Technology
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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By Mike Heiber, Ph.D., Materials Informatics Manager Enthought, Materials Science Solutions The American Chemical Society (ACS) is a premier scientific organization with members all over the world from both academia and industry. Some of my team and I recently returned from their primary annual convening, the ACS 2023 Fall Meeting, held in San Francisco. I…
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There’s a long history of scientists who built new tools to enable their discoveries. Tycho Brahe built a quadrant that allowed him to observe the path and distance of a comet as it crossed the solar system, helping to prove the heliocentric model of the way the stars and planets move. Galileo Galilei built his…
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With the increasing importance of AI and machine learning in science and engineering, it is critical that the leadership of R&D and IT groups at innovative companies are aligned. Inappropriate budgeting, policies, or vendor choices can unnecessarily block critical research programs; conversely an “anything goes” approach can squander valuable resources or leave an organization open to novel security threats.
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