Even though the origins of artificial intelligence (AI) date back to the 1940s, AI has never captured the imagination of the business, government, and the public like we have seen in recent years. It’s suddenly even common to find AI breakthroughs covered in national news broadcasts.
A huge part of this groundswell of interest stems from the simple fact that there’s serious money to be made in the field of AI. PwC’s Global Artificial Intelligence Study predicts a potential AI-driven contribution of $15.7 trillion for the global economy by 2030. The arrival of generative industrial AI applications, enabled by rapidly improving and expanding large language models, has opened the eyes of many more people to creative and intuitive experiences that can benefit from deep learning. Per the Exploding Topics website, the popularity of the term “Generative AI” has grown 8,200% since 2020.
The forces driving AI’s expansion
Although the exponential advancements in industrial AI are driving intense excitement today, they have, in reality, been a long time coming, with very good reason. Avathon Chief Science Officer Bruce Porter details the path AI has taken to get where it is today and identifies four critical enablers behind the current AI revolution:
Connectivity
The ability of devices to connect to each other and to the internet has led to an explosion in the number of devices that can collect and share data. This data is used to train AI systems, enabling more accurate predictions and decision making.
Computation
Computers can perform more complex calculations and process larger amounts of data faster than ever. Advances in computer hardware have made it possible to train and run AI models more quickly. Today’s powerful machines can handle the massive amounts of data required to train deep learning algorithms.
Algorithms
Algorithms for industrial AI include supervised learning, unsupervised learning, and reinforcement learning, among others. Advances in algorithms have made it possible to solve problems previously considered unsolvable.
Data
Advances in data storage and processing have made it possible to collect and store massive amounts of data. Work has been done to improve our ability to collect more diverse and representative data to train AI models and improve their accuracy.
A representative case study
“AI will be as transformational as some of the major technological inventions of the past several hundred years,” stated JPMorgan Chase Chairman and CEO Jamie Dimon in a recent annual report. He offered a forward-looking perspective, starting with his unequivocal statements on the massive impact of AI.
“While we do not know the full effect or the precise rate at which AI will change our business—-or how it will affect society at large—-we are convinced the consequences will be extraordinary and possibly as transformational as some of the major technological inventions of the past several hundred years, e.g., the printing press, the steam engine, electricity, digital computing, and the Internet.”
Dimon pointed out that since he first cited AI as a strategic initiative in his 2017 shareholders’ letter, the company has increased its AI activities substantially, with more than 2,000 AI and machine learning experts and data scientists using predictive analytics across over 400 use cases in production today. He said that these AI projects “are increasingly driving real business value across our businesses and functions.”
The list of industry leading firms now implementing AI technology is immense and growing rapidly, including applications ranging from safety and cybersecurity to product quality inspection and failure prediction in industrial systems.
Avathon’s role in the evolution of industrial AI
At Avathon, our solutions solve critical problems that leverage the ability to predict, prescribe, and automate industrial processes efficiently. One of the most significant challenges we address is preventing downtime in industrial assets. For example, on average, offshore oil and gas operators rack up 27 days of downtime annually, creating a $38 million+ cost for the industry. Using proprietary AI algorithms applied to historical data produced by critical O&G equipment, Avathon uses anomaly detection models to understand a system’s ‘normal’ operating state, continuously evaluating the incoming data stream of sensor data to generate alerts when irregular conditions are detected, giving O&G operators more lead time—often as much as thirty days—before a critical issue becomes urgent, so they can schedule repairs, address the problem, and avoid costly downtime.
Industrial AI offers a unique approach to problem-solving, with the potential to tackle a much broader spectrum of problems than ever before by simulating human intelligence and learning capabilities. For Avathon’s clients, AI use cases include predictive maintenance, prescriptive insights to speed up repairs, and visual analytics to reduce on-the-job accidents. As we reflect on the evolution of problem-solving through the lens of the emerging impact of industrial AI, while the details may change, the pattern is the same. Human ingenuity is unbeatable; when technology catches up to it, the world changes fast.
Learn more about Avathon’s AI-enabled solutions and how they can enhance the performance of your industrial assets.