ChatGPT is the fastest growing consumer application in history. No other technology has been adopted this quickly, and it will be followed by more large language model (LLM) applications.
But while some generative AI tools can be used by anyone to create, summarize, translate, or even write code, their accuracy and reliability are still a work in progress. Enterprise applications requiring precision, dependability, and explainability are most likely not a good fit for these AI tools—at least not yet.
Gartner’s recent FAQ on ChatGPT suggests that organizations shouldn’t provide ChatGPT-powered experiences directly to customers—for now the risk is just too high.
Some of these risks include:
- Inaccuracies and Hallucinations: Despite improvements, GPT-4 can “hallucinate” facts and make reasoning errors. This limits its application to use cases where high accuracy is not required. Across a variety of categories, factual accuracy ranges from 70% to 80%.
- Bias: These models can generate outputs that represent social biases and worldviews derived from its training data.
- Privacy Concerns: Data sent to non-API versions (like ChatGPT’s non-subscription offering) are sometimes used to improve the models. On the other hand, API versions have more privacy assurances, but it’s still important to read the terms of service to evaluate the privacy risk for your specific use cases.
How Enterprises can Use Large Language Models in Hyperscience
Generative AI has a massive potential, but so far, it’s mostly gone untapped. Watch the demo below to see how large language models and generative AI can be used in everyday business processes.
At Hyperscience, it’s our mission to help enterprises transform their business processes by harnessing the power of AI. To do so, we empower organizations to easily train machine learning models using their own data.
Our market leading document processing solution is a key piece for extracting data from any document, no matter how complex or unstructured. It enables the use of such data as prompts or training material for generative AI tools like chatGPT, Google’s Bard, or other LLM APIs available in the market.
When Combined with Hyperscience’s Flow Studio (a low-code development environment), customers can use these powerful LLMs in tandem with human-in-the-loop supervision to ensure accuracy, mitigate risks, and manage an endless number of advanced use cases.
Know Your Customer: An Example of Generative AI in Business Processes
“Know Your Customer” or KYC processes are used everywhere. Any time you’ve had to show an ID, pay stub, or even a passport, you’ve participated in an organization’s KYC validation.
Hyperscience allows customers to use data extracted from documents for their KYC processes. This data can be used to validate information or suggest changes during the approval process, making it easier for customer representatives to complete their tasks accurately and efficiently.
While Hyperscience could handle the validation of complex KYC submissions without the help of large language models, the advantage of using these models is that they understand rules the way humans understand them—it’s no longer necessary to hardcode them.
This means that entire sets of rules can be replaced in a matter of seconds. If a process changes, giving the LLM a written command will prompt it to take the new rules into account (shown in the demo above). With legacy solutions like RPA or OCR, process changes require a large time investment, and sometimes require development resources to keep processes running smoothly.
Beyond enabling organizations to use their own data in AI-driven processes, Hyperscience can also add customizable human supervision to guarantee the required accuracy—ensuring enterprises can trust the application’s outputs.
NOTE: ChatGPT is a public API, and customers are best advised not to send any PII or other sensitive information to public APIs. This can be dealt with by redacting sensitive data (a feature available in Hyperscience) before sending it to the API, but we are also exploring the use of open source language models that can be deployed on-premise to prevent the leaking of any sensitive data. If you’re interested in using an open-source language model hosted by Hyperscience, let us know here.
What’s in the Demo?
In the demo, you’ll see how Hyperscience can extract data from various documents such as KYC applications, bank statements, passports, and payslips. The demo will show how to load database files, as well as a rules engine in CSV format. The ChatGPT API is used to validate the submission against these rules. After the submission is validated, ChatGPT will generate an appropriate email response to the customer.
You’ll also see a customizable supervision interface, where human supervisors can either approve or reject the submission and inform the customer about the status of their application via email. As mentioned above, the demo highlights how business rules can be changed in seconds to accommodate changes in business processes—without the need to code—saving time and development costs.
The use of large language models extends well beyond this use case, too. For example, they can generate training data for unstructured documents, or it can help users locate answers to queries based on the data ingested and prompt information.
Additionally, Hyperscience and ChatGPT can generate shortened summaries of documents or emails, extract key content, and perform software code generation, translation, explanation, and verification. Furthermore, the platform can compare two paragraphs in a document or across documents.
LLMs and generative AI solutions might not be ready for unmonitored incorporation in today’s business processes, but with care, they can still be used to optimize business processes and deliver an exceptional customer experience. Solutions like Hyperscience can help bring the power of these AI solutions to the enterprise, granting short term ROI while providing a simple point of entry for greater AI-led transformation.
To learn more about bringing AI-powered document processing to your organization, contact us here.