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The State of the Union for IoT Intelligence

A digital transformation revolution in manufacturing is underway, and data is the primary currency paving the way for more efficient ways of doing business.

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By Frank Antonysamy

Frank Antonysamy is Vice President of Cognizant’s Global IoT and Engineering Services

A digital transformation revolution in manufacturing is underway, and data is the primary currency paving the way for more efficient ways of doing business. Gone are the days when data analysis was left to Monday night quarterbacking by poring over static results. Today, thanks to a central wireless ecosystem which links relevant mobile devices, Internet of Things (IoT) connected machines and connected employees, data gathering and analysis in a smart factory is immediate and real-time optimization drives significant efficiencies.

But not all smart factories are created equal.

Given that enterprises are all on different points on the path to complete digital maturity, it helps to take stock of the state of IoT intelligence — where we are now and where we are headed — and what industrial organizations need to be successful.

Laying the foundation for intelligence

One of the key advantages of Industry 4.0 is the promise of the Internet of Things (IoT) or Industrial Internet of Things (IIoT). Sensors connected to raw materials, factory floor equipment and final products can relay information, over a Wi-Fi connection, about their health and usage patterns to wider enterprise asset management software and enterprise resource planning systems.

Equally important, stakeholders can access this data in real-time and on-demand. Companies can leverage this data to deliver insights across three channels:

  • Product intelligence
  • Service intelligence
  • Operational intelligence

There is significant overlap across these three pillars but their power to deliver a smart factory with new avenues for monetization, is revolutionary.

Here’s an overview of the IoT landscape with respect to its three core pillars of intelligence.

Case Study: Fast-tracking innovation with a PLM platform

Product intelligence shakes up traditional PLM

The smart factory runs on smart products whose intelligence can be leveraged to read the tea leaves of market demand. At its core, product intelligence is defined as intelligence derived from an intelligent (read…IoT-enabled) product. In the IoT world, traditional rules of product lifecycle management (PLM) no longer apply. Gone are the rinse-and-repeat cycles of concept, design, manufacture, marketing and sales. In traditional manufacturing, the ideation-sale stage took years if not decades and slight changes in market demand had a whiplash effect on the process.

IoT has rebooted the PLM conversation to move it away from the product and make it more about the customer. IoT-enabled products can now deliver intelligence post sales about how the product is being used (or not), how it is being disposed of, and a whole host of other downstream information. Such product intelligence is useful in two primary ways: as a method of refining the product to make it more agile and responsive to consumer needs (thereby leading to potentially more sales) and as new avenues of monetizing such product intelligence.

The future of product intelligence is a complete “closed-loop” product development, with real-time customer feedback woven into the process. It bears stressing that while customer focus groups and behaviors have always been part of the design and manufacture process, IoT has effectively compressed that time cycle and expanded the scale of parameters that might be considered — and monetized.

Service intelligence delivers customer-focused monetization

Monetization in the new smart factory landscape need not be restricted to product intelligence alone. Service intelligence, for example, is about delivering aftermarket intelligence in the form of added services to an existing or expanding customer base. A customer who buys Widget A from a manufacturing company might also be interested in understanding how to optimize the use of that widget for their own tailored environments.

[Download]: Designing Manufacturing’s Digital Future

While aftermarket services are not entirely new, the addition of IoT has the capability of delivering service intelligence on steroids. In the future, service intelligence providers will use IoT to tailor measurements of key performance indicators (KPIs) and delivery of data insights depending on exactly what the end customer is looking for. Tailoring service intelligence to the customer potentially leads to greater client stickiness. What’s more, IoT is capable of slicing and dicing intelligence for each and every customer, making the net results that much more insightful and leading to more bountiful monetization opportunities.

Operations intelligence squeezes the most out of machines

Monetization also comes from picking the low-hanging fruit in production processes. Arguably one of the best ways to squeeze the most out of IoT is to use it to increase manufacturing uptime. IoT is also favorably impacting the ability to fine-tune production processes by being able to connect, visualize and analyze data from a whole host of new players such as machines on the plant floor. RFID and computer vision layers also add to such intelligence.

