encompassing multiple subfields.
Machine Learning (ML):
A subset of AI focused on developing systems that can learn automatically from data. Instead of programming every rule, we allow the machine to discover patterns on its own — for example, a product recommendation system based on your purchase history.
Deep Learning (DL):
An advanced branch of Machine Learning that employs multi-layered artificial neural networks to process massive amounts of data. It is particularly effective for unstructured data such as images, sound, and text.
Data:
Often described as the fuel for AI. Whether in the form of text, numbers, images, or sound, data is what AI models use to learn and make inferences. The quality and quantity of data directly influence the performance of AI systems.
Algorithm:
A set of rules or step-by-step procedures that a computer follows to process data, learn, or solve a specific problem. It serves as the blueprint for how AI operates.
Model:
The outcome of training an AI algorithm on a dataset. It is a refined system ready to apply what it has learned to new data for prediction or classification purposes.
Training:
The process of feeding data into an AI algorithm so it can learn to perform a specific task. Similar to human learning, this process involves continuous model adjustments to improve accuracy.
Testing:
After training, the model must be evaluated using a completely new dataset (one it has never seen before) to assess its real-world performance and ensure its ability to generalize.
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Exploring Advanced AI Terminology
Once you are familiar with the basics, it’s time to delve deeper into more complex concepts that explain how AI truly operates.
Artificial Neural Network (ANN):
Inspired by the structure of the human brain, ANNs are computational models consisting of multiple interconnected processing units (neurons) arranged in layers. They form the core of Deep Learning, enabling the system to process complex relationships within data.
Natural Language Processing (NLP):
A field that allows computers to understand, interpret, and generate human language. Applications include machine translation, virtual assistants, sentiment analysis, and text summarization.
Computer Vision (CV):
Gives computers the ability to “see” and interpret visual content from images or videos, similar to human vision. It is widely applied in object recognition, autonomous vehicles, medical image analysis, and quality control in manufacturing.
Reinforcement Learning (RL):
A machine learning approach in which AI learns to make optimal decisions through trial and error — receiving rewards for correct actions and penalties for mistakes. It is commonly used in robotics, strategic optimization, and game playing.
Big Data:
Refers to extremely large and complex datasets characterized by high Volume, Velocity, and Variety — exceeding the capacity of traditional data-processing tools. AI, particularly ML and DL, thrives on Big Data to uncover hidden patterns and valuable insights.
Generative AI:
A rapidly advancing branch of AI capable of creating original content such as text, images, music, or code based on learned data. Models like GPT or Stable Diffusion are prime examples, ushering in a new era of creative generation.
Prompt:
A command, instruction, or question provided to a Generative AI model to produce a desired output. The quality and level of detail in the prompt greatly influence the model’s output.
Accuracy:
A metric that measures the proportion of correct predictions made by an AI model out of all predictions. While commonly used to evaluate performance, it should be considered alongside other metrics depending on the task.
Bias:
Refers to unfairness or systematic errors in AI model results, often stemming from non-representative or socially biased training data. Identifying and mitigating bias is a crucial challenge in building responsible AI systems.
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The world of artificial intelligence is constantly expanding, and understanding its language will not only help you keep up but also enable you to apply it effectively in your work and daily life. Continue exploring with Công Nghệ AI VN to stay updated with the latest knowledge and news about artificial intelligence!
