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Generative AI as a personal agent for knowledge management processes

Unleash the power of Artificial Intelligence and generative models in the pharmaceutical industry together with Boehringer Ingelheim by developing personalized AI agents that support health workers in knowledge management, research & content creation.

Shape the future of human-machine interaction and master demanding challenges in the pharmaceutical industry together.

#KnowledgeManagement #PersonalAIagent #GenerativeAI

  • ūüŹÜ  Rewards
    EUR 3,000 prize pool + versatile collaboration models + hiring opportunities
  • ūüēĎ  Deadline
    Sep 27, 2023, 9:59:00 PM
  • ūüĆé Scope

    Open to students, researchers, industry experts as well as startups & companies

  • ‚Ěď Q&A Call
  • ūüĆĪ Innovate 2030 All SDGs

    This challenge is part of the INNOVATE2030 program

Brief
Submission
Rewards
Opportunity Areas
FAQ

Here you can read more about the challenge.

Generative AI as a personal agent

Information workers in the pharmaceutical industry often struggle with information overload, making it challenging to extract key insights from an ever-growing pool of research papers, clinical trial results, regulatory guidelines, and market reports.

 With the new possibilities provided by generative AI, various work processes can be simplified and made more efficient. We are looking for a text processing model that can revolutionize the way knowledge is summarized and facilitate efficient decision-making processes.

Be part of the challenge and develop innovative solutions for advanced AI models to act as personal agents for knowledge management processes.

The Challenge

The goal of this Challenge is to develop a large language model (LLM) that can analyze and process a wide range of sources, leveraging either an existing (open source) LLM or building a new one. The objective is to generate high-quality pharmaceutical summaries and articles that effectively capture essential information in a coherent and accurate manner.


Key Questions

  • How can your text generation model effectively analyze and summarize large volumes of diverse information from scientific articles, clinical trials, regulatory guidelines, and other relevant sources?
  • How does your model ensure the accuracy and coherence of the generated summaries and articles, while maintaining the original meaning and context of the source material?
  • What techniques or approaches does your model employ to overcome challenges such as information overload, ambiguity, and potential biases in the source data?

Goals and outcome

The goal is to find innovative solutions that help to design such an interaction and interface between humans and machines.

For this purpose, a concept with proof-of-concept is to be submitted in the first phase, as well as the concept and outline for further implementation.

The participation is aimed at start-ups, companies and researchers who have chances for partnerships up to (research) collaborations and hiring.

To approach the problem step by step, participants can refer to the instructions in the tab ‚ÄúOpportunity Areas‚ÄĚ for inspiration.

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