providing solutions to challenges of the 21st century
The future of AI relies on small, precise models used at the edge, as highlighted by IEEE Spectrum. These models, like the RxScanner Lite, are crucial for providing services in areas with limited resources. With the potential to transform healthcare and agriculture, small AI may reach more people than larger models, despite underlying infrastructure challenges.
The World Bank warns that the AI revolution could exacerbate inequalities between rich and poor countries, as poorer nations struggle to benefit from AI advancements. Adebayo Alonge emphasizes the need for "small AI," tailored to local contexts, advocating for precision in development and funding to adequately support impactful applications that save lives.
The author challenges the notion that speed and distance are opposites, advocating for a unified approach to tackling systemic issues like counterfeit drugs, financial exclusion, and energy poverty. By sharing resources across ventures, the author demonstrates how diverse projects can coexist under a single trust architecture, leading to significant impact and financial success.
In 2016, Adebayo Alonge founded RxAll, initially developing an AI-powered spectrometer to combat counterfeit medications. Despite early struggles, a pivot during COVID-19 turned RxAll into a comprehensive pharmacy workflow platform, enhancing success and profitability. By addressing pharmacies' operational needs, adoption surged from 5% to 92%, solidifying their market presence.
Trust is a system that can be structured for effectiveness, yet institutions are struggling with public skepticism. Compliance-driven approaches fail to address underlying trust issues. Successful solutions redesign systems for transparency and verification, improving adoption and building credibility. The future favors those who establish robust trust architectures over mere reputational marketing.
The EU AI Act, enforcing significant penalties for noncompliance, seeks to regulate high-risk AI systems, emphasizing moral governance alongside technical compliance. While necessary, this framework lacks moral architecture, risking harm despite adherence to rules. Builders must prioritize community needs to ensure AI systems serve and do not exclude vulnerable populations.
