Artificial intelligence, machine learning, and deep learning are often discussed together and even used as synonyms. But they are not parallel relationships, but rather relationships that are contained layer by layer. Artificial intelligence is the largest category, machine learning is a subfield of artificial intelligence, and deep learning is a type of machine learning method based on deep neural networks. Understanding this relationship and looking at various artificial intelligence news and popular science content will make it much clearer.
Artificial intelligence aims to build systems that can simulate human intelligent behavior. Its scope is very broad, covering various technical routes such as logical reasoning, search algorithms, probabilistic reasoning, etc. Machine learning is just a subset of the artificial intelligence family. Traditional artificial intelligence often requires humans to manually write a large number of rules to tell machines to perform certain actions when encountering certain situations. Machine learning has taken a different approach, allowing machines to fit mathematical models onto massive observational data, learn patterns from the data, and make judgments and decisions based on them.
In the past decade, the field of machine learning has experienced explosive growth. Due to its high popularity, many people directly regard machine learning as synonymous with artificial intelligence, which is actually a common misconception.
Among the numerous models in machine learning, there is a special type of model called deep neural networks. When deep neural networks are trained and fitted with data, this process is called deep learning. Deep learning is currently one of the most powerful and widely implemented machine learning solutions, which has penetrated into every aspect of daily life. Machine translation can convert foreign text into Chinese, which involves natural language processing; Image retrieval can upload a photo and find similar objects on the Internet, relying on computer vision; Voice assistants can understand what we say to our phones and respond through voice recognition. Many of these daily features are driven by deep learning techniques.
If we summarize the relationship between the three in one sentence, artificial intelligence is the largest circle, machine learning is the circle within artificial intelligence, and deep learning is the smaller circle within machine learning. Deep neural networks are a special type of machine learning model, and the process of training and fitting them with data is called deep learning. Machine learning is a subfield of artificial intelligence that fits mathematical models onto observed data. It can be roughly divided into supervised learning, unsupervised learning, and reinforcement learning, and deep neural networks contribute to these fields.
Artificial intelligence is not equivalent to machine learning, and machine learning is not equivalent to deep learning either. Deep learning is just one particularly impressive branch of the flourishing development of artificial intelligence. By understanding this subordinate relationship and reading various artificial intelligence news and popular science articles, it is not easy to be confused by terms.
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