artificial intelligence and machine learning in fm

eFMinsig ht Issue 45 | June 2018

FM GLOBALLY

THE SHIFT

TOWARDS INCREASED GLOBAL FM Jelle van der Kluit

Artificial Intelligence and Machine Learning in FM

Comments on FM Henrik Järleskog

Strategic Sourcing and Procurement of FM Per Anker Jensen

Michael May

ISSN 1993-1980 | A EuroFM Publication

22 HOW TO LISTEN TO YOUR EMPLOYEES WHEN IMPLEMENTING A HEALTH AND SAFETY PLAN

Networks, hospitality and FM

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AI and Machine Learning in FM

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Q & A with Ivan Volkov

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Redesigning the entry to FM education and

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increasing the number of students FM Young Professional - Florian Engesser

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From the EuroFM Chair

20

How to listen to your employees when

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implementing a Health and Safety Plan

37 COMMENTS ON FM HENRIK JÄRLESKOG

Is trust missing from contracts?

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FM Globally - The shift towards increased global

26

FM Strategic Sourcing and Procurement of FM

28

Comments on FM - Henrik Järleskog

37

Buildings as Nodes for Sustainable Development

38

in the Fourth Industrial Revolution FM Education Hub

44

Think Beyond Standard Health Care to Improve

46

Patient Well-Being

64

From the EuroFM RNG Chair

51

FM in times of digital transformation

52

Could AI be the Future of FM?

57

Risk management: an advantage and a need for

58

every company

RELIABILITY-CENTERED MAINTENANCE: THE FUTURE OF MAINTENANCE

Measuremen Presents Workspace Excellence

60

A Manifesto for Change

62

From the EuroFM Ambassador -

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Reliability-Centered Maintenance

germany

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN FM

by Michael May

About the author Michael May has been a professor of computer sciences and facility management at the University of Applied Sciences HTW Berlin since 1994. He earned his PhD in mathematics in 1981 and his habilitation in information technology in 1990 at the Berlin Academy of Sciences. His current research is related to digitization, FM knowledge management, game-based learning, facility layout automation, IT integration, BIM, CAFM/lWMS, augmented reality and sustainability. He is the editor and author of several books including ‘The Facility Manager’s Guide to Information Technology’. He is a board member of the German Facility Management Association (GEFMA) and head of GEFMA’s IT (CAFM) work group. He represents GEFMA at the international level, e.g. at EuroFM and IFMA.

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Undoubtedly, “Artificial Intelligence” (AI) is one of the most fascinating fields of research in computer science with unforeseen opportunities but also risks – this also applies to the use of AI in real estate and FM. The basics of AI go back to the 17th century, when Charles Babbage had the revolutionary idea of an “Analytical Engine”. Unlike all previously developed machines, this one should have its own memory and a self-calculating and decision-making unit (“mill”). The control was to be a program stamped on punched cards – an idea he had copied from Jacquard looms that were able to create extremely complex weave patterns. By considering what would happen if such an Analytical Engine modified its own program, the idea of a “mechanized” intelligence was born. However, it was not until the 20th century that the conditions were created to not only conceive but also build a functioning (electronic) computer.

In the 1950s and 1960s, we were still talking reverently of electronic brains – a recognition that we might soon be able to use again. AI tries to simulate both human perceptions as well as human decisions and actions by machines. Many disciplines work together for this. In the beginning, these were the theories of axiomatic reasoning, mechanical calculations, and the psychology of intelligence (Hofstadter 1979). Today, other areas such as cognitive science, neurology, evolution, statistics, linguistics and even philosophy have joined. After decades of intensive research, we had to realize that a “thinking” machine cannot be constructed without intensively researching human thinking. The boundaries between intelligent and non-intelligent behaviour are still not well defined. It is undisputed that intelligence includes at least learning, creativity, emotional response, sense of beauty and self-confidence. But when is a machine intelligent? This question has occupied the AI researchers for many decades. The socalled Turing test from the year 1950, named after the famous mathematician Alan Turing, who named this test “Imitation Game”, is considered a generally recognized measuring instrument.

‘‘But when is a machine intelligent?’’

A human communicates in parallel with another human and a machine without visual or hearing contact, e.g. via a keyboard and screen. Both interlocutors (human and machine) answer questions from the questioner and try to convince him that they are thinking people. If, after the conversation, the tester cannot clearly decide which of the interlocutors is the machine, the machine has passed the test and may be considered intelligent. So far, no computer program has been known to have passed the Turing test – possibly an indication that the complexity of natural intelligence is more comprehensive than we commonly suspect. However, with today’s chatbots, we are already approaching the limit where we can no longer easily decide whether we are dealing with a human or a machine. In the past, AI has repeatedly been able to celebrate successes in certain limited areas of responsibility. These include board games such as chess and the much more complex Go, the use of robots in manufacturing and healthcare but also mathematical theorem proving. An example is mentioned for the latter.

