This new age with the potential of writing history has finally arrived through LLMs and generative AI. In fact, the arrival of the new age of technology is asserted to deal with a gargantuan amount of complex nature-based problems. Besides, it will be bringing out the new creativity and innovation age. And most importantly, it promises to deliver data, text, images, sound, code, and other forms of content. In fact, we are discussing generative AI, and it can reverse any sector of the economy.
IoT turned whole industries in the previous decade on their heads. It was an auto-pilot system that developed an innovation that transformed what could be done previously. IoT turned the traditional million-strong industry forces on their head with intelligent gadgets and, uh, the birth of this new technology era that promised to solve a gargantuan spectrum of complex problems. It will also lead to a new imagination and innovation. This blog shall, therefore, primarily cover Generative AI in IIoT.
Before we dive into those facts, let me tell you about the basic elements of this blog, such as generative AI and IIoT, so that you people can get a basic idea about the subject which we are going to discuss later on.
What Exactly is Generative AI?
Generative AI is one of those deep-learning models that can produce text, images, or any content depending on how it has been trained. From many trends that AI has ridden out, the increase in ChatGPT has become a game-altering trend even to cynics. OpenAI’s chatbot, powered by a large language model, can write poems, jokes, essays, and even song lyrics that sound human-made.
Generative AI has also revolutionized computer vision, with selfies reimagined as Renaissance masterpieces and faces aged automatically. Its greatest leap now is in language processing so that AI can generate responses indistinguishable from human intelligence on just about any topic. And it’s not limited to text—these programs can be trained to generate computer code, molecule structures, and images resembling nature, too.
As its applications grow, technology companies are using generative AI to speed up computer coding, discover new materials, and develop advanced chatbots. It’s even being used to generate artificial data, which is used to train AI without compromising privacy and copyright protection. And that’s just the beginning.
What is Industrial Internet of Things (IIoT)?
Industrial Internet of Things (IIoT) is the application of smart devices, actuators, and other hardware, i.e., radio frequency identification tags, to optimize industrial and manufacturing processes. They are networked hardware that offers data gathering, processing, and transmission. Data obtained using it gives higher efficiency and reliability. IIoT may be termed as the industrial internet. It is utilized in all industries, e.g., energy management, manufacturing, utilities, oil and gas.
IIoT exploits the capability of smart machines and real-time processing to access the data dumb machines have been generating in the last decades in factories. The IIoT concept is that smart machines not only gather and process information better than human beings in real-time but also provide crucial insights that can be used for business decision-making quicker and better.
Embedded sensors and actuators allow companies to identify inefficiencies and problems sooner, conserving time and cost savings, as well as adding to business intelligence software. In manufacturing alone, IIoT has the potential to offer quality control, green and sustainable operations, traceability, and supply chain efficiency as a whole. In an industrial environment, IIoT is at the hub of functions like predictive maintenance, field service optimization, asset tracking, energy management, and maintenance.
The Rise of Generative AI
The future of the world’s largest industries, such as finance, media, and technology, is generative AI, or a tool of artificial intelligence capable of creating new content based on input provided. Indeed, its widespread use for activities like content creation and data analysis has gigantic impacts on the future of work.
Mark Murphy, who heads U.S. enterprise software research at J.P. Morgan, believes the advent of generative AI is a bigger technology inflection point than the iPhone or the Internet. Murphy predicted a massive explosion in productivity in one to three years, followed by profound white-collar job redefinition between four and eight years on.
J.P. Morgan’s 5th Annual Global Machine Learning Conference during October 2023 provides an indication of investor expectations. Most (28%) anticipate the maximum impact on marketing, followed by insurance and legal services (21%), media (20%), data analytics (18%), and consumer tech (13%). Generally speaking, these surveys are an indication of the underlying industry changes, a pointer towards far-reaching changes to come.
Thus, when paired with the promise of IoT, generative AI has the potential to deliver a new world of promise and possibility. Generative AI provides a suite of capabilities that can be leveraged to enrich IoT applications and use in numerous ways.
Some of these capabilities include:
- Synthetic data and data augmentation.
- Anomaly detection
- Data anonymization.
- Natural language interface
- Automation
We will now discuss each of these capabilities in detail.
Synthetic Data and Data Augmentation
IoT devices generate vast volumes of data, which need to be used to train machine learning models. Realistic IoT application data in actual environments is costly and time-consuming to acquire, primarily due to sophisticated IoT deployments, security, and privacy.
Generative AI offers an escape path by generating synthetic datasets with high similarity to real-world environments. It is achieved through apt training and validation of machine learning models on varying IoT settings. Through generating data within the backdrop of similar actual device telemetry, generative models can be a tool in the diversity and richness enhancement of datasets as well as to counteract sparsity, unbalance, or incompleteness of data. Further, wealthier data is more useful to be involved in improving the machine learning constructs for applications like energy estimation and occupation management and in the sustainability of IoT devices.
