This repository contains code and resources related to my research on artificial intelligence (AI), machine learning (ML), (DL) deep learning and, Cyber Security. The main goal of this project is to explore different approaches to solving problems in these fields and to document my findings.
This repository is a collection of my research work in the exciting and rapidly evolving fields of AI, ML, and DL and Cyber Security. It contains Python scripts, Jupyter notebooks, datasets, and documentation related to various topics such as text to speech, AI-assisted coding, natural language processing, computer vision, and deep reinforcement learning, among others.
The code implementations are written using popular Python libraries used across the AI (Artificial Intelligence), ML (Machine Learning), DL (Deep Learning) and Cyber Security fields. The repository is well-organized and easy to navigate, with README.md files providing an overview of the contents and subdirectories containing specific research topics and related code. Most of the work in this repo was generated with the aid of multiple language models and AI's currently available in the private and open-source communities. I encourage others to contribute to the repository by sharing their own research findings and code implementations.
The use of AI: For the purpose of this repository, AI is being used as a learning tool. It is not being used to solve specific problems, but rather to help me better understand coding concepts and techniques to solve those problems. By leveraging AI and machine learning, I am able to gain a deeper understanding of the underlying principles of coding and data science, which in turn allows me to create better code and more effective research experiments.
Validating AI-generated answers: As this project progresses, one of my main objectives is to develop a robust methodology for testing the answers and examples generated by AI. It is important to ensure that the AI is providing accurate and reliable results, and that any errors or inaccuracies are identified and corrected. I will be using a variety of methods to validate the AI-generated answers, including manual inspection, testing against known datasets, and comparing results with those obtained using traditional coding techniques.
Learning to code with AI: As a beginner coder, I am using AI to assist me in learning coding concepts and developing my skills. By using AI, I am able to gain a deeper understanding of the underlying principles of coding, as well as the various tools and libraries available for Python development. Over time, I hope to build my expertise in the field of AI and machine learning, and to use this knowledge to contribute to the wider research community.
Is going to be very easy to know which parts of this repo where written with the help of AI, and which parts where written by me alone =). This section is an example of that. I want to keep this section dynamic and i want to remain well in control because it's inevitable that the Repo structure is going to change as this project progresses. And Yes the typos and grammar errors will stay. Thats how you'll know it is me and only me speaking.
To keep this Readme from becoming an encyclopedia I will link to the docs folder which is located in the same relative path as the readme you are currently reading. This pattern is also applied to other readme files found in child directories. If i need to expand on the topic being discussed i will link to Readme.md files that are titled accordingly. I apologize in advance for any instances where I reference private repositories or public work that goes beyond the scope of this repository topic. These references are my 'Secret Sauce' and are necessary for providing a complete understanding of the topic.
AI research presents several challenges and complexities that need to be addressed to ensure effective and ethical solutions. Some of the key challenges that this project aims to address are organized in
[Problem Statement]: Data quality and scarcity are major issues in AI research. Poor quality data can lead to incorrect results and unreliable models, while limited data can prevent researchers from developing accurate and robust models. This project aims to explore ways to improve data quality and availability for AI research.
[Proposed Solutions]: One way to improve data quality is to incorporate data preprocessing techniques, such as data cleaning, normalization, and feature selection. These techniques can help identify and remove outliers and irrelevant features, improving the quality and accuracy of the data used for model training and evaluation.
Another approach is to use data augmentation techniques, which involve generating new data samples from existing data through techniques such as rotation, translation, and flipping. This can help increase the size of the dataset and improve model performance, especially in cases where limited data is available.
To address the issue of data scarcity, transfer learning can be used to transfer knowledge from related tasks or domains to the target task. This can help improve model performance and reduce the amount of data required for training.
References:
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Chollet, F. (2018). Deep learning with Python. Manning Publications.
Zhang, Y., & Yang, Q. (2017). A survey on multi-task learning. arXiv preprint arXiv:1707.08114.
[Problem Statement]: As AI models become more complex, understanding how they arrive at their decisions and predictions becomes increasingly difficult. Model interpretability is crucial for making informed decisions and ensuring that AI models are transparent and trustworthy. This project aims to investigate techniques for improving model interpretability in AI research.
[Proposed Solutions] Model interpretability is a critical aspect of AI research that enables users to understand how a model arrives at its decisions and predictions [1]. Improving model interpretability can lead to better decision-making and increase transparency and trustworthiness of AI models.
Techniques for improving model interpretability include feature importance, counterfactual what-if, and explainable AI (XAI) [2][4][7]. These techniques help users understand the impact of different features on a model's output, observe how perturbations in features affect predictions, and provide clear evidence of how a model is making decisions.
In addition, researchers are exploring new approaches to improving model interpretability, such as using sense-making techniques and developing human-computer interaction collaborations [6][10]. By incorporating these approaches, researchers can improve the transparency and trustworthiness of AI models, enabling better decision-making and ultimately advancing the field of AI research.
Overall, improving model interpretability is essential in AI research to ensure transparent and trustworthy AI models. By utilizing existing techniques such as feature importance and XAI, and exploring new approaches such as sense-making, researchers can improve the transparency and reliability of AI models.
References:
[1] https://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability
[2] https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard
[4] https://www.forbes.com/sites/forbestechcouncil/2023/01/13/explainable-ai-the-importance-of-adding-interpretability-into-machine-learning/
[6] https://arxiv.org/abs/2206.15465
[7] https://medium.com/edge-analytics/interpretability-tools-for-understanding-your-machine-learning-models-part-1-927324ec873e
[10] https://www.semanticscholar.org/paper/Sensible-AI%3A-Re-imagining-Interpretability-and-Kaur-Adar/2ae9a9ec0c6a614d5bc522e64983cded8e6f4fb9
[Problem Statement]: AI models are only as unbiased as the data they are trained on. Bias in algorithms can perpetuate discrimination and reinforce systemic inequalities. This project aims to explore ways to mitigate and eliminate bias in AI algorithms.
[Proposed Solutions]
[Problem Statement]: Many AI research tools and technologies are expensive, which can pose a significant challenge for researchers with limited budgets or resources. This project aims to identify and explore cost-effective solutions for AI research.
[Proposed Solutions]
[Problem Statement]: AI has the potential to impact society in significant ways, both positive and negative. This project aims to consider the potential ethical implications of AI research and to develop solutions that prioritize ethical considerations.
[Proposed Solutions]