Decoding True AI and Its Potential in Healthcare

Classification of AI

There are multiple kinds of AI classification, depending on the objective, some simply break it down into fuzzy and discrete logic AI. The most common classification of AI is based on the evolution of its capabilities and divides it into three broad types — Artificial Narrow Intelligence (Weak) — ANI, Artificial General Intelligence (Strong) — AGI and Artificial Super Intelligence — ASI.

Types of AI based on Level of Progress

For the uninitiated, ANI refers to machines with a narrow range of abilities. They are designed to perform a given set of tasks but cannot mimic human intelligence. Almost all AI applications that we see around us come under this category, including IBM’s Watson, Siri by Apple and Alexa by Amazon. AGI or strong or deep AI has been evading researchers and developers so far. It refers to machines that are as capable as human beings. Creating such machines is a huge challenge as the human brain and its functioning remains a mystery. Unless scientists can program machines for cognitive abilities and truly understanding the true meaning/context, the human beliefs and emotions, AGI seems unachievable. There are current attempts on to make AI explainable too as well as responsible. ASI refers to intelligent machinery that is superior to human beings in intelligence as well as capabilities. It is only a theory now that we perhaps get to see only in Hollywood Sci-Fi movies.

Types of AI based on Functionality

While that’s the theory of it, functionality-wise AI is segregated into four types — Reactive Machines, Limited Memory, Theory of Mind and Self-aware AI. Reactive AI systems respond to stimuli but cannot learn or perform memory-based functions. Limited memory systems go a step ahead- they have the ability to draw from past data to find the most apt response. Most AI systems today, including bots, are limited memory machines. Theory of Mind artificial intelligence attempts to understand the human mind functioning with the aim of developing human like simulation abilities. This however remains just a concept for now — just like the next AI type, which is the Self-Aware AI. If we live to see self-aware intelligent systems, it would be surreal, and scary, to see machines surpass human capabilities in every aspect.

The Third Typification

Well-known AI technologist, Francesco Corea, argues that though commonly found classification of AI works well to educate novices, it holds little utility for those who possess deeper domain knowledge. According to him, a usage centric classification that creates an AI Knowledge Map (AIKM) will be more instrumental in furthering the research and innovation endeavours of mature AI users. Using the below given graphic representation, he explains in a Forbes article, how AI Paradigms and AI Problem Domains interact to create a knowledge bank that can be used by leading AI users. In the process, he categorizes AI according to different methodologies used by researchers. The categories are logic-based tools, knowledge-based tools, probabilistic methods, machine learning, embodied intelligence, and search optimization.

Sources/References

Author : VirooPax B. Mirji. — — Viroo Mirji (@ vpax)

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VirooPax Mirji

VirooPax Mirji

Entrepreneur, innovation leader, Industry disrupter, Data Ops Automation, Mobile/media/cable domain, OTT, Maker, Design thinker, & CRM/Billing services leader