Making global IoT connectivity seamless with TELUS Global Connect
Navin Arora, President, TELUS Business Solutions, Executive Vice-president, TELUS
TELUS Global Connect lets you customise and manage IoT device connectivity on mobile networks worldwide
Communications technology company TELUS is a leader in IoT connectivity and technologies in Canada. We are constantly striving to provide better value and more flexibility to our IoT business customers, who already enjoy access to Canada’s largest dedicated IoT network.
As their businesses grow and expand internationally, customers look to TELUS for the same enterprise-grade network operation to power their products and services, from connectivity through to built-in security and world-class support, across the globe.
TELUS Global Connect provides a platform for customers to enjoy that same reliable connectivity on their cellular IoT devices beyond Canada's borders in nearly every country on the planet.
Powered by an integration with Eseye’s AnyNet Connectivity Platform, TELUS Global Connect has near 100 per cent global connectivity management across 700 mobile networks in 190 countries through a single eSIM with up to 10 carrier profiles. It is currently being used by more than 2,000 customers worldwide, connecting seamlessly over two million devices. The platform also enables integration with AWS and Microsoft Azure, providing flexibility for customers and developers to integrate their own cloud-based services.
By simplifying access to global networks, TELUS Global Connect removes a barrier to international expansion for many businesses, contributing to a stronger economy for us all. Further, customers have always-on access to the TELUS IoT Centre of Excellence, and a dedicated support team to help with everything from onboarding to technical troubleshooting.
TELUS (TSX: T, NYSE: TU) is a dynamic, world-leading communications technology company with $16 billion in annual revenue and 16 million customer connections spanning wireless, data, IP, voice, television, entertainment, video and security. We leverage our world-leading technology and compassion to enable remarkable human outcomes. Our longstanding commitment to putting our customers first fuels every aspect of our business, making us a leader in customer service excellence and loyalty. In 2020, TELUS was recognised as having the fastest wireless network in the world, reinforcing our commitment to provide Canadians with access to superior technology that connects us to the people, resources and information that make our lives better.
For more information on TELUS Global Connect, visit telus.com/iotconnectivity
INDUSTRY VIEW FROM TELUS
Artisan robots with AI smarts will juggle tasks, choose tools, mix and match recipes and even order materials – all without human help
Failure of a machine in a factory can shut it down. Lost production can cost millions of dollars per day. Component failures can devastate factories, power plants and battlefield equipment
To return to operation, skilled technicians use all the tools in their kit - machining, bending, welding and surface treating, making just the right part as quickly and as accurately as possible. But there’s a declining number of technicians with the right skills, and the quality of things made by hand is subject to the skills and mood of the artisan on the day the part is made.
Both problems could soon be solved by artificially intelligent robotic technicians. These systems can take measurements; shape, cut or weld parts using varied tools; pass parts to specialized equipment; and even purchase needed materials – all without human intervention. Known as hybrid autonomous manufacturing, this process involves automated systems that seamlessly use multiple tools and techniques to build high-quality components where and when they are needed.
I am a professor of metallurgical engineering. My colleagues and I design the recipes to make materials and components with just the right internal structure to create properties like strength and fracture resistance. With a network of colleagues at Ohio State and other universities, I have been developing a plan to give birth to these autonomous artisans.
How things are made
Components are either mass-produced or custom-made.
Most things people touch daily have been mass-produced. Quality is assured by using well-honed processes based on testing and monitoring large numbers of parts and assuring the process is done the same way every time.
Custom fabrication – making components on demand – is often essential, sometimes to conform to a patient’s specific anatomy or to replace aircraft landing gear that was forged and is no longer being made. Processes for making metallic parts – material removal, deposition, deformation, transformation, inspection – can all be done with small tools, with incremental actions rather than the kind of bulk processes, usually with big tools and dies, used in mass production.
Automation has long been a part of mass production, which includes sophisticated robots that handle parts and weld on automobile assembly lines. Additive manufacturing often referred to as 3D printing, is increasingly being used with a variety of materials to make components.
Now in development are robotic blacksmiths – robots that can hammer metallic parts into shape instead of cutting, building up or moulding them.
