SAG_LinkedIn_MEME_Artificial_Text_Sep16.jpgThe next generation Internet of Things (IoT) platforms will probably be called AIOT, Artificial Intelligence of Things; the IoT – but enhanced by Artificial Intelligence (AI).

This year has shown peak investment in AI, with start-ups in the US alone having raised $1.5 billion, meaning that we will see the fruits of those investments in our daily lives very soon.

For me, as an IoT-watcher, the way AI will augment current IoT solutions and offerings is particularly interesting. In order to understand where AI will play a role we need to understand what AI is – and what it is not.

AI is an algorithm powered by statistical models allowing the AI to “learn” through feedback loops. So, rather than deterministic models where an algorithm uses predefined rules upon which to base its decisions, other models are applied. For example, Google makes use of a technique that’s called deep learning; much of the work in this area is inspired by how the human brain works. Those models are no longer deterministic and, as such, could mean that how an AI comes to a certain decision might become opaque. This could give rise to unforeseen situations; witness Microsoft’s AI chatbot that learned to be racist within hours through analyzing twitter feeds.

Will AI become all-knowing? The current AI’s will certainly not, they are trained on specific domains and will not be able to apply that knowledge in other contexts, but future generations of AI might (that is certainly Google’s goal).

So where will AI augment IoT? We think the area where loads of attention will go is in manufacturing, an industry that is already spending heavily on IoT.  The use case manufacturing is attacking with AI is pre-dominantly predictive maintenance. The form of AI they are doing this with is called Machine Learning.

Why are manufacturers chasing predictive maintenance? Well there are some real and tangible benefits around the corner. The low-hanging fruit is increased uptime and less (un)planned downtime, allowing organizations to lower the cost of maintenance and repair.

But there is more at stake. Having those capabilities will allow manufacturers to adopt new business models to better compete in the marketplace. For example, in some areas there is a need to move from capital intensive investment to more operational investments (they call that the shift from Capex to Opex), so instead of offering a machine for a fixed price, a machine is rented and paid for only when it is used (IoT will allow the monitoring of usage).

However, a side effect to this is that the manufacturer is not paid when the machine breaks down, so uptime is in his direct interest, likewise the lifetime of the goods. If the lifetime can be extended then the margin on the rent will go up. Having predictive maintenance capabilities are essential to reaching those goals.

So if predictive maintenance is so important, why isn’t there a full adoption going on yet? Well, there are some steep hurdles. The lack of reliable sensors for monitoring performance and behaviour of machines is one; the challenges of getting reliable connectivity into shop floor operations another. Both are prerequisites to collecting the data that is necessary to test the statistical models. 

Then there is a lack of statistical models that can predict behaviour, largely because of a shortage of data scientists that can build and test those models. And the real world is complex; machines are shipped all over and work under different conditions. For example, the vibration of a machine will be influenced based on the type of floor it stands on; a wooden floor will influence the measurements differently than concrete.

Manufacturers often manufacture many different machines in different versions and models. Those machines often are constructed on parts that were ordered through different vendors and suppliers. Although designers tend to set the quality to certain standards, spare parts delivered by third party vendors might behave slightly differently, undercutting the models in unseen ways.

We are certain that deterministic models will be insufficient to deal with the above situations effectively and that the only way forward to tackle these challenges will be AI-inspired real-time analysis approaches.

In my next blog I will look into AI-inspired chatbots and the role those will play in propelling IOT into the future.

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