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The future of IoT and machine learning – what role will humans play?

Despite having been around for over two decades, machine learning and its integration into business models is yet to become commonplace. Jari Salminen, Managing Director of Cumulocity, has witnessed the unfolding of machine learning and noted the progress made in its adoption into a wide range of industries over recent years. He says that rather than spend time in building grand strategies then assume that value can immediately be realized, companies should take pragmatic steps to connect their assets and start collecting data.

“What we are seeing today is that there typically exists a bit of a delay when companies start connecting assets and collecting information to be able to rely on machine learning algorithms and their accuracy,” says Salminen. “The training of these algorithms requires large amounts of data and thus time. It takes time for any individual company to move through the cycle of starting with very basic use cases and moving onto more complex algorithms and dependencies, and eventually introducing machine learning.”

At Cumulocity, Salminen deals with several manufacturing and industrial companies. He recognizes companies that require warehouses – or those whose supply chains do – currently expect sophisticated IoT solutions from a production and manufacturing point of view.

“Things are changing at such a pace that it is now very cost efficient even for smaller companies to deploy off-the-shelf IoT solutions for their supply chains.”

Salminen encourages companies who have examined the cost of IoT solutions for manufacturing or supply chain management over recent years to do so again. “Things are changing at such a pace that it is now very cost efficient even for smaller companies to deploy off-the-shelf IoT solutions for their supply chains as the price of hardware, connectivity and software has dramatically reduced over the last 5 years,” he reasons.

Holding algorithms accountable

The accuracy and efficiency at which these solutions can be implemented will greatly depend on the algorithms triggering them. According to Salminen, the more automated these algorithms become, the greater influence they will have not only on supply chains, but beyond them as well.

“Today, most actions are still done by human users and there are several reasons why that is. For example, accountability – in the long-term, we will need to ask how this will change. Will decisions be made based solely on the algorithms that machine learning will make possible? Maybe. However, such questions and their answers go beyond technology itself, as they are concerned with making sure that somebody other than a computer takes responsibility for decisions. It’s a complex domain,” Salminen says.

So, how to legally and ethically approach decision-making when no human is involved in the process? Very similar questions are currently being asked in the automotive industry.

Closely tied to the accountability of machine-to-machine decision-making within IoT projects is the matter that is security. In general, issues surrounding security are never too far away when connectivity, machines and material networks are concerned.

Multilevel security management

“The issue with security is that it exists on so many levels in IoT projects: from hardware – meaning devices, machines and assets – and connectivity, whether that entails using mobile connections or new narrowband IoT solutions, to the backend, meaning the cloud or servers. On top of all that you might have special applications for company users, partners or even consumers. Security needs to be controlled and monitored at all these levels.”

“Often, security breaches take place when more than one area has been overlooked. A most common area attracting hackers and attacks is wherever any connected device exposes ports reachable from the internet. These are being scanned by hackers,” Salminen continues. He reminds companies that if they are dealing with hardware, that they should keep their wide area network ports closed; from a connectivity perspective, they must make sure that everything is fully encrypted in transit.

“If you are providing cloud services on top of hardware and connectivity, make sure that your whole system is robust and secure. Ultimately, there is no single thing companies must consider when it comes to security, but rather, it is an area that must be owned end-to-end by someone in the project.”

Setting future standards

When asked how he sees the future of machine learning and IoT taking shape, Salminen is simultaneously restrained and excited. He sees industries as being past the initial hype. The implementation of more advanced and sophisticated use cases is now becoming a reality.

“The connectivity costs per device are decreasing, and thus enabling many new developments when it comes to industrial solutions. This is happening across widespread assets, meaning that for example, narrowband technologies are becoming more popular. However, what is still lacking from IoT and will need to be addressed in the next few years is standardization.”

While Salminen is adamant that a comprehensive philosophy regarding standardization must be adopted, he does not foresee the use of one single underlying standard that will apply across industries.

“My guess is that there won’t be an overarching standard that will include everything from devices to data structures. There will be so many different IoT use cases that it will be impossible to create something that would cover all of them,” he says. “We see lot of traction with MQTT (Message Queuing Telemetry Transport) as a messaging protocol due to the fact it doesn’t even try to standardize all parts of the IoT stack. For example, it does not deal with message payload format which is left to the developer to decide.”

