Artificial narrow intelligence (ANI), also referred to as weak AI or narrow AI, is the only type of artificial intelligence we have successfully realized to date. Narrow AI is goal-oriented, designed to perform singular tasks - i.e. facial recognition, speech recognition/voice assistants, driving a car, or searching the internet - and is very intelligent at completing the specific task it is programmed to do.
While these machines may seem intelligent, they operate under a narrow set of constraints and limitations, which is why this type is commonly referred to as weak AI. Narrow AI doesn't mimic or replicate human intelligence, it merely simulates human behaviour based on a narrow range of parameters and contexts.
Consider the speech and language recognition of the Siri virtual assistant on iPhones, vision recognition of self-driving cars, and recommendation engines that suggest products you make like based on your purchase history. These systems can only learn or be taught to complete specific tasks.
Narrow AI has experienced numerous breakthroughs in the last decade, powered by achievements in machine learning and deep learning. For example, AI systems today are used in medicine to diagnose cancer and other diseases with extreme accuracy through replication of human-esque cognition and reasoning.
Narrow AI's machine intelligence comes from the use of natural language processing (NLP) to perform tasks. NLP is evident in chatbots and similar AI technologies. By understanding speech and text in natural language, AI is programmed to interact with humans in a natural, personalised manner.
Narrow AI can either be reactive, or have a limited memory. Reactive AI is incredibly basic; it has no memory or data storage capabilities, emulating the human mind's ability to respond to different kinds of stimuli without prior experience. Limited memory AI is more advanced, equipped with data storage and learning capabilities that enable machines to use historical data to inform decisions.
Most AI is limited memory AI, where machines use large volumes of data for deep learning. Deep learning enables personalised AI experiences, for example, virtual assistants or search engines that store your data and personalise your future experiences.