This engaging panel discussion delves into the critical differences between proprietary and public data, emphasising the distinct advantages and disadvantages associated with each. Explore how the accessibility and vast quantities of public data facilitate robust generalisation within AI models, contrasting with the nuanced strengths of proprietary data.
Public data's accessibility and abundance offer significant advantages, enabling broad generalisation within AI models. Conversely, proprietary data boasts higher quality, enhanced control, and minimal risk of contamination, catering specifically to niche topics with detailed coverage.
Delve into the advantages of public data, its scalability, and the challenges it poses, juxtaposed against the precise and controlled nature of proprietary data. Gain valuable insights into navigating the trade-offs between the two, understanding their impacts on model performance, ethical and regulatory considerations, and innovation within the realm of AI.
Tom Kersten
Tom is a distinguished R&D Engineer specialising in AI within the aerospace sector. Armed with a background in computer science and AI, Tom possesses a comprehensive understanding of AI systems. Within his company, he stands out as a leading visionary delving into the integration of generative AI in space, in particular to support the efforts of the Dutch government and its military in this domain. His pioneering work involves exploring and harnessing the potential of GenAI models to revolutionise satellite operations, mission planning, earth observation and space exploration. Tom's dedication to pushing the boundaries of AI in aerospace extends to leveraging generative AI's capabilities, envisaging transformative applications that could redefine the landscape of space technology.