“Domain expertise is the secret sauce that separates Industrial AI from more generic AI approaches. Industrial AI will guide innovation and efficiency improvements in capital-intensive industries for years to come,” said Willie K Chan, CTO of AspenTech. Chan was one of the original members of the MIT ASPEN research program that later became AspenTech in 1981, now celebrating 40 years of innovation.
Incorporating that domain expertise gives Industrial AI applications a built-in understanding of the context, inner workings, and interdependencies of highly complex industrial processes and assets, and takes into account the design characteristics, capacity limits, and safety and regulatory guidelines crucial for real-world industrial operations.
More generic AI approaches may come up with specious correlations between industrial processes and equipment, generating inaccurate insights. Generic AI models are trained on large volumes of plant data that usually does not cover the full range of potential operations. That’s because the plant might be working within a very narrow and limited range of conditions for safety or design reasons. Consequently, these generic AI models cannot be extrapolated to respond to market changes or business opportunities. This further exacerbates the productization hurdles around AI initiatives in the industrial sector.
By contrast, Industrial AI leverages domain expertise specific to industrial processes and real-world engineering based on first principles that account for the laws of physics and chemistry (e.g., mass balance, energy balance) as guardrails for mitigating risks and complying with all the necessary safety, operational, and environmental regulations. This makes for a safe, sustainable, and holistic decision-making process, producing comprehensive results and trusted insights over the long run.
Digitalization in industrial facilities is critical to achieving new levels of safety, sustainability, and profitability—and Industrial AI is a key enabler for that transformation.
Industrial AI in action
Talking about Industrial AI as a revolutionary paradigm is one thing; actually seeing what it can do in real-life industrial settings is another. Below are a few examples that demonstrate how capital-intensive industries can leverage Industrial AI to overcome digitalization barriers and drive greater productivity, efficiency, and reliability in their operations.
- A process plant may deploy an advanced class of Industrial AI-enabled Hybrid Models, drawing on deeper collaboration between domain experts and data scientists, machine learning, and first principles for more comprehensive, accurate, and performant models. These hybrid models can be used to optimally design, operate, and maintain plant assets across their lifecycles. Because they are reliably relevant for a longer period, they also provide a better representation of the plant.
- A chemical plant could leverage Industrial AI for yielding real-time insights from integrated industrial data from the edge to the cloud, using the Artificial Intelligence of Things (AIoT) to enable agile decision-making across the organization. Using richer, dynamic workflows, supply chain and operations technologies are seamlessly linked together to detect changes in market conditions and automatically adjust the operating plan and schedule in response.
- A refinery can use Industrial AI to evaluate thousands of oil production scenarios simultaneously, across a diverse set of data sources, to quickly identify optimal crude oil slates for processing. Combined with AI-rich capabilities, enterprise-wide insights, and integrated workflows to improve executive decision-making, this approach empowers workers to allocate their time and efforts to more strategic, value-driving tasks.
- A next-generation industrial facility could apply Industrial AI as the plant’s “virtual assistant” to validate the quality and efficiency of a production plan, in real time. AI-enabled cognitive guidance ultimately helps reduce reliance on individual domain experts for complex decision-making, and instead institutionalizes historical decisions and best practices to eliminate expertise barriers.
These use cases are by no means exhaustive, but just a few examples of how pervasive, innovative, and broadly applicable Industrial AI’s capabilities can be for the industry and for laying the groundwork for the digital plant of the future.
The digital plant of the future
Industrial organizations need to accelerate digital transformation to stay relevant, competitive, and capable of addressing market disruptors. The Self-Optimizing Plant represents the ultimate vision of that journey.
Industrial AI embeds domain-specific know-how alongside the latest AI and machine-learning capabilities, into fit-for-purpose AI-enabled applications. This enables and accelerates the autonomous and semi-autonomous processes that run those operations—realizing the vision of the Self-Optimizing Plant.
A Self-Optimizing Plant is a self-adapting, self-learning and self-sustaining set of industrial software technologies that work together to anticipate future conditions and act accordingly, adjusting operations within the digital enterprise. A combination of real-time data access and embedded Industrial AI applications empower the Self-Optimizing Plant to constantly improve on itself—drawing on domain knowledge to optimize industrial processes, make easy-to-execute recommendations, and automate mission-critical workflows.
This will have numerous positive impacts on the business, including the following:
- Curbing carbon emissions caused by process upsets and unplanned shutdowns or startups, helping to meet corporate environmental, social, and governance goals. This reduces both production waste and carbon footprint, driving a new era of industrial sustainability.
- Boosting overall safety by significantly reducing dangerous site conditions and reallocating staff on the operations and production floors to safer roles.
- Unlocking new production efficiencies by tapping into new areas of margin optimization and production stability, even during downturns, for greater profitability.
The Self-Optimizing Plant is the ultimate end goal of not just Industrial AI, but the industrial sector’s digital transformation journey. By democratizing the application of industrial intelligence, the digital plant of the future drives greater levels of safety, sustainability, and profitability and empowers the next generation of the digital workforce—future-proofing the business in volatile and complex market conditions. This is the real-world potential of Industrial AI.
To learn more about how Industrial AI is enabling the digital workforce of the future and creating the foundation for the Self-Optimizing Plant, visit
This article was written by AspenTech. It was not produced by MIT Technology Review’s editorial staff.
This startup’s AI is smart enough to drive different types of vehicles
Jay Gierak at Ghost, which is based in Mountain View, California, is impressed by Wayve’s demonstrations and agrees with the company’s overall viewpoint. “The robotics approach is not the right way to do this,” says Gierak.
