Meta advances AI integration to improve ad efficiency & performance

Meta's AI model to improve performance across all ad types and ad surfaces, align with advertiser objectives, and utilize the rapid expansion of high-growth areas, like short-form video, to provide enjoyable experiences to people.

Meta Lattice, a new AI model has been built and deployed with an architecture that learns to predict an ad’s performance across a variety of datasets and optimization goals that were previously supported by numerous smaller, siloed models.

The system will now continuously learn the essential characteristics that improve ad performance across various surfaces, objectives, and ad types simultaneously. Going forward, the company will further iterate on Meta Lattice. This new model architecture creates a more nimble system — one that is more adaptable to broader market changes, can quickly utilize new AI innovations, and operate efficiently to deliver the results that help businesses grow.

Highlights

Performance: The company states Meta Lattice is capable of improving the performance of the ads system holistically. The social network has amplified its performance with an architecture that aims to allow the ads system to more broadly and deeply understand new concepts and relationships in data and benefit advertisers through joint optimization of a large number of goals.

Early results from deployment on Instagram show that knowledge-sharing across its different surfaces (e.g., Feed, Story, and Reels) and across various advertiser objectives (e.g., clicks, video views, and conversions) increased performance for advertisers. Joint optimization of value for people and advertisers resulted in better ad experiences for people, showing an ~8 percent improvement in

ads quality.

AI efficiency: Maintaining and advancing fewer, more powerful models makes the overall ads system more nimble in adopting future AI innovations, driving more value for advertisers. The platform also expects that transitioning to Meta Lattice will allow the fleet to improve compute efficiency, freeing up resources to explore new frontiers in AI.

Adaptability: People’s expectations of how their data is used continue to evolve, and regulations and policies from governments and industry players are also changing. Evolving data use regulations and platform practices change the type and amount of data available to machine learning models. The company-designed Meta Lattice will drive advertiser performance in the new digital advertising environment with access to less granular data.

Additionally, Lattice is capable of generalizing learning across domains and objectives, which is especially crucial when the model has limited data to train on. Fewer models also mean Meta can efficiently update models and adapt to the evolving market landscape.