When people think of artificial intelligence (AI), scenes of android killers and computer-gone-rouge from sci-fi movies usually come to mind. What was once a figment of our imaginations has become one of the hottest buzzwords today with AI taking root in our everyday lives. These days, when you browse the internet for news on AI, you will see attention-grabbing headlines such as:

• AI-powered Alpha Go beats the best human chess player;
• AI robots help surgeons carry out surgeries much easier;
• Google’s AI Assistant sounds like a human;

That’s all good… but… what is AI exactly and why is it important?

To help businesses understand and leverage AI to supercharge customer experiences, we have distilled the technicalities and complexities around AI applications, in the most non-technical and easy to understand way.

First up, what is AI?

In short, AI is used to describe any machine that can mimic human behaviours. By processing large amounts of data and recognising patterns, machines can perform specific tasks, which are traditionally done by humans like speaking, monitoring and moving around, etc. Based on the type of human behaviour to replicate, AI can be categorised loosely into two groups.

1. Humans can see through their eyes and interpret what they see. The equivalence of this in AI is called computer vision.

By imitating a human’s vision, AI technology enables machines to process and identify objects in images and videos, just like we do.

Depending on which part of the vision capability machines try to mimic, computer vision can be further divided into the following subcategories. Some of the more important ones are:

• Object recognition – What is in the picture?
• Object classification – Which category does the object in the picture belong to?

E.g. Is the animal in the picture a dog?

• Object detection – Where is the object in the picture?

E.g. Where is the dog in the picture?

• Object tracking– how the object in the video has moved?

E.g. Where did the dog run to in the video? …

Some classic applications of computer vision include:

• Facial recognition – Banks use facial recognition to verify customers’ identities and therefore prevent fraud.
• Search by image – E-commerce giants like Taobao allows users to search for wanted products by photos.
• Self-driving cars – By identifying different objects in the environment, the car can navigate the right path on its own.

2. Humans can use language to communicate thoughts and understand each other. The similar ability of AI is achieved by a technology called Natural Language Processing.

With this technology, AI can converse with a human and deal with queries or perform a task we require. For example, asking a virtual assistant about the weather forecast, or opening an app on a smartphone. Some of the subcategories of natural language processing include:

• Automatic Speech Recognition, which helps machines transcribe spoken words into text. We can think of it as the human’s ear.
• Natural Language Understanding, which enables machines to process and understand the meaning of words. It is similar to the part of our brain that interprets what other people say.
• Natural Language Generation, used by machines to transfer data into natural language. It is equivalent to the process when humans turn thoughts into speech or writing.
• Sentiment Analysis, based on which machines can figure out what topics have been discussed relating to a certain issue, and whether they are negative or positive.

Some common usage of Natural Language Processing are:

• Voice assistant – For instance, Google Assistant, Alexa, Siri, etc.
• Chatbot – Today many companies use chatbots to deal with customer inquires and to promote products, which has the potential to scale while keeping operating costs low.
• Voice AI agents – Similar to the chatbot, voice AI agents can also be used to automate customer service, product promotion, verifying information, etc.

The difference is that while chatbot sends customers messages on the screen, voice AI “talks” with customers directly over the phone. Aside from performing functions such as responding to customers immediately, voice AI can also acquire and interpret instant responses “from” customers. This advantage puts voice AI one step ahead of the chatbot as getting customer feedback is crucial for improving the quality of engagement.

AI vs Machine Learning vs Deep Learning

“Machine Learning” and “Deep Learning” are two terms that often appear together with “AI”. Sometimes they are even used interchangeably, but what are the key differences between these popular concepts?

Simply put, Machine learning is the foundation of AI. The essence of AI is to have machines perform human tasks. Similar to how humans learn before carrying out a task, so do machines and that is where machine learning comes from.

Before any machine is able to perform a task, we need to train it (via an algorithm) with large amounts of data derived from historical context. For instance, for voice AI to converse with customers on a certain topic seamlessly, it needs to learn from millions of similar real-life conversations beforehand. The more (high-quality) data the machine is exposed to, the better it learns, and the better quality decisions it would make.

That is also the key difference that distinguishes AI from other traditional software– while the former improves its performance with more data, the latter is more “static” and can only be updated by human engineers.

On the other hand, deep learning is simply a type of “advanced” machine learning. It is inspired by the way the human brain functions. With something called neural networks, it improves its performance as time passes by. Object recognition, for instance, is achieved by deep learning.

In Summary…

• AI is the technology that allows machines to replicate human ability.
• The two main categories of AI are Computer Vision and Natural Language Processing, the former resembles a human’s vision system while the latter mimics a human’s language ability.
• Machine learning lies the foundation of AI, it is the process in which machines learn from previous data and make predictions in a new environment. The more high-quality data the machine is exposed to, the better it performs.
• Deep Learning is a subset of Machine Learning and is inspired by how the human brain works.