But he’s not sold on Wayve’s total commitment to deep learning. Instead of a single large model, Ghost trains many hundreds of smaller models, each with a specialism. It then hand codes simple rules that tell the self-driving system which models to use in which situations. (Ghost’s approach is similar to that taken by another AV2.0 firm, Autobrains, based in Israel. But Autobrains uses yet another layer of neural networks to learn the rules.)
According to Volkmar Uhlig, Ghost’s co-founder and CTO, splitting the AI into many smaller pieces, each with specific functions, makes it easier to establish that an autonomous vehicle is safe. “At some point, something will happen,” he says. “And a judge will ask you to point to the code that says: ‘If there’s a person in front of you, you have to brake.’ That piece of code needs to exist.” The code can still be learned, but in a large model like Wayve’s it would be hard to find, says Uhlig.
Still, the two companies are chasing complementary goals: Ghost wants to make consumer vehicles that can drive themselves on freeways; Wayve wants to be the first company to put driverless cars in 100 cities. Wayve is now working with UK grocery giants Asda and Ocado, collecting data from their urban delivery vehicles.
Yet, by many measures, both firms are far behind the market leaders. Cruise and Waymo have racked up hundreds of hours of driving without a human in their cars and already offer robotaxi services to the public in a small number of locations.
“I don’t want to diminish the scale of the challenge ahead of us,” says Hawke. “The AV industry teaches you humility.”
Russia’s battle to convince people to join its war is being waged on Telegram
Just minutes after Putin announced conscription, the administrators of the anti-Kremlin Rospartizan group announced its own “mobilization,” gearing up its supporters to bomb military enlistment officers and the Ministry of Defense with Molotov cocktails. “Ordinary Russians are invited to die for nothing in a foreign land,” they wrote. “Agitate, incite, spread the truth, but do not be the ones who legitimize the Russian government.”
The Rospartizan Telegram group—which has more than 28,000 subscribers—has posted photos and videos purporting to show early action against the military mobilization, including burned-out offices and broken windows at local government buildings.
Other Telegram channels are offering citizens opportunities for less direct, though far more self-interested, action—namely, how to flee the country even as the government has instituted a nationwide ban on selling plane tickets to men aged 18 to 65. Groups advising Russians on how to escape into neighboring countries sprung up almost as soon as Putin finished talking, and some groups already on the platform adjusted their message.
One group, which offers advice and tips on how to cross from Russia to Georgia, is rapidly closing in on 100,000 members. The group dates back to at least November 2020, according to previously pinned messages; since then, it has offered information for potential travelers about how to book spots on minibuses crossing the border and how to travel with pets.
After Putin’s declaration, the channel was co-opted by young men giving supposed firsthand accounts of crossing the border this week. Users are sharing their age, when and where they crossed the border, and what resistance they encountered from border guards, if any.
For those who haven’t decided to escape Russia, there are still other messages about how to duck army call-ups. Another channel, set up shortly after Putin’s conscription drive, crowdsources information about where police and other authorities in Moscow are signing up men of military age. It gained 52,000 subscribers in just two days, and they are keeping track of photos, videos, and maps showing where people are being handed conscription orders. The group is one of many: another Moscow-based Telegram channel doing the same thing has more than 115,000 subscribers. Half that audience joined in 18 hours overnight on September 22.
“You will not see many calls or advice on established media on how to avoid mobilization,” says Golovchenko. “You will see this on Telegram.”
The Kremlin is trying hard to gain supremacy on Telegram because of its current position as a rich seam of subterfuge for those opposed to Putin and his regime, Golovchenko adds. “What is at stake is the extent to which Telegram can amplify the idea that war is now part of Russia’s everyday life,” he says. “If Russians begin to realize their neighbors and friends and fathers are being killed en masse, that will be crucial.”
The Download: YouTube’s deadly crafts, and DeepMind’s new chatbot
Ann Reardon is probably the last person whose content you’d expect to be banned from YouTube. A former Australian youth worker and a mother of three, she’s been teaching millions of loyal subscribers how to bake since 2011. But the removal email was referring to a video that was not Reardon’s typical sugar-paste fare.
Since 2018, Reardon has used her platform to warn viewers about dangerous new “craft hacks” that are sweeping YouTube, tackling unsafe activities such as poaching eggs in a microwave, bleaching strawberries, and using a Coke can and a flame to pop popcorn.
The most serious is “fractal wood burning”, which involves shooting a high-voltage electrical current across dampened wood to burn a twisting, turning branch-like pattern in its surface. The practice has killed at least 33 people since 2016.
On this occasion, Reardon had been caught up in the inconsistent and messy moderation policies that have long plagued the platform and in doing so, exposed a failing in the system: How can a warning about harmful hacks be deemed dangerous when the hack videos themselves are not? Read the full story.
DeepMind’s new chatbot uses Google searches plus humans to give better answers
The news: The trick to making a good AI-powered chatbot might be to have humans tell it how to behave—and force the model to back up its claims using the internet, according to a new paper by Alphabet-owned AI lab DeepMind.
How it works: The chatbot, named Sparrow, is trained on DeepMind’s large language model Chinchilla. It’s designed to talk with humans and answer questions, using a live Google search or information to inform those answers. Based on how useful people find those answers, it’s then trained using a reinforcement learning algorithm, which learns by trial and error to achieve a specific objective. Read the full story.
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