Documentation on the emergence, current iterations, and possible future of Artificial Intelligence with a focus on Large Language Models
Dyde, Travis (2023)
Dyde, Travis
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023120434240
https://urn.fi/URN:NBN:fi:amk-2023120434240
Tiivistelmä
This thesis presents a comprehensive exploration of artificial intelligence and large language models, discussing their historical evolution, definitions, and contemporary significance. The research delves into the foundational aspects of natural language processing, making clear its fundamentals, including text processing, tokenization, parsing, and Part-of-Speech Tagging. A critical examination of machine learning for natural language processing introduces concepts such as supervised vs. unsupervised learning and word embeddings.
The focus then shifts to leading large language models, providing an overview of prominent models like Generative Pre-Trained Transformer 4, Claude 2, and Pathways Language Model v2, along with key milestones in their development. Ethical and societal implications of artificial intelligence, addressing bias, privacy, security concerns, and environmental impact, are explored in this thesis, highlighting mitigation strategies.
Furthermore, the thesis contemplates the future of artificial intelligence, envisioning potential advancements. The objective is to offer a comprehensive understanding of artificial intelligence and large language models, emphasizing their role, ethical considerations, and future trajectories. The research employs literature review, case studies, and critical analysis of existing models.
The conclusion drawn is that while artificial intelligence presents unprecedented opportunities, responsible development and deployment are imperative to mitigate ethical challenges. This research contributes to the discourse on artificial intelligence, serving as a resource for navigating the evolving landscape of artificial intelligence.
The focus then shifts to leading large language models, providing an overview of prominent models like Generative Pre-Trained Transformer 4, Claude 2, and Pathways Language Model v2, along with key milestones in their development. Ethical and societal implications of artificial intelligence, addressing bias, privacy, security concerns, and environmental impact, are explored in this thesis, highlighting mitigation strategies.
Furthermore, the thesis contemplates the future of artificial intelligence, envisioning potential advancements. The objective is to offer a comprehensive understanding of artificial intelligence and large language models, emphasizing their role, ethical considerations, and future trajectories. The research employs literature review, case studies, and critical analysis of existing models.
The conclusion drawn is that while artificial intelligence presents unprecedented opportunities, responsible development and deployment are imperative to mitigate ethical challenges. This research contributes to the discourse on artificial intelligence, serving as a resource for navigating the evolving landscape of artificial intelligence.