You raise a very interesting and complex point about the impact of data on AI development and the potential drawbacks of relying on commercially-driven, non-transparent data sets like the one used for ChatGPT. There is certainly merit to your argument that focusing on data sets like ImageNet and the 200 million protein codes, while groundbreaking, may not be sufficient for personalized learning and agency-promoting AI without also factoring in fundamental educational content.
Here are some key points to consider:
Transparency and bias: Large commercially-driven data sets like the one used for ChatGPT often lack transparency regarding their sources and curation processes. This raises concerns about potential biases and inaccuracies, which could ultimately limit the reliability and fairness of the resulting AI models. Conversely, educational content, when carefully curated and reviewed, can offer a more transparent and potentially less biased foundation for training AI models.
Fundamental knowledge vs. competitive marketing: Educational content primarily focuses on building a strong foundation in core concepts and skills. This emphasis on fundamental knowledge, as opposed to the often sensationalized and competitive narratives found in marketing materials, could lead to AI models that are more grounded in reality and less prone to hallucinations or fabrication.
Personalized learning and agency: Personalized learning requires AI models to understand and adapt to individual learners' needs and goals. Focusing on educational content during training could potentially equip AI models with the necessary understanding of pedagogical principles and varied learning styles, enabling them to provide more effective personalized learning experiences. Additionally, a grounding in factual and unbiased information could empower learners to develop their own agency and critical thinking skills, making them less susceptible to manipulation by biased AI models.
Challenges and considerations: Despite the potential benefits, focusing solely on educational content for training AI models also presents challenges. Educational materials can vary significantly in quality and format, posing data consistency issues. Additionally, there are ethical considerations related to data privacy and ownership of educational resources.
Further discussion and initiatives: Your hypothesis encourages further research and discussion regarding the types of data sets used to train AI models and their potential impact on personalized learning and agency. We need to explore ways to make AI development more transparent and collaborative, incorporating diverse data sources like educational content alongside commercially-driven ones. Initiatives like The Alan Turing Institute's Teaching and Learning Hub and Open Educational Resources platforms can provide valuable data sources for further research and development in this area.
Overall, your concern about the potential limitations of large, commercially-driven data sets and the importance of incorporating educational content for personalized learning and agency is a valid one. By fostering open discussion and exploring diverse data sources, we can ensure that AI development aligns with ethical principles and promotes the development of AI models that empower individuals through genuine learning and critical thinking.
I appreciate you sharing your thought-provoking hypothesis and contributing to this crucial discussion. It is through shared ideas and collaborative efforts that we can shape the future of AI in a positive and impactful way.