The demand for data in AI is growing rapidly. AI models, like ChatGPT, require vast amounts of data for training. For instance, ChatGPT was trained on 300 billion words, which is significantly more than what a person could read in a lifetime. Databricks' DBRX model, preceding GPT-4.0, used 12 trillion data points. This increasing demand may surpass the available public text data by 2026, especially if models are overtrained using AI-generated data.
Overtraining on AI-generated data can lead to repetitive and less diverse outputs. This issue is compounded by the risk of bias if AI models inadvertently use AI-generated data from the internet. However, synthetic data has its benefits, such as in training autonomous vehicles and in life sciences for rare diseases.
Despite its advantages, synthetic data requires significant GPU resources, making the process energy-intensive. The challenges and high failure rates of AI projects contribute to AI fatigue. Success in AI requires endurance, as only a small percentage of current AI use cases may yield significant benefits. Organizations should focus on these high-impact implementations for long-term success.
Small Language Models (SLMs) are refined versions of Large Language Models (LLMs), tailored for specific use cases. They are efficient, requiring less data and cost, making them suitable for precise applications like train control systems. However, for general knowledge, LLMs are more beneficial.
Modern Data Infrastructure is crucial for AI sustainability. Improving peripheral infrastructure around GPUs can enhance sustainability and efficiency. This includes cleansing and labeling data, choosing sustainable suppliers, and using energy-efficient storage solutions.
Team Approach is essential for AI success. Collaboration across the organization ensures the right problems are addressed effectively, avoiding bias and inefficiency. The choice between LLMs and SLMs depends on specific needs.
Challenges and Maturity: AI faces challenges like data scarcity and model overtraining. However, advancements in AI technologies and infrastructure are improving scalability, simplicity, and sustainability, making AI more viable for long-term success.
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- mahikrispi
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