Automated customization – not an oxymoron
To automate custom fabrication, my colleagues and I are developing an automated suite of tools that can carry out all the steps for making a wide range of components, using multiple processes without human intervention. Sensors will also be central to hybrid autonomous manufacturing to control the processes and maintain and assure quality.
Such autonomous manufacturing systems will make the myriad decisions needed to create a component of the right strength, size and surface finish. Artificial intelligence will be required to handle the enormous number of choices of materials, machine settings and process sequences. Rather than finding a mass production recipe and never deviating, these autonomous manufacturing systems will choose from a very large set of possible recipes to create parts and will have the intelligence to assure that the chosen path produces components with the appropriate material properties.
Robots could either position small tools on manufactured components or transfer the component from one piece of equipment to another. A fully autonomous system could manufacture a wide range of products with a versatile set of tools. The systems could source materials and possibly even send work out to specialized cutting and deformation tools, just like a human artisan.
The production rate of such systems would not rival those of mass production, but because robots can work continuously they can be more productive than human technicians are. Data from sensors provide a digital record of all the steps and processes with critical temperatures, machine settings and even images. This record can assure quality by, for example, making sure the material was deformed the right amount and cracks were not produced during the process and covered up.
Manufacturing at or near the operating room is one example of a process that can be enabled with hybrid autonomous manufacturing. Often when patients with bone fractures undergo trauma surgery, metallic plates of varied shapes are required to hold bones together for healing. These are often created in the operating room, where the surgeon bends plates to fit the patient, sometimes using a 3D-printed model created from medical images of the patient as a form to bend the metal against.
Bending by hand is slow and imprecise, and stressing the plate in the wrong place can cause it to fracture. A robotic technician could cut and bend and finish a plate before surgery. Patients do better and save money if they spend less time in the hospital.
The road to robotic artisans
Numerous companies are now showing the way forward in autonomous manufacturing, including three venture-funded startups. FormLogic is developing automated high-quality machine shops. Path Robotics is putting the skills of a welder into a robot. And Machina Labs is out to create robotic blacksmiths. Other companies are developing systems to automate design and logistics.
Hybridization – the ability to carry out different tasks in different ways with multiple tools – is the next step. The key pieces of hybrid autonomous manufacturing exist now, and fully autonomous systems could be common in a decade. Companies adopting this approach to custom fabrication will need to draw on a new generation of students with the skills to combine these technologies.
The investments proposed in the United States Innovation and Competition Act passed by the Senate on June 8, 2021, and those in the Biden administration’s proposed American Jobs Plan could support the development of these kinds of advanced manufacturing technologies. Funds for the development of advanced manufacturing technologies and the associated skills base could make U.S. manufacturing more competitive.
Solving IoT trust to build smarter cities
The technological advances triggered by the pandemic offer us the opportunity for smart cities to thrive. Truly smart cities will be those that allow open and interoperable exchange of data via the Internet of Things (IoT). According to IoT analytics, IoT connections reached 12 billion in 2020, surpassing non-IoT connections. 2021 will bring dramatic changes to how IoT is scaling.
This is due to two drivers. Firstly, the health crisis brought to the fore the importance of data-driven strategic decisions for businesses in all sectors. Secondly, it is due to the proliferation of low-power wide-area network (LPWAN) wireless technology, specifically designed for M2M devices with low-bandwidth at long range with resilient cellular networks – for example, gas and water supply data.
The amount of data generated even by a small city is vast, and city officials must ensure that data at the origin, at rest and across all data exchanges is trusted and secure.
For a device and its data to be truly trusted, security must be built in from the start. The familiar SIM already offers a tried-and-tested blueprint. Enhanced features with new SIM standards – such as embedded SIM (eSIM) that are soldered into the device and bolstered by GSMA IoT Safe – provide a way to safeguard businesses from disruptive outages or change in suppliers while offering the device maker and service provider remote management.
Innovations like integrated SIM (iSIM) help unlock entirely new IoT use cases while offering additional out-of-the-box functionality packaged with security for applications services to build on. This technology is already catalysing new areas of innovation in urban mobility, bringing more e-bikes and e-scooters to our streets or smart tracking solutions that enable real-time visibility of critical goods and supplies.
Trust frameworks and transparency will need to be woven into all IoT layers for our cities to dispel concerns and ensure these new technologies can help our smart cities thrive.
by Loic Bonvarlet is VP of Product Marketing at Kigen.
