Artificial intelligence (AI) technology simulates human intelligence using computer systems powered by advanced machine learning algorithms. AI technology can perform many functions that previously could only be performed by humans - including speech recognition, natural language processing, and computer vision.
AI development leverages programming skills, data science skills, massive datasets, and specialized hardware, to enable machines to simulate human cognitive tasks. Most current AI solutions are considered Narrow AI, because they can perform only specific functions. Multiple organizations are working on applications of General-Purpose AI which can rival human cognitive capabilities for any task.
Two key concepts in AI technology are machine learning and deep learning:
- Machine learning involves training models to make accurate classifications and predictions according to input data.
- Deep learning is a subset of machine learning algorithms that use artificial neural networks, inspired by the structure of the human brain, which enable computer systems to perform complex, unstructured cognitive tasks.
In this article:
- Why Is AI Important?
- Machine Learning vs Deep Learning
- What Is Computer Vision?
- What Is Natural Language Processing (NLP)?
- AI Deployment Models
- AI Infrastructure
- Trends Driving the Future of AI Development
- Explainable AI
- Large Language Models
- Who Is Building AI Technology? AI Organizational Roles
- How Is AI Technology Used? Example Applications
- AI Infrastructure Virtualization with Run:ai
Why Is AI Important?
Artificial intelligence allows computer programs to think and learn like humans. AI generally refers to any problem or task that would normally require human intelligence to handle.
AI applications offer huge advantages, revolutionizing many professional sectors. These include:
- Automated repetitive learning—AI typically handles high volumes of frequent, repetitive tasks rather than simply automating manual tasks. These computerized tasks are reliable and can process large amounts of data without fatigue. Most AI systems require a human to set up and manage them.
- Progressive learning—algorithms that consume data and can progressively program themselves. They can identify patterns and acquire more accurate skills over time. For example, algorithms can learn to play chess or recommend suitable products to online customers, adapting to new inputs.
- Multi-layered data analysis—neural networks have multiple hidden layers to analyze deep data, enabling the creation of tools such as AI-based fraud detection systems. The availability of big data and improved computing power enable deep learning models to train directly on huge datasets.
- Fast decision-making—AI-based technologies can make decisions and perform actions faster than humans. Humans tend to analyze multiple emotional and practical factors when making decisions, while AI quickly analyzes data in a structured way to deliver fast results.
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