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How to Train Generative AI Using Your Company's Data
Original article seen at: hbr.org on July 6, 2023
tldr
- 🚀 Generative AI models like GPT-4 can enhance company performance by capturing and disseminating proprietary knowledge.
- 💡 There are three main approaches to incorporating proprietary content into a generative model: creating a domain-specific model from scratch, fine-tuning an existing LLM, and prompt tuning.
- 🔑 Managing generative AI content to ensure quality and avoid 'hallucinations' is crucial.
- 🏢 Companies need to develop a culture of transparency and accountability to manage potential risks of generative AI applications to knowledge management.
summary
The article discusses the use of large language models (LLMs) like GPT-4 for knowledge management within organizations. It highlights the potential of these AI models to capture and disseminate proprietary knowledge, thereby enhancing company performance, learning, and innovation capabilities. The article outlines three primary approaches to incorporating proprietary content into a generative model: creating a domain-specific model from scratch, fine-tuning an existing LLM, and prompt tuning. It provides examples of companies like Bloomberg, Google, and Morgan Stanley that have employed these approaches. The article also discusses the challenges in implementing these approaches, including the need for high-quality data, substantial computing power, and data science expertise. It emphasizes the importance of managing generative AI content to ensure quality and avoid 'hallucinations' or incorrect facts. The article concludes by stressing the need for companies to develop a culture of transparency and accountability to manage potential risks of generative AI applications to knowledge management.starlaneai's full analysis
The use of AI models for knowledge management can potentially revolutionize the way organizations capture and disseminate proprietary knowledge. However, the implementation of these models requires substantial investment in data and computing resources, as well as technical expertise. Therefore, organizations need to carefully consider the potential benefits and challenges before adopting these models. Furthermore, the management of AI content to ensure quality is crucial to avoid 'hallucinations' or incorrect facts. As such, organizations need to develop a culture of transparency and accountability to manage potential risks. Despite these challenges, the potential benefits of these models in enhancing company performance, learning, and innovation capabilities make them a promising tool for knowledge management.
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starlaneai's Ratings & Analysis
Technical Advancement
75 The article discusses advanced AI models like GPT-4, indicating a high level of technical advancement.
Adoption Potential
60 The adoption potential is moderate as the implementation of these models requires high-quality data, substantial computing power, and data science expertise.
Public Impact
50 The public impact is moderate as these models are primarily used within organizations for knowledge management.
Innovation/Novelty
70 The use of AI models for knowledge management is a relatively novel concept, indicating a high level of novelty.
Article Accessibility
40 The accessibility is relatively low as the implementation of these models requires technical expertise.
Global Impact
55 The global impact is moderate as these models can be used by organizations worldwide.
Ethical Consideration
45 The article discusses the importance of managing AI content to ensure quality, indicating a moderate level of ethical consideration.
Collaboration Potential
65 The collaboration potential is high as the implementation of these models requires collaboration between data scientists, knowledge managers, and content creators.
Ripple Effect
60 The ripple effect is moderate as these models can potentially impact adjacent industries or sectors.
Investment Landscape
70 The AI investment landscape is high as these models require substantial investment in data and computing resources.