- Computing system
- Data and data management
- Advanced artificial intelligence algorithm (code)
The closer the expected results are to humans, the higher the data volume and processing power requirements.
The origin of artificial intelligence
Since at least the first century BC, humans have been interested in the feasibility of making machines to simulate the human brain. In modern times, John McCarthy coined the term “artificial intelligence” in 1955. In 1956, McCarthy and others organized a conference called “Dartmouth College Summer Artificial Intelligence Research Project.” Starting from this, machine learning, deep learning, and predictive analysis came into being, and it has been developed to the current standardized analysis. In addition, a new field of research has also emerged: data science.
What is the importance of artificial intelligence?
Today, the huge amount of data generated by humans and computers is far beyond the ability of humans to absorb, interpret, and make complex decisions accordingly. Artificial intelligence forms the basis of all computer learning and represents the future of all complex decisions. For example, tic-tac-toe (circular cross-game) has 255,168 different moves, of which 46,080 moves will result in a draw.
But despite this, most people can figure out how to not lose the game. Checkers have 18 different possible moves over 500 x 10, so very few people can be called masters. The computer can calculate the permutations and combinations of these moves extremely efficiently and draw the best countermeasures. Artificial intelligence (and the logical evolution of its machine learning) and deep learning lay the foundation for the future of business decision-making.
Artificial intelligence use cases
AI applications can be seen in many everyday scenarios, such as financial service fraud detection, retail purchase forecasting, and online customer support interaction. Here are a few examples:
Fraud detection. There are two uses of artificial intelligence in the financial services industry. One is to use artificial intelligence in the initial scoring of credit applications to understand the creditworthiness of applicants. The other is to use a more advanced artificial intelligence engine to monitor and detect payment card fraud transactions in real-time.
Virtual Customer Assistants (VCA). In addition to human interaction, the call center also uses VCA to predict and respond to customer inquiries. Voice recognition combined with simulated human dialogue is the first point of interaction in customer service inquiries. Inquiries of a higher level are transferred to human customer service.
When a customer initiates a chat (with a chatbot) on a web page, they usually first interact with a computer running specialized artificial intelligence. If the chatbot cannot explain or solve the problem, the manual customer service will step in and communicate directly with the customer. These unexplainable examples will be fed to machine learning computing systems for improving artificial intelligence applications to better interact in the future.