Applied AI Research Team
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An Applied AI Research Team is a AI research team that is an applied research team (conducts applied research to develop practical AI solutions for real-world problems).
- Context:
- It can translate Theoretical AI Advances into practical implementations through research application.
- It can develop AI Solutions for specific domains through applied research methodology.
- It can validate AI Systems in real-world environments through empirical testing.
- It can bridge research gaps between theoretical advances and practical needs.
- It can optimize AI Models for specific use cases through iterative refinement.
- ...
- It can often collaborate with domain experts to understand practical requirements.
- It can often iterate on prototypes based on user feedback.
- It can often publish research findings in applied research journals.
- It can often develop proof of concepts for industry partners.
- ...
- It can range from being a Small Specialized Team to being a Large Multi-Domain Group, depending on its research scope.
- It can range from being a Single Domain Focused Team to being a Cross Domain Research Group, depending on its application area.
- ...
- It can maintain research partnerships with industry collaborators.
- It can provide technical consultations to implementation teams.
- It can support knowledge transfer to development teams.
- ...
- Examples:
- Industry Research Teams, such as:
- Corporate AI Labs, such as:
- Industry-Specific Teams, such as:
- Academic-Industry Partnerships, such as:
- ...
- Industry Research Teams, such as:
- Counter-Examples:
- AI System Development Teams, which focus on product development rather than research application.
- Pure AI Research Teams, which focus on theoretical advancements rather than practical implementation.
- AI Implementation Teams, which focus on deployment rather than research and development.
- See: Research Team, AI Research, Applied Research, AI Development, Research Management.
References
2022
- (Subramonyam et al., 2022) ⇒ Hariharan Subramonyam, Jane Im, Colleen Seifert, and Eytan Adar. (2022). “Solving Separation-of-Concerns Problems in Collaborative Design of Human-AI Systems through Leaky Abstractions.” In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. ISBN:9781450391573 doi:10.1145/3491102.3517537
- NOTES:
- Applied AI Research Team: A core research team within an organization that focuses on advancing AI capabilities with potential commercial applications. According to the paper, these teams typically sit between pure AI research and product teams, exploring AI innovations that could eventually be integrated into products, though often still disconnected from immediate user needs. In large organizations, they're staffed by computer scientists and ML researchers who work on adapting and advancing AI technology while considering potential product applications, though they may still prioritize technical advancement over user experience. This team structure is distinct from both academic AI research teams (who focus on theoretical advances) and AI product teams (who implement established AI capabilities in products).
- AI Engineer: A technical professional who implements AI systems, focusing on model development, model training, and model optimization. The paper shows they often face challenges in translating UX requirements into technical specifications and may lack understanding of how their technical decisions impact the user experience. They typically work with data points and performance metrics rather than user needs.
- AI-First Workflow: An approach to product development where AI capabilities are developed before considering user experience design. The paper critiques this workflow, showing how it often leads to solutions that don't align with user needs. In this approach, AI technology drives the product direction rather than human needs shaping the AI development.
- NOTES: