As a discipline, AI involves a range of subfields, each with its own specific methods.

Some of these focus on very specific types of capabilities, for example natural language processing, which is concerned with understanding, processing, and generating language.

For others, the focus is on developing techniques that can be applied across many types of problems, for example machine learning or knowledge representation and reasoning.

Naturally, there are strong connections between these overlapping areas. AI research comes in many flavours,  ranging from the speculative to the very application-specific, where big communities have emerged that develop AI for use in areas such as: 

• Medicine
• Engineering 
• Education 
• Humanities 

Beyond the ever-increasing connectivity of AI to other disciplines relevant to this research, AI naturally also has close connections to most other areas of computing, such as databases, cybersecurity, hardware design, and software engineering. 

Throughout its history, AI has undergone several “hype cycles”, where impressive successes led to inflated expectations that were later followed by disillusionment. Despite these changes in public opinion, continued research efforts often led to success in the long-term.