Generative AI has quickly emerged as an impactful force in science research and development across various domains such as healthcare, materials science, biology, and chemistry. Leveraging machine learning's abilities, it enables scientists to generate, simulate, or augment scientific data models and experiments using neural networks - revolutionizing scientific discovery processes along the way!
Generative AI in Science industry size is expected to be worth around USD 45.9 Bn by 2032 from USD 3.2 Bn in 2022, growing at a CAGR of 31.4% during the forecast period from 2023 to 2032.
Generative AI has great promise but still faces several significant hurdles to its adoption in science. Chief among them is its need for large volumes of high-quality training data; many scientific datasets tend to be smaller, making accurate generative model training difficult. Ethical considerations, data privacy concerns, and interpretability challenges also often prohibit AI-driven solutions in research institutions. Finally, computational resources required for training and deploying sophisticated generative models may prove too expensive for some research institutions to afford.
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