With the arrival of generative AI and LLMs, it gave birth to a new era which has the potential to shape history. Indeed, this new era’s dawn that’s driven by technology has the potential to resolve multitudes of complex challenges. It will also establish a new age of innovation and creativity. Besides, with the capability to generate content, text, images, audio, data, or even code. Yes, we are talking about the generative AI, which holds the the power to transform any industries far and wide.
Over the past decade, the Internet of Things (IoT) completely revolutionized industries. It was an auto-driving force and gave birth to an innovation that overhauled what was hitherto possible. IoT upturned the conventional million-strong industry dynamics. With smart devices and sensors cropping up everywhere, writing themselves into the fabric of industries, IoT became the pillar upon which contemporary industry benchmarks are established.
What is Generative AI?
In short, generative AI is deep-learning models that can produce high-quality text, images, and other material based on what they have learned. Eventhough Artificial intelligence has experienced numerous cycles of hype, but even to cynics, the launch of ChatGPT appears to be a game-changer. OpenAI’s chatbot, powered by its latest large language model, can compose poems, telpoetry, crack jokes, and write essays as if they had been written by humans. Prompt ChatGPT with a few words and outcomes love poems in the form of Yelp reviews or song lyrics in the style of Leonard Cohen.
Previously, computer vision had made breakthroughs in generative AI. Selfies became Renaissance portraits, and impossibly aged faces flooded social media streams. Five years on, it’s the advance in natural language processing and the capacity of massive language models to improvise on virtually any topic that has captivated the popular imagination. And not just language: Generative models can learn the syntax of computer program code, molecules, natural images, and much more.
The applications of this technology are growing every day, and tech experts around the world re just starting to explore its limitless possibilities. Now many tech giants are working to help their customers use generative models to speed up software development, discover new molecules, and train reliable conversational chatbots grounded on enterprise data. They’re even using generative AI to create synthetic data to build more robust and trustworthy AI models and to stand in for real data protected by privacy and copyright laws. Before we jump in further, let’s cover a few basics first.
What Exactly is Industrial Internet of Things (IIoT)?
Industrial Internet of Things (IIoT) is the application of intelligent devices, actuators, and other devices, i.e., radio frequency identification tags to improve industrial and manufacturing operations. They are networked devices that offer data collection, analysis, and sharing. Knowledge obtained with this method enhances more efficiency and reliability. IIoT is also referred to as the industrial internet. It is applied across a broad array of industries, e.g., energy management, manufacturing, utilities, oil and gas.
IIoT leverages the ability of smart machines and real-time processing to take advantage of the information that dumb machines have been generating for decades in factory environments. The philosophy of IIoT is that smart machines not only capture and process information more efficiently than human beings in real-time but are also able to communicate important information that can be leveraged for business decision-making quicker and more effectively.
Integrated actuators and sensors allow businesses to spot inefficiencies and issues earlier, saving time and capital, as well as complementing business intelligence initiatives. Within manufacturing alone, IIoT can offer quality control, green and sustainable processes, supply chain traceability and supply chain efficiency overall. Within an industrial environment, IIoT is the core of functions like predictive maintenance, improved field assistance, tracking assets, energy control, and maintenance.
The Rise Of Generative AI
The future of the world’s biggest industries, such as technology, finance, and media, is generative AI, a type of artificial intelligence program that can generate new content from given data. Indeed, its extensive application to tasks such as data analysis and content generation has monumental effects on the future of work.
Mark Murphy, J.P. Morgan’s Head of U.S. Enterprise Software Research, sees the emergence of generative AI as a greater technology moment than the Internet or the iPhone. He forecasted a gigantic productivity explosion in one to three years, followed by deep white-collar job redistribution in four to eight years on.
