AI and the Future of Work: Artificial Intelligence in the Workplace, Business, Ethics, HR, and IT for AI Enthusiasts, Leaders and Academics
Host Dan Turchin, PeopleReign CEO, explores how AI is changing the workplace. He interviews thought leaders and technologists from industry and academia who share their experiences and insights about artificial intelligence and what it means to be human in the era of AI-driven automation. Learn more about PeopleReign, the system of intelligence for IT and HR employee service: http://www.peoplereign.io.
AI and the Future of Work: Artificial Intelligence in the Workplace, Business, Ethics, HR, and IT for AI Enthusiasts, Leaders and Academics
314: Mike Schuster, Two Sigma AI Leader and Google Translate Pioneer, On AI in Finance, Data Challenges, Collaboration, and Future Trends
Dr. Mike Schuster is the head of the AI Core team at Two Sigma, where he leads engineers and quantitative researchers in advancing AI technologies across the firm's investment strategies and internal efficiencies. With over 25 years of expertise in machine learning and deep learning, Mike has been at the forefront of AI trends in tech and finance. Prior to Two Sigma, he spent 12 years at Google, contributing to transformative projects like Google Translate as part of the Google Brain team. Dr. Schuster holds a PhD in Electrical Engineering from the Nara Institute of Science and Technology in Japan and is recognized as a pioneer whose work has significantly shaped the AI landscape.
In this conversation, we discuss:
- The challenges and importance of building collaborative teams for complex AI systems in finance.
- Key differences between developing AI technologies in tech companies like Google versus finance firms like Two Sigma.
- The evolution of neural networks and their transformative impact on applications like Google Translate.
- The ethical considerations and risks of using AI in finance compared to other industries.
- Insights into data quality challenges and strategies for addressing bias in financial modeling.
- Predictions for the future of AI, focusing on efficiency, data quality, and practical advancements over the next five years.
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