HYPERSCIENCE AI 100M TIGER STRIPES BESSEMER HOW TO
The demo will show how to load database files, as well as a rules engine in CSV format.
In the demo, you’ll see how Hyperscience can extract data from various documents such as KYC applications, bank statements, passports, and payslips. If you’re interested in using an open-source language model hosted by Hyperscience, let us know here. 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. NOTE: ChatGPT is a public API, and customers are best advised not to send any PII or other sensitive information to public APIs. With legacy solutions like RPA or OCR, process changes require a large time investment, and sometimes require development resources to keep processes running smoothly.īeyond 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. 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). This means that entire sets of rules can be replaced in a matter of seconds. 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 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. Hyperscience allows customers to use data extracted from documents for their KYC processes. Any time you’ve had to show an ID, pay stub, or even a passport, you’ve participated in an organization’s KYC validation. “Know Your Customer” or KYC processes are used everywhere. Know Your Customer: An Example of Generative AI in Business Processes 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. 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. Our market leading document processing solution is a key piece for extracting data from any document, no matter how complex or unstructured. To do so, we empower organizations to easily train machine learning models using their own data.
Watch the demo below to see how large language models and generative AI can be used in everyday business processes.Īt Hyperscience, it’s our mission to help enterprises transform their business processes by harnessing the power of AI. Generative AI has a massive potential, but so far, it’s mostly gone untapped. How Enterprises can Use Large Language Models in Hyperscience 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. Privacy Concerns: Data sent to non-API versions (like ChatGPT’s non-subscription offering) are sometimes used to improve the models.Bias: These models can generate outputs that represent social biases and worldviews derived from its training data.Across a variety of categories, factual accuracy ranges from 70% to 80%. This limits its application to use cases where high accuracy is not required. Inaccuracies and Hallucinations: Despite improvements, GPT-4 can “hallucinate” facts and make reasoning errors.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. Enterprise applications requiring precision, dependability, and explainability are most likely not a good fit for these AI tools-at least not yet. No other technology has been adopted this quickly, and it will be followed by more large language model (LLM) applications.īut 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.
ChatGPT is the fastest growing consumer application in history.
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