AI Marketplace: the digital platform for tomorrow’s innovations
Posting challenges, finding solutions, and gathering know-how: this is what the AI Marketplace promises. For the past year, experts from science and industry have been working on a platform for AI in product development. The platform is now live in a beta version and brings together AI experts, suppliers, and users.
Whether it's automating technology scouting or optimising design data, the potential of AI in product creation is manifold. CLAAS, an internationally active agricultural machinery manufacturer, has recognised these possibilities.
The company is testing a special use case for the integration of AI in computer-aided design in the AI Marketplace, an initiative of the leading-edge cluster It’s OWL. The project is one of six pilot projects in which companies are working together with research institutions in the AI Marketplace on AI solutions for specific use cases.
The topics range from intelligent product monitoring and smart vehicle diagnosis to AI-supported manufacturability analysis. In addition to CLAAS, companies including Diebold Nixdorf, Düspohl Maschinenbau, Hella Gutmann Solutions, Westaflex and Ubermetrics Technologies are also involved in the pilot projects.
However, countless other partners will benefit from the AI Marketplace. After all, providers, users, and experts can jointly develop and exchange AI solutions on the platform of the same name.
“The AI Marketplace offers companies a central place to solve their product development challenges with the help of AI,” says Leon Özcan, Project Coordinator and Research Associate at Heinz Nixdorf Institute (Germany), part of Paderborn University. “AI providers gain direct access to their customers in this way.”
Sneak peek at the AI Marketplace platform: Suppliers, users and experts can develop and exchange AI solutions. Image provided courtesy of It’s OWL.
What sets the AI Marketplace apart
The AI Marketplace platform will be successively expanded by functionalities in the course of the project. Initially, users will be offered a matching function: for example, companies can post challenges while AI providers create competence profiles.
A subsequent matching of challenges with competence profiles brings together suitable platform actors, who can now jointly develop solutions.
“Our competitive analysis has shown that our concept of a marketplace for AI in product creation addresses a blank spot on the competitive map,” says Ruslan Bernijazov, Technical Project Coordinator and Research Associate at Fraunhofer Institute for Mechatronic Systems Design IEM. “Although there are numerous solutions that address concrete problems from product creation, there is currently no central point of contact for solution providers, AI experts, and users.”
In addition to matching, the next step is to create a protected data room for development and test data in order to continuously improve AI applications and adapt them to customer needs.
Here, the AI Marketplace relies on certified standards that guarantee data sovereignty while forming the basis for fair cooperation. An app store for AI solutions is also to be added, as well as a range of standardised AI building blocks that can be combined as required and used to develop new AI solutions.
More than a platform
The AI Marketplace already offers free services with which small and medium-sized enterprises in particular can tap into the potential of artificial intelligence in product creation.
By means of a Potential Analysis, structured application possibilities for AI in companies can be identified. In addition to concrete AI use cases, companies also receive recommendations for action for implementation. Using these, manufacturing companies can optimise their development capacities and shorten development cycles.
Companies can find out whether they are ready for the application of artificial intelligence in the AI Readiness Check on the AI Marketplace. In addition to determining the maturity level, companies receive helpful tips here on how to best prepare for the use of AI in product development.
In addition, companies can have their current corporate data governance assessed by the AI Marketplace with the help of a Data Check-Up. As a result, companies immediately receive a classification of their maturity level as well as concrete tips and information on how they can improve their corporate data governance so that nothing stands in the way of the use of AI applications.
The AI Marketplace creates a unique ecosystem that brings together AI experts, vendors, and users to realise the potential of AI. Image Source: Adobe Stock
A large network is behind the AI Marketplace
The success of the platform and its services is guaranteed by a consortium of 19 research institutions, networks, and companies, whose nucleus is It’s OWL. In this technology network, 200 companies and research institutions are jointly developing new technologies for tomorrow’s production.
Other networks also ensure a broader impact. prostep ivip Association, for example, bundles know-how in product development, while the International Data Spaces Association (IDSA) – both members of FIWARE Foundation – ensures secure data spaces.
