Computing power in the digital age: Unlocking the power of artificial intelligence

In the context of artificial intelligence (AI), "computing power" refers to the ability of a computer or computer network to perform complex calculations, process large amounts of data, and run complex algorithms quickly. This is a key aspect of AI because many AI tasks, such as machine learning and deep learning, require large amounts of computing resources to be performed efficiently.

The importance of computing power in AI can be explained with an analogy: Imagine a detective trying to solve a complex puzzle. The more clues and evidence the detective can juggle simultaneously and the faster he or she can think, the sooner he or she can piece together the solution. Similarly, more computing power enables AI models to process and analyze large amounts of data, leading to increased accuracy and faster decision-making.

Computing power is primarily determined by two key components:

(1) Processor (CPU/GPU/TPU): The processor is like the brain of the computer. It performs all the calculations and operations required for AI tasks. The CPU (central processing unit) can handle general tasks well, but for AI, specialized units such as GPU (graphics processing unit) or TPU (tensor processing unit) are often used. These specialized units can perform multiple calculations at the same time, allowing them to perform AI tasks faster.

(2) Memory (RAM): Memory is like a workspace where computers can quickly access and store data. In artificial intelligence, large data sets need to be processed, so having enough RAM is crucial. If the memory is limited, the computer may have to waste time constantly fetching data from the storage, slowing down the process.

Having enough computing power available makes breakthroughs in AI possible. Researchers and developers can experiment with larger and more complex models, leading to advances in a variety of AI applications, including natural language processing, computer vision, and autonomous systems.

After ChatGPT came out, many people had this question, "Can Chinese companies make a large model like ChatGPT?"

Among these, the algorithm may not be the link with the biggest gap. The real bottleneck is the computing power that many people cannot imagine.

Computing power is very important for training large models.

A model like GPT-3.5 itself consists of 175 billion parameters. Parameters are like knobs that the model learns to adjust during training to produce appropriate responses. Having a large number of parameters enables the model to capture complex patterns in language, understand context, and generate coherent and contextually relevant responses.

This massive computing power and large number of parameters contribute to ChatGPT’s ability to engage in natural and human-like conversations. It can understand nuanced statements, follow context, and provide detailed answers or responses across a wide range of topics.

Artificial intelligence cannot do without massive computing power even for a day.

Source: Chongqing Radio Science Popularization Experience Center

Audit expert: Zhang Qiyi

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