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• Risk Management: Facilitates early identification and mitigation of risks related to data quality,
model performance, deployment challenges, and ethical considerations, minimising potential
disruptions.
• Quality Assurance: Ensures rigorous testing, evaluation, and validation of AI models to meet
desired performance standards and business requirements, enhancing reliability and usability.
• Continuous Improvement: Supports iterative development and enhancement of AI solutions
based on feedback, new data insights, and evolving business needs, fostering innovations, and
adaptation over time.
• Modularity: Encourages the design of AI solutions in a modular fashion, allowing components
to be developed, tested, and integrated independently, promoting flexibility and scalability in
project development.
Task #Creativity & Innovativeness
Case Study: Cleaning Oceans
The world's oceans are increasingly polluted with plastic waste, which poses a severe threat to marine life
and ecosystems. Identifying the location and density of plastic waste patches is challenging due to the
vast and dynamic nature of the ocean. Traditional methods of detecting and removing plastic waste are
inefficient and labour-intensive. Coastal communities and environmental organisations lack the advanced
tools needed to monitor and clean up these plastics effectively, leading to continued environmental
degradation and harm to marine biodiversity.
Can we solve this problem with AI? How?
Now that you are aware of AI concepts, plan to use them in accomplishing your task. Start with listing
down all the factors which you need to consider saving the oceans. This system aims to:
Problem Scoping: Define the problem statement, objectives, and constraints of the AI project. Identify
stakeholders, understand the business context, and outline the scope of work to be undertaken.
Define the scope to develop AI models for identifying and removing ocean plastic waste. Identify key
challenges such as ocean dynamics, plastic detection limitations, and scalability of cleanup efforts.
Data Acquisition: Gather relevant datasets required for training and evaluating AI models. Ensure data
is collected from reliable sources, adheres to legal and ethical standards, and is prepared in a format
suitable for analysis.
Gather satellite imagery, drone footage, and oceanographic data from various sources to create a
comprehensive dataset. Utilise data from environmental organisations, governments, and research
institutions.
190 Touchpad Artificial Intelligence (Ver. 3.0)-IX