Furthermore, the open-source platform FIWARE and the platform operator Inno-Focus are leaders in their fields. Artificial Intelligence and Machine Learning (ML) techniques are increasingly being incorporated in architectures powered by FIWARE to support the adoption of smarter decisions, going a step further in the automation of processes.
The AI Marketplace project is funded until December 2022 by the innovation competition “Artificial Intelligence as a Driver for Economically Relevant Ecosystems” of the German Federal Ministry for Economic Affairs and Energy (BMWi).
To learn more about our work and how you can get involved, visit the AI Marketplace webpage.
Hendrik Fahrenwald is a Marketing Manager at It’s OWL.
Header image courtesy of It’s OWL.
Make the future of work a strategic competency
Contrary to popular belief, the future of work isn’t something that happens to you or your firm. It’s not merely some new technology your organisation will eventually get around to adopting. In fact, there’s no single future of work – there will be many, built in the context of specific problems. But here’s the key: the future of work is a strategic imperative that you must actively create for your company, your employees, and your own career. It’s a strategy you build and a set of actions you take – continuously. And like all strategic imperatives, it requires elevating the discipline to a strategic competency, an initiative with a vision, a strategy, and a plan.
Think of the future of work as a constellation of innovations that address new or accelerating challenges for organisations aiming for customer-obsessed outcomes. You can dip into these innovations as you see fit. Some are technological, such as robotic process automation (RPA), where bots or software entities take on some of the more repetitive and predictable tasks that human workers conduct digitally. Other future of work innovations are organisational, such as Chinese manufacturer Haier’s use of micro-enterprise teams that elect their own leaders.
Often, technological and organisational innovations will go hand-in-hand. Deploying RPA bots will be a powerful tool to automate some tasks, but they depend upon an organisation’s automation strike teams – business-responsive but technologically savvy organisational units that help provide guidance and expertise – or similar centres of excellence. Creating a distributed workforce as an organisational tool can diversify your talent base, but will depend upon successfully deploying remote collaboration software to help far-off colleagues work together. Technology and organisational structures are intertwined.
Considering the future of work an ongoing innovation initiative places agency in your hands. Turning it into a strategic competency will drive investment at three levels. First, you must prepare yourself, personally, to have the underlying attitude and inclination to succeed. A world in which bots and AI are becoming ever more common requires every employee – from leaders to technologists to non-technical employees – to bring curiosity, collaboration and adaptability to the table.
Second, you must help every employee increase their understanding of how to interact with automation, AI and other technologies. Today, most employees aren’t ready. Forrester’s Business Technographics data reveals that only 21 per cent of information workers agree with the statement “I know when to question the results of an automated technology”, while only 18 per cent agree that “my career path in a world of automation is clear to me.” Why? Because learning and development programmes haven’t begun to train employees about advanced technologies. Only 17 per cent agree that “we have established a structured training programme for working with automation technologies.”
Third, you must create a strategic plan. The influx of AI and automation into the workplace will change its composition: the automation economy will eliminate some jobs while creating others. Your five-year plan should forecast which roles you’ll need fewer of, like employees performing administrative tasks. These workers must be trained for new positions or not be backfilled as they depart. You must also gain a whole host of technical and business skills in AI and automation. These are workers you must cultivate through training or hiring – training can take years, and hiring is competitive, so you must plan ahead.
Succeeding in the future of work requires a commitment to longer-term (up to 10 years) thinking, which can be challenging. Yet companies and leaders that successfully invest in elevating the future of work to the status of a strategic competency are the ones most likely to thrive over the next decade.
Find out more about Forrester’s research on the Future of Work here.
by JP Gownder, Vice President and Principal Analyst, Forrester
How to make more time and money from your manufacturing operation
2020 will see a further increase in the deployment of industrial digital technologies within UK manufacturing operations. These technologies can help manufacturers address some of their pain-points and create new gains for their customers, shareholders and workers.
Let’s face it, there’s been no shortage of excitement or promotion around so-called Industry 4.0 technologies such as the internet of things, robotics and automation, machine learning, 3D printing, artificial intelligence and augmented reality.
Cutting through all the jargon, we at the Institution of Engineering and Technology (the IET) would like to de-mystify some of the hype that beckons you to jump on the “digital bandwagon”, particularly if you are a small or medium-sized enterprise owner, manager or investor.
How will any of this improve the things that really matter?
