The achievements of generative AI, since ChatGPT grabbed media attention almost a year ago, have redefined digital transformation, the user experience, KM, and the enterprise value of data. The potential applications for generative AI are still being discovered and discussed.
Applying large language models (LLMs) to produce content for sales and marketing is the least of it. Employing ChatGPT for retrieving information, amalgamating and summarizing results, and turning search into question-answering is only the beginning.
In addition to absorbing unstructured text, documents, ideas, and approaches for fulfilling business objectives, foundation models can also generate images, videos, and digital objects. The user experience is transformed when this content is paired with textual applications to simplify requirements for completing tasks so that the model “does it for you,” commented Jeff Kaplan, SkyView Innovations CEO.
With these digital replications of real-time production and data systems, “things like training and simulations and supply chain optimization could be enormous,” Kaplan said. The visual accuracy and conformity to real-world systems depicted by digital twins for such use cases are impressive. “
There is an AI technology that allows you to take a video and then bring it into Unreal Engine, which is the gaming platform that powers Fortnite and things like that, and now you have a full digital twin of your environment,” Kaplan divulged.
Data privacy: Perhaps the foremost concern orga- nizations have about employing LLMs, ChatGPT, and other iterations of foundation models is the issue of privacy. According to Kaplan, it’s not always apparent how to deploy these models so companies can successfully “adopt them to your business processes and meet your security needs, your data needs, your privacy, your authentication.”
The application of these (and countless other) foundation models to the very way humans work, communicate, and use technology is quietly producing an even more profound result. According to Medhat Galal, Appian senior VP of engineering, “Think of the spectrum of human-to-computer interactions. Normally, we use low code to bridge the gap between what users want and what the application can do. LLMs bring that notion, that continuous spectrum, closer to the user, where they get to use their language, as opposed to the [system’s] language, to understand things.”
Foundation models, trained on vast quantities of data, function as general-purpose platforms for any number of AI applications, including generative AI. According to the U.K.’s CMA, an independent, non-ministerial U.K. government department, foundation models “have the potential to transform much of what people and businesses do across the spectrum of human activity, from searching, to learning, to creating, to how we solve problems across health, engineering, design and education, to name just a few domains. In the process, as with any technology breakthrough, they will disrupt existing markets and create new ones (assets.publishing.service.gov. uk/media/64528e622f62220013a6a491/AI_Foundation_Mod- els_-_Initial_review_.pdf).
These are some of the more utilitarian applications that users can commission via natural language:
♦ Knowledge management: Foundation models can create entire data models, taxonomies, knowledge graphs, descriptions of content, and pertinent classifications—which humans can oversee and readily adjust as needed.
♦ Business intelligence: These models can not only search through desired data for ad hoc question-answering, but also generate visualizations, reports, and diagrams to illustrate them.
♦ Intelligent process automation: Foundation model techniques are employed to create digital versions of documents and other content that becomes what Galal termed “web-friendly” and amenable to upstream or downstream processes in IT systems.
The most powerful of these use cases, which also involve application development and digital twins, combine textual and visual capabilities. They can be performed on-the-fly, in real time, and according to human-specified constraints to eliminate incorrect results.
Nonetheless, none of these possibilities negate the fact that generative AI does not solve all digital transformation dilemmas. “There’s a whole lot of things that enterprises require for these things to be sensible, viable, and not a risk,” added Sean Martin, Cambridge Semantics CTO. “There’s a whole list of tick boxes that have to be ticked off. ChatGPT all by itself doesn’t tick those boxes.”
ChatGPT is well on its way to becoming synonymous with LLMs, but that’s inaccurate. It’s a digital agent that uses LLM techniques to understand prompts in natural language and perform various activities. It can search through vast amounts of information (typically electronic) related to a prompt, synthesize the results, issue them in natural language according to user specifications, or generate endless varieties of text on demand. Depending on the prompt, ChatGPT may be able to answer questions and generate written content based on its training data without additional searching.
Nevertheless, ChatGPT is not a search engine. “LLMs are not a knowledge store,” Martin cautioned. “The knowledge that’s in them is a by-product of how they were trained.” LLMs are foundation models trained on vast quantities of text used to understand and generate additional text. Foundation models are advanced machine learning models trained on enormous data quantities that are applicable to multiple tasks; they typically involve transformer architecture.