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Realising Practical AI, Part One

Artificial Intelligence (AI) is an ensemble of technologies that allow machines to use an existing body of knowledge to provide valuable insights. These insights can take the form of decisions for action by machines themselves (for example in control systems commonly found in robotics) or can be of informative nature to humans, which can process them and subsequently make decisions (for example recommender systems for online marketplaces).

Although AI has demonstrated its potential for transforming entire industries, it is often regarded as a collection of technologies immediately applicable to a specific domain. In fact, AI is only the last part of a lengthy and costly process that involves digitalization of knowledge (see figure 1).

The lack of digitalized (“machine readable”) knowledge is one of the prime reasons for slow AI adoption, as in many cases, the critical mass of such knowledge required in order for AI to provide meaningful insights is missing. Automation of insight extraction process is therefore required to accelerate AI adoption and reduce time-to-market.

Insight extraction process begins by processing of raw data into information. This information contains metadata, i.e. semantic description of the data. For example, for datum “23”, the metadata could be “temperature”. The next step in the process is the transformation of information into knowledge. This process includes the creation of entity-relationship graphs, which identify the relationship between information entities. For example, if one entity is about “temperature” and another about “location”, then a relationship from the latter to the former could be characterized as “has temperature”. These entity-relationship graphs are known also as knowledge graphs and can be used by AI to produce insights. Back to our simple example, assuming that in an entity relationship graph we have the following relationships:

Location Stockholm, Sweden has temperature of 23 degrees.

If temperature is more than 20 degrees, then the weather is hot outside.

Then, using an AI technique called reasoning, we can deduce that:

It is hot in Stockholm, Sweden

The challenge for automating the insight-generation process end-to-end is twofold:

First, there exist a plethora of tools and AI techniques for data transformation and insight generation respectively. Some tools are optimized for performance, while others support more features.

Second, the requirements of the data owner differ depending on the use-case. These requirements range from the way to visualize the insights to performance (i.e. speed of insight generation and accuracy of generated results), data governance (geofencing), security and privacy, etc.


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