On the other hand, Salminen believes that standards like Lightweight M2M by Open Mobile Alliance are not being picked up by market players because probably it doesn’t fit many use cases, among other reasons.

Nonetheless, he reminds those looking to initiate IoT projects to certainly consider available standards, but not to be limited by them. He also states that the key thing to ensure is that you are not locked into any standard, but to have flexibility in case your needs change in the future.

“If I were starting an IoT project, I would be looking at the most recent connectivity options, the role of standards, if such exist, and whether or not they are relevant to my business. However, I wouldn’t force any standards at the moment, as there are in fact very few that are relevant,” Salminen concludes.

 

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Interview w/ Jari Salminen

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How to build Big Data competencies

There are two separate topics to consider when it comes to Big Data. One involves finding a solution to an existing problem or challenge. The second has to do with building something new. Matti Vakkuri, Head of Technology, Internet of Things at ‎Tieto, outlines ways in which companies can build their Big Data competencies. He also discusses the aggregation of unstructured and machine IoT data in a manufacturing context.

“If we narrow the discussion to cover only the data drivers of Big Data, we are going in the wrong direction,” says Vakkuri. As an example, in B2B operations in the maritime industry like port optimization or fuel consumption optimization in vessels, data delivers huge benefits for a shipping business. “Less polluting engines – or ones that generate more power from less consumption – could be designed based on data that is collected. This would affect productivity and competitiveness because shipping is a volume business.”

Vakkuri adds that this data needs to be utilized so that all the information isn’t amassed for the sake of it. “You always ought to build something with or on the data. This brings an opportunity for data-driven firms,” he continues. “In nuclear power plants in Finland, there’s a regulation that data has to be stored for a relatively long period. Before Big Data, it was mainly in passive storage only. With Big Data, however, all data can be online. This means that we can do research based on the data that already exists.” Vakkuri points out that this is one of the paradigm shifts – that instead of having inaccessible passive data, we now have available data from which we can build analysis, business, opportunities and innovations.

Case examples

Amazon is a good example. “The truly remarkable thing about their business is that they see who is checking their products, putting them in their basket but not buying them. Checking but not buying – taking things out of your basket and not proceeding to check out – these send very significant signals. Big Data allows this to be done, but it must be understood that Big Data is also about analytics and machine learning,” continues Vakkuri. The same kind of implementation that is being applied in webstores can be also be used in brick and mortar retails stores using indoor positioning.

Tieto’s Intelligent Building Product Line is another case of effective Big Data usage. “This allows us to establish who is where in our new headquarters at any given moment. As long as I have given my permission, for instance, I can be followed around the office. Knowing where co-workers are at any time helps us to be more collaborative,” shares Vakkuri. Beyond determining where people are, the company is also gathering data to optimize and remodel how space is used in their offices.

A question of ethics

To CIOs and CDOs keen on building Big Data competencies in their respective companies, Vakkuri offers this piece of advice. “The answer is really simple: multi-disciplined people. You need technology guys who possess not just domain competence but also ethics. There must be somebody who is capable of understanding ethical aspects and good manners not based on gut feeling, but rather on an understanding of the logic behind whatever ethics is used.” Ethics, Vakkuri notes, is vital in technologies such as AI and Big Data.

“Another thing is that you need a team of people in which every person is different, and from a different background, but who are able and willing to build things together. Never hire just one person to be a data scientist, but a team made up of individuals with a variety of experiences, ages and educational backgrounds, and who come from diverse cultures.” He believes that the more heterogeneous your team is, the better.

“Never hire just one person to be a data scientist, but a team made up of individuals with a variety of experiences, ages and educational backgrounds, and who come from diverse cultures”

Combining unstructured and machine IoT data

“Let’s start with instruments: Instrument everything. This means – as Twitter co-founder Jack Dorsey once advised – log, measure and test everything. Store everything, store all relevant data,” suggests Vakkuri. “If you install video cameras in your factory, you don’t need to collect the data of the raw image, but more so the metadata of what is in the image, timestamps, location data, etc.”

