AUGUST 2024LIFE SCIENCES REVIEW8IN MY OPINIONhile maybe not the newest kid on the block, generative AI is certainly one of the hottest technology topics of the time. Specifically, the release of Openai's ChatGPT sparked a plethora of discussions that range from fear to enthusiasm. But besides the sometimes very public discussions around AI, there is also an impressive amount of work going on behind the scenes in developing new capabilities and finding use cases where this set of technologies can be applied. Here, the release of AutoGPT has created another wave of enthusiasm and awe, as it combines the power of ChatGPT with other applications and tools. While many of the discussed and shared use cases may not be revolutionary at first sight, there is significant potential that these technologies, and the ones to come and build upon them, will have a profound impact on the way we work and do business. And this holds true for Biotech as much as for any other industry. The first and obvious capability that generative AI offers is interaction in natural language. I can simply tell the system what I want, and it `understands' my intention. This is fundamentally different from the need to program or learn a certain set of rules and commands. While prompt engineering is a particularly important topic (generative AI uses your input as a starting point, and the better the input, the better the response), the fundamental idea of being able to `communicate' in natural language is a central characteristic. With this I can also ask the system to e.g. summarize a particular topic or text that I provide or to generate some text that I need, from a friendly email to a journal article like this or potentially a document for regulatory authorities. The ability to synthesize By Oliver Hesse, CMC Digital Transformation & Data Science Lead, Bayer PharmaceuticalsGENERATIVE AI (ARTIFICIAL INTELLIGENCE) IN BIOTECH WOliver Hesse
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