God has given mankind amazing learning ability. We learn complex tasks, such as language and image recognition, from the very beginning of birth, and continue to make corrections based on this first learning experience in the lifetime. After that, it seems natural that we use this learning concept to accumulate knowledge, and we can 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 the so-called "artificial intelligence."
Just a game
In the late 1990s, the artificial intelligence world arrived at a decisive moment. In 1996, chess master Gary Kasparov defeated IBM's "dark blue" computer and won 4-2. A year later, Kasparov played against Deep Blue again. This time, Deep Blue laughed to the end. This victory has completely changed the way outsiders think about artificial intelligence. Chess masters must constantly perform very complex calculations, taking into account a variety of different moves and corresponding strategies. They can also study on their own and create novel moves. If you can imitate this process and even apply it to special tasks such as chess, it will reveal the true potential of artificial intelligence technology.
Thanks to these successes, artificial intelligence continues to evolve and we have therefore entered a mature and cutting-edge stage. DeepMind, a company owned by Google, 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 being applied to the game, assuming its name. DeepMind adopted the idea of ​​“human-machine matchâ€, this time challenging a very complicated Go game. DeepMind's description of the game is "the number of chess pieces is greater than the number of atoms in the universe." Therefore, this is a perfect challenge for artificial intelligence technology. DeepMind uses deep learning algorithms to train how to deal with professional players. The intelligent Go system developed by the company is the famous AlphaGo. Its winning percentage against other Go programs is 99.8%, and it has achieved 5 wins and 4 wins in the recent match against professional player Li Shishi.
It seems like this is just a game, but in fact it proves the technology and shows that artificial intelligence can learn how to build models and predict results like humans. The match with Li Shishi proves that the computer has this kind of ability, and now artificial intelligence technology is entering a mature stage. At this point, the technology will be applied to solve more realistic problems. After AlphaGo's success, Google learned the benefits of these technologies and immediately integrated AlphaGo technology into the company’s cloud based on the Google Machine Learning Platform.
Some definitions in the artificial intelligence world
In this chapter, we need to pay attention to some terms and definitions of artificial intelligence technology.
We can understand it this way: 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 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 often trained and learn from them.
Machine Learning: In the man-machine battle we used as an example above, machine learning uses chess players to train. By learning the moves and strategies of players, the system can use very large data sets as training input, and then they use these data sets to predict results. Machine learning based systems can use 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 data entered. In the case of game play, this learning helps the machine win more games.
Deep learning: This is the branch of machine learning and an implementation of machine learning. The typology of the system is very important; in learning, the key is not "big" but in surface area or depth. More complex problems can be solved by more neurons and layers. 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, which is passed on to the next neuron to determine the output of the 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 artificial intelligence applied to games, then in the real world commercial applications? Artificial intelligence has now been applied to a variety of process flows and systems.
For example, at the French IT giant Sopra Steria Group, we use artificial intelligence in solutions for industries such as banking and energy. We integrate natural language processing and voice recognition capabilities from partner solutions such as IBM Watson or Microsoft's Microsoft Cortana. Natural language processing, speech recognition (and in the near future, including image recognition) are now widely used and integrated in a variety of applications. For example, for the banking industry, text and speech recognition is used as a qualification assistant for help desks and customer service departments. Voice and personal assistance technologies such as Siri and Google Now have led artificial intelligence out of 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 named VIV. It is based on autonomous learning algorithms and its topology is more in-depth than Siri's linear path. VIV created an artificial intelligence platform that can access multiple tasks, creating more significant opportunities for developers. Google also recently announced similar improvements to its highly acclaimed assistant, Google Now.
Machine learning is also used for multiple back-end processes, such as the required scores for obtaining bank loans and mortgage loans. In the banking industry, machine learning can be used to provide product personalization, which gives banks a competitive advantage.
Deep learning has been applied to more complex tasks where rules are more ambiguous and more complex. The era of big data will provide some tools that are more conducive to promoting the use of 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.
With the help of these more sophisticated and sophisticated algorithms, artificial intelligence is entering a new era. This is the next disruptive technology - many of Gartner's predictions for 2016 and beyond are based on artificial intelligence and machine learning. Artificial intelligence captures the key to those problems that cannot be solved - these problems we previously thought were the only humans to solve. In the end, even a job like writing this one day can be done by the machine.
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