IoT-embedded devices on the plant floor can spit out data that measures machine health, which can be fed into machine learning algorithms for predictive maintenance. If a rotor heats up past a preset temperature setting, for example, it can trigger the algorithm to send an alert to a plant worker or even proactively shut the machine down. Machine learning capabilities derived from IoT enhance KPIs such as manufacturing uptime.

[Download]: Creating a Digital PLM Platform to Promote Collaboration and Increase Efficiency

In the future, expect a move toward increasingly segmented manufacturing, possibly sliced and diced into ever smaller batches. Operations intelligence will allow manufacturers to segment the production process — and fine-tune each — to fulfill a variety of specialty orders at the same time.

What it takes to deliver on the promise of IoT

While IoT intelligence in its various forms promises a truly smart factory with a wealth of monetization opportunities, it needs a robust infrastructure to truly deliver. Elements of this winning infrastructure include, among others: a C-suite willing to address negative attitudes of incumbency; standardization of data aggregation and analytics processes such as machine learning; and future-proofing technologies through increasing reliance on open-source models.

Since data is the lifeblood of IoT, enterprises need to ensure that they don’t get mired in the data lake — that the data they’re working with is clean and structured, relevant to the KPIs they want measured, and fed to algorithms in a consistent format. Once data is clean and uniform, smart factories can leverage IoT to feed machine learning algorithms that learn from the data and eventually deliver an almost lights-out production stream.

Since the future of intelligence also involves its monetization — vendors up and down the digital supply network will pay for insights — it will be important to connect stakeholders to the central nervous system of the smart factory in new ways. Customer service agents (or even customers themselves) for example should be able to see where product orders are in the production process and fine-tune their forecasts accordingly. IoT delivers transparency to all stakeholders — within reason, keeping intellectual property concerns in mind.

[Download]: Advancing Smart Manufacturing Operations Value with Industry 4.0

IoT in manufacturing is not limited to the production floor either. IoT sensors in warehouses can detect when supplies are going bad, when inventory is low and beef up accordingly. Remote weather events that can affect vendor delivery can trigger automated backups. The IoT-driven smart factory touches many processes and products much beyond the plant floor.

Until true digitization from start to finish is a total reality, companies are figuring out stop-gap measures that will leverage the promise of IoT. A “nerve center,” which serves as a central repository for data gathering and analytics can serve to overcome the problem of data connectivity across locations and devices.

The ripple effect from IoT intelligence is not limited to the manufacturing floor alone. By placing the digital core at the center, it reshapes processes up and down key constituencies such as supply chain and asset management.

How tomorrow’s tech might impact IoT intelligence

IoT is already being incorporated in the smart factory of today. Tomorrow, expect acceleration with respect to monetizing closed-loop product intelligence, an increased focus on the customer through service intelligence and using operations intelligence by improving businesses processes on the way to a truly smart factory.

The road is expected to get even smoother with the advent of 5G technology which will decrease latency of IoT for edge computing devices. 5G will deliver even faster access to data in real time which will make real-time analysis even more accurate. The technology has special ramifications for production processes where time is of the essence. Devastating machine shutdowns can be averted in split seconds by machine learning algorithms fed through 5G connections from IoT-enabled equipment. This means smart factories of faster computing speeds and greater agility. The state of the union for IoT intelligence is strong and only expected to grow stronger as new technologies such as 5G make data competencies that much more robust.

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How businesses can protect themselves from the rising threat of deepfakes

Dive into the world of deepfakes and explore the risks, strategies and insights to fortify your organization’s defences

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In Billy Joel’s latest video for the just-released song Turn the Lights Back On, it features him in several deepfakes, singing the tune as himself, but decades younger. The technology has advanced to the extent that it’s difficult to distinguish between that of a fake 30-year-old Joel, and the real 75-year-old today.