At any one time, there are a myriad of issues facing SME manufacturers, many of them completely beyond your control. The challenges are many, varied and specific to each firm and its niche or sector. And it’s no secret that with challenges come opportunities too!
• Lack of visibility
• Skills and staff shortages
• Fulfilling customer orders
• Rising costs, such as energy
• Productivity improvements
• Product quality/consistency
• Machine downtime
• Power outages
• Legacy premises and old equipment not fit for purpose
• Retaining existing customers
• Getting paid on time
• Keeping a constant flow
• Prototyping costs and time
• Too much time spent firefighting
• Winning new orders
• Time to market taking too long
• Matching capacity to demand
• Limited funds for CAPEX
• Supply chain issues
Industry 4.0 technologies won’t necessarily solve any of these issues for you outright, but they will enable you to hone in on and quantify solutions to those things you can directly inspire, inform and influence. Harvesting, analysing and acting on the right data in real time offers increased speed and ability to address your pain points within the business and lies at the very heart of Industry 4.0.
Why should I even spend time thinking about all this?
Fundamentally, there are two reasons. First, reduced costs. Your operating costs should fall and your available time should rise as a result of using the right digital tools within your business.
Second, that you should stay ahead. It’s likely that many of your competitors, collaborators and clients may well be exploring or increasing their use of digital technologies within their businesses. Stay in the game, get yourself up to speed and avoid getting left behind by innovating before they do.
Where do I start?
Set your sights high but start with a grounded view. Don’t spend money on “digital”, if you haven’t already optimised your “physical”. The adage remains: get lean, then get digital. You need to find out what’s really happening within your manufacturing operation, or as we say, create a single version of the truth. To do this you will need to digitally connect your existing machines and information systems across the business.
This used to be the privilege of big businesses that could afford expensive bespoke programmes to connect their systems. The new digital tools bring such connectivity between systems such as ERP (enterprise resource planning) and CRM (customer relationship management) within the grasp of any SME.
To complete this task, it’s likely that you will need to add some simple and relatively inexpensive sensors to your existing machines (at the cost of a few pounds) and some new connecting protocols to your network.
To do this and make sense of the data generated, you may need to get help. Challenge your new apprentices or latest recruits to work with your champion on this. Failing that, try contacting your local further education college, university engineering department, equipment supplier or catapult centre.
Having gained a better understanding of the key factors at play within the business, you’ll be in a much better position to shine the spotlight on those parts of your operation which require deeper examination, and that will give you savings and increased flexibility. It’s vital to act on these insights of your operation and reap the rewards before moving forward to the more advanced steps where you will need to invest your hard-earned cash on further technology.
As anyone who has ever been through a new ERP or control system implementation knows, there is no point at all in digitising poor productivity (at best) or digitising chaos (at worst).
Creating new gains
Industry 4.0 is all about taking your existing human capital, shop floor equipment and back office systems and connecting these valuable assets, giving you a clearer and faster view of your world, and enabling your team to save money and time, invest your savings in the right technology at the right time with clear return on investment, and spend more time with your existing and new customers to grow your business.
by John Patsavellas, Senior Lecturer, Cranfield University and expert panel member at the Institution of Engineering and Technology
‘Can I see your parts list?’ What AI’s attempted chat-up lines tell us about computer-generated language
Have you ever wondered what flirting with artificial intelligence would look like? Research scientist and engineer Janelle Shane have given us an idea by training a neural network – an algorithm loosely inspired by biological brain structures – to produce chat-up lines.
Some of the results are hilarious and completely nonsensical, such as the inelegant: “2017 Rugboat 2-tone Neck Tie Shirt”. But some of them turned out pretty well. At least, if you’re a robot:
I can tell by your red power light that you’re into me. You look like a thing and I love you. Can I see your parts list?
But how were these lines generated, and why do the results vary so much in terms of quality and cohesiveness? That’s down to the types of neural networks Shane worked with: all based on GPT-3, the world’s largest language model to date.
GPT stands for generative pre-trained transformer. Its current version, developed by OpenAI, is the third in a line of ever-improving natural language processing systems trained to produce human-like text or speech.
