AI Research
In addition to my Scientific Research, I also conduct Research and Development (R&D) in various types of Artificial Intelligence (AI), there underlining infrastructure, and methodologies.
Furthermore, I focus on ensuring that the Data, Algorithms, and Models used to Design and Develop Artificial Intelligence (AI) are Responsible, Ethical, and adhere to Human Rights, Civil Rights, Equal Rights, Women's Rights, Disability Rights, LGBTQIA Rights, and Diversity, Equity, and Inclusion (DEI),
My Research and Development (R&D) includes but is not limited to the following:
- Generative Artificial Intelligence (GenAI)
- Agentic Artificial Intelligence (Agentic AI)
- Prompt Engineering
- Context Engineering
- Large Language Models (LLMs)
- Large Reasoning Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Cache-Augmented Generation (CAG)
- Deep Learning (DL)
- Artificial Neural Networks (ANNs)
- Feedforward Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) Networks
- Generative Adversarial Networks (GANs)
- Transformer Networks
- Fluid Artificial Intelligence (FAI)
- Crystallied Artificial Intelligence (CAI)
- Edge AI
- Distributed AI
- Long and Short Term Memory (LSTM)
- Natural Language Processing (NLP)
- Natural Language Understanding (NLU)
- Natural Language Generation (NLG)
- Natural Language Acquisition (NLA)
- Neural Machine Translation (NMT)
- Machine Learning (ML)
- Reinforcement Learning (RL)
- Generative AI
- Agentic AI
- Predictive AI
- Computer Vision
- Machine Vision
- Algorithms (various types)
- Supervised Learning
- Unsupervised Learning
- Open Source Software and Tools
- Open Source Algorithms and Models
- Long Chain Framework
- Long Graph Framework
- and a lot more
