God has given humans amazing learning ability. We have been learning complex tasks from birth, such as language and image recognition, and have been constantly revising based on this first learning experience throughout our lives. Later, it seems natural that we use this learning concept to accumulate knowledge, and to be able to build models and predict results, and even apply this concept to computer-related programs and tasks. And these technologies involved in the above calculation process are called "artificial intelligence."

Just a game

In the late 1990s, a decisive moment in the artificial intelligence world came. In 1996, chess master Gary Kasparov played against IBM's "dark blue" computer and won 4-2. A year later, Kasparov and Dark Blue once again played against each other. This time, dark blue laughed to the end. This victory has completely changed the perception of artificial intelligence. Chess masters must constantly perform very complex calculations, considering a variety of different moves and corresponding strategies. They can also learn on their own and create novel moves. If you can imitate this process and even apply it to a special task like chess, it will reveal the true potential of artificial intelligence technology.

Thanks to the above successes, artificial intelligence has continued to evolve and we have entered a mature and cutting-edge stage. Google's DeepMind uses deep learning algorithms. These algorithms are based on the idea of ​​allowing humans to learn neural pathways or networks. Artificial intelligence is once again applied to the game, thinking that it is correct. DeepMind adopted the idea of ​​"human-machine battle", this time challenging a very complicated Go game. DeepMind's description of the game is "the number of pieces in the game is more than the number of atoms in the universe." Therefore, this is the perfect challenge for artificial intelligence technology. DeepMind uses deep learning algorithms to train itself on how to deal with professional players. The company's intelligent Go system is the famous AlphaGo, which has a winning percentage of 99.8% against other Go programs, and has won 5 games and 4 wins in the recent game against Li Shishi.

It seems that this is just a game, but in fact, it proves this technique, showing that artificial intelligence can learn how to build models and predict outcomes like humans. The game with Li Shishi proved that the computer has this ability, and now the artificial intelligence technology is entering a mature stage, and the technology will be applied to solve more realistic problems. After the success of AlphaGo, Google learned about the benefits of these technologies and immediately integrated AlphaGo technology into the company's cloud based on the Google Machine Learning Platfom.

Some definitions in the world of artificial intelligence

In this chapter, we need to pay attention to some terms and definitions of artificial intelligence technology.

We can understand this: deep learning is a branch of machine learning; 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 a human-like IQ 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 human-machine battles we used as examples, machine learning uses the chess player's game for training. By learning the player's moves and strategies, the system can use very large data sets as training inputs, which they then use to predict outcomes. Machine learning-based systems can use both classic and non-classical algorithms. One of the most valuable aspects of machine learning is adaptability. Adaptive learning can improve the accuracy of predictions. This, in turn, facilitates the processing of all possibilities and combinations and provides optimal results based on the input data. In the case of game play, this learning helps the machine win more games.

Deep learning: This is a branch of machine learning, an implementation of machine learning. The typology of the system is very important; when learning, the key is not "big" but the surface area or depth. More complex problems can be solved by more neurons and slabs. This system is used to train the system and apply known questions and answers to solve any given problem, which creates a feedback loop. The training result is a weighted result that is passed to the next neuron to determine the output of the neuron—in this way, it establishes a more accurate result based on various possibilities.

Artificial intelligence in the real world

We have seen artificial intelligence applied to games, so what about commercial applications in the real world? Artificial intelligence is now used in a variety of processing processes and systems.

For example, in the French IT giant Sopra Steria Group, we use artificial intelligence in solutions in industries such as banking and energy. We have integrated Natural Language Processing and speech recognition from partner solutions such as IBM Watson or Microsoft 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, for the banking industry, text and speech recognition are used by the Help Desk for the Help Desk and Customer Service. Voice and personal assistance technologies such as Siri and Google Now have brought artificial intelligence to the lab and into the mainstream. These assistants use artificial intelligence and predictive analytics to answer our questions and plan our schedule. Siri now has a smarter successor called VIV. It is based on autonomous learning algorithms whose topology is deeper than the linear path of Siri. VIV creates an artificial intelligence platform that can access multiple tasks, creating more significant opportunities for developers. Google also recently announced similar improvements to its award-winning assistant Google Now.

Machine learning is also used in multiple back-end processes, such as obtaining the required ratings for bank loans and mortgages. In the banking industry, machine learning can be used to personalize products, giving banks a competitive advantage.

Deep learning has been applied to more complex tasks where rules are less clear and more complex. The big data era will provide tools that are more conducive to promoting deep learning. We can see that deep learning is applied to anything related to pattern recognition, such as facial recognition systems, voice assistants, and behavioral analysis to prevent fraud.

Artificial intelligence is entering a new era with the help of these more sophisticated and sophisticated algorithms. This is the next disruptive technology - many of Gartner's predictions for technology in 2016 and beyond are based on artificial intelligence and machine learning. Artificial intelligence captures the key to problems that cannot be solved—these problems we thought were only humans to solve. In the end, even one day's work like writing this article can be done by the machine.

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