What is AI? Ingredients for Intelligence

This introductory overview of artificial intelligence acts as a layman’s guide what AI is, and what it is made up of.

From the movie Ex Machina, via the @exmachinamovie Twitter feed

When I tell people that I work at an AI company, they often follow up with “So what kind of machine learning/deep learning do you do?” This isn’t surprising, as most of the market attention (and hype) in and around AI has been centered around Machine Learning, and its high profile subset, Deep Learning, and around Natural Language Processing, with the rise of the chatbot and virtual assistants. But while machine learning is a core component for artificial intelligence, AI is in fact more than just ML.

So what does it reallymean for an application to be “intelligent”? What does it take to create a system that is “artificially intelligent?

In Blade Runner, Harrison Ford and co. used the Voight-Kampf machine to see if a suspect is a replicant. In the real world, the late and great Alan Turing came up a test to measure whether a machine is able to exhibit behaviour is that equivalent to that of a human, aptly known as the Turing Test. “A computer would deserve to be called intelligent if it could deceive a human into believing that it was human,” said Turing. Since its advent in the 1950s, it has been the most common litmus test employed in AI. The test is rudimentary but it presents a baseline that needs to be reached before it can be considered intelligent.

  Now, let’s look behind the curtain to see how to can create a system that can pass the Turing Test. According to Norvig and Russell:

Now, I assume you already trust that I’m quoting from an authoritative source, but in case there’s any doubt (or if your memory needs jogging), Norvig and Russell are AI heavyweights. Dr Norvig is currently Director of Research at Google, and Dr Russell is a Professor of Computer Science at UC Berkeley. They literally wrote the textbook on artificial intelligence used in more than 1100 universities in the world.

Each component outlined above serves a different purpose to create a system that mimics human-like intelligence. Natural Language Processing allows a machine to communicate and receive information in an organic human form, rather than as unwieldy lines of code. Machine Learning systems gives it the ability to adapt to new circumstances and detect new patterns and facts.

  As the lesser-known components of AI, Knowledge Representation and Automated Reasoning aren’t as commonly spoken about in the press but nonetheless play a key role in the creation of intelligent systems.

Knowledge Representation allows us to make sense of the complexity in information. I like to think of it like a “mental map” of how things are related to each other in the real world and its context.