International research teams trace AI’s growth from neural network–based supervised and reinforcement learning to large language and generative models accelerated by GPUs, and they highlight pruning and emerging neuromorphic hardware to balance performance with ethical and energy considerations.

Key points

• Alan Turing’s intelligence concept and McCarthy’s 1955 AI coinage set AI foundations
• Artificial neural networks learn via supervised, unsupervised, and reinforcement paradigms
• GPUs accelerate large-scale neural network training by parallelizing matrix operations
• Generative AI models combine vast datasets with large language and diffusion architectures
• Pruning and physics-constrained learning methods reduce computational and energy costs
• Neuromorphic hardware architectures aim to co-locate memory and compute for brain-like efficiency

Why it matters: AI’s shift toward more powerful generative and agentic models can transform scientific workflows and industry practices but also raises critical concerns over energy consumption, model reliability, and ethical oversight, prompting new methods to reduce hardware costs and enhance transparency.