Tackling artificial intelligence using architecture

intelligence (‘AI’) is more and more sneaking up into our daily activities. Anyone using Google, Facebook or a Microsoft product knows this. It’s far from perfect, but it’s improving at a quick pace. Not every enterprise is using AI at the same pace. Has your organization started looking into using AI yet? Do you have any clue on how to tackle and implement AI in your organization? How should your enterprise and business architects examine AI? Where should they start? This article will try to answer these questions using a wealth management example.

What is artificial intelligence?

The first mention of artificial intelligence was about 60 years ago. AI has been defined in several ways. The10-minute video below, “What Is Artificial Exactly?,” explains AI very well and elaborates on a few definitions:

I also find Wikipedia’s definition very appropriate:

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Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.”

Much of the recent enthusiasm about AI has been the consequence of developments in deep learning, which is based on learning data representations, called neural networks, as opposed to task-specific algorithms. Deep learning can be supervised, semi-supervised or unsupervised. Deep learning networks can now easily have over ten layers, with simulated neurons running into the millions, as mentioned in “The promise and challenge of the age of artificial intelligence.”

The deployment challenge

Not everyone has the deep pockets and the technical know-how of Google, Facebook or a Microsoft. Artificial Intelligence will most likely provide value, but its development, its implementation and its practical use is and will remain a real challenge for most enterprises, not to mention for most public organizations. Technical know-how and resources are scarce. Getting the right to, accessing and then analysing existing collected data will continue to be an issue in some circumstances. Finally, positive results from concrete artificial intelligence initiatives may prove longer to materialize then anticipated.

As mentioned by Andrew Ng, founder of Google Brain, in a recent article in Forbes:

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