Reducing Hallucinations and Streamlining Drug Discovery Workflows with Generative AI - with Asif Hasan of Quantiphi
Feb 22, 2024
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Asif Hasan, Co-founder of Quantiphi, discusses AI's impact on reducing hallucinations in healthcare language models and streamlining drug discovery for rare disorders. They explore how Generative AI is revolutionizing drug discovery processes, addressing cost-effective solutions, and enhancing language models with retrieval mechanisms to improve accuracy and reduce hallucinations in regulated contexts. The conversation also highlights the importance of streamlining AI implementation and having experienced champions in organizations
Generative AI accelerates drug discovery for rare disorders by reducing costs and timelines significantly.
Large language models revolutionize life sciences, enhancing understanding of protein structures and improving drug development processes.
Adopting a retrieval augmented generator pipeline can reduce errors in probabilistic systems like generative AI, enhancing response accuracy.
Deep dives
Generative AI Impact in Drug Discovery for Rare Disorders
Generative AI plays a crucial role in accelerating drug discovery for rare disorders by reducing costs and timelines significantly. Large language models streamline drug development through enhanced understanding of protein and molecule structures, providing a solution to the complex and time-consuming process.
New Normals in Life Sciences with Generative AI
Generative AI, particularly large language models, is shaping new norms in life sciences by revolutionizing areas like commercialization, manufacturing, and clinical trials. Key factors for impactful use cases include human-level performance, tackling repetitive tasks, and utilizing simple natural language prompts for precise instructions.
Addressing Hallucinations and Improving Response Accuracy in Generative AI
To combat flaws like hallucinations in probabilistic systems such as generative AI, a retrieval augmented generator pipeline is recommended. Grounding language model responses in context can significantly reduce errors and improve response accuracy by providing a structured approach to information processing.
Key Steps for Successful Generative AI Adoption in Life Sciences
Successful adoption of generative AI in life sciences requires being intentional about program vision, identifying strong leadership, developing robust governance, selecting the right use cases, nurturing talent, and establishing a center of enablement. Finding the right partner with experience in generative AI is crucial to navigate the complexities of adoption.
Role of Champions in Driving Generative AI Adoption
Champions of generative AI initiatives play a pivotal role in driving successful adoption, leading initiatives through intentionality and vision. Strong governance and collaboration, including stakeholder involvement, are crucial in steering generative AI programs to achieve impactful success in the life sciences sector.
Today’s guest is Asif Hasan, Co-founder of Quantiphi. Quantiphi is an AI-first digital engineering company that designs AI-driven services and platforms for broad applications across industries. In conversation with Emerj CEO and Head of Research Daniel Faggella, Asif discusses reducing hallucinations in healthcare language models and the latest developments in AI’s impact on drug discovery for rare disorders. If you’ve enjoyed or benefited from the insights in this episode, consider leaving us a five-star review on Apple Podcasts and let us know what you learned or liked most about this show or what you’d like to see in the future.
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