The importance of capturing relevant data for Sustainable Economic Development using AI.

By DR. RAYNER @ HENRY PAILUS

Faculty of Computing & Informatics

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To begin our onion pealing process on this topic, we are going break this topic into 3 main topics

• First, what is Ai, how is it processed, how relevant data is crucial in making a better Ai.
• Second, what are the main pillars of Sustainable Economic Development
• Third, what are the suggested Ai relevant data and how can it create a Sustainable Economic Development

What is Ai?

• Ai, or artificial intelligence, refers to the development of computer systems that can perform tasks and make decisions that typically require human intelligence.

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• This includes areas such as speech recognition, problem-solving, learning and decision-making. Ai is designed to mimic human cognitive functions and has a wide range of applications in various industries including healthcare, finance, transportation and manufacturing.

So what does AI do to human?

• Out of the simplicity, at this stage, Ai makes, helps, and designs for anyone, despite looking at the colour of their skin, willingly to assist students from deep in rural areas able to access ChatGPT from the parent’s handphone who are not fortunate enough to receive such English grammar and writing skills guidance that any student from town would receive.
• To an Engineer, a Programmer, or a professor or a Grandpa ChatGPT Ai gives the opportunity for them to get their message across without a glitch. Well, that is not true all, Chat GPT Ai can only suggest but we ……. are the one who evaluates them.

You are what you eat, well that is Ai.

• Well, you can say that Ai is like a child, if coming from parenting perspective or a Frankenstein or a Jarvis coming from scientist perspective.
• Ai is shaped based on the colossal data that you feed to it. These data are extracted into features and continue with features selection process.
• These features are processed with some existing and hidden nodes, also known as black box which consist colossal computer neurons that are as complex as the brain neuron, and these neurons path complexity is your algorithm modeling which is the pillar of Ai foundation.

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• As you feed Ai with new data and as Ai learn from your respond based on your assessment process these algorithms models either will or will not change.
• So why does it, or why doesn’t it change.? This is our main issue of today’s topic
• What is mostly and crucially important is this:

“the data that is able to assist every Data Scientist to get closer to the ideal output is called relevant data”.

Relevant Data.

Testing AI MODEL

Computer Science Vs Ai

• In contrast with Computer Science, AI is a subfield of computer science focused on building computer systems capable of intelligent behaviour. This includes tasks such as machine learning, natural language processing, robotics, and computer vision.
• AI systems strive to simulate human intelligence by using algorithms to make decisions and solve problems autonomously. It’s also important to note that AI is not only about creating machines that can think like humans. It also has practical applications in many areas such as finance, healthcare, retail, transportation, and logistics.
• At the same time, they have some major differences as well. Computer science focuses on understanding how computers can be used to solve problems efficiently.
• AI focuses on understanding how computers can learn and think intelligently. Computer science requires a deep understanding of algorithms while AI requires an understanding of machine learning techniques.

What is Sustainable Economic Development?

• To seek new levels of efficiency, to produce more with fewer resources (How?) and less waste (How?).
• Stop Industry that produce pollution like coal mining. To fight against pollution to the water where Government has to enforce Environment free from toxic like provide a deep dumbing ground and purify it in a long run.
• To use of recyclable materials, and the development of recovering or recyclable output components (How?).
• Economic growth can be achieved only through the synergy of pluralistic institutions, technological innovations, and the market economic system (How?).

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Don’t Go To The Moon Just Because we destroy the Earth.

• Most of us will only think about doing the right thing when our government initiates the campaign and forget about it the very next day, because we only want to show our support during the campaign.
• One of these campaigns is recycling. Please raise your hands if your own house has recycling bins.
• “Ask not what your country can do for you – ask what you can do for your country,” JFK challenged every American to contribute in some way to the public good, civic action, and to consider how it applies to their own lives.
• Each and every one of you here can help in maneuvering the future of our state or country or the mother Earth as a whole.

Economics Studies

• is the study of how society allocates resources to satisfy human needs.
• is an analysis of how the economic system allocates scarce resources, must include both the flow of natural resources undisturbed and environmental resources into the production process and the flow of wastes from the production and consumption processes back to the natural environment. (Sustainable Economic Development. In: Wang Y. Inflation and Growth in China).
• The quality of the natural environment directly affects the standard of living of society.

Sustainable Economics Studies

Sustainability and Economic Growth

• Classical Economists emphasized on the Country ‘s production productive factors, such as labor and land.
• Classical Economists always paid full attention to the sustainable exploitation of renewable resources and attached special significance to the fertility of land to allow continual crop production. Some country like Indonesia has to burn the farm for fertility and some endangered their ecological system.
• One can read the extensive literature on economic growth without ever realizing that ecological system, natural resources and sustainability by recycling might be a determinant of growth potential.
• Sustainability Factors:Social issues, Economic diversification, Environment, Resources, Education, Industrial structure, Urban and rural hierarchy, Infrastructure

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What can Ai do in sustaining economic development especially to rural areas ?

