In a major leap for artificial intelligence (AI) and photonics, researchers at the University of California, Los Angeles (UCLA) have created optical generative models capable of producing novel images using the physics of light instead of conventional electronic computation.
In a major leap for artificial intelligence (AI) and photonics, researchers at the University of California, Los Angeles (UCLA) have created optical generative models capable of producing novel images using the physics of light instead of conventional electronic computation.
Published in Nature, the work presents a new paradigm for generative AI that could dramatically reduce energy use while enabling scalable, high-performance content creation.
Generative models, including diffusion models and large language models, form the backbone of today’s AI revolution. These systems can create realistic images, videos, and human-like text, but their rapid growth comes at a steep cost: escalating power demands, large carbon footprints, and increasingly complex hardware requirements. Running such models requires massive computational infrastructure, raising concerns about their long-term sustainability.
The UCLA team, led by Professor Aydogan Ozcan, has charted a different course. Instead of relying solely on digital computation, their system performs the generative process optically—harnessing the inherent parallelism and speed of light to produce images in a single pass. By doing so, the team addresses one of AI’s greatest bottlenecks: balancing performance with efficiency.
The models integrate a shallow digital encoder with a free-space diffractive optical decoder, trained together as one system. Random noise is first processed into « optical generative seeds », which are projected onto a spatial light modulator and illuminated by laser light.
As this light propagates through the static, pre-optimized diffractive decoder, it produces images that statistically follow the target data distribution.
