Artificial Intelligence and Its Applications

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Brief Introduction to Machine Learning, Deep Learning, Weak and Strong Artificial Intelligence, Neural Networks

Definition of Artificial Intelligence

So let’s look at the definition of artificial intelligence, what is artificial intelligence, which is what we mean by ai. Artificial intelligence is a new technical discipline that researches and develops theories, methods, technologies, and application systems for simulating the extension and expansion of human intelligence. Our use of artificial intelligence research is to hope that machines can perform some complex tasks that require intelligent humans to complete. 

Machine Learning and Deep Learning

When it comes to artificial intelligence, we have to mention two aspects: machine learning and deep learning.

Machine Learning

First of all, machine learning is a core concept in artificial intelligence. All of us have to learn, and our human knowledge transfer is also carried out through such a method of learning. We learn the knowledge of our ancestors, and then create new knowledge by inference. We also hope that the machine has such ability: By learning the previous information, the machine is more like having intelligence and can react accordingly for new input in the future. This is called machine learning.

ML

Deep Learning

When we talk about artificial intelligence, we often hear concepts such as machine learning and deep learning. In fact, they are an inclusive relationship, and artificial intelligence includes machine learning and deep learning, and a specific form of learning in machine learning is called deep learning. It mainly based on algorithms of neural networks. At present, deep learning has made great progress in fields of image recognition, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, medical image analysis, and board game programs.

Deep Learning

Weak and Strong Artificial Intelligence

Artificial intelligence has two different forms, one called weak artificial intelligence, and one called strong artificial intelligence. So what are the two of them talking about?

Weak Artificial Intelligence

What weak artificial intelligence is saying is that the machine is not really intelligent. The key is that it cannot have an autonomous consciousness. It can only have corresponding intelligence in a specific field, which is similar to a very advanced, a kind of bionics. Only in one aspect, such as watching, listening, and speaking, it appears to be intelligent, but it doesn’t like humans who have complete consciousness.

Strong Artificial Intelligence

Strong artificial intelligence means that the machines can appear conscious and reach or even surpass human intelligence. This is not just a field of computer science, and it involves many aspects such as psychology, philosophy, and so on. It belongs to a kind of intelligence created by people, and can even be called life.

So the biggest difference between weak artificial intelligence and strong artificial intelligence is what intelligence level this machine can reach, and whether the machine has its own consciousness. This is the most essential difference between these two types of artificial intelligence. Don’t afraid our world will be conquered by machines like Skynet or something else first. At present, the range of artificial intelligence we are studying is still in the range of weak artificial intelligence.

What is Neural Networks?

When it comes to artificial intelligence, we have to mention a well-known algorithm in artificial intelligence, called neural networks. Then the neural network is the same as the neural transmission of the human brain, from one input unit to the next input unit to get a result. This is the principle of a simple neural network, which is to simulate the transmission of information from nerves in the human brain. It transfers information from one neuron to another and then passes down.

BP Neural Network

After the invention of the neural network algorithm, many problems have been solved to a certain extent. At the same time, people are constantly optimizing this algorithm. First, a very widely used and very classic one is the BP neural network. BP neural network has one more hidden layer than the original neural network. There are additional hidden layers in the input layer and the output. It can greatly reduce the amount of calculation and the difficulty of calculation by way of gradient descent.

Convolutional Neural Network

But after we have the BP neural network, we find that the computational load of the BP neural network is still very large. It sometimes fails to give the optimal solution within our acceptable time range, or it takes too long to give the optimal solution, which does not meet the needs of some of our applications. Then came the convolutional neural network (CNN), which is also a kind of neural network algorithm in essence, but it optimizes the content in the BP neural network, it makes the calculation faster, and it can get the most on many problems. Excellent solution. It improves the efficiency of its calculation by processing related information highly concurrently. At the same time, it greatly reduces the computational complexity between BP neural networks. Therefore, the convolutional neural network can currently reach the optimal solution in a fast time on many problems.

Let’s see what artificial intelligence can do.

Image Recognition

Image recognition is now widely used in our lives. For example, the identity of a person can be identified based on photos or when a person’s face is captured with a camera. In many train stations in cities of China, you can swipe an ID card, the machine collects a face image of you with a camera, and then identify and verify your identity. Some building access control uses image recognition for identification, and you no longer need an access card or a key. Other applications include advanced human-computer interaction, video surveillance, automatic indexing of images, and video database, among others.

Speech Recognition

Speech recognition provides a faster and more convenient way for us to interact with computers. When we speak to the computer, it can know what we are talking about and interact with us. This method is completely different from what we used to type on the keyboard. This way of interacting with the computer can bring us many extended applications. Virtual assistants like Siri, Google Assistant, Alexa can perform tasks or services for an individual based on commands or questions.

Summary

Artificial intelligence is a very comprehensive discipline. It can do image recognition (it can see), speech recognition (it can hear), auto-driving (it can walk), consumer finance, and so on. It is more and more human-like to listen, to see, to say, with these perceptions.

To implement all of these, we have to know about machine learning and deep learning, which is the core content of artificial intelligence. That is to let the machine acquire certain intelligence through the method of learning.