Natural language processing, or NLP, refers to the application of computers to process and generate large amounts of coherent spoken or written text. Whether you ask Siri for a weather update, request for Alexa to turn on the lights, or you use Google to translate a message from French into English, you’re able to do so because of developments in NLP.
It takes a variety of NLP tasks – from speech recognition to picking apart sentence structures – for applications such as Siri to successfully requests. The virtual assistant, much like any other language-based tool, is trained using many thousands of sentences, ideally as varied and diverse as possible.
Because human language is extremely complex, the best NLP applications rely increasingly on pre-trained models that allow “contextual bidirectional learning”. This means considering a word’s wider context in a sentence, scanning both left and right of any given word to identify the word’s intended meaning. More recent models can even pay attention to more nuanced features of human language, such as irony and sarcasm.
GPT-3 is such a successful language-generating AI because it doesn’t need retraining over and over again to complete a new task. Instead, it uses what the model has already learned about language and applies it to something new – such as writing articles and computer code, generating novel dialogue in video games, or formulating chat-up lines.
Compared to its predecessor GPT-2, the third-generation model is 116 times bigger and has been trained on billions of words of data. To generate its chat-up lines, GPT-3 was simply asked to automate the text for an article headlined: “These are the top pickup lines of 2021! Amaze your crush and get results!”
Because GPT-3’s training updates have been added gradually over time, this same prompt could also be used on smaller, more basic variants – generating weirder and less coherent chat-up lines:
Hey, my name is John Smith. Will you sit on my breadbox while I cook or is there some kind of speed limit on that thing? It is urgent that you become a professional athlete. CAPE FASHION
But GPT-3’s “DaVinci” variant – its largest and most competent iteration to date – delivered some more convincing attempts which might actually pass for effective flirting – with a little fine-tuning:
You have the most beautiful fangs I’ve ever seen. I love you. I don’t care if you’re a doggo in a trenchcoat. I have exactly 4 stickers. I need you to be the 5th.
The latest variant of GPT-3 is currently the largest contextual language model in the world and is able to complete a number of highly impressive tasks. But is it smart enough to pass as a human?
As one of the pioneers of modern computing and a firm believer in true artificial intelligence, Alan Turing developed the “Imitation Game” in 1950 – today is known as the “Turing Test”. If a computer’s performance is indistinguishable from that of a human, it passes the Turing Test. In language generation alone, GPT-3 could soon pass Alan Turing’s test.
But it doesn’t really matter if GPT-3 passes the Turing Test or not. Its performance is likely to depend on the specific task the model is used for – which, judging by the technology’s flirting, should probably be something other than the delicate art of the chat-up line.
And, even if it were to pass the Turing Test, in no way would this make the model truly intelligent. At best, it would be extremely well trained on specific semantic tasks. Maybe the more important question to ask is: do we even want to make GPT-3 more human?
Learning from humans
Shortly after its reveal in summer 2020, GPT-3 made headlines for spewing out shockingly sexist and racist content. But this was hardly surprising. The language generator was trained on vast amounts of text on the internet, and without remodelling and retraining, it was doomed to replicate the biases, harmful language and misinformation that we know to exist online.
Clearly, language models such as GPT-3 do not come without potential risks. If we want these systems to be the basis of our digital assistants or conversational agents, we need to be more rigorous and selective when giving them reading material to learn from.
Still, recent research has shown that GPT-3’s knowledge of the internet’s dark side could actually be used to automatically detect online hate speech, with up to 78% accuracy. So even though its chat-up lines look unlikely to kindle more love in the world, GPT-3 could be set, at least, to reduce the hate.
It’s time to deliver on the promise of IoT security
Haydn Povey, CEO and founder of Secure Thingz, an IAR Systems Group company, and Member of the Executive Steering Board at IoT Security Foundation.
In the past, electronic devices had fixed functionality and their actions in the real world were controlled by the user.
Fast forward to today and practically every connected device is equipped with sensors and actuators that interact with the outside world without direct human intervention. This is true across consumer electronics, modern industrial systems and, of course, critical national infrastructures such as oil pipelines, water treatment plants and cityscape lighting.
Unfortunately, this means that if attackers can gain access to these systems, they can alter the decision-making processes that drive these autonomous actions in the real world. This has huge implications for how we build and manage our connected devices, how we manage vulnerabilities over the lifecycle of our products, and how we manage and constrain data and credentials.
