Artificial Intelligence (AI) is one of the most transformative technologies of the 21st century. From automation to personalisation. AI is having so much potential.
AI also has limitations, both technically and socio-economically. In particular, for developing and low-income countries, large-scale AI adoption presents challenges.
Technical Limits of AI
-Data Dependency:
AI systems are heavily reliant on large amounts of data to train their models. Like machine learning algorithms, neural networks, require vast datasets to gain accuracy and reliability.
Inaccurate, incomplete, or biased datasets can lead to skewed results, limiting the effectiveness of AI models.
-Lack of Human-Like Understanding:
In natural language processing and machine learning, AI still lacks human-like reasoning, empathy, and emotional intelligence. While AI can process large amounts of data and recognize patterns, it cannot fully grasp because of the complexity of human emotions or behaviours.
-Regulatory Issues:
AI development raises various ethical concerns, like job displacement, privacy, and surveillance.
Many countries have yet to establish AI regulations to address these challenges. Governments must create policies that ensure AI is used ethically.
Economic Limits of AI in Poor Countries
-Infrastructure and Resource:
Infrastructure is one of the key barriers in poor Countries. AI requires high computing power, cloud services, and high-speed internet connectivity. In developing nations electricity supply is unstable, and internet access is limited, deploying AI systems on a large scale becomes nearly impossible.
The hardware required for AI, such as GPUs and data servers, is expensive and beyond the reach of many low-income countries.
Microsoft is planning to power its artificial intelligence (AI) data centers with the Three Mile Island nuclear power plant in Pennsylvania.
-Skill Gap:
AI expertise is concentrated particularly in developed countries like the United States, China, and Europe. The shortage of skilled AI professionals in poor countries is also a big challenge. AI systems require expertise in programming, data science, and machine learning.
-Impact on Employment:
Large-scale AI adoption could lead to job displacement, with workers lacking the skills to transition to new roles in the AI-driven economy. Without social safety, this disruption could worsen poverty and inequality in low-income countries.
Can Poor Countries Afford AI at Large Scale?
AI can be a powerful tool to solve challenges in developing countries, like better healthcare, smarter farming, and improved education. But making it affordable and accessible to everyone is still a big challenge.
Here are some ways AI could be made more affordable and useful in these countries:
-Governments and organizations need to plan AI projects that suit the needs of local communities and focus on solutions that have the most impact.
-Developed countries and tech companies can partner with developing countries to share knowledge, tools, and resources, helping reduce costs and making AI more accessible.
-Instead of relying on expensive infrastructure, AI tools can be designed to work with simpler technology, like mobile phones, making them more affordable for wider use.
With the right efforts and support, AI can be made more affordable and helpful in improving lives in developing countries.