Generative AI: 1. Ethics 2.CLIP: Difference between revisions

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===Milestone 1===
===Milestone 1===


* Define Research Questions: Establish clear, focused questions to guide the project.
* '''Define Research Questions''': Establish clear, focused questions to guide the project.
* Literature Review: Conduct a comprehensive review of existing studies in AI ethics.
* '''Literature Review''': Conduct a comprehensive review of existing studies in AI ethics.
* Ethical Theory Exploration: Investigate various ethical theories to ground your research in a solid theoretical framework.
* '''Ethical Theory Exploration''': Investigate various ethical theories to ground your research in a solid theoretical framework.
* Ethical Dataset Identification: Locate datasets for quantitative AI ethics evaluation, such as red teaming datasets.
* '''Ethical Dataset Identification''': Locate datasets for quantitative AI ethics evaluation, such as red teaming datasets.


===Milestone 2===
===Milestone 2===

Revision as of 22:25, 4 December 2023

Project Plan and Milestones

Weekly Plan

Date Task Completion
Week 4
  • Paper reading.
  • Existing RLHF and RLAIF exploring.
  • Red-teaming dataset exploring.
Week 5
  • Familiarizing with Dromedary, SALMON, Llama base models.
Week 6
  • Evaluation of different base models.
  • Choice of using Llama 2 model as our baseline.
Week 7
  • Red teaming dataset exploration.
  • Reading about ethical theories.
Week 8
Week 9
  • ETHICS dataset formatting for Llama fine-tuning and evaluation.
  • Llama supervised model fine-tuning
Week 10
  • Evaluation of Llama model before and after fine-tuning with ETHICS dataset.
  • Mid-term Presentation & Start writing the Wikipedia page with the plan.
Week 11
  • Read about Reinforcement learning using PPO.
  • Re-formatting deontology dataset.
  • Creation of the preference model.
Week 12
Week 13
Week 14
  • Write the Wikipedia page & Final presentation

Milestone 1

  • Define Research Questions: Establish clear, focused questions to guide the project.
  • Literature Review: Conduct a comprehensive review of existing studies in AI ethics.
  • Ethical Theory Exploration: Investigate various ethical theories to ground your research in a solid theoretical framework.
  • Ethical Dataset Identification: Locate datasets for quantitative AI ethics evaluation, such as red teaming datasets.

Milestone 2

  • Refine our research questions.
  • Finish the whole dataset.
  • Run the model and fine-tuned it on the GPU.
  • Evaluate our fine-tuned supervised model.

Milestone 3

  • Get our Preference and the Reinforcement learning models.
  • Analyze the results.
  • Write the Wikipedia page.