The wave of artificial intelligence is sweeping the world, and many words are always lingering in our ears: artificial intelligence (Artificial Intelligence), machine learning (Machine Learning), deep learning (Deep Learning). Many people always have a vague understanding of the meaning of these high-frequency words and the relationship behind them. This article clarifies the meaning of these words in the simplest language, which can help everyone better understand the relationship between them.
From cars to smartphones, to digital assistants, and even robots. We're not just talking about the groundbreaking new features that pop up every day. What's more, devices, computers, and machines are all performing tasks intelligently. How do they do it? — Through artificial intelligence, aka AI.
The term "artificial intelligence" was first coined by cognitive scientist John McCarthy, who wrote in his book: "This research is based on the assumption that any learning behavior or other intellectual characteristic can in principle be A precise description, so that a machine can be built to simulate it." Since then, artificial intelligence has been haunting people's minds and slowly hatched in scientific research laboratories. In the decades that followed, artificial intelligence has been reversing its polarities, or been called a prophecy of the dazzling future of human civilization, or thrown into the garbage heap as the fantasies of technological lunatics. Until 2012, both voices existed simultaneously.
Figure | Artificial Intelligence Research Branch
After 2012, thanks to the increase in the amount of data, the improvement of computing power and the emergence of new machine learning algorithms (deep learning), artificial intelligence began to explode. However, the current scientific research work is concentrated on the weak artificial intelligence part, and it is very hopeful that a major breakthrough will be made in the near future. Most of the artificial intelligence in the movies are depicting strong artificial intelligence, and this part is difficult to achieve in the current real world. (Artificial intelligence is usually divided into weak artificial intelligence and strong artificial intelligence. The former enables machines to have the ability to observe and perceive, and can achieve a certain level of understanding and reasoning, while strong artificial intelligence enables machines to acquire adaptive capabilities and solve some problems that were not previously problems encountered).
Weak artificial intelligence is expected to make breakthroughs, how is it achieved, and where does "intelligence" come from? This is largely thanks to one approach to artificial intelligence – machine learning.
We use the simplest method to visualize the relationship between the three.
Artificial Intelligence
Simply put, artificial intelligence describes the various ways in which a machine interacts with the world around it. With advanced, human-like intelligence, an AI machine or device can mimic human behavior or perform tasks like a human through the combined effects of software and hardware.
We can see a lot about artificial intelligence today, such as speech recognition (used in smart personal assistant devices), facial recognition (used in filters that are currently popular on social media), or object recognition (such as searching for apple and picture of oranges).
Machine Learning
Machine learning is an approach or subset of artificial intelligence that emphasizes "learning" rather than computer programs. A machine uses complex algorithms to analyze large amounts of data, identify patterns in the data, and make a prediction, a process that does not require a human to write specific instructions in the machine's software. After mistaking a cream puff for an orange, the system's pattern recognition improves over time as it learns from its mistakes and corrects itself like a human.
Deep Learning
Deep learning is a subset of machine learning that drives great strides in computer intelligence. It uses a lot of data and computing power to simulate deep neural networks. Essentially, these networks mimic the connectivity of the human brain, classifying datasets and discovering correlations between them. With newly learned knowledge (without human intervention), the machine can apply its insights to other datasets. The more data a machine processes, the more accurate its predictions will be.
For example, a deep-learning device could examine big data—such as the color, shape, size, ripening time, and origin of the fruit—to accurately determine whether an apple is a green apple or an orange is a blood orange.
The differences between AI, machine learning and deep learning are not as obvious as apples and oranges, they are more subtle. Machine learning is a method to achieve artificial intelligence, and deep learning is a technology to achieve machine learning. Now do you understand the difference between them?
