Ask Not What AI Can Do for You, Ask What You Can Do for AI
As society undergoes a transformation driven by artificial intelligence (AI), it is crucial to consider not only what AI can accomplish for us but also what steps we must take to maximize its value while minimizing associated risks. This necessitates a pragmatic approach to AI development that prioritizes essential elements to ensure its safe and beneficial implementation.
Key Elements Needed for AI Success
For AI to flourish and benefit society as a whole, three critical elements must converge: infrastructure, ecosystem, and governance.
1. Infrastructure
The infrastructure supporting AI encompasses a wide range of components, from data centers to wearable technology, significantly influencing AI’s cost and value across financial, societal, and environmental dimensions.
Cloud-edge-connectivity continuum: The trend towards incorporating more computational power at the edge—within devices such as smartphones, sensors, and industrial equipment—is accelerating. This shift allows AI models to process data locally, thereby reducing latency and enhancing privacy, which enables real-time decision-making.
However, the need for robust, high-speed networks remains paramount. Edge devices require synchronization with centralized cloud systems to update models and share insights for comprehensive analysis. A practical example includes a fleet of agricultural drones that process immediate data on-site but still depend on cloud connectivity for broader data aggregation and model retraining.
Power and the power of innovation: The energy requirements for training large-scale AI models, such as GPT-4, can be substantial, raising concerns about sustainability. As AI models grow in complexity, their carbon footprint increases, emphasizing the need for innovations that improve model efficiency and reduce power consumption.
2. Ecosystem
Application ecosystem: Developing AI architecture should prioritize inclusivity, bridging the gap between technologists and the broader AI-consuming population. The role of the developer community is pivotal in shaping AI’s future, influencing its technical performance and societal impact through their choices in algorithms, data, and frameworks.
The emergence of low-code and no-code platforms has democratized AI development, enabling non-technical individuals and small businesses to leverage AI technology for practical challenges. This accessibility is crucial for inclusive innovation, allowing historically marginalized communities to participate in the AI era.
Device ecosystem: The advancement of AI is closely tied to the integration of AI capabilities into everyday devices. For AI to provide real-time, context-aware intelligence, it must be seamlessly embedded in smartphones, sensors, wearables, and industrial equipment. Without a robust device ecosystem, AI risks being confined to centralized environments, which limits its effectiveness and adaptability.
3. Governance
The governance of AI requires a blend of top-down and bottom-up approaches. Top-down design should focus on creating flexible guardrails for AI implementation, while bottom-up design must emphasize observability and control through capabilities like explainable AI (XAI). Such frameworks should reflect human values, ensuring that fairness, accountability, transparency, and safety are continuously refined according to societal needs.
Bridging the digital divide is essential for empowering communities to establish domain-specific security and governance frameworks, fostering a responsible approach to AI development. The responsibility lies with us, the human stewards of technology, to ask the right questions and define appropriate objectives to create effective AI systems.