This is where tech is being used for good. But when it’s used with bad intent, it can spell disaster. In mid-February, a report showed a clerk at a Hong Kong multinational who was hoodwinked by a deepfake impersonating senior executives in a video, resulting in a $35 million theft.

Deepfake technology, a form of artificial intelligence (AI), is capable of creating highly realistic fake videos, images, or audio recordings. In just a few years, these digital manipulations have become so sophisticated that they can convincingly depict people saying or doing things that they never actually did. In little time, the tech will become readily available to the layperson, who’ll require few programming skills.

Legislators are taking note

In the US, the Federal Trade Commission proposed a ban on those who impersonate others using deepfakes — the greatest concern being how it can be used to fool consumers. The Feb. 16 ban further noted that an increasing number of complaints have been filed from “impersonation-based fraud.”

A Financial Post article outlined that Ontario’s information and privacy commissioner, Patricia Kosseim, says she feels “a sense of urgency” to act on artificial intelligence as the technology improves. “Malicious actors have found ways to synthetically mimic executive’s voices down to their exact tone and accent, duping employees into thinking their boss is asking them to transfer funds to a perpetrator’s account,” the report said. Ontario’s Trustworthy Artificial Intelligence Framework, for which she consults, aims to set guides on the public sector use of AI.

In a recent Microsoft blog, the company stated their plan is to work with the tech industry and government to foster a safer digital ecosystem and tackle the challenges posed by AI abuse collectively. The company also said it’s already taking preventative steps, such as “ongoing red team analysis, preemptive classifiers, the blocking of abusive prompts, automated testing, and rapid bans of users who abuse the system” as well as using watermarks and metadata.

That prevention will also include enhancing public understanding of the risks associated with deepfakes and how to distinguish between legitimate and manipulated content.

Cybercriminals are also using deepfakes to apply for remote jobs. The scam starts by posting fake job listings to collect information from the candidates, then uses deepfake video technology during remote interviews to steal data or unleash ransomware. More than 16,000 people reported that they were victims of this scam to the FBI in 2020. In the US, this kind of fraud has resulted in a loss of more than $3 billion USD. Where possible, they recommend job interviews should be in person to avoid these threats.

Catching fakes in the workplace

There are detector programs, but they’re not flawless. 

When engineers at the Canadian company Dessa first tested a deepfake detector that was built using Google’s synthetic videos, they found it failed more than 40% of the time. The Seattle Times noted that the problem in question was eventually fixed, and it comes down to the fact that “a detector is only as good as the data used to train it.” But, because the tech is advancing so rapidly, detection will require constant reinvention.

There are other detection services, often tracing blood flow in the face, or errant eye movements, but these might lose steam once the hackers figure out what sends up red flags.

“As deepfake technology becomes more widespread and accessible, it will become increasingly difficult to trust the authenticity of digital content,” noted Javed Khan, owner of Ontario-based marketing firm EMpression. He said a focus of the business is to monitor upcoming trends in tech and share the ideas in a simple way to entrepreneurs and small business owners.

To preempt deepfake problems in the workplace, he recommended regular training sessions for employees. A good starting point, he said, would be to test them on MIT’s eight ways the layperson can try to discern a deepfake on their own, ranging from unusual blinking, smooth skin, and lighting.

Businesses should proactively communicate through newsletters, social media posts, industry forums, and workshops, about the risks associated with deepfake manipulation, he told DX Journal, to “stay updated on emerging threats and best practices.”

To keep ahead of any possible attacks, he said companies should establish protocols for “responding swiftly” to potential deepfake attacks, including issuing public statements or corrective actions.

How can a deepfake attack impact business?

The potential to malign a company’s reputation with a single deepfake should not be underestimated.

“Deepfakes could be racist. It could be sexist. It doesn’t matter — by the time it gets known that it’s fake, the damage could be already done. And this is the problem,” said Alan Smithson, co-founder of Mississauga-based MetaVRse and investor at Your Director AI.

