“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.
Yann LeCun has a bold new vision for the future of AI
Melanie Mitchell, an AI researcher at the Santa Fe Institute, is also excited to see a whole new approach. “We really haven’t seen this coming out of the deep-learning community so much,” she says. She also agrees with LeCun that large language models cannot be the whole story. “They lack memory and internal models of the world that are actually really important,” she says.
Natasha Jaques, a researcher at Google Brain, thinks that language models should still play a role, however. It’s odd for language to be entirely missing from LeCun’s proposals, she says: “We know that large language models are super effective and bake in a bunch of human knowledge.”
Jaques, who works on ways to get AIs to share information and abilities with each other, points out that humans don’t have to have direct experience of something to learn about it. We can change our behavior simply by being told something, such as not to touch a hot pan. “How do I update this world model that Yann is proposing if I don’t have language?” she asks.
There’s another issue, too. If they were to work, LeCun’s ideas would create a powerful technology that could be as transformative as the internet. And yet his proposal doesn’t discuss how his model’s behavior and motivations would be controlled, or who would control them. This is a weird omission, says Abhishek Gupta, the founder of the Montreal AI Ethics Institute and a responsible-AI expert at Boston Consulting Group.
“We should think more about what it takes for AI to function well in a society, and that requires thinking about ethical behavior, amongst other things,” says Gupta.
Yet Jaques notes that LeCun’s proposals are still very much ideas rather than practical applications. Mitchell says the same: “There’s certainly little risk of this becoming a human-level intelligence anytime soon.”
LeCun would agree. His aim is to sow the seeds of a new approach in the hope that others build on it. “This is something that is going to take a lot of effort from a lot of people,” he says. “I’m putting this out there because I think ultimately this is the way to go.” If nothing else, he wants to convince people that large language models and reinforcement learning are not the only ways forward.
“I hate to see people wasting their time,” he says.
The Download: Yann LeCun’s AI vision, and smart cities’ unfulfilled promises
“We’re addicted to being on Facebook.”
—Jordi Berbera, who runs a pizza stand in Mexico City, tells Rest of World why he has turned to selling his wares through the social network instead of through more conventional food delivery apps.
The big story
“Am I going crazy or am I being stalked?” Inside the disturbing online world of gangstalking
Jenny’s story is not linear, the way that we like stories to be. She was born in Baltimore in 1975 and had a happy, healthy childhood—her younger brother Danny fondly recalls the treasure hunts she would orchestrate. In her late teens, she developed anorexia and depression and was hospitalized for a month. Despite her struggles, she graduated high school and was accepted into a prestigious liberal arts college.
There, things went downhill again. Among other issues, chronic fatigue led her to drop out. When she was 25 she flipped that car on Florida’s Sunshine Skyway Bridge in an apparent suicide attempt. At 30, after experiencing delusions that she was pregnant, she was diagnosed with schizophrenia. She was hospitalized for half a year and began treatment, regularly receiving shots of an antipsychotic drug. “It was like having my older sister back again,” Danny says.
On July 17, 2017, Jenny jumped from the tenth floor of a parking garage at Tampa International Airport. After her death, her family searched her hotel room and her apartment, but the 42-year-old didn’t leave a note. “We wanted to find a reason for why she did this,” Danny says. And so, a week after his sister’s death, Danny—a certified ethical hacker—decided to look for answers on Jenny’s computer. He found she had subscribed to hundreds of gangstalking groups across Facebook, Twitter, and Reddit; online communities where self-described “targeted individuals” say they are being monitored, harassed, and stalked 24/7 by governments and other organizations—and the internet legitimizes them. Read the full story.
The US Supreme Court has overturned Roe v. Wade. What does that mean?
Access to legal abortion is now subject to state laws, allowing each state to decide whether to ban, restrict or allow abortion. Some parts of the country are much stricter than others—Arkansas, Oklahoma and Kentucky are among the 13 states with trigger laws that immediately made abortion illegal in the aftermath of the ruling. In total, around half of states are likely to either ban or limit access to the procedure, with many of them refusing to make exceptions, even in pregnancies involving rape, incest and fetuses with genetic abnormalities. Many specialized abortion clinics may be forced to close their doors in the next few days and weeks.
While overturning Roe v Wade will not spell an end to abortion in the US, it’s likely to lower its rates, and force those seeking them to obtain them using different methods. People living in states that ban or heavily restrict abortions may consider travelling to other areas that will continue to allow them, although crossing state lines can be time-consuming and prohibitively expensive for many people facing financial hardship.
The likelihood that anti-abortion activists will use surveillance and data collection to track and identify people seeking abortions is also higher following the decision. This information could be used to criminalize them, making it particularly dangerous for those leaving home to cross state lines.
Vigilante volunteers already stake out abortion clinics in states including Mississippi, Florida and North Carolina, filming people’s arrival on cameras and recording details about them and their cars. While they deny the data is used to harass or contact people seeking abortions, experts are concerned that footage filmed of clients arriving and leaving clinics could be exploited to target and harm them, particularly if law enforcement agencies or private groups were to use facial recognition to identify them.
Another option is to order so-called abortion pills to discreetly end a pregnancy at home. The pills, which are safe and widely prescribed by doctors, are significantly less expensive than surgical procedures, and already account for the majority of abortions in the US.