LLMs such as GPT-4 can improve decision-making, simplify operations, and promote innovation. Customised LLMs fine-tuned to an organisation can be even more efficient as they are trained on specific data, providing a tailored AI experience that enhances organisational performance.
However, companies that wish to train fine-tuned models face three immediate challenges:
1) the necessity of having substantial and high-quality data,
2) the possibility of bias in AI models due to being trained on publicly available data and
3) the risk of privacy violations when using personal data to train models without explicit consent.
Platforms that offer privacy-preserving training on abundant, high-quality data can help overcome these challenges and create a more inclusive, diverse, and universal AI.
Source: Harvard Business Review
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