Technology
As we step into 2025, R&D organizations are bracing for another year of rapid-pace, transformative shifts.
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Learn how AI Supermodels are allowing for faster, more accurate predictions with far fewer data points.
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In today’s competitive R&D landscape, selecting the right technology partner is one of the most critical decisions your organization can make.
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Leveraging advanced technology like generative AI through digital transformation (not digital enhancement) is how to get the biggest returns in scientific R&D.
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There is much more to digital transformation than technology, and a holistic strategy is crucial for the journey.
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In the rapidly-evolving landscape of drug discovery and development, traditional approaches to R&D in biopharma are no longer sufficient. Artificial intelligence (AI) continues to be a game-changer.
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Leveraging large language models (LLMs) in materials science and chemical R&D isn’t just a speculative venture for some AI future. There are two primary use cases that are ready for adoption in research labs today.
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R&D leaders and scientists need a working understanding of key AI concepts so they can more effectively develop future-forward data strategies and lead the charge towards groundbreaking discoveries.
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Scattered and siloed data is one of the top challenges slowing down scientific discovery and innovation today. What every R&D organization needs is a data fabric as part of their technology solution set.
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It doesn’t take ✨magic✨ to integrate ChatGPT into your Jupyter workflow. Integrating ChatGPT into your Jupyter workflow doesn’t have to be magic. New tools are seemingly coming out daily to help write code using large language models (LLMs). They appear to have a considerable positive impact on developers’ lives. GitHub claims 88% percent of developers…
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