Five core elements supporting the success or failure of artificial intelligence technology

According to foreign media (VentureBeat), in the late 1980s, with many startups, government departments and large enterprises deploying new systems to perform work that could only be done by experts, artificial intelligence ushered in a new upsurge. These systems run on a rule basis. Unlike the strict program logic that relied on traditional programming languages ​​in the past, the new system encodes behavior in rules. As hardware configurations such as memory increase, the system can handle more sophisticated computing tasks such as machine learning, scheduling, and understanding of natural language. In today's big data era, many people think that AI has shifted its technology field, but it is not. As the TalkingHeads sang in the song: things are as always.
The core of smart applications has always been the same. It was only in the 1980s and 1990s that the technology used in the space shuttle, space telescope and space station was commercialized in the following years. It is on this basis that we have been able to develop complex businesses such as e-commerce, enterprise resource planning (ERP) and customer relationship management (CRM). People are currently using AI applications to process massive amounts of data. AI applications may be different, but their cores are similar.
These applications include:
Life science applications can learn from clinical trial data and provide doctors with the best advice and medication advice;
The cyber threat security system can predict the weakest factors in the business and give insurance purchase advice in advance;
The Internet of Things (IoT) system uses Frequency Identification (RFID) to monitor changes in material location for more efficient planning and more accurate predictions, and to prevent criminal activity.

支撑人工智能技术成败得失的五大核心元素

In addition, there are a large number of AIs serving humanity in everyday life. Siri and Alexa are waiting for your voice commands at any time. More and more cars are starting to be equipped with automatic parking or even automatic driving. Intercity trains are unmanned, AlphaGo plays Go, IBM super computer Watson beats humans to win TV competitions. And so on.
Despite the changes in specific applications, AI has five core elements that have been around for a long time, connecting and supporting the successes and failures of artificial intelligence technology for forty years. These AI applications must process large amounts of data, react to the surrounding environment, learn to improve performance, face the future, and have the ability to support millions of people and systems at the same time.
Data-intensive intake <br> Data-intensive AI systems process large amounts of data, often in billions of units. Processing such a large amount of data in real time is one of the daunting tasks that an AI program must accomplish. In addition, it must be able to handle continuous stream data (such as uninterrupted data from IoT sensors) and batch data (such as large historical data sets).
Adaptive <br> Adaptive applications use machine learning techniques to improve themselves, and their performance can grow over time. Machine learning workflows require data scientists to perform model selection, feature engineering iterations, algorithm selection, and parameter adjustments in an experimental environment. The application developer then deploys the model, and when new data comes in, the model program can sort the data according to the settings. The application then reviews the results of the classification and uses these results for repeated training.
Reactions <br> Modern AI systems are able to react in real time to changes in their surroundings, unlike traditional batch programs. AI applications continuously monitor data input, which in reality is usually from streaming data platforms. When a situation occurs that meets certain conditions, the program performs further arithmetic processing. In short, the program is ready to process data at any time.
Forward-looking <br> Many AI systems focus not only on solving current problems, but also predicting future possibilities to determine the best solution. Planning systems, games, and even language parsing systems need to be processed in the most forward-looking way to get the best solution. This requires the AI ​​system to have the ability to adapt to new input data. (For example, the latest news shows that the typhoon caused the delay of China's shipping accessories, and the AI ​​system needs to propose an optimal re-planning based on various assumptions)
Simultaneous <br> Like traditional applications, AI programs must handle task interactions from multiple people or systems simultaneously. They use the techniques used to develop distributed systems in the operating system and database domains to maintain ACID attributes.
These five features enable modern AI systems to deliver performance that is satisfactory to the user. In addition, as data volumes increase and response times shrink, well-built systems can easily scale their technology infrastructure without having to rebuild everything. Given the importance of these programs to individuals and businesses, maintaining online and operability may be a common feature of all AI systems.

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