JUNE 2024LIFE SCIENCES REVIEW8By Oliver Hesse, VP & Head of Biotech Data Science & Digitalization, Bayerhile 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 enormous amounts of information in seconds and respond in natural language will be an invaluable productivity booster on many fronts. Key to the success of using generative AI in a business context will be the availability and access to internal data to get meaningful responses to a particular business use case. But imagine the possibility of asking your `personal assistant' a question like `What was the difference in manufacturing from product a vs b' and get an answer in seconds. Similarly for questions about your regulatory filings in different markets or an analysis of the competitive landscape in a specific area. But the applicability does not stop here, it is merely the starting point. The tools are really good when you have structured input and or output. The prime example WGENERATIVE AI (ARTIFICIAL INTELLIGENCE) IN BIOTECH IN MY OPINION
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