The Last Word: Putting Generative AI into Perspective

On Oct. 30, 2023, President Joseph R. Biden issued an executive order that called for a “society-wide effort that includes government, the private sector, academia and civil society” to address artificial intelligence (AI). The order stated that AI holds extraordinary potential for both promise and peril; “Responsible AI use has the potential to help solve urgent challenges while making our world more prosperous, productive, innovative and secure,” he said.  

Automation.com is taking on the challenge by increasing its reporting on the industrial uses of artificial intelligence of all types, including generative AI. But industrial organizations can only leverage generative AI successfully by solving the industrial data problem first. Companies must take steps to ensure the promise of AI and avoid the peril. 

What is generative AI? 

According to Gartner, generative AI can learn from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it. “It can produce a variety of novel content, such as images, video, music, speech, text, software code and product designs.”  

Generative AI uses several continually evolving techniques. AI foundation models are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. “Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms. Generative AI most commonly creates content in response to natural language requests—it doesn’t require knowledge of or entering code—but the enterprise use cases are numerous and include innovations in drug and chip design and material science development.” 

“The Definitive Guide to Generative AI for Industry” is a recently released book from Cognite, maker of industrial operational technology software and an Industrial DataOps platform, that discusses the transformative potential of industrial AI. In it, the authors note: “Machines are now equipped with AI-powered capabilities that enable them to adapt, learn and optimize their performance. AI algorithms can now analyze vast amounts of data collected from various sources to optimize operations holistically, identifying patterns, predicting failures and making intelligent decisions.”   

"Generative AI is fundamentally reshaping operational processes and it will be the digital mavericks who will be early adopters of this technology,” said Cognite CEO Girish Rishi.  

"Safe, secure, hallucination-free generative AI is critical to paving the road to sustainable and profitable global energy supply and manufacturing excellence.”

Generative AI in action 

Reece Hayden, senior analyst, artificial intelligence lead at ABI Research explained, “In our opinion, there are three main use case buckets. The first is employee augmentation, and we can all understand what that means as simply enabling your employees to [use] generative AI chatbot or summarization tools within their daily workflow. The next is new products and services. This could be implementing predictive capabilities or generative capabilities within software that you provide to your customers. And the third and the highest risk, the highest value use case is automation and optimization.” 

Sharing a realistic opinion of the state of the market, Hayden said that most users are moving from simply understanding generative AI and using it daily “toward incorporating AI into products and services—like implementing predictive or generative capabilities within the software they provide to their customers.” 

The reason is technological maturity. The models, applications and ecosystem aren’t ready to support new AI products and services in customer-facing applications, said Hayden. “Enterprises aren’t ready to expose themselves to the risk required to produce and deploy these products and services, be that reputational risk or simply that data privacy aspect,” he explained. 

Hayden said that one of the key trends that will impact the manufacturing sector and the wider generative AI market is open source. “We’ve seen, in lots of software, that open source is inevitably the winner. The reasons behind this are numerous, [but] the main reason [is] that once you’ve downloaded and deployed the open source model, you own the output, you own the input, and you can control the data flow. From a researcher’s perspective, this is where the growth is: open-source generative AI. We’re predicting huge growth in terms of software revenue as we move forward to 2030.” 

James Iversen, industry analyst at ABI Research said that ABI has been looking into generative AI for manufacturing predominantly through the lens of both current and potential use cases. “Presently, the use cases, which are the most built out and widely used, come from the design aspect of manufacturing and they are often offered through a collection of software providers in this space.”