The wave of Artificial Intelligence is sweeping the world, and many words are always in our ears: Artificial Intelligence, Machine Learning, Deep Learning. Many people always have a vague understanding of the meaning of these high-frequency words and the relationship behind them.

In order to help everyone better understand artificial intelligence, this article uses the simplest language to explain the meaning of these words, clarify the relationship between them, hoping to be helpful to the peer who just started.

Artificial intelligence: From conception to prosperity

In 1956, a group of computer scientists gathered at a Dartmouth conference to come up with the concept of “artificial intelligence,” dreaming of using then-nascent computers to build complex machines with the same essential properties as human intelligence. Since then, artificial intelligence has been in people’s minds and incubated in research laboratories. In the decades since, ARTIFICIAL intelligence has been flipped from pole to pole, heralded as the dazzling future of human civilization or consigned to the dustbin as the fancies of a technological maniac. Until 2012, both voices existed at the same time.

After 2012, ARTIFICIAL intelligence exploded thanks to rising data volumes, improved computing power and the emergence of new machine learning algorithms (deep learning). As of the first quarter of 2017, there were more than 1.9 million technical talents in the field of AI(artificial intelligence) worldwide based on linkedin platform, with a shortage of more than 5 million in China alone, according to the global AI Talent Report released by linkedin.

The research field of artificial intelligence is also expanding. Figure 1 shows the various branches of artificial intelligence research, including expert systems, machine learning, evolutionary computing, fuzzy logic, computer vision, natural language processing, recommendation systems, etc.

FIG. 1 Research branch of artificial intelligence

But the current research work is focused on the weak ai this part, and hopefully for a major breakthrough in the near future, most of the artificial intelligence in the movie are strong ai, and this part in the real world is difficult to achieve (artificial intelligence (ai) is usually divided into weak strong artificial intelligence and artificial intelligence, let the machine have the ability to observe and perception of the former, A degree of understanding and reasoning is possible, while strong AI allows machines to adapt to solve problems they have never encountered before.)

A promising breakthrough in weak AI, how is it achieved and where does “intelligence” come from? This is largely due to a method of implementing ARTIFICIAL intelligence called machine learning.

Machine learning: an approach to artificial intelligence

At its most basic, machine learning uses algorithms to parse data, learn from it, and then make decisions and predictions about real-world events. Unlike traditional software programs that are hard-coded to solve specific tasks, machine learning takes large amounts of data and “trains” them, using algorithms to learn from the data how to perform a task.

To take a simple example, when we browse online shopping malls, we often see the information of product recommendation. It’s the store’s way of identifying what you’re really interested in and willing to buy, based on your past shopping history and lengthy collection lists. Such a decision-making model can help the mall offer suggestions to customers and encourage product consumption.

Machine learning is directly derived from the early field of artificial intelligence. Traditional algorithms include decision tree, clustering, Bayesian classification, support vector machine, EM, Adaboost and so on. In terms of learning methods, machine learning algorithms can be divided into supervised learning (such as classification problems), unsupervised learning (such as clustering problems), semi-supervised learning, integrated learning, deep learning and reinforcement learning.

The application of traditional machine learning algorithms in fingerprint recognition, face detection based on Haar, object detection based on HoG feature and other fields has basically reached the requirements of commercialization or the commercialization level of specific scenes, but every step is extremely difficult until the emergence of deep learning algorithm.

Giiso, founded in 2013, is a leading technology provider in the field of “artificial intelligence + information” in China, with top technologies in big data mining, intelligent semantics, knowledge mapping and other fields. With its strong technical strength, the company has received angel round investment at the beginning of its establishment, and received pre-A round investment of $5 million from GSR Venture Capital in August 2015. Giiso’s research and development products include editing robots, writing robots and other artificial intelligence products.

Deep learning: A technique for implementing machine learning

Deep learning is not an independent learning method, and it also uses supervised and unsupervised learning methods to train deep neural networks. However, due to the rapid development of this field in recent years, some unique learning methods have been proposed (such as residual network), so more and more people regard it as a learning method alone.

The original deep learning is a learning process that uses deep neural networks to solve feature expression. Deep neural network itself is not a new concept, which can be roughly understood as a neural network structure containing multiple hidden layers. In order to improve the training effect of deep neural network, the neuron connection method and activation function are adjusted accordingly. In fact, a lot of ideas had been put forward in the early years, but due to the lack of training data and backward computing ability, the final effect was not satisfactory.

Deep learning performs tasks so devastatingly that it makes all sorts of machine-assisted functions seem possible. Driverless cars, preventive health care, even better movie recommendations are all in sight, or within reach.

The difference and connection of the three

Machine learning is a method to achieve artificial intelligence, and deep learning is a technology to achieve machine learning. Let’s visualize the relationship between them using the simplest method, concentric circles.

FIG. 2 Relationship diagram of the three

At present, more and more development engineers want to understand or change careers in artificial intelligence, but mathematics has become the threshold that they cannot overcome. Machine learning is the only way to get into artificial intelligence, and machine learning algorithms need a foundation in advanced college mathematics, probability theory and mathematical statistics, linear algebra (or matrix theory).

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