AI in Biotechnology
One of the most important advances in the application of computing science to real world problems of the last decade has been the realisation that AI algorithms give better predictions about the real world when fed with plentiful or valuable data. Industry, however, knows that this involves a trade-off: more data means better decision making but comes with increased associated costs (acquisition, integration, interpretation, storage, procesing, etc). The rapid progress of the last years in the field of Artificial Intelligence means that now AI can be applied to a large variety of workflows, jobs and tasks within a Biotechnology enterprize. In order to decide whether, when, where or how to invest in AI, Biotechnology companies will need to have answers to questions such as:
- What is it that the company is trying to achieve with AI?
- What does a company need to do in order to make better decisions and how does the AI fit in this picture?
- How do predictions from AI are to be fed into human value judgment?
- What are the metrics for success in the use of AI?
- What data would be or could be available to the AI for training? as input for prediction? as feedback for improvement?
Ultimately, Biotechnology companies will need to understand the value that AI brings to their entire business from essential predictions, to decision making, to tools, strategy and broader societal implications of its adoption. This AIMday will help industry and other stakeholders to start understanding these and other issues related to the development and adoption of AI in Biotechnology.