Most people are concerned about some of the AI's productive things, some practical use cases. Of course, there is Hawking’s “Artificial Intelligence Threat Theoryâ€. But through the appearance to see how this "machine" is "running", here we talk about the four basic elements that need to be understood: classification, grading, machine learning and collaborative filtering.
Artificial intelligence (AI) is sweeping the globe, and there are many innovative use cases that are used in almost all industries. Although it is still a few decades away to make things that sound like science fiction with intelligent robots instead of doctors, artificial intelligence is now providing decision-making and solutions for experts from all walks of life. Help with the problem. It will also provide some very convenient features for our consumers, such as listening to songs.
Most people are concerned about some of the AI's productive things, some practical use cases. Of course, there is Hawking’s “Artificial Intelligence Threat Theoryâ€. But for me, I prefer to look at how this "machine" works by looking at it. Here we talk about the four basic elements that need to be understood: classification, grading, machine learning and collaborative filtering.
Classification involves the creation of metrics (such as finance, networks) that are specific to the domain of the problem to be solved. Grading includes determining how relevant the data is to the problem to be resolved. Machine learning involves anomaly detection, clustering, deep learning, and linear regression. Collaborative filtering involves looking across application models for large data sets.
classification
AI requires a lot of data related to problem solving. The first step in creating an artificial intelligence solution is to create what I call a "design intent metric" to classify the problem. Whether users attempt to build a system to help doctors diagnose cancer or help IT administrators diagnose wireless network problems, they need to define metrics so that the problem is broken down into small pieces. For example, in wireless networks, key metrics are user connection time, throughput, coverage area, and roaming. In cancer diagnosis, key indicators are white blood cell count, ethnic background and X-ray scan.
Grading
Once the user has a clear classification of the problem to be solved, the next step is to rank each category to help the user move in the direction in which meaningful conclusions can be obtained. For example, when training an artificial intelligence system, the user must first classify whether the question belongs to a simple text or a pun, and then classify by time, person, thing, or location. In a wireless network, once the user knows the type of problem, it is necessary to begin to categorize the factors that cause the problem: association rules, authentication, Dynamic Host Configuration Protocol (DHCP), or other wireless, wired, and device factors.
Machine learning
The problem now is to divide it into specific domain blocks of metadata, and users need to "feed" this information to a magical, powerful computer that can be swallowed and learned, that is, machine learning. There are many algorithms and techniques in the field of machine learning, and supervised machine learning (ie, deep learning) using neural networks has become one of the most popular methods. The concept of neural networks is now in 1949. With the enhancement of computing and storage capabilities, neural networks have begun to be trained to solve practical problems ranging from image recognition to natural language processing to network performance prediction. Other applications include anomaly discovery, time series anomaly detection, and event-related root cause analysis.
Collaborative filtering
Most people experience collaborative filtering when they watch videos or e-commerce platforms online, and receive recommendations for movies or products they might like. In addition to recommendations, collaborative filtering is also used to sort large amounts of data and drop the final stroke in the development of artificial intelligence solutions. In the process, all data collection and analysis becomes meaningful insights and actions. Whether in the game, or for doctors, network administrators, collaborative filtering is a means of providing high-reliability answers. It is like a virtual assistant that can help you solve complex problems.
Artificial intelligence is still an emerging field, but its impact is far-reaching and will become more intense as it will slowly become part of our lives. Choosing an artificial intelligence solution is very similar to buying a car. We not only look at the shape of the car, but also the things under the hood that really represent the performance of the car. In this way, we can know if this car can meet our needs.
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