It would be difficult nowadays not to come across a piece of news, article or posting related to Artificial Intelligence, or AI. Whether it is smart appliances and home automation systems with intelligent digital assistants, autonomous vehicles, advanced medical diagnostics, virtual reality, or even human-like automatons supplanting humans themselves – such as the recent news of the humanoid robot piloting a spacecraft and attempting to dock at the International Space Station – there seems to be no stopping AI.

Brief History

AI is not a new concept. In fact, it was coined over sixty years ago by John McCarthy during a conference with fellow scientists at Dartmouth College in New Hampshire. Yet AI, described as the ability for machines to simulate intellectual processes characteristic of humans, is an area of study that has existed long before its inception as an academic discipline. Alan Turing, the British mathematician known for his cryptanalysis of the Enigma during the Second World War, and whose life is featured in the 2014 film, “The Imitation Game”, invented his “a-machine” (automatic machine) in 1936, and is widely considered to be the father of theoretical computer science and AI.

Clarifying Terminology

Even in our world of facility and energy management, the term artificial intelligence, and a long list of buzzwords that include data mining, machine learning, predictive analytics, data science, deep learning, and many others have been trending upwards and gaining renewed popularity. However, even when these words are springing up in our everyday conversations and media consumption, for many of us, the concepts they convey are often fuzzy at best and incomprehensible at worst.

This article aims at providing some clarity to this ‘word salad’, giving basic definitions while explaining their relationship to each other, and eventually suggesting that, when it comes to smart buildings, the most suitable and all-encompassing term is probably predictive analytics. A follow-up blog will build up from there and tackle the more practical question, “How can predictive analytics help save energy and improve occupant comfort?”

The diagram below shows the various disciplines, methodologies, processes and applications, and how they relate to each other. Obviously, there are many different points of view on how these concepts are defined, inter-related and applied. Through careful research, this diagram is our best attempt at developing a simplified framework to help us understand some key ideas.

Figure 1: Data Science: disciplines, methodologies, processes and applications – and their relationship to each other

 

In essence, figure 1 depicts the multidisciplinary field of data science with its two primary disciplines, statistics and computer science represented by the two largest intersecting circles. Data mining, shown at the intersection of both disciplines, is typically understood as an interdisciplinary process involving Artificial Intelligence (AI), statistics and database systems, shown as the three intersecting circles. Machine Learning (ML) is widely accepted as a methodology and subset of AI (others include natural language processing, machine vision, etc.) hence represented as a circle inside AI, with Deep Learning (DL) being a subset of ML, represented by the smallest circle contained within. Predictive modelling, shown inside the circle representing statistics methodologies, is defined as a process tightly coupled with statistics to predict outcomes. Database systems commonly refer to the software applications that organize data for analysis, with big data, the related field that handles data sizes exceeding traditional hardware and software, represented by the inner circle. Predictive analytics is generally understood as an application of data science requiring its inter-related disciplines, methodologies and processes to generate the expected outcomes.

Predictive Analytics

In a nutshell, predictive analytics is the analysis of historical information to find patterns useful in making informed predictions about future events. This type of evaluation is achieved using data mining processes and ML techniques with a variety of algorithms including decision tree regression, Bayesian networks, genetic algorithms and Artificial Neural Networks (ANN) – a deep learning subset of ML. It’s indeed the basis for Fault Detection and Diagnostics (FDD) methods based on process history as described in this blog.

As mentioned earlier, this article is foundational to the next one in our series, where we learn practical examples on how predictive analytics helps raise the bar to traditional methodologies aimed at reducing energy and operational costs while protecting valuable assets and increasing occupant well-being.

At CopperTree, continuous product innovation is very important to us. That’s why we are actively involved in research partnerships with leading institutions, working on initiatives that would enhance our predictive analytics platform and help our clients realize their building potential. Questions? Let us know!

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CopperTree Analytics

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