He would like companies to forget the notion that storing and analyzing data is expensive – they really are not nowadays. “The paradigm has changed so that infrastructure costs are down, so invest in human capital and in people who can analyze the data. But if you want to conduct a comprehensive analysis, you need to combine the data sets together which has never been done before, so be provocative and think outside the box. While you’re at it, forget the box altogether.” He adds that this also depends on how open-minded and competent your employees are, and what types of ideas they propose. “In the end, it’s all about what innovations you can get to with the data.”

On Big Data and machine to machine implementation

As far as Big Data and machine-to-machine (M2M) implementation are concerned, Vakkuri has this to share with manufacturers. “M2M communication needs to be more standardized. This also means using the right tools for the right purposes. How do you know what’s right? Only by building proof of concepts and then testing,” he explains.

Vakkuri likewise recommends establishing a partnership involving the research side – such as universities and similar institutions – within the ecosystem. “Carrying out a research collaboration usually means that your costs will be lower when developing something new and quite often new innovations come from the applied research side.”

Last, data quality and information security are two other points Vakkuri would like to underline when it comes to M2M communication. “Information security needs will be more complex. The more machines and data are connected, the more challenges will arise in terms of information security.”

Matti Vakkuri works as Head of Technology, Internet of Things at ‎Tieto.

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Interview w/ Matti Vakkuri

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The problem with IIoT design

As with all trends and innovations in their infancy, there is bound to be some premature efforts deemed as groundbreaking, when in reality they fail to sustain their relevant functionality beyond initial hype. According to EE Times editor Rich Quinnell, this has been the case with IoT design. “All too often the design behind these [IoT] devices is not all that smart. It’s clever, it’s innovative, but IoT designs are also all too often piecemeal and rushed to market. What’s being created is a system of systems, without the system-level design issues getting addressed,” Quinnell writes.

The Object Management Group (OMG) is nonetheless providing a remedy for “correcting IoT’s trajectory.” For his EE Times piece, Quinnell spoke to Matthew Hause and Graham Bleakly of OMG to find make sense of the issues surrounding current approaches to IoT design. “We’re trying to get people away from building the IoT by hacking,” says Bleakley.

Read more on Hause’s and Bleakly’s thoughts at: http://www.eetimes.com/author.asp?section_id=8&doc_id=1331075

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The learning curve: From the Internet to Big Data to IoT

The technological developments born within the boundaries of the IT industry, and conversations that follow outside these boundaries create trends that are greater than the sum of their individual parts. Challenges are becoming less unique to manufacturers of particular products, and opportunities more ubiquitous to a wide range of service providers and manufacturers alike. In other words, technologies, industries and societies will become increasingly related to, and contingent on, one another in 2017.

Mikko Marsio, Vice President of Digital Business and IoT at Empower group, says that what has unfolded over the past two decades and led companies to where they are today can be understood as both an evolution from a technological perspective, as well as a revolution from an industry and business perspective. From the speculative nature of the IT bubble, to the profoundness of the Internet of Things, Marsio explains how consolidating technology with business is now more imperative than ever before.

“I remember a prediction that was made before I attended an MIT Executive Education course on the Internet in 2000. It envisioned the Internet becoming like electricity, meaning something that we don’t even acknowledge when using,” Marsio reminisces. “If you look at what was laid out in 2000 in conjunction with the IT bubble – for example that the best years for the pulp and paper industry were then and there – no one could actually have predicted how many paper mills would be shut down over the following 15 years.

In order for these mills to stay relevant, they must adapt what they are producing. Companies in general need to understand how both digitalization and end-users are causing their businesses to change. Over the past few years, increasingly many have come to recognize this,” he continues.

An affordable evolution

Despite the predictions regarding the impact of the Internet made at the beginning of the millennium, it would have been impossible for companies to imagine the extent of its integration into businesses. Many companies, and even industries, are now at a point where they are faced with a similar integration problem to solve concerning IIoT. For Marsio, integrating the Internet was the first hurdle for businesses to overcome, Big Data and analytics the second, and IoT the third.

“Big Data is the result of an evolution and I’m not sure that IIoT and IoT can, or indeed should, be separated as distinct developments. I say this because what essentially facilitates Big Data are the digital interfaces created for customer connectivity to machines.”

Machine connectivity and digital interfaces. Sounds very IoT doesn’t it? Marsio recalls an early example of this kind of machine connectivity from his time at Hewlett-Packard, when IoT or IIoT terminology had yet to see the light of day.