Synthetic data or AI model-synthesized data to mimic real data also supports predictive maintenance. Generative AI, based on history data, failure behavior, and real-time data, can generate synthetic data to simulate different operating conditions and failure modes predict probable equipment malfunction, and propose preventive measures to avoid it. This avoids bulk-scale downtime and maintenance costs in Industrial Internet of Things (IIoT) solutions.
Anomaly Detection
Any surge or tendency in IoT statistics can exhibit an indication of an imminent piece of equipment malfunction, security breach, or irregular usage and, therefore, must be resolved as swiftly as possible.
Generative AI offers a solution to IoT network anomaly detection by learning typical patterns of behavior from vast amounts of device data and flagging deviations that can lead to future problems or security vulnerabilities. Generative AI models, unlike conventional threshold-based systems, are capable of comprehending difficult-to-analyze, multi-dimensional device data and can detect subtle anomalies that may have been missed.
This ability is especially worth it in application scenarios where IoT sensors are tracking vital infrastructure, manufacturing operations, or weather conditions, where early anomaly detection will avoid equipment failure, factory shutdown, or environmental risks. With a more advanced IIoT platform, discoveries like these can be utilized to enable proactive decision-making.
Data Anonymization
Data sharing is critical to analytics use cases, research studies, and third-party collaboration, like system integrators in the IoT industry. Data in the IoT is mostly gathered as personal or confidential data and, therefore, is intricate in nature and challenging to share.
Generative AI models have a solution of anonymization of data. Data anonymization, by the very term, leads to anonymization of the data but does not impact the statistical nature of data. Anonymized data sets so obtained can be used for analysis, development, and testing under the data protection laws without affecting data privacy.
The elegance and potency of generative AI for data anonymization make it something worth an undetermined amount to businesses who wish to subject IoT data to analytics, machine learning, or even sharing with third-party vendors.
Natural Language Interface
End-user industrial IoT usage is probably going to be accompanied by complex graphs, complex data tables, and complex user interfaces. Usage of these graphs and user interfaces by working and learning requires a minimum amount of technical knowledge and an understanding of the structure of the IoT application.
Generative AI creates a potential for streamlining this process. With generative AI, one is able to use natural language in order to consume IoT data that is sophisticated, to normalize the complexities that come with traversing data graphs, tables, and interfaces. Natural language allows one to query, instruct, or interrogate IoT devices using conversational speech, resulting in interaction being natural and compelling.
Major Uses of GenAI in IIoT
Now that we have discussed the capabilities and limitations of GenAI in IoT, let’s look at its applications in some different sectors. This is, again, not an exhaustive list but an indication of how beneficial GenAI is going to be in IoT.
Industrial Manufacturing
With integrated to LLMs like CoPilot, ChatGPT, BARD, etc., GenAI can have web data incorporated to further add the equipment telemetry data with suggestions of the probable action and likely effect, excerpts of other papers or data with a bit more technical information, each fix success rate, etc.
GenAI can fill the data gap that predictive analytics typically has. GenAI also fulfills the vision of what AI for IoT can be defined as authoritatively:
- Equipment failures: Predict equipment/machine failures before stopping production
- Optimized maintenance: Predict remaining useful life estimates for component replacement and maintenance at the right time
- Digital twins: Accelerate the development of digital twins for advanced operations planning, performance engineering, energy consumption optimization, etc.
- Waste minimization: Discover opportunities through what-if simulation and analysis.
This use case is indeed a great example of Generative AI in IIoT.
Medical and Healthcare
- Privacy protection: Construct representative patient data and protect actual patient data for privacy enforcement
- Clinical summarization: Create automated clinical summary reports of tests and wearables information
- Prescriptions: Streamline prescription of procedures and drugs based on tests and wearables data
- Image processing: Apply data augmentation procedures (denoising, reconstruction, registration, etc.) to medical images like CTs, MRIs, ultrasounds, and X-rays
- Pharmacological research: Facilitate discovery/invention of new medicines
This use case is also another good example of Generative AI in IIoT.
Leveraging AI
Organizations can make their IoT solutions smarter, more intuitive, innovative, and user-centric by unlocking the potential of generative AI. These are able to improve operational efficiency, productivity, and customer satisfaction and induce paradigm shifts in industries. While there are shining opportunities offered by generative AI for IoT, hardly any challenges have to be addressed to achieve its full potential. Also, today, businesses aiming to reach and use their full potential should learn the correct use of Generative AI in IIoT. The prominent software development services in the UK have also started to use GenAI to enhance their software solutions as well.
In addition, one of the most critical among them is the cost and complexity of constructing generative models. Also, integrating these generative AI models into existing IoT systems governed by legislation and many more are also some of the enormous challenges. To fight these issues, there should be an inter-team coordination of technology communities, regulatory bodies, and industries to harness generative AI’s capability in responsibly enriching IoT settings. In brief, GenAI will indeed change the world, with endless possibilities and finite options for humankind; let us be cautious in our take-up and advancement.
