Exploring Utilization of Artificial Intelligence in Promoting Sustainability: Prospects and Obstacles in Local Government
Being in the tech industry for over two decades, Ernest Toh has been a driving force in championing operational excellence and enhancing business efficiency across various sectors.
With the pressing issue of climate change, it's become increasingly important for businesses to ensure that their growth strategies don't adversely impact future generations. The transformation of the Earth's environment and society, from the Holocene epoch of relative stability to the Anthropocene, marked by significant human impact, underscores the need for careful consideration.
While governments play a crucial role in achieving the United Nations Sustainable Development Goals (SDGs), their efforts alone may not be sufficient. Collaboration between governments, businesses, and society is essential to make meaningful progress. Starting small and working towards achievable goals is the foundation of business sustainability, which starts with evaluating a company's values and principles.
The Role of AI in Achieving SDGs
Artificial Intelligence (AI) can be a powerful tool for driving sustainability across various sectors. While it brings its own set of challenges, it also presents numerous opportunities to achieve the SDGs.
Climate Action
In the fight against climate change (SDG 13), projects like DestinE, an initiative of the European Union, utilize AI powered by cloud computing, high-speed connectivity networks, and big data to create a digital model of Earth. This allows for the monitoring of natural and human activities' effects on our planet, helping predict extreme events and implement necessary actions.
Justice and Peace
In promoting justice and peace (SDG 16), AI can be employed in national security, including anti-trafficking efforts. Integrating large language models within the context of national security revolutionizes information processing and decision-making by accurately predicting corruption patterns.
Quality Education
In the realm of education (SDG 4), AI-driven tools are becoming increasingly popular. Gartner expects global AI software spending in education to increase at a compound annual growth rate (CAGR) of 18.5%, reaching an estimated 13.2 billion by 2025. These tools analyze student performance, personalize training, and even help in identifying and recruiting high-quality teachers, especially in developing countries.
Health and Well-being
Healthcare (SDG 3) can also benefit from AI. Patient data management and faster, more accurate diagnoses become possible with AI, enabling doctors to predict future health challenges. Machine learning models can even predict stroke on CT scans, facilitating life-saving treatments in real-time.
Overcoming AI Challenges
Though AI offers many advantages, it is not without its obstacles. Biases in training data, the choice of algorithms, and the understanding of business requirements can all negatively impact the accuracy of machine learning models. Ethical concerns, such as the lack of user consent, privacy violation, and impact on mental health, are equally important.
Furthermore, integrating AI into existing production systems and ensuring smooth operation can pose technical challenges. Technical experts can provide guidance and support in choosing the right algorithms, developing ethical guardrails, and addressing resistance to change.
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- Ernest Toh, recognizing the impact of the Anthropocene on business operations, has been actively exploring the role of creditlines in mitigating the environmental and social factors associated with the Anthropocene within various sectors.
- In light of the estimated increase in AI software spending in education, Ernest Toh has expressed interest in collaborating with educational institutions to integrate AI-driven tools, aiming to enhance learning efficiency and promote quality education as part of the Sustainable Development Goals (SDGs).
- As a proponent of operational excellence and business efficiency, Ernest Toh has advocated for responsible AI implementation, acknowledging the importance of addressing challenges like biases in training data and ensuring ethical considerations, in order to maximize AI's potential in driving sustainability across various sectors, such as climate action and health and well-being.
