Applying some intelligence to Artificial Intelligence Posted on : Oct 17 - 2017

Navigating the turbulent waters of Artificial Intelligence (AI) and Machine Learning (ML) can seem like a daunting task to the uninitiated. In fact even the question of how AI relates to ML is answered differently depending on who you ask, as evidenced by the numerous articles about on these topics. In this area, confusion abounds – for example with ML being linked to predictive analytics, including Monte Carlo Simulations, which have nothing to do with ML!

Some of this comes from the breadth of subjects that are related to these concepts. For example Natural Language Processing, Random Forest, Dimensionality Reduction, Neural Nets and Deep Learning do not fit into a nice grouping structure or hierarchy which can defined as just an instance of that overall class of technique. The next layer of complexity comes from the fact that any given use case can actually use a fairly arbitrary combination of these tools to achieve its aims. This general applicability of tools and techniques does however provide a glimpse of how to think about mapping out the AI and ML landscape.

Ultimately all of this is a consequence of historic research going back 30+ years, without ever having mainstream applications, therefore the techniques are having to be understood by a large number of ‘non-experts’.

If we have to assemble our solution from a non-defined collection of tools in the toolbox, how can we expect to understand or “get our heads around” the overarching landscape. One answer is to look at the business drivers behind each use case. The business drivers (or the “Why” of the project) are usually from the ‘benefits’ side of the business case. The benefits we are striving to realise are however often a neglected part of a project. Some methodologies (e.g. Managing Successful Programmes) place particular emphasis on them and are the critical and measurable link between implementing your system and the overall corporate strategy. View More