1. Agriculture : AI can help farmers make more informed decisions about planting times, water usage, and pest control.
• Ai-enabled systems can also monitor farms for disease, undernourishment, and sustainability.
• Ai Chatbots can provide real-time advice on farm management, and solar-powered devices can predict crop threats.
2. Education: Ai-powered educational platforms can help students in rural areas overcome learning barriers.
3. Infrastructure: Ai and digital technology can solve issues by freeing service providers from the need for expensive infrastructure.
4. Economic diversification: Ai solutions can be applied throughout the production process, from research and development to distribution and recycling.
5. Renewable energy: Ai algorithms can help optimize renewable energy deployment and reduce greenhouse gas emissions.
6. Sustainability: Ai has the potential to help protect the environment by detecting energy emission reductions, monitoring deforestation, and predicting extreme weather conditions.

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Why Relevant Data Collection is essential:

1. Fast In Decision Making : Accurate data helps identify the specific needs until to a specific person(which we can extract from my Sejahtera) and priorities of rural communities, ensuring that development initiatives address the most pressing . Policymakers can use data to craft effective policies that target the unique conditions of rural areas, leading to better outcomes ( No need to go through many red taps).
2. Better Resource Allocation: Efficient Use of Resources: Data allows for precise allocation of resources such as funding, manpower, and materials, maximizing impact and minimizing waste. Prioritization : It helps in prioritizing projects based on urgency and potential benefits, ensuring that the most critical areas receive attention first.
3. Better Monitoring and Evaluation: Fast in Progress Tracking : Regular data collection enables the monitoring of development projects, ensuring they stay on track and meet their goals. Impact Assessment: Data provides a basis for evaluating the effectiveness of interventions, helping to understand what works and what doesn’t.

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Why Relevant Data Collection is essential:

4. Allow Customization of Solutions: Tailored Interventions: Data helps in designing interventions that are specifically tailored to the local context, culture, and economic conditions, enhancing their effectiveness. Scalability: Understanding local conditions through data allows for the development of scalable solutions that can be adapted to similar rural contexts.
5. Allow Empowerment and Participation By All: Community Involvement: Data collection often involves community participation, which can empower residents by giving them a voice in the development process. Transparency and Accountability: Open data practices promote transparency and hold stakeholders accountable, building trust within the community.
6. Technological Integration: Smart Solutions: Data is the backbone of AI and other smart technologies that can drive rural development, such as precision farming and telemedicine. Infrastructure Planning: Data on population density, resource availability, and environmental conditions informs the planning and implementation of infrastructure projects.

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Why Relevant Data Collection is essential:

7. Economic Development: Market Insights: Data on local economic activities, consumer behavior, and market trends helps businesses and entrepreneurs make informed decisions, driving economic growth. Financial Inclusion: Financial data can help in designing inclusive financial services and microcredit schemes that cater to the needs of rural populations.
8. Environmental Management: Sustainability Practices: Data on environmental conditions and resource usage supports sustainable agricultural and conservation practices. Disaster Preparedness: Data on weather patterns and natural hazards helps in preparing for and mitigating the impact of natural disasters.
9. Healthcare Improvement: Health Monitoring: Health data enables the tracking of disease outbreaks, health trends, and the effectiveness of health interventions. Resource Distribution: Data ensures that healthcare resources such as medicines, vaccines, and medical personnel are distributed where they are most needed.
10. Education Enhancement: Learning Outcomes: Data on student performance and educational resources helps in identifying gaps and improving the quality of education. Access and Inclusion: Data can highlight areas with poor access to education, guiding efforts to bridge educational disparities.

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What are the reasons why clean data is essential?�

Ai can only assisted the creation of tailored solutions from clean Data

1. Accuracy: Clean data ensures that the information being used for AI analysis and decision-making is accurate and reliable. Inaccurate or erroneous data can lead to flawed conclusions, incorrect predictions, and unreliable AI models.
2. Consistency: Clean data ensures consistency in format, structure, and quality, which is crucial for training AI models effectively. Consistent data enables the model to learn patterns and make predictions more reliably.

What are the reasons why clean data is essential?�

3. Relevance: Clean data includes only relevant information that is necessary for the specific tasks or objectives of an AI system. Irrelevant or redundant data can introduce noise into the analysis, making it more difficult for AI models to extract meaningful insights.

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4.Efficiency: Processing clean data requires less time and resources compared to dealing with messy or incomplete datasets. This improves the efficiency of training AI models and allows them to be deployed faster.

What are the reasons why clean data is essential?�

5. Interpretability: Clean data makes it easier to interpret the results generated by AI systems because there are no ambiguities caused by inconsistent or inaccurate information.

6. Trustworthiness: Using clean data enhances trust in the results produced by AI systems among stakeholders, users, and decision-makers who rely on the technology's outputs.

7. Compliance: In many industries, compliance regulations mandate that organizations must use accurate, complete, and reliable datasets in their operations; clean data helps ensure compliance with these regulations.

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THANK YOU �

• Any Question ?