Drug design: Generative AI has been used to design drugs for various uses within months, offering pharma significant opportunities to reduce both the costs and timeline of drug discovery.
Material science: Generative AI is impacting the automotive, aerospace, defence, medical, electronics and energy industries by composing new materials targeting specific physical properties.
Chip design: Generative AI can use reinforcement learning (a machine learning technique) to optimise component placement in semiconductor chip design (floorplanning), reducing product-development life cycle time from weeks with human experts to hours with generative AI.
Synthetic data: Generative AI is one way of creating synthetic data, which is a class of data that is generated rather than obtained from direct observations of the real world. This ensures privacy of the original sources of the data used to train the model.
Design of parts: Generative AI enables industries, including manufacturing, automotive, aerospace and defence, to design parts that are optimised to meet specific goals and constraints, such as performance, materials and manufacturing methods.From ‘Beyond ChatGPT:The Future of Generative AI
for Enterprises’, Gartner
