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AI machine learning
AI machine learning
God gives mankind amazing learning ability. We learn complex tasks from birth, such as language and image recognition, and then revise them throughout our lives on the basis of this first learning experience. After that, it seems natural that we use this learning concept to accumulate knowledge, to model and predict results, and even to apply it to computer-related programs and tasks. And these technologies involved in the above calculation process are so-called AI.
It's just a game.
At the end of the 1990s, a decisive moment in the AI world came. In 1996, chess master Gary Casparov won 4-2 against IBM's Deep Blue computer. A year later, Kasparov fought again with deep blue. This time, dark blue laughs last. This victory has completely changed the view of artificial intelligence. Chess masters must constantly perform very complex calculations, considering a variety of different moves and corresponding strategies. They can also learn by themselves and create new ways of doing things. If you can mimic this process, and even apply it to special tasks like chess, it will show the real potential of artificial intelligence technology.
Thanks to the above successes and the continuous development of artificial intelligence, we have entered the stage of maturity and sophistication. Google's DeepMind company uses deep learning algorithm. These algorithms are based on the idea of allowing humans to learn neural pathways or networks. AI is once again applied to the game to assume its name. DeepMind adopted the idea of "man-machine warfare". This challenge is a very complicated go game. DeepMind's description of the game is "the number of pieces is larger than the number of atoms in the universe". Therefore, this is a perfect challenge for AI technology. DeepMind uses deep learning algorithm to train himself how to deal with professional level players. The company's smart Go system, known as AlphaGo, has won 99.8 percent against other Go programs, and has won five games and four games in a recent game against professional Go player Li Shishi.
It may seem like a game, but in fact, it demonstrates the technology that artificial intelligence can learn to model and predict results just like humans do. The match with Li Shishi proved that computers have this ability. Now artificial intelligence technology is entering a mature stage, and this technology will be used to solve more realistic problems. After the success of AlphaGo, Google learned the benefits of these technologies and immediately integrated them into the company's Google Machine Learning Platfom-based cloud.
Some definitions in the AI world
In this chapter, we need to pay attention to some terms and definitions of AI technology.
We can understand that deep learning is a branch of machine learning and machine learning is a branch of artificial intelligence.
Artificial Intelligence: This general term is used to describe a technology created by humans that can achieve IQ levels similar to those of humans when solving problems. It may (or may not) use biological structures as a potential basis for its intelligent operations. Artificial intelligence systems are usually trained and learned from them.
Machine learning: In the above-mentioned man-machine combat we use as an example, machine learning uses chess scores for training. By learning the moves and Strategies of a chess player, the system can use very large datasets as training inputs, which are then used to predict results. Machine learning based systems can use classical and non classical algorithms. One of the most valuable aspects of machine learning is adaptability. Adaptive learning can improve the accuracy of prediction. This, in turn, facilitates the processing of all possibilities and group mergers to provide optimal results based on input data. In the game match situation, this kind of learning helps the machine win more games.
Deep learning: This is a branch of machine learning and a way to achieve machine learning. Systematic typology is very important; in learning, the key is not "big" but surface areas or depths. More complex problems can be solved by more neurons and blocks. The system is used to train the system and apply known questions and answers to any given problem, creating a feedback loop. The training result is a weighted result, which is passed to the next neuron to determine the output of that neuron - in this way, it builds a more accurate result based on various possibilities.
Application of artificial intelligence in the real world
We have seen that AI is applied to games. What about commercial applications in the real world? AI has now been applied to many processes and systems.
For example, at the French IT giant Sopra Steria, we use artificial intelligence in banking and energy solutions. We have integrated natural language processing with voice recognition capabilities from partner solutions such as IBM Watson or Microsoft Cortana. Natural language processing, speech recognition (and in the near future, including image recognition) are now widely used and integrated into a variety of applications. For example, in banking, text and voice recognition are used as qualifying assistants at consulting desks and customer service departments. Siri and Google N
It's just a game.
At the end of the 1990s, a decisive moment in the AI world came. In 1996, chess master Gary Casparov won 4-2 against IBM's Deep Blue computer. A year later, Kasparov fought again with deep blue. This time, dark blue laughs last. This victory has completely changed the view of artificial intelligence. Chess masters must constantly perform very complex calculations, considering a variety of different moves and corresponding strategies. They can also learn by themselves and create new ways of doing things. If you can mimic this process, and even apply it to special tasks like chess, it will show the real potential of artificial intelligence technology.
Thanks to the above successes and the continuous development of artificial intelligence, we have entered the stage of maturity and sophistication. Google's DeepMind company uses deep learning algorithm. These algorithms are based on the idea of allowing humans to learn neural pathways or networks. AI is once again applied to the game to assume its name. DeepMind adopted the idea of "man-machine warfare". This challenge is a very complicated go game. DeepMind's description of the game is "the number of pieces is larger than the number of atoms in the universe". Therefore, this is a perfect challenge for AI technology. DeepMind uses deep learning algorithm to train himself how to deal with professional level players. The company's smart Go system, known as AlphaGo, has won 99.8 percent against other Go programs, and has won five games and four games in a recent game against professional Go player Li Shishi.
It may seem like a game, but in fact, it demonstrates the technology that artificial intelligence can learn to model and predict results just like humans do. The match with Li Shishi proved that computers have this ability. Now artificial intelligence technology is entering a mature stage, and this technology will be used to solve more realistic problems. After the success of AlphaGo, Google learned the benefits of these technologies and immediately integrated them into the company's Google Machine Learning Platfom-based cloud.
Some definitions in the AI world
In this chapter, we need to pay attention to some terms and definitions of AI technology.
We can understand that deep learning is a branch of machine learning and machine learning is a branch of artificial intelligence.
Artificial Intelligence: This general term is used to describe a technology created by humans that can achieve IQ levels similar to those of humans when solving problems. It may (or may not) use biological structures as a potential basis for its intelligent operations. Artificial intelligence systems are usually trained and learned from them.
Machine learning: In the above-mentioned man-machine combat we use as an example, machine learning uses chess scores for training. By learning the moves and Strategies of a chess player, the system can use very large datasets as training inputs, which are then used to predict results. Machine learning based systems can use classical and non classical algorithms. One of the most valuable aspects of machine learning is adaptability. Adaptive learning can improve the accuracy of prediction. This, in turn, facilitates the processing of all possibilities and group mergers to provide optimal results based on input data. In the game match situation, this kind of learning helps the machine win more games.
Deep learning: This is a branch of machine learning and a way to achieve machine learning. Systematic typology is very important; in learning, the key is not "big" but surface areas or depths. More complex problems can be solved by more neurons and blocks. The system is used to train the system and apply known questions and answers to any given problem, creating a feedback loop. The training result is a weighted result, which is passed to the next neuron to determine the output of that neuron - in this way, it builds a more accurate result based on various possibilities.
Application of artificial intelligence in the real world
We have seen that AI is applied to games. What about commercial applications in the real world? AI has now been applied to many processes and systems.
For example, at the French IT giant Sopra Steria, we use artificial intelligence in banking and energy solutions. We have integrated natural language processing with voice recognition capabilities from partner solutions such as IBM Watson or Microsoft Cortana. Natural language processing, speech recognition (and in the near future, including image recognition) are now widely used and integrated into a variety of applications. For example, in banking, text and voice recognition are used as qualifying assistants at consulting desks and customer service departments. Siri and Google N