J.P. Morgan’s 5th Annual Global Machine Learning Conference in October of 2023 provides a clue to investors’ expectations. Most (28%) believe generative AI will most profoundly impact marketing, followed by insurance and legal services (21%), media (20%), data analytics (18%), and consumer tech (13%). Overall, these surveys hint at fundamental industry shifts, reflecting broad changes to come.
So, when combined with the potential of IoT, generative AI can offer a world of new possibilities and potential. Generative AI offers a range of capabilities that can be used to augment IoT applications and uses in a range of ways. Some of these capabilities are:
- Synthetic data and data augmentation.
- Anomaly detection
- Data anonymization.
- Natural language interface
- Automation
We will now explain each of these capabilities in detail. Before jumping into those details, let’s understand the core aspects of this blog, such as generative AI and IIoT, so you guys can have a better understanding of the topic we are going to discuss further.
Synthetic Data and Data Augmentation
Internet of Things devices produce large amounts of data, which are required to train machine learning models. Obtaining realistic IoT application data in real life is time-consuming and expensive, most often because of complex IoT deployments, security, and privacy issues.
Generative AI offers an exit by creating synthetic datasets with high similarity to real-world situations. This is accomplished through efficient training and testing of machine learning models against different IoT scenarios. By creating data similar to actual device telemetry, generative models have the capability to enrich dataset diversity and richness and alleviate sparsity, imbalance, or incompleteness problems in data. Richer data enables the construction of more accurate machine learning models for applications, including energy forecasting and occupancy planning, leading to increased functionality and efficiency of IoT systems.
Synthetic data, or data created by AI models to simulate actual data, also supports predictive maintenance. Generative AI, using past history data, failure trends, and current data, can create synthetic data to simulate diverse operating conditions and failure modes, which can predict likely malfunction of equipment and suggest preventive actions to prevent it. This prevents mass-scale downtime and maintenance expenses in Industrial Internet of Things (IIoT) solutions.
Anomaly Detection
Sudden spikes or trends in IoT data can indicate potential equipment failure, security violation, or unusual activity, which needs to be addressed as soon as possible.
Generative AI provides a solution to anomaly detection in IoT networks by learning typical operating behaviors from enormous volumes of device information and identifying deviations that may point to impending issues or security weaknesses. Generative AI algorithms, unlike conventional threshold-based systems, are able to comprehend complex, multidimensional device data, allowing them to detect subtle anomalies that would go undetected.
This ability is especially valuable in applications where IoT sensors are tracking important infrastructure, production processes, or environmental factors, where early detection of anomalies can avoid equipment failure, loss of production, or environmental risk. In combination with a more powerful IIoT platform, these insights can be used to the maximum for proactive decision-making.
Data Anonymization
Sharing of data is critical to analytics use cases, research practices and collaboration with third parties like system integrators in the IoT ecosystem. Data in IoT mostly comprises personal or confidential data and hence is intricate and challenging to share.
Generative AI models have a solution based on data anonymization. Data anonymization, as its name indicates, supports anonymization of the data without violating the statistical properties of data. These anonymized data sets can be used for analysis, development, and testing purposes based on data protection legislation without violating data privacy.
The ease and ability of generative AI in anonymizing data make it of immense utility to organizations seeking to use IoT data for analytics, machine learning, or even sharing with third parties.
Natural Language Interface
The use of industrial IoT data by end-users tends to accompany sophisticated graphs, sophisticated data tables, and sophisticated user interfaces. Operating and understanding these user interfaces and graphs involve some level of technical expertise and experience with the organization of the IoT application.
Generative AI offers a chance to make this process easier. Generative AI allows one to utilize natural language to consume IoT data that is complicated, helping to smooth out the complexities that come with traversing data graphs, tables and user interfaces. By means of natural language, users are able to query, command, or request information from IoT devices using conversational language, rendering the interaction natural and welcoming.
Key Applications of GenAI in IIoT
Having discussed the possibilities and limitations of GenAI in IoT, let’s discuss the applications in a few varied industries. This is, again, not an exhaustive list but rather an indication of how beneficial GenAI is going to be in IoT.
