Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. In simpler terms, it is making computers think like humans. The term is used to describe machines that mimic cognitive functions such as learning and problem solving.
While the term was coined in 1956, AI has since advanced by leaps and bounds thanks to advanced algorithms, increased data volumes, and improvements in computing power and technology. In the 1950s, early AI research delved into topics such as problem solving and symbolic methods. Ten years later, the US Department of Defense expressed interest and began to train computers to mimic basic human reasoning. By 2003, intelligent personal assistants were produced long before Siri or Alexa were introduced.
Popular examples of artificial intelligence include AI autopilots on commercial flights, spam filters, mobile check deposits, and voice-to-text features on mobile devices.
How AI works
To understand how AI works, understanding the sub domains of AI and how these domains can be applied to various industry fields is critical.
- Machine learning (ML): ML teaches a machine to make inferences and decisions based on past experiences. It’s a type of data analysis that uses algorithms to learn from data. This ability to reach a conclusion by evaluating data saves time and helps make better decisions.
- Deep learning: Deep learning is a subset of ML that processes data and creates patterns for use in decision making. Deep learning models are typically image-based.
- Neural networks: Neural networks attempt to imitate how a human brain works. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain would.
- Natural language processing (NLP): NLP analyzes, understands, and generates the languages that humans use naturally in order to interface with computers in both written and spoken contexts.
- Natural language generation (NLG): NLG generates combinations of words in text format automatically, based on human language patterns and formation. Sometimes it is based in machine learning.
- Computer vision: A computer vision algorithm attempts to understand an image by breaking down and studying different parts of it. This helps the machine classify and learn from a set of images to create a better output decision based on previous observations.
- Cognitive computing: Cognitive computing algorithms attempt to mimic a human brain by analyzing text, speech, images, and objects in a human manner and tries to give the desired output.
- DevOps automation: AI can perform technological processes, such as software testing, quickly and reliably, freeing development and operations teams to do fewer manual tasks.
Examples of AI in industries
AI is being used in every industry, and the demand for AI capabilities only continues to grow.
- Healthcare: AI provides personalized medicine and X-ray readings. Personal health care assistants can remind patients to take medicine, exercise, or eat healthier.
- Retail: AI provides virtual shopping capabilities that offer personalized recommendations. Stock management and website layout technology are also improved with AI.
- Manufacturing: AI analyzes IoT data as it streams from connected equipment to forecast expected load and demand using recurrent networks.
- Banking: AI enhances speed, precision, and effectiveness of human efforts. It can be used to identify which transactions are likely to be fraudulent and automate manual data management tasks.
- Automotive: AI-powered software allows vehicles to understand their immediate environment and safely navigate it. Self-driving cars are becoming a popular topic of interest concerning AI.
- Blockchain: Developing AI models through blockchain technology allows everyone participating in the development to view records of progress in a secure environment.
This article was updated April 2021 by Jenna Phipps.