Life Sciences
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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OpenAI’s ChatGPT, Google’s Bard, and other similar Large Language Models (LLMs) have made dramatic strides in their ability to interact with people using natural language. Users can describe what they want done and have the LLM “understand” and respond appropriately.
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Mammalian cell culture is a fundamental tool for many discoveries, innovations, and products in the life sciences.
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In the digital era, robust data tools are crucial for all companies and the science-driven industries like the life sciences, materials science, and chemistry are no exception.
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As a company that delivers Digital Transformation for Science, part of our job at Enthought is to understand the trends that will affect how our clients do their science. Below are three trends that caught our attention in 2022 that we predict will take center stage in 2023. ChatGPT This one just showed up on…
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Enthought’s team in Japan exhibited at the Pharma IT & Digital Health Expo 2022 life sciences conference in Tokyo, to meet with pharmaceutical industry leaders gathering for technological insight and to revitalize market growth. 200 companies exhibited across the 3-day in-person event, which drew over 6,700 attendees. With digital transformation a headline theme, the show…
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Labs are resetting the trajectory for drug development: reducing timelines from years to months; decreasing costs from billions to millions; and gaining an advantage by delivering drugs to market in months rather than decades. This value combination is a compelling case for investment in digital capability and organizational transformation. A 100x or 1000x advance in…
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Introduction If you have used TensorFlow before, you know how easy it is to create a simple neural network model using the Keras API. Just create an instance of the Sequential model class, add the number of desired layers and accompanying layer nodes, define the activation functions to be used by each layer, and compile…
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Scaling innovations in R&D can be considered challenging in two dimensions: from lab to lab; and from the lab to the end customer. A 100x or 1000x advance in R&D efficiency challenges an organization to adapt, from R&D to engineering, manufacturing, and to the marketplace, where there is a significant business opportunity. Incremental Advances…
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