“Building a brand is hard, and then it can be destroyed in a second,” Smithson told DX Journal. “The technology is getting so good, so cheap, so fast, that the power of this is in everybody’s hands now.”

One of the possible solutions is for businesses to have a code word when communicating over video as a way to determine who’s real and who’s not. But Smithson cautioned that the word shouldn’t be shared around cell phones or computers because “we don’t know what devices are listening to us.”

He said governments and companies will need to employ blockchain or watermarks to identify fraudulent messages. “Otherwise, this is gonna get crazy,” he added, noting that Sora — the new AI text to video program — is “mind-blowingly good” and in another two years could be “indistinguishable from anything we create as humans.”

“Maybe the governments will step in and punish them harshly enough that it will just be so unreasonable to use these technologies for bad,” he continued. And yet, he lamented that many foreign actors in enemy countries would not be deterred by one country’s law. It’s one downside he said will always be a sticking point.

It would appear that for now, two defence mechanisms are the saving grace to the growing threat posed by deepfakes: legal and regulatory responses, and continuous vigilance and adaptation to mitigate risks. The question remains, however, whether safety will keep up with the speed of innovation.

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The new reality of how VR can change how we work

It’s not just for gaming — from saving lives to training remote staff, here’s how virtual reality is changing the game for businesses

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Until a few weeks ago, you might have thought that “virtual reality” and its cousin “augmented reality” were fads that had come and gone. At the peak of the last frenzy around the technology, the company formerly known as Facebook changed its name to Meta in 2021, as a sign of how determined founder Mark Zuckerberg was to create a VR “metaverse,” complete with cartoon avatars (who for some reason had no legs — they’ve got legs now, but there are some restrictions on how they work).

Meta has since spent more than $36 billion on metaverse research and development, but so far has relatively little to show for it. Meta has sold about 20 million of its Quest VR headsets so far, but according to some reports, not many people are spending a lot of time in the metaverse. And a lack of legs for your avatar probably isn’t the main reason. No doubt many were wondering: What are we supposed to be doing in here?

The evolution of virtual reality

Things changed fairly dramatically in June, however, when Apple demoed its Vision Pro headset, and then in early February when they were finally available for sale. At $3,499 US, the device is definitely not for the average consumer, but using it has changed the way some think about virtual reality, or the “metaverse,” or whatever we choose to call it.

Some of the enhancements that Apple has come up with for the VR headset experience have convinced Vision Pro true believers that we are either at or close to the same kind of inflection point that we saw after the release of the original iPhone in 2007.Others, however, aren’t so sure we are there yet.

The metaverse sounds like a place where you bump into giant dinosaur avatars or play virtual tennis, but ‘spatial computing’ puts the focus on using a VR headset to enhance what users already do on their computers. Some users generate multiple virtual screens that hang in the air in front of them, allowing them to walk around their homes or offices and always have their virtual desktop in front of them.

VR fans are excited about the prospect of watching a movie on what looks like a 100-foot-wide TV screen hanging in the air in front of them, or playing a video game. But what about work-related uses of a headset like the Vision Pro? 

Innovating health care with VR technology

One of the most obvious applications is in medicine, where doctors are already using remote viewing software to perform checkups or even operations. At Cambridge University, game designers and cancer researchers have teamed up to make it easier to see cancer cells and distinguish between different kinds.

Heads-up displays and other similar kinds of technology are already in use in aerospace engineering and other fields, because they allow workers to see a wiring diagram or schematic while working to repair it. VR headsets could make such tasks even easier, by making those diagrams or schematics even larger, and superimposing them on the real thing. The same kind of process could work for digital scans of a patient during an operation.

Using virtual reality, patients and doctors could also do remote consultations more easily, allowing patients to describe visually what is happening with them, and giving health professionals the ability to offer tips and direct recommendations in a visual way. 

This would not only help with providing care to people who live in remote areas, but could also help when there is a language barrier between doctor and patient. 

