Biggest Challenges for a Gen AI Scientist at a Property Software Company
Gagandeep's greatest challenge as a Gen AI Scientist involves the "rapid pace of change in AI," requiring constant learning and adaptation, as seen in their recent transition to large language models like GPT 3.5. This is further complicated by managing stakeholder expectations, addressing AI limitations and risks like bias, and maintaining team motivation and productivity amidst rapid technological advancements.
Artificial Intelligence, Machine Learning, Project Management, Team Leadership, Staying Current in Tech
Advizer Information
Name
Job Title
Company
Undergrad
Grad Programs
Majors
Industries
Job Functions
Traits
Gagandeep Singh
Gen AI Scientist
Property Software Company
Uttar Pradesh Technical University
Arizona State University (ASU) - W. P. Carey
Computer Science
Real Estate, Technology
Data and Analytics
None Applicable
Video Highlights
1. The rapidly evolving nature of AI and machine learning requires constant learning and adaptation to new models, techniques, and best practices.
2. Balancing innovation with practical implementation is crucial; exploring cutting-edge technologies while ensuring stable, production-ready solutions is a key challenge.
3. Managing stakeholder expectations and addressing the limitations and potential risks of AI systems, such as bias and hallucinations, is vital. Additionally, maintaining team motivation and productivity while fostering continuous learning presents a significant managerial challenge.
Transcript
What is your biggest challenge in your current role?
The biggest challenge in my role is multifaceted. Firstly, keeping up with the rapid pace of change in AI and machine learning is a constant struggle. New models, techniques, and best practices emerge almost daily, and it's crucial to stay updated while also delivering on our current projects.
For instance, transitioning from traditional NLP models to using large language models like GPT 3.5 required a significant pivot in our approach. Rapid learning and balancing innovation with practical implementation can be tricky. We need to explore cutting-edge technologies while ensuring we're delivering stable and production-ready solutions.
Another major challenge is managing stakeholder expectations. Many might not fully understand the complexities of AI development. Explaining the limitations and potential risks of AI systems, especially regarding bias or hallucinations in large language models, can be difficult but necessary.
Lastly, as a manager, I face the challenge of keeping my team motivated and up to date while also meeting project deadlines. Encouraging continuous learning while maintaining productivity is a delicate balance.
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