Furthermore, the impact of a major compromise can be massive to bottom-line revenues and profitability, with deep brand value ramifications. Beyond the initial business impacts of a compromise, there are other good reasons to implement security – not least to protect critical intellectual property. If you spent millions of dollars on R&D, you really do not want someone to re-use your efforts. The EU has estimated the impact of IP theft in Europe alone as approaching $60 billion, with nearly 300,000 jobs lost to this insidious crime over the past few years.
New regulation and legislation for IoT security and privacy is being rapidly introduced globally, such as Consumer IoT EN 303 645, Industrial IoT ICE 62443 frameworks, and the US IoT Cybersecurity Improvement Act. Demonstrating compliance to these regulations is an emerging challenge for all organisations, especially given these cover technical and operational activities, and require long-term support of products within the end-user environment.
To assist in resolving these tasks, the IoT Security Foundation, a non-profit industry association of which Secure Thingz is a founding member, has developed an IoT Security Compliance Framework, enabling organisations to implement a self-certification methodology that covers the 13 best practices for security and secure by design guidelines. The Consumer IoT Security Standard EN 303 645, based on the 13 best practices, is widely regarded as the security benchmark for consumer IoT. Both the standard and the guidelines contain core requirements for applications, which developers must achieve within their applications.
IAR Systems is the world’s leading provider of software for the programming of processors in embedded systems, with approximately 50,000 customers globally. As a division of IAR Systems, Secure Thingz is a global domain expert in device security, embedded systems and lifecycle management.
To mass customise is to produce through custom eyes
The concept of a circular economy is an indication that a ‘sustainable world’ doesn’t necessarily require a decrease in the quality of life for consumers. Its primary focus is to manage our resources systematically, and one way in which we’re able to maximise the output of our resources is through mass customisation.
Henry Ford initialised the concept of mass production with his standardised assembly line back in 1913, but we’re now interested in how we can normalise mass customisation. Mass customisation is often described as a way of adding mass value to a consumer in the most efficient manner. It is the process of producing goods or a service as and when a consumer requires it and simultaneously tailoring it to their wants and needs without disrupting the production process and keeping costs aligned. This may initially sound visionary, yet with the utilisation of the right technology we are closer to achieving this paradigm than we believe.
Also known as made-to-order, mass customisation has already begun its journey through various companies in tailoring products and services to suit each individual. The development of different social media platforms has exponentially increased the choices available for consumers and, in turn, their wants and desires. Modern society has become more informed and connected than in any other previous generation. A sense of individual empowerment can be achieved simply through being able to demonstrate to the world that something has been tailored directly to your needs or co-created by your own ideas.
With this in mind, does that complicate the ability to create a unique product for each customer? Perhaps it unlocks a horizon of opportunity? The developed technologies we have allow us to address the barriers of mass customisation that may previously have been a hindrance. The possibilities are now endless. With 3D modelling we are able to envision a product or service before an actual output, enabling a saving in both time and cost. For example, we can see what a particular item of clothing looks like on a replica of ourselves or even foresee any physical drawbacks of a mechanical construction before it has been developed. It promotes a buyers’ market where lean production is the ulterior motive.
Moreover, the data collected from allowing consumers to customise each item they purchase activates an ability not only to adapt to users’ habits but also to produce and store data that can be used to influence future research and development. This level of data can save initial development costs while also formulating a platform for dynamic pricing by which we can price according to spending behaviour. All this is achievable using minimum labour hours and with minimal resources. Using social media as a form of free marketing also allows an ability to fully understand the wants of a consumer – something that had not previously been a possibility.
Essentially, mass customisation is only as powerful as the outcome of productivity crossed with the ability to customise. With the technology readily available and generations becoming increasingly active on social media, both high productivity levels, and the ability to customise, are not only achievable but formulate the ultimate example of market segmentation. With artificial intelligence being utilised to enable the manufacturing process to learn from previous errors and collect data to customise before investment, the risk of overspending on R&D and stockpiling can be diminished. Many companies are already adopting methods of mass customisation and those who haven’t yet may find the steps are far more reachable than they ever have been before.
