JUNE 2023LIFE SCIENCES REVIEW9ability 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 here is certainly coding. I can simply tell the system to write me some python code to do x or to review or document my code that I generated. While coding may not be at the heart of work in biotech, there is nevertheless a lot of applicability, and this is where tools like autogpt come into play where you can combine various tools. To make this more tangible imagine the following scenario where you simply instruct the tool to plan your process development: to gather information about your new product, summarize the findings, develop an experimental plan, provide the respective programming of your equipment, check for the availability of resources and materials needed, order them if necessary, compile all this into a summary and send it off to people that need to be informed. 5 min later the tool is done. Sounds like Sci-Fi, but that is the power if you combine the capabilities of multiple tools through something like autogpt, where you can instruct in plain language. Here, various agents then start creating and executing tasks and creating new task and due to their ability to `translate' from natural language to structured code for e.g., API (Application Programming Interface) calls (i.e., interfacing with another system) they are able connect all these different systems without the need to explicitly code all these transactions. And you can still have the human in the loop to e.g., allow for certain transactions. Now this is of course a) just one example of what could be done and b) will not be done tomorrow, but it demonstrates some of the powers and the potential that these new tools can provide. Despite the exciting potential of generative AI, there are also risks and challenges. One major concern is the lack of transparency in the algorithms used by generative AI. This can make it difficult to understand how the algorithms arrived at a particular result and assess the validity of the generated solutions, which is particularly important in the context of developing medicines where patient safety is paramount. While the regulatory framework in pharma is often seen as an obstacle to the implementation of novel approaches, this should not be seen as an excuse to not assess these technologies more and better understand their potential and pitfalls.Oliver Hesse - Life Science Review Article - DRAFT In conclusion, generative AI has the potential to transform the way we work and do business in many areas, including Biotech and we only start to understand how transformative these tools will be. We still need to learn about the best way to use them, but we can certainly not ignore them. 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
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