I. Why is artificial intelligence so crucial?
Thanks to the surge in data and the maturing innovations in cloud processing and computing power, the application of artificial intelligence is climbing at an unprecedented pace. Businesses now have access to unprecedented volumes of data, including previously unrecognized dark data. These valuable resources are a boon to the development of artificial intelligence.
When used properly, artificial intelligence (AI) can become a crucial source of business value. AI has long been recognized as a powerful force for business innovation. With supporting infrastructure in place, businesses are beginning to see how AI adds value. Automation reduces costs and brings greater consistency, speed, and scalability to business processes. In fact, some Accenture clients have found time savings of up to 70%. However, even more compelling is AI's ability to drive growth. Companies that have successfully scaled their technology have seen three times the ROI of their peers still in the experimental phase. Therefore, it's easy to understand why 84% of CEOs agree that AI must be leveraged to achieve their growth goals.
II. What are the main technologies included in artificial intelligence?
Artificial intelligence technology is a broad field of research, with major components including computer vision, deep learning, natural language processing, machine learning, and speech recognition.
1. Computer Vision
Computer vision aims to enable computers to autonomously adapt to their environment, allowing them to observe and understand the world through vision, much like humans. It primarily uses image acquisition devices and computer imaging systems to replace visual organs, identifying, tracking, detecting, and controlling targets. The images are then processed and analyzed to extract useful information. It is widely used in medical imaging analysis, facial recognition, security monitoring, and autonomous driving, among other fields.
2. Deep Learning
Deep learning is a type of machine learning that learns from existing data and represents a new field within machine learning. By building upon and mimicking the activity of neurons in the neocortex, it imitates the mechanisms of the human brain to understand data patterns through learning hierarchical structures, representation levels, and abstraction levels, thereby interpreting data such as text, images, and sounds, enabling machines to possess analytical and learning capabilities similar to humans.
3. Natural Language Processing
Natural Language Processing (NLP) is the process of using computers to intelligently process natural language and extract effective information. The most challenging part of artificial intelligence technology is to enable machines to have the same text processing capabilities as humans. Through the analysis of speech, grammar, lexical and semantics, machines can understand human language, communicate with humans in a natural language manner, realize natural language communication between humans and machines, and ultimately possess "intelligence". Commonly used voice dialogue robots have this capability.
4. Machine Learning
Machine learning is an essential step towards achieving artificial intelligence. It refers to the ability of computers to improve their performance using data from known situations. Machine learning is trained, rather than explicitly programmed. Similar to the human learning process, it automatically discovers situations during learning and makes predictions and judgments about new situations based on past experience, such as sales and inventory forecasting and oil exploration.
5. Speech recognition
Speech recognition converts human language into computer-readable data. First, the speech signal is converted into an electrical signal. After preprocessing, features are extracted. Then, the extracted features are compared with an acoustic model in a database. After decoding and searching, the spoken content is recognized, facilitating computer understanding and operation. However, factors such as speech rate, environment, noise, accent, and emotion can significantly affect the generation of the speech signal. With the development of deep learning, speech recognition has entered a new stage and is commonly used in social chat, voice navigation, voice dialing, and smart speakers.
