Intelligence, automation, or intelligent automation?

Intelligence, automation, or intelligent automation?

There is a lot of excitement about artificial intelligence (AI), and also a lot of fear. Let’s set aside the potential for robots to take over the world for the moment and focus on more realistic fears. There is a growing acceptance that AI will change the way we work. There is also agreement that it is likely to result in a number of jobs disappearing or being replaced by AI systems, and others appearing.

This has fueled the discussion on the ethics around intelligence, especially AI. Thoughtful commentators note that it is unwise to separate the two. Some have suggested frameworks for the ethical development of AI. Underpinning ethical discussion, however, is a question of what AI will be used for exactly. It is hard to develop an ethics framework out of the blue. In this blog, this issue will be unpicked a little, sharing thoughts about where and how AI is used and how this will affect the value that businesses obtain from AI.

Defining intelligence

Artfiicial Intelligence has been defined as the ability of a system to interpret data, learn from it, and then use what it has learnt to adapt and therefore achieve particular tasks. There are therefore three elements to AI:

1. The system has to correctly interpret data and draw the right conclusions.

2. It must be able to learn from its interpretation.

3. It must then be able to use what it has learnt to achieve a task. Simply being able to learn or, indeed, to interpret data or perform a task is not enough to make a system AI-based.

As consumers, most of our contact with AI is with systems like Alexa and Siri. These are definitely "intelligent," in that they take in what we say, interpret it, learn from experience and perform tasks correctly as a result. However, in business, there is general acceptance that much of the real value from AI will come from automation. In other words, AI will be used to mimic or replace human actions. This is now becoming known as 'intelligent automation'.

Where does intelligent start and automation stop though? There are plenty of tasks that can be automated simply and easily, without any need for an intelligent system. A lot of the time the ability to automate tasks is overshadowing the need for intelligence to drive the automation. This typically results in very well-integrated systems, which often have decision-making capabilities. However, the quality of those decisions is often ignored.

Good AI algorithms can suggest extremely good options for decisions. Ignoring this limits the value that companies can get out of their investments in AI. Equally, failing to consider whether the quality of the decision is good enough can lead to poor decisions being made. This undermines trust in the algorithm. This results in less use for decisions, again reducing the value. But how can you assess and ensure the quality of the decisions made or recommended by the algorithm?

Balancing automation and intelligence

An ideal AI deployment should have a balance between automation and intelligence. If you lean too much towards the automation side and rely on simple rules-based automation, all you will be able to do is collect all the low-hanging fruit in this case. You will therefore miss out on the potential to use the AI system to support more sophisticated decision making. Lean too much towards other direction though, and you get intelligence without automation or systems like Alexa and Siri. Useful for consumers, but not so much for businesses.

In business, analytics needs to be at the heart of an AI system. The true measure of a successful AI deployment lies in being able to mimic both human action and human decision making.

An AI deployment has a huge range of components, it would not be unreasonable to describe it as an ecosystem. This ecosystem might contain audio-visual interpretation functions, multisystem and/or multichannel integration, and human-computer interface components. However, none of those would mean anything without the analytical brain at the centre. Without that, the rest of the ecosystem is simply a lifeless body. It needs the analytics component to provide direction and interpretation of the world around it.

Author: Yigit Karabag

Source: SAS