Industrial Manufacturing
When combined with LLMs such as CoPilot, ChatGPT, BARD, etc., GenAI is able to incorporate web data to further enrich the equipment telemetry data with recommendations regarding possible actions and their probable impact, citations of other documents or data with additional technical data, success rates of every remediation, etc.
GenAI has the ability to fill the data gap that predictive analytics generally suffers from. Also, GenAI satisfies the idea that AI in IoT can be defined with certainty:
- Equipment breakdowns: Forecast equipment/machine failures ahead of stopping production
- Optimized maintenance: Forecast remaining useful life forecasts for component replacement and maintenance at the appropriate time
- Digital twins: Speed development of digital twins for intricate operations planning, performance engineering, energy consumption optimization, etc.
- Waste minimization: Identify opportunities through what-if simulation and analysis.
Medical and Healthcare
- Privacy protection: Build representative patient data and safeguard real patient data for privacy enforcement
- Medical summarization: Build automatic medical summary reports of tests and wearables data
- Prescriptions: Automate prescription of procedures and medications on the basis of tests and wearables data
- Image processing: Apply data augmentation operations (denoising, reconstruction, registration, etc.) on medical images such as CTs, MRIs, ultrasounds, and X-rays
- Pharmaceutical research: Speed up discovery/invention of new medicines
Illustrative Examples
Since this blog discusses GenAI in the context of IoT, let’s jump in to the key concepts by using real-world examples, and the following examples are great examples of the use cases of GenAI in IoT.
Scenario 1: Production Stoppages for Manufacturing Equipment Malfunction
- Analysis: Rotating equipment is a normal source of failures, as parts that rotate would normally have parts that move. Rotations develop characteristic vibrations, and deviations from these vibrations could predict and indicate failure.
- Challenge: There wasn’t sufficient vibrational data to establish a correct baseline to determine anomalies because the equipment was relocated and re-mounted; the new mounting parts and position introduced fluctuations in the patterns of vibration and thus created a data gap since most needed baseline conditions weren’t in the dataset due to the relocation. Essentially, many correct operating conditions were labeled as anomalous.
- Remediation: GenAI was utilized to enhance the correct baseline dataset for detecting abnormal vibration.
Scenario 2: Estimation of Damage and Repair Estimation of Vehicles for Motor Insurance Claims
- Analysis: Utilize decades’ worth of images of wrecked vehicles and repair expenditure to conduct image analysis of wrecked vehicles to estimate repair costs.
- Challenge: Overall, there are not sufficient samples of all the typical kinds of damage on every make and model of automobile for insurers. Wrecking autos to photograph such kinds is costly and impractical.
- Remediation: GenAI extracts various kinds of damage details from the available inventory of vehicle images that have been damaged and subsequently uses the damage details in relation to the target brand, make, and model. The resulting generated images are then put through available AI methods in an attempt to automate damage inspection.
Key Takeaways
- GenAI plays only a limited but important role in IoT
- GenAI can bridge data gaps
- GenAI’s LLM makes predictions using AI easier
Harnessing AI
By leveraging generative AI capability, organizations can create more innovative, intuitive, intelligent, and user-focused IoT solutions. These solutions can improve operational efficiency, productivity and customer satisfaction and cause transformative change in industries. Though there are radiant opportunities presented by generative AI for IoT, hardly any challenges need to be met to tap its complete potential.
Moreover, among the most pressing challenges are the complexity and expense of developing generative models. Also the deployment of these generative AI models into current IoT environments, compliance with laws, and numerous others are also are a few major challneges. To combat these challenges, an inter-team effort of technology teams, regulatory agencies, and industries is required to tap the potential of generative AI to enrich IoT environments in a responsible way. In a nutshell, GenAI will undoubtedly transform the world, with limitless potential and limited opportunities for humanity; let us remain vigilant in our adoption and advancement.
