The amount of data generated by humans and computers is so vast that it far exceeds the ability of humans to absorb, interpret, and make complex decisions based on it. And artificial intelligence forms the basis of all computer learning and represents the future of all complex decision-making. Artificial intelligence (and its logical evolution of machine learning) and deep learning lay the foundation for the future of business decision-making.
The operating environment of this tutorial: Windows 7 system, Dell G3 computer.
Artificial intelligence (AI) refers to the creation and use of algorithms to build a dynamic computing environment to simulate the basis of human intelligence processes. Simply put, the goal of artificial intelligence efforts is to make computers think and act like humans.
To achieve this goal, three key elements are required:
Computing System
Data and Data Management
Advanced artificial intelligence algorithm (code)
The closer the expected results are to humans, the higher the requirements for data volume and processing power.
The Origin of Artificial Intelligence
Since at least the first century BC, humans have been interested in the feasibility of creating machines that 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 the "Dartmouth College Summer Artificial Intelligence Research Project." Starting from this, machine learning, deep learning, and predictive analysis emerged as the times require, and have developed to the current standardized analysis. In addition, a new field of research has emerged at the same time: data science.
What is the importance of artificial intelligence?
Today, the sheer volume of data generated by humans and computers has far exceeded the ability of humans to absorb, interpret, and make complex decisions based on it. Artificial intelligence forms the basis of all computer learning and represents the future of all complex decision-making.
For example, Tic-Tac-Toe (a cross-circle game) has 255,168 different moves, 46,080 of which result in a tie. But despite this, most people can figure out how to not lose the game. Checkers has over 500 x 10 to the power of 18 different possible moves, so very few people can be considered masters. Computers can calculate permutations and combinations of these moves extremely efficiently and come up with the best strategy.
Artificial intelligence (and its logical evolution of machine learning) and deep learning lay the foundation for the future of business decision-making.
Artificial Intelligence Use Cases
The application of artificial intelligence can be seen in many daily scenarios, such as financial services fraud detection and retail purchase forecasting and interact with online customer support, etc. Here are a few examples:
1. Online chat
1) Chat robot:
Such a robot generally does not require a large knowledge base , but it requires professional language analysis. It is not technically difficult. You only need to give an answer. There is no requirement for recall rate, and there is no requirement for accuracy rate. This is not technically difficult.
2 ) Personal Assistant:
This is common for everyone. The biggest difficulty is intention recognition. Intention recognition also includes language, text, expression, and body movement recognition. It requires the robot's strong learning ability and can be skipped directly. A single-round conversation must satisfy multiple rounds of conversation, which is not easy to do
3) Customer service robot:
The customer service robot realizes single-round and multi-round conversations through knowledge base retrieval. It does not require intent recognition, but it needs to analyze various messages and provide effective feedback to visitors. This requires a hit rate, so the difficulty is not small. Fortunately, the technology is relatively mature and has been commercialized. Support from many users
2. Data model construction
This is rarely mentioned, but we really need it. Everyone knows that the later stages of business competition are all about data share. Only with data can you have combat effectiveness. The existing data analysis models are nothing more than manual formulation, and at most they support a high degree of customization, and the cost of verifying the rationality of the model is quite
. Artificial intelligence can create the best data model through self-learning, refinement and integration. , this is simply an exciting thing, and it is also a matter of innovation and reform
3. Voice interaction
Voice interaction, through recording and process, realizes voice The commercialization of robots focuses on product promotion and after-sales service, which is very convenient for our lives
4. AI educational robots, nanny robots, government services, and medical diagnosis:
Early childhood education, housekeeping, green plants, retail, etc., these can all be liberated through artificial intelligence. This type of artificial intelligence does not need to have the ability to learn by itself, but only needs to complete clear tasks according to established rules. Medical treatment has been applied to some extent, but the effect is poor. The recognition rate of CT films in the United States is 80%, while in China it has always stayed at 60%
5. Industry:
INTELLIGENT CAR, SECURITY, SMART HOME
In the industrial field, artificial intelligence can only perform some narrow types of work, but it can be combined in order to complete the replacement of human labor.
Manpower will perform more business activities, and artificial intelligence will be far more efficient and accurate. Supermanpower
6. Agriculture and animal husbandry:
Soil quality testing, natural environment monitoring, agricultural management strategy analysis
Agriculture is the most primitive industry of mankind. , but has one characteristic, non-standardization. We all know that agriculture is undergoing mechanization advancement, but China's agriculture is still in the experience stage, and the scientific stage has not yet been fully popularized. Artificial intelligence can only perform some subdivision nodes, such as pesticide spreading and fruit collection, and this is still in the In the mechanical automation stage, advanced artificial intelligence does not yet have application fields. The more important reason here is that it will still take some time to abstract the basic data model.
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