This years’ IoT Solutions World Congress has yielded so many interesting insights that it was hard for us to select our highlights. From industry leaders managing FTSE100 companies to start-ups and SMEs disrupting the industry, we were able to see what the future may hold for a connected world, and interact with clients and partners to discover new solutions, challenge the norm and discuss new ideas.
As exhibitors ourselves, we pulled our weight offering our expertise on new approaches, solutions, and industry challenges, and we enjoyed the interactions with peers, partners, and friends, as well as hearing the success stories from across the industry.
Here are some of our insights from the IOT Solutions World Congress and the opportunity machine learning presents
But first things first, why as a data science and machine learning company did we go to an IoT solutions congress aside from the fact we were invited to present our R&D Sailing AI project? Well, our main aim was to showcase and demonstrate how machine learning and data science brings additional advantages to develop and optimize intelligent IoT solutions. But what exactly is IoT?
The Internet of Things (IoT)
can be defined as all the objects with computing devices in them that are able to connect to each other and exchange data using the internet.
Vodafone Business, for instance, showcased a variety of connected devices such as security cameras, smoke detectors, water leak sensors, smart lighting and smart heating helping make properties more secure and energy-efficient. All of them connected to the internet and to each other and exchanging data to improve your experience.
Machine Learning (ML) then, as a branch of Artificial Intelligence (AI), is the ability of a machine to automatically learn and especially improve without being directly programmed. Image recognition is probably the most known example of what machine learning can do. If you go through the airport security with an electronic passport, it’s the machine that scans your face and grants you access (or sometimes not!).
ML & IoT combined then impact sets of tools and processes that allow devices, whilst connecting to each other, to gather data, optimise a process and deliver real-time suggestions. Imagine an employee in a factory that gets notified by tablet that in 5 minutes the probability of them making a mistake increases by 40% – its time to take a break. A great example of the two working together.
We understand that these concepts can be slightly abstract so what about a real example from one of the talks that we attended?
Think about Hugo Boss. This renowned fashion brand, created in the 1930s, is known to be a symbol of cool and style yet is also known to be one of the most technologically advanced companies in the world of fashion. Erkut Ekinci, Head of IT, explored how they were able to expand and maintain their Smart Factory in Turkey during his presentation.
Their IoT and ML development analyses data and delivers real-time suggestions to the employees whilst tracking and gathering further data to verify the effectiveness and efficiency of its prediction.
This translates into a smaller number of imperfections (less than 2%) and higher R.O.I.
But the beauty of Machine Learning doesn’t stop here: Teaching AI how to sail
This project started almost casually when our co-founder Eric Topman met with his fellow university friend turned professional sailor, Jack Trigger, founder of Trigger Racing, and started discussing the possible implications of machine learning applied a sailing boat. The discussion then evolved towards how to apply Machine Learning and Data Science to a sailing boat during a sailing race.
The main challenge was to create an asset for Jack, without breaking any rule of the competition or giving him an unfair advantage. The solution leaned toward the possibility to enhance the autopilot of this high-performance sailing boat, where we were then capable of using recurrent neural networks, deep reinforcement and optimization techniques to produce a digital twin of the sailor.
Machine Learning & Food Treatment Machines
During our third and final day at the congress, we had the opportunity to learn more about how our partner Microsoft, were able to help Marel, a food processing company, to expand from Iceland and become one of the biggest firms in that industry. The talk, led by Marel’s IoT architect, Edward Voermans and Tolli Einisson, project manager, explained how through predictive maintenance, they were able to deliver impeccable service and optimize their machines’ processes.
Imagine a 7km chain of hanging poultry that wears off over time, but you don’t know where or when. This information could tip the scale between success and failure for a business.
By applying ML models of predictive analysis to its process, the company was able to forecast the condition of the chain, removing the downtime of the machine and optimise its operations. Consequently, reducing its costs and improving revenues and R.O.I.
The IOT Solutions World Congress is over for another year but these opportunities are everywhere. Every day businesses are exploring new technologies and new processes to gain a competitive edge. New products and services are emerging and combining technology in different ways to deliver a vast range of benefits. From reducing costs or response times to improving performance or customer experience, the main challenge is where to start, and often not whether the technology exists.
So if you’re embarking on your project; ‘Think Big, Start Small but Move Forward’, and if you want to find out more about our unique approach and how it’s proved successful in developing Machine Learning for clients, book a call to speak with the team.
