Navigating the AI Dilemma: Challenges Faced when Large Language Model Training Hits a Roadblock
Understanding the Consequences and Solutions for Software Engineering
Introduction: As AI continues to advance, replacing numerous jobs across industries, we must examine its impact on software engineering and potential roadblocks faced during large language model training.
Body:
AI’s growing presence threatens countless professions, including those beyond software engineering.
To understand how LLMs function, refer to the “Stable Diffusion” video by Computerphile on YouTube.
LLMs train similar to meat mincers, producing new content based on existing material and prompts.
Overfitting issues arise even with large datasets; observed in GPT-4, Gemini, and Copilot.
Challenge: LLMs struggle to generate basic code for older OS versions, like Macintosh System 7.5.
Addressing the challenges posed by AI and ensuring responsible development remains crucial for progress.
Conclusion: By recognizing and addressing the limitations of large language models, we can ensure their successful integration into our technological landscape while mitigating negative impacts on employment and industry standards.
