<aside> đź’ˇ This page is a work-in-progress since June 2023. Updates are made to the page once a week.
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Even though AI research has been around for at least 5 decades (as far as a I can remember from my CS101 class), ChatGPT, DALL-E and the recent developments in the AI space since mid-2022, have captured the curiosity of millions of users both inside and outside the tech industry. This movement in the last year had made AI basic.
This wiki aims to establish an intuition around key AI terms to demystify some of the inner workings of these developments.
<aside> đź’ˇ Difference between AI and ML
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<aside> đź’ˇ Since I have some background in this space, it is imperative to reiterate the difference between these two terms that keep being used interchangeably as the conversation moves to Main Street & Wall Street - there is no difference. Artificial intelligence (AI) is an umbrella term for different strategies and techniques you can use to make machines more human-like. Whereas ML is the science of developing algorithms and statistical models that computer systems use to perform complex tasks without explicit instructions.
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Machine Learning is the science and engineering of making intelligent machines, especially intelligent computer programs with a goal to understand human intelligence and offer ways to further human intelligence.
This definition was given more shape after Alan Turing’s paper, “Computer Machinery & Intelligence” (1950), was published where the famous question was asked:
Can machines think?
He also designed the Turing Test, where a human interrogator would try to distinguish between a computer and human text response.
Another paper was also published Artificial Intelligence: A Modern Approach, where four potential goals are outlined
| A. Human Approach | B. Ideal Approach |
|---|---|
| 1. Systems that think like us | 1. Systems that think rationally |
| 2. Systems that act like us | 2. Systems that act rationally |
At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.