With the rapid development of artificial intelligence technology, the combined application of "AI+computing power" has become a hot topic in the technology industry, and even a hot Internet equation of "AI+computing power = the strongest leader" has been born. The combination will not only improve computing efficiency, but also bring more powerful data processing and analysis capabilities to various industries, thereby driving innovation and growth.
AI + computing power
AI
Artificial intelligence (AI) is a technology that simulates human intelligent thinking, which can realize human cognition and thinking activities. Through this technology, computers can simulate human thinking and intelligence, so that they can complete many complex tasks, such as image recognition, speech recognition, natural language processing, decision making, etc.
features
The characteristics of artificial intelligence (AI) can be summarized as follows:
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Autonomy (meaning that computer programs or robots can independently make decisions and actions without human intervention. The autonomy of artificial intelligence is one of the important prerequisites for the realization of artificial intelligence, and it is also one of the differences between artificial intelligence and human intelligence)
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Learning ability (the learning ability of artificial intelligence refers to the ability of computer programs or robots to improve their performance through learning, so as to better complete tasks. The learning methods of artificial intelligence include supervised learning, unsupervised learning and reinforcement learning, etc.) Supervised
learning : Supervised learning is a training method in machine learning, which refers to the process of using a set of samples of known categories to adjust the parameters of the classifier to achieve the required performance, also known as supervised training or teacher learning. In supervised learning, each instance is a pair consisting of an input object (usually a vector) and a desired output value (also called a supervisory signal).
Unsupervised learning: Unsupervised learning refers to learning on unlabeled data sets, that is, no manual labeling of data is required. The goal of unsupervised learning is to discover structure and patterns in data, not to predict output outcomes. Commonly used unsupervised learning algorithms include clustering, dimensionality reduction, association rule mining and other
reinforcement learning: Reinforcement learning is a learning method of machine learning, which corresponds to supervised learning and unsupervised learning. The goal of reinforcement learning is to maximize cumulative reward by interacting with the environment, not by predicting the output. The idea of the reinforcement learning algorithm is very simple. Taking games as an example, if a certain strategy can be adopted in the game to obtain higher scores, then this strategy should be further "strengthened" in order to continue to achieve better results. Common reinforcement learning algorithms include Q-learning, SARSA, Deep Q-Network, etc. -
Adaptability (the adaptability of artificial intelligence means that the artificial intelligence system can adapt to different environments and tasks, and adjust its behavior according to the situation. The adaptability of the artificial intelligence system can be achieved by representing and reasoning knowledge in order to solve complex problems. problem. AI systems can also interact with humans, for example through technologies such as speech recognition, natural language processing, and image recognition. AI systems can also self-optimize and improve to improve performance and accuracy.)
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Adaptability (meaning that the artificial intelligence system can automatically adjust its behavior and strategy according to changes in the environment and tasks to adapt to different situations. The adaptability of the artificial intelligence system can be realized by representing and reasoning knowledge in order to solve complex problems. AI systems can also interact with humans, for example through technologies such as speech recognition, natural language processing, and image recognition)
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Interpretability (refers to the ability of humans (including non-experts in machine learning) to understand the choices a model makes in its decision-making process, and why those options are chosen. Interpretability is one of the key factors in whether artificial intelligence can be widely used one)
Application field
The application fields of artificial intelligence are very extensive, mainly including the following aspects:
- Intelligent manufacturing: including automatic identification equipment, human-computer interaction systems, industrial robots and CNC machine tools, etc.
- Smart factory: including smart design, smart production, smart management and integrated optimization.
- Smart home: A complete home ecosystem is formed through smart hardware, software, and cloud computing platforms.
- Smart finance: automatic customer acquisition, identity recognition, big data risk control, smart investment advisory, smart customer service and financial cloud, etc.
- Smart healthcare: mainly through deep integration with the medical industry through technologies such as big data, 5G, cloud computing, AR/VRh, and artificial intelligence.
computing power
Computing power refers to the ability of a computer system to process data, usually measured by the number of floating-point operations that can be performed per second.
features
The characteristics of computing power include:
- The size of the computing power represents the strength of the ability to process digital information.
- The measurement indicators and benchmark units of computing power include general computing power, special computing power, super computing power, etc.
- The types of chips that provide computing power for artificial intelligence include GPUs, FPGAs, and ASICs.
Application field
The application fields of computing power are very extensive, including but not limited to:
- Artificial Intelligence: Computing power is the foundation of artificial intelligence. Without computing power, there is no powerful artificial intelligence.
- Cloud computing: Cloud computing requires a lot of computing power to support, so computing power is also an important part of cloud computing.
- Blockchain: Blockchain requires a lot of computing power to ensure its security and reliability.
- Digital currency mining: Digital currency mining requires a lot of computing power to ensure its security and stability.
Computing power provides the ability to process data required for the development of AI, while AI uses algorithms and machine learning technology to achieve intelligent behavior. AI needs the support of computing power, and computing power also needs AI to play a greater role. The two complement each other. With the combination of the two, it is believed that more and more fields will realize more applications and innovations.