1. The application of generative AI in the field of drug design According to a 2010 study, the average cost of a drug from research and development to marketing is about 1.8 billion US dollars, of which drug research and development costs account for about one-third, and the entire research and development process requires Up to 3 to 6 years. Generative AI has been used to reduce drug design cycles to months for various purposes, reducing drug development costs and time in the pharmaceutical industry.
2. Applications of Generative AI in Materials Science Generative AI is impacting the automotive, aerospace, defense, medical, electronics, and energy industries by combining new materials with specific physical properties. The process, known as inverse engineering, defines desired properties and then explores materials that might have those properties, rather than relying on chance to find materials with them, so it can be found, for example, to be more efficient than those currently used in the energy and transportation industries. Materials with electrical conductivity or magnetic attraction, materials meeting corrosion resistance requirements, etc.
3. Application of generative AI in chip design Generative AI can use reinforcement learning (a machine learning technique) to optimize the placement of components in semiconductor chip designs (floor planning), reducing product development cycles from weeks (using human experts) to ) down to hours (using generative AI).
4. Application of Generative AI to Synthetic Data Generative AI can be used to create synthetic data. Synthetic data is generated data that does not come from direct observations of the real world. This protects the privacy of the original source of model training data. For example, medical data for research and analysis can be artificially generated, which avoids disclosing the identity of the patient on the medical records used and protects the privacy of the patient.
5. Generative design of parts With generative AI, industries such as manufacturing, automotive, aerospace, and defense can design parts that best meet specific goals and constraints such as performance, materials, and manufacturing processes. For example, automakers can use generative design to create lighter designs, helping them achieve their goal of making cars more fuel-efficient.