From Dialects to Peptides: Scalable and Efficient AI for People

Overview

We argue that real-world impact at global scale potentially rests on three pillars: rigorous quantification of what is missing, explicit modeling of culture rather than mere linguistic form, and strict efficiency constraints that make deployment feasible. Centered on speech and language technologies for approximately 1.7 billion people in Africa and the Arabic-speaking world, we introduce a unified toolkit for decoding complex, noisy signals in low-resource settings.

We begin by addressing linguistic diversity through measurement-first evaluation. We consolidate fragmented resources into coherent, comparable assessment protocols that stress-test systems under dialectal variation, code-switching, informal registers, and domain-specific conditions such as health, education, and governance. We then extend beyond surface-level correctness to cultural competence by examining whether models adhere to community norms, values, figurative meaning, and contextual appropriateness.

Throughout, efficiency is treated as a first-class scientific objective: we develop training and inference strategies that preserve capability while reducing computational cost, latency, and data demands, thereby widening access beyond high-resource environments. Finally, we demonstrate the broader methodological reach of this agenda in proteomics. Treating mass spectra as structured evidence, we build fast, accurate sequencing methods that incorporate model self-correction. Together, these threads yield a unified approach to decoding the complex languages of both humans and biology.