June 2018

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century ago, there was no real interest in ANN‘s until the mid-1985s, when it was discovered that certain complex optimization problems could be solved by ANN’s and improved learning procedures (e.g. back propagation) were developed. germany Input layer

Hidden layers 1 ... n

Output layer

Figure 1: 1: Structure of an Artificial NetworkNeural Network Fig. Structure of anNeural Artificial

As early as the late 1950s, software programs were able

recognized and predictions made. Learning can be either

ANN’s also often (but exclusively) basisorfor the different forms of machine to find new mathematical proofsnot for Euclidean geom- are the trained uncontrolled. For complex ANN’s this usually learning (ML), which are becoming increasingly important in real estate and facility etry theorems, for example, a very elegant proof of the happens via so-called deep learning. management. ML is about enabling machines to learn independently. This often fifth proposition (pons asinorum) from the first book of In the meantime, numerous open source frameworks are requires patterns to be recognized and predictions made. Learning can be either Euclid’s “Elements”, which states that the base angles in available in addition to commercial development envitrained or uncontrolled. For complex ANN’s this usually happens via so-called deep an isosceles triangle are equal. ronments for ML applications (Hwang 2017). learning. A good overview of other fields of application of AI’s in In the meantime, numerous open source frameworks are addition to Meanwhile, the scientists are much more ambitious. So the areas of design andavailable construction, in RE and Smart Citcommercial development environments for ML applications (Hwang 2017). there are plans to build a so-called General AI – a system ies as well as FM is given by Hoar et al. (2017) and May

thatgood not onlyoverview does a clearlyof defined task fields but understands (2018). the area Facility Services, example, and A other of application of InAI’s in ofthe areas offordesign the world comprehensively, can Smart orient itself in it and construction, RE and Cities ascanwell as catering, FM is reception/helpdesk, given by Hoarcleaning, et al.security, (2017) and solve any problems 2017).of It’sFacility about nothing less maintenance andreception/helpdesk, logistics can be listed. May (2018). In(Göring the area Services, forinspection, example, catering, than developing machines that are as smart asmaintenance humans, They also expressly point disruptive nature also of cleaning, security, inspection, and logistics can out bethelisted. They expressly point outresearchers the disruptive nature of AIAIwith thedemand demand is high or even smarter. For many it is obvious that with the that it that is highittime to buildtime up theto build up already the necessary in theSoRE sectors ininorder this technology today’s AI’s have forms ofskills consciousness. theyand FMnecessary skills the RE to anduse FM sectors in order to use profitably. However, it also addresses ethical issues. are curious, creative and show individuality. this technology profitably. However, it also addresses ethical issues. AI’s will influence our life and thus our (FM) working environment dramatically – more Many of today’s advances in AI are basedtechnology on Artificial sustainable than any other in recent decades. We need to get used to Neural Networks (ANN). This AI’s will influence our life and thus our (FM) working sharing our planet withcomprises AI. hardware and software methods that attempt to simulate the nervous system of the human brain.

References

environment dramatically – more sustainable than any other technology in recent decades. We need to get used to sharing our planet with AI.

The information processing is similar to that in nature,

Göring M (2017): Begegnung mit einer unbekannten Art. National Geographic, Juli whereby information about connections between the References 2017, 58-81 (artificial) neurons are forwarded (see figure 1). AlGöring M (2017): Begegnung mit einer unbekannten Art.

though the first investigations date back as farEscher, as three Bach – National Geographic, Juli 2017, 58-81 Hofstadter, D.R. (1979): Gödel, An Eternal Golden Braid. Basic Books, quartersYork, of a century Hofstadter, D.R. (1979): Gödel, Escher, Bach – An Eternal New 1979ago, there was no real interest in ANN‘s until the mid-1985s, when it was discovered that

Golden Braid. Basic Books, New York, 1979

propagation) were developed.

2017, 28 S

Hoar, C; Atkin, B.; King K (2017): Artificial intelligence: What it means he built certain complex optimization problems could be solved Hoar, C; Atkin, B.; King K (2017): Artificial fort intelligence: What environment. RICS Report, October 2017, 28 S by ANN’s and improved learning procedures (e.g. back it means fort he built environment. RICS Report, October Hwang, K. (2017): Cloud Computing for Machine Learning

ANN’s also often (but not exclusively) are the basis for the different forms of machine learning (ML), which are becoming increasingly important in real estate and facility management. ML is about enabling machines to learn independently. This often requires patterns to be

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and Cognitive Applications. The MIT Press, Cambridge, London, 2017 May, M. (2018): Das CAFM-Handbuch – Digitalisierung im Facility Management erfolgreich einsetzen. 4. Auflage, Springer Vieweg, Berlin Heidelberg, to appear: 2018

eFMi Issue 45