Artificial intelligence (AI) refers to the ability of computer systems to perform complex tasks that were previously only possible with human intelligence. Insufficient hardware capabilities, deviations in its development path, and algorithmic flaws led to a period of stagnation in AI development during the 1980s and 90s. In recent years, the convergence of four major catalysts—low-cost massively parallel computing, big data, deep learning algorithms, and brain-computer interfaces—has resulted in an upward inflection point in AI development.
We all know that big data refers to massive amounts of information. Using simple addition, subtraction, multiplication, and division would definitely overload a computer. However, these aren't ordinary computers; they typically have data processing centers with high-end commercial servers. Even with high-end servers, processing them using simple algorithms is still very time-consuming.
Therefore, neural network algorithms, machine learning, and other technologies are needed to process the data in the database by combining software and hardware. These algorithms, machine learning, and other analytical techniques belong to artificial intelligence.
In fact, artificial intelligence is a general term for many technologies, including robotics, speech recognition, image recognition, natural language processing, and expert systems. Because artificial intelligence is still in the development stage, there is no very precise definition. Within the industry, artificial intelligence is inseparable from big data, and many big data applications (such as cloud computing platforms) can be attributed to artificial intelligence.
According to statistics from relevant companies, the total amount of global data will reach 163 ZB by 2025. This means that the total amount of data in 2025 will be more than 10 times that of the total amount of data generated globally in 2016. Among them, the amount of data belonging to data analysis will increase 50 times compared to 2016, reaching 5.2 ZB (ten quadrillion bytes); the amount of data belonging to cognitive systems will reach more than 100 times. This explosive growth in data is driving the emergence and expansion of new technologies, providing fertile ground for training computer vision technology using deep learning methods.
Big data mainly includes data collection and preprocessing, storage and management, analysis and processing, visualization computing, and data security. It is characterized by its ever-expanding scale, diverse types, rapid generation, high processing capacity requirements, strong timeliness, strict reliability requirements, high value, but relatively low density. This provides abundant data accumulation and training resources for artificial intelligence. For example, Baidu needs 200 million facial images to train its facial recognition system.
The field of artificial intelligence is rich in massive amounts of data, and traditional data processing technologies struggle to meet the demands of high-intensity, high-frequency processing. The emergence of AI chips has significantly improved the efficiency of large-scale big data processing. Currently, GPUs, NPUs, FPGAs, and various dedicated AI-PU chips have appeared. Traditional dual-core CPUs, even for training simple neural networks, require days or even weeks, while AI chips can increase computing speed by approximately 70 times.
Driven by the exponential growth of computing power and high-value data, artificial intelligence (AI) at its core is continuously expanding the breadth of its technological applications, deepening technological breakthroughs, and accelerating the pace of technology implementation (commercial monetization). For example, in the new retail sector, the combination of big data and AI technologies can improve the accuracy of facial recognition, allowing merchants to better predict monthly sales. In the transportation sector, the combination of big data and AI technologies, based on massive amounts of traffic data, enables intelligent traffic flow prediction, intelligent traffic management, and other AI applications to achieve intelligent control of the entire transportation network. In the healthcare sector, the combination of big data and AI technologies can provide more convenient and intelligent medical services such as medical image analysis, assisted diagnosis, and medical robots. Meanwhile, at the technological level, big data technology is basically mature and is driving AI technology to advance at an astonishing pace; at the industrial level, intelligent security, autonomous driving, and medical imaging are all accelerating their implementation.
With the rapid application and popularization of artificial intelligence, the continuous accumulation of big data, and the continuous optimization of algorithms such as deep learning and reinforcement learning, big data technology will be more closely integrated with artificial intelligence technology, possessing the ability to understand, analyze, discover, and make decisions based on data. This will enable us to obtain more accurate and deeper knowledge from the data, uncover the value behind the data, and give rise to new business forms and models.