Nikesh Mistry is Sector Head – Industrial Automation at Gambica. If you would like to share your opinions with like-minded people as to how the digitalised world is evolving, visit gambica.org.uk. Contact Nikesh on LinkedIn or Twitter.
by Nikesh Mistry, Sector Head – Industrial Automation, Gambica
AI is increasingly being used to identify emotions – here's what's at stake
Imagine you are in a job interview. As you answer the recruiter’s questions, an artificial intelligence (AI) system scans your face, scoring you for nervousness, empathy and dependability. It may sound like science fiction, but these systems are increasingly used, often without people’s knowledge or consent.
Emotion recognition technology (ERT) is in fact a burgeoning multi-billion-dollar industry that aims to use AI to detect emotions from facial expressions. Yet the science behind emotion recognition systems is controversial: there are biases built into the systems.
Many companies use ERT to test customer reactions to their products, from cereal to video games. But it can also be used in situations with much higher stakes, such as in hiring, by airport security to flag faces as revealing deception or fear, in border control, in policing to identify “dangerous people” or in education to monitor students’ engagement with their homework.
Shaky scientific ground
Fortunately, facial recognition technology is receiving public attention. The award-winning film Coded Bias, recently released on Netflix, documents the discovery that many facial recognition technologies do not accurately detect darker-skinned faces. And the research team managing ImageNet, one of the largest and most important datasets used to train facial recognition, was recently forced to blur 1.5 million images in response to privacy concerns.
Revelations about algorithmic bias and discriminatory datasets in facial recognition technology have led large technology companies, including Microsoft, Amazon and IBM, to halt sales. And the technology faces legal challenges regarding its use in policing in the UK. In the EU, a coalition of more than 40 civil society organisations have called for a ban on facial recognition technology entirely.
Like other forms of facial recognition, ERT raises questions about bias, privacy and mass surveillance. But ERT raises another concern: the science of emotion behind it is controversial. Most ERT is based on the theory of “basic emotions” which holds that emotions are biologically hard-wired and expressed in the same way by people everywhere.
This is increasingly being challenged, however. Research in anthropology shows that emotions are expressed differently across cultures and societies. In 2019, the Association for Psychological Science conducted a review of the evidence, concluding that there is no scientific support for the common assumption that a person’s emotional state can be readily inferred from their facial movements. In short, ERT is built on shaky scientific ground.
Also, like other forms of facial recognition technology, ERT is encoded with racial bias. A study has shown that systems consistently read black people’s faces as angrier than white people’s faces, regardless of the person’s expression. Although the study of racial bias in ERT is small, racial bias in other forms of facial recognition is well-documented.
There are two ways that this technology can hurt people, says AI researcher Deborah Raji in an interview with MIT Technology Review: “One way is by not working: by virtue of having higher error rates for people of color, it puts them at greater risk. The second situation is when it does work — where you have the perfect facial recognition system, but it’s easily weaponized against communities to harass them.”
So even if facial recognition technology can be de-biased and accurate for all people, it still may not be fair or just. We see these disparate effects when facial recognition technology is used in policing and judicial systems that are already discriminatory and harmful to people of colour. Technologies can be dangerous when they don’t work as they should. And they can also be dangerous when they work perfectly in an imperfect world.
The challenges raised by facial recognition technologies – including ERT – do not have easy or clear answers. Solving the problems presented by ERT requires moving from AI ethics centred on abstract principles to AI ethics centred on practice and effects on people’s lives.
When it comes to ERT, we need to collectively examine the controversial science of emotion built into these systems and analyse their potential for racial bias. And we need to ask ourselves: even if ERT could be engineered to accurately read everyone’s inner feelings, do we want such intimate surveillance in our lives? These are questions that require everyone’s deliberation, input and action.
Citizen science project
ERT has the potential to affect the lives of millions of people, yet there has been little public deliberation about how – and if – it should be used. This is why we have developed a citizen science project.
On our interactive website (which works best on a laptop, not a phone) you can try out a private and secure ERT for yourself, to see how it scans your face and interprets your emotions. You can also play games comparing human versus AI skills in emotion recognition and learn about the controversial science of emotion behind ERT.
Most importantly, you can contribute your perspectives and ideas to generate new knowledge about the potential impacts of ERT. As the computer scientist and digital activist Joy Buolamwini says: “If you have a face, you have a place in the conversation.”