“In 2006, when I was working for HP, we were working on how to connect all our office equipment, especially multifunctional machines, to the Internet. This made the remote storage and analyses of data possible, and it also allowed the company to deliver a new kind of value for customers. Since 2006, Big Data has evolved to partly define what IoT is today, as we are now able to gain insights from thousands of data points, analyze these insights in real-time and ultimately use them to drive services. Moreover, in 2017 this can all be done affordably.”

From the short to the long-term

The short-term benefits of such insights can already be seen. However, long-term outlooks still require work. According to Marsio, companies must begin to address how they will develop the execution capacity necessary to scale up tangible opportunities not only now, but also in the future.

“In the manufacturing side, we will see less errors and faults in the short-term, which means companies will improve their overall equipment efficiency. Moreover, companies will not only gain insight into processes from, say the control room of a pulp and paper mill, but they will be able to do so remotely. In the long-term what will be more challenging, for example for players in the pulp and paper industry, will be addressing the ‘paper’ part of their businesses.” This is to say that as end-users’ needs change, the customer-value of paper will need to as well.

According to Marsio, short-term objectives and long-term perspectives can be maintained and executed in parallel. This necessitates a systematic management approach to IIoT opportunities and inherently entails considering the future.

“Within the last few years, there has been a change in how companies approach future developments. This means that companies are now anticipating more of a journey with regard to IIoT, as opposed to a project to be tackled, executed and moved on from. Therefore, future trends, developments and opportunities will be considered as a continuous flow of things.”

“Short-term objectives and long-term perspectives can be maintained and executed in parallel. This necessitates a systematic management approach to IIoT opportunities and inherently entails considering the future.”

Not just thinking, but acting ahead

How do companies and organizations evaluate what they could, should and must do now, and what are the potential consequences of those actions, Marsio asks. Part and parcel of a journey mentality is evaluating the future, which can be challenging especially in industries that have been set in their ways for many years, or even decades. When envisaging what a company will be in 20 years, and who and what it will serve, Marsio encourages leaders to think beyond their businesses and consider societies at large.

“Take Tesla. If in the future, we will all indeed have electric self-driving cars, why buy one at all? The same car that drives you could be used by others when you don’t need it. What would happen to companies offering parking spaces in city centers? Or the driving experience itself? German automotive manufacturers typically market the driving experience as the number one thing to consider, but if there is no driver, what’s the relevance of the experience?”

Regardless of leaders thinking ahead, the questions posed above require action in order to gain answers, and that’s what is currently so compelling about IIoT and IoT. The more ordinary and accessible products like Tesla’s become, the more products will be transformed into services, and thus, the more answers companies will have. However, waiting for that to happen, as opposed to making it happen and becoming accustomed to what IIoT allows for, will result in an opportunity lost. As with electricity and the Internet, Marsio holds that companies should aim for such a profound awareness of IoT that it becomes intuitive to corporate mindsets.

“Ultimately, it is essential for companies to consider how they can get to a point where they no longer acknowledge the fact that they are using IoT or IIoT,” he concludes.

 

Mikko Marsio works as Vice President of Digital Business and IoT at Empower Group

Image credit: chombosan / Shutterstock.com

Interview w/ Mikko Marsio

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The Augmented Reality and Virtual Reality revolution in manufacturing

According to Leroy Spence, Head of Sales Development at EU Automation, “like any disruptive technology with roots in the consumer market, industry viewed VR with a certain level of scepticism to begin with.” That is to say, industrial manufacturers didn’t at first consider developments in VR as having value in terms of production. However, for example in the automotive industry, designers and engineers use immersion labs where Oculus Rift headsets support the virtual testing of designs on vehicles. In his article for automation.com, Spence notes how one of the biggest indicators of the potential of AR and VR for industry has come from a shift in recruitment at major engineering companies.

Spence goes on to say that recently, firms have been very open about actively recruiting graduates with game design degrees. “Astute with VR, Android and mobile technology, this next generation of engineering recruits are helping make Industry 4.0 and Internet of Things (IoT) applications a reality.”

Read more about the potential of AR and VR for industry at:
http://www.automation.com/automation-news/article/the-augmented-reality-and-virtual-reality-revolution-in-manufacturing

Image credit: Yuganov Konstantin / Shutterstock.com

Via Automation.com

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