Impacting industry worldwide

One technology consulting firm writes that using a Vision Pro or other VR headset to streamline assembly and quality control in maintenance tasks. Overlaying diagrams, 3D models, and other digital information onto an object in real time could enable “more efficient and error-free assembly processes,” by providing visual cues, step-by-step guidance, and real-time feedback. 

In addition to these kinds of uses, virtual reality could also be used for remote onboarding for new staff in a variety of different roles, by allowing them to move around and practice training tasks in a virtual environment.

Some technology watchers believe that the retail industry could be transformed by virtual reality as well. Millions of consumers have become used to buying online, but some categories such as clothing and furniture have lagged, in part because it is difficult to tell what a piece of clothing might look like once you are wearing it, or what that chair will look like in your home. But VR promises the kind of immersive experience where that becomes possible.

While many consumers may see this technology only as an avenue for gaming and entertainment, it’s already being leveraged by businesses in manufacturing, health care and workforce development. Even in 2020, 91 per cent of businesses surveyed by TechRepublic either used or planned to adopt VR or AR technology — and as these technological advances continue, adoption is likely to keep ramping up.

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5 tips for brainstorming with ChatGPT

How to avoid inaccuracy and leverage the full creative reign of ChatGPT

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ChatGPT recruited a staggering 100 million users by January 2023. As software with one of the fastest-growing user bases, we imagine even higher numbers this year. 

It’s not hard to see why. 

Amazon sellers use it to optimize product listings that bring in more sales. Programmers use it to write code. Writers use it to get their creative juices flowing. 

And occasionally, a lawyer might use it to prepare a court filing, only to fail miserably when the judge notices numerous fake cases and citations. 

Which brings us to the fact that ChatGPT was never infallible. It’s best used as a brainstorming tool with a skeptical lens on every output. 

Here are five tips for how businesses can avoid inaccuracy and leverage the full creative reign of generative AI when brainstorming.

  1. Use it as a base

Hootsuite’s marketing VP Billy Jones talked about using ChatGPT as a jumping-off point for his marketing strategy. He shares an example of how he used it to create audience personas for his advertising tactics. 

Would he ask ChatGPT to create audience personas for Hootsuite’s products? Nope, that would present too many gaps where the platform could plug in false assumptions. Instead, Jones asks for demographic data on social media managers in the US — a request easy enough for ChatGPT to gather data on. From there he pairs the output with his own research to create audience personas. 

  1. Ask open-ended questions

You don’t need ChatGPT to tell you yes or no — even if you learn something new, that doesn’t really get your creative juices flowing. Consider the difference: 

  • Does history repeat itself? 
  • What are some examples of history repeating itself in politics in the last decade?

Open-ended questions give you much more opportunity to get inspired and ask questions you may not have thought of. 

  1. Edit your questions as you go

ChatGPT has a wealth of data at its virtual fingertips to examine and interpret before spitting out an answer. Meaning you can narrow down the data for a more focused response with multiple prompts that further tweak its answers. 

For example, you might ask ChatGPT about book recommendations for your book club. Once you get an answer, you could narrow it down by adding another requirement, like specific years of release, topic categories, or mentions by reputable reviewers. Adding context to what you’re looking for will give more nuanced answers.

  1. Gain inspiration from past success

Have an idea you’re unsure about? Ask ChatGPT about successes with a particular strategy or within a particular industry. 

The platform can scour through endless news releases, reports, statistics, and content to find you relatable cases all over the world. Adding the word “adapt” into a prompt can help utilize strategies that have worked in the past and apply them to your question. 

As an example, the prompt, “Adapt sales techniques to effectively navigate virtual selling environments,” can generate new solutions by pulling from how old problems were solved. 

  1. Trust, but verify

You wouldn’t publish the drawing board of a brainstorm session. Similarly, don’t take anything ChatGPT says as truth until you verify it with your own research. 

The University of Waterloo notes that blending curiosity and critical thinking with ChatGPT can help to think through ideas and new angles. But, once the brainstorming is done, it’s time to turn to real research for confirmation.

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