Tasks and Duties
Objective
The goal of this task is for you to identify a compelling research problem in the field of Artificial Intelligence that is of relevance to current academic trends. You are expected to perform a comprehensive literature review to justify the problem statement and its importance. The final deliverable is a DOC file that contains your defined research problem, a detailed literature review, and a summary of emerging trends and gaps in the field.
Expected Deliverables
- A clearly defined research problem statement.
- A literature review section covering at least 10 scholarly sources.
- A discussion regarding the relevance of the research problem within the context of AI research.
- A concluding section summarizing identified trends and research gaps.
Key Steps
- Identify and select a research problem area within AI.
- Gather at least 10 scholarly articles or publicly available research papers related to your topic.
- Summarize the methodologies, approaches, and findings from each source, focusing on emerging trends.
- Construct a detailed literature review narrative tying together your findings.
- Compile your work into a DOC file ensuring clear formatting and section wise arrangement.
Evaluation Criteria
Your submission will be assessed based on the clarity and relevance of your research problem, the depth and critical analysis of your literature review, the logical structure and flow of information, and proper citation of sources. The document should demonstrate analytical thinking, thorough research, and clear communication. Use headings and sub-headings where necessary to clearly organize your work.
Objective
This task focuses on the design and planning phase for an AI model tailored to address the research problem previously defined. You will design a robust model architecture, explaining the rationale behind chosen algorithms, layers, and data handling techniques. The final deliverable will be a DOC file that includes model diagrams, design choices, and a comprehensive explanation of how your model addresses the research problem.
Expected Deliverables
- A detailed description of the AI model architecture including diagrams or flowcharts.
- An explanation of the algorithms, layers, and data preprocessing strategies involved.
- A rationale for design choices and anticipated benefits and challenges.
- A section on potential modifications or scalability aspects of the proposed architecture.
Key Steps
- Review the research problem and identify key challenges that need to be addressed.
- Select an appropriate AI framework and design the model architecture using publicly available references.
- Create detailed diagrams or flowcharts to represent component interactions.
- Provide a written explanation of each chosen component and strategy.
- Compile all information into a well-structured DOC file.
Evaluation Criteria
Your submission will be evaluated based on the clarity of the architecture design, logical connections between components, depth of technical explanation, and the justification of your design decisions. Attention to detail and clear presentation will be critical in achieving a high score.
Objective
The third task is aimed at designing a simulation environment and formulating a set of experiments to validate the AI model architecture proposed in Week 2. This task challenges you to design an experiment that simulates real-world scenarios and test cases. You must outline experiment parameters, define success metrics, and predict possible outcomes. Your final deliverable is a DOC file containing a detailed simulation and experiment design plan.
Expected Deliverables
- A comprehensive simulation design that outlines the virtual environment setup for testing the AI model.
- Detailed experiment plans including parameters, control variables, and success metrics.
- An analysis section predicting outcomes and potential challenges.
- Diagrams or flowcharts that illustrate the experiment workflow.
Key Steps
- Review the AI model architecture and identify key functionalities to be tested.
- Design a simulation environment using publicly available references or frameworks.
- Define clear objectives and measurable metrics for each experiment.
- Create a step-by-step plan that includes control variables and data collection methods.
- Compile your entire simulation and experiment design into a DOC file with clear annotations and illustrations.
Evaluation Criteria
Your work will be judged on the clarity and comprehensiveness of the simulation environment, logical experiment design, feasibility of the proposed tests, and the precision of the outcome predictions. The documentation should reflect meticulous planning, rigorous thought process, and clear articulation of experiment design.
Objective
This week’s task requires you to design data preprocessing and feature engineering strategies necessary for preparing input data for your AI model. Your challenge is to structure an approach that cleans, transforms, and extracts meaningful features from raw data. The final deliverable is a DOC file detailing your preprocessing workflow, feature extraction methods, and a hypothetical case study using publicly available data as reference.
Expected Deliverables
- A step-by-step data preprocessing workflow including data cleaning, normalization, and transformation techniques.
- A detailed description of feature extraction and engineering strategies applied.
- An explanation connecting the preprocessing steps with the success factors of the AI model.
- A hypothetical case study that applies these methods to a publicly available dataset.
Key Steps
- Identify the types of data challenges associated with your research problem.
- Develop a workflow to systematically preprocess and clean data.
- Outline feature extraction methods that enhance the model's performance.
- Construct a hypothetical case study demonstrating the application of these techniques.
- Document your strategies and rationale in a DOC file with sections, diagrams, and flowcharts as needed.
Evaluation Criteria
Submissions will be evaluated for the depth of analysis in data preprocessing, clarity and logical flow of the workflow, the relevance of chosen feature engineering techniques, and the alignment of these methods with the objectives of your AI research. Accuracy, creativity, and technical clarity will be the key criteria.
Objective
In this task, your objective is to evaluate the performance of the AI model prototype and propose optimization strategies. You are required to compose a comprehensive evaluation plan that includes performance metrics, analysis techniques, and potential iterative improvements. The deliverable is a DOC file containing your analysis framework, evaluation results (hypothetical or simulated data), and proposed optimization recommendations.
Expected Deliverables
- A detailed plan for evaluating the AI model’s performance.
- An explanation of chosen performance metrics (e.g., accuracy, precision, recall, F1-score, etc.).
- A hypothetical analysis using sample or simulated data to demonstrate evaluation methods.
- A discussion on optimization strategies and iterative improvements based on evaluation outcomes.
Key Steps
- Review the model design and identify key performance indicators relevant to your research problem.
- Create an evaluation framework outlining the metrics and tools to be used.
- Simulate model behavior with basic data or hypothetical scenarios if actual data is unavailable.
- Propose optimization strategies based on simulated analysis and expected performance gaps.
- Document the evaluation plan, findings, and recommendations in a DOC file, including graphs and tables where applicable.
Evaluation Criteria
Your submission will be graded on the robustness of the evaluation framework, logical coherence of the analysis, clarity of your proposed optimization strategies, and overall presentation. The report should reflect critical understanding, analytical rigor, and potential for real-world application.
Objective
The final task for the internship is to produce a comprehensive research report that encapsulates the entirety of your work from the previous weeks. You are expected to compile findings from problem definition, literature review, model design, simulation, data preprocessing, and model evaluation. Additionally, propose future extensions and research directions for further enhancement of the AI model. The final deliverable is a DOC file that serves as a polished research report, complete with sections for introduction, methodology, results, discussions, and future work.
Expected Deliverables
- A complete research report including background, literature review, methodology, simulation design, evaluation results, and a discussion section.
- Detailed sections for each phase of the project with clear transitions and comprehensive analysis.
- A section outlining potential future work and recommendations for subsequent research.
- Diagrams, charts, and tables to support your discussion and analysis.
Key Steps
- Assemble all the components of your previous work into a cohesive document.
- Ensure each section is clearly defined and flows logically from one to the next.
- Integrate diagrams, charts, and tables where appropriate to support your data and analysis.
- Draft a detailed discussion that not only reflects on your past work but also lays out future research directions, potential challenges, and innovative strategies for improvement.
- Review your document to ensure clarity, coherence, and professional presentation before compiling into a DOC file.
Evaluation Criteria
The final report will be evaluated on overall comprehensiveness, coherence, clarity, and depth of analysis. Special emphasis will be placed on the quality and relevance of the future work proposal, the logical structure of the document, and the professional presentation of your research. Demonstration of cumulative learning across multiple AI research facets is critical for a successful submission.