Daniel Fried is a computer scientist and entrepreneur specializing in Artificial intelligence and Machine learning. He is best known as a co-founder of Adept AI, a company focused on building AI agents that can automate software tasks. Before founding Adept, Fried worked as a research scientist at Google Brain, where he contributed to research on large language models and their applications.
Fried's research interests include neural networks, Deep learning, and the development of AI systems that can interact with digital environments. His work has been published in major conferences and journals, and he is recognized for his contributions to the field of generative AI.
Early Life and Education
Fried received his PhD in computer science from Carnegie Mellon University, where he worked under the supervision of Alexei Efros and others. His doctoral research focused on grounded language understanding and learning from human feedback. He also holds a bachelor's degree from the University of Toronto, where he studied computer science and mathematics.
Career at Google Brain
After completing his PhD, Fried joined Google Brain as a research scientist. At Google Brain, he worked on projects involving transformers and sequence-to-sequence models. He contributed to research on improving the efficiency and reliability of large language models, including work on model pruning and data augmentation. His research helped advance the understanding of how these models can be trained and deployed effectively.
Co-founding Adept AI
In 2022, Fried co-founded Adept AI with several colleagues, including Ashish Kumar and others. The company aims to build AI systems that can perform complex tasks in software environments, such as navigating web interfaces and using tools like spreadsheets. Adept's technology leverages large language models and reinforcement learning to create agents that can follow natural language instructions. The company has raised significant funding and has been recognized as a leader in the emerging field of AI agents.
Research Contributions
Fried has published numerous papers on topics such as multi-head attention, cross-attention, and positional encoding. His work on reinforcement learning from AI feedback has been influential in improving the alignment of AI systems with human intentions. He has also explored curriculum learning and learning rate schedules to enhance training efficiency. His research has been cited widely and has contributed to the development of more capable and robust AI systems.
Recognition and Impact
Fried's contributions to Artificial intelligence have been recognized through invitations to speak at major conferences and workshops. He is considered a thought leader in the area of AI agents and their potential to transform productivity. His work at Adept AI continues to push the boundaries of what is possible with generative AI, and he remains an active contributor to the research community.
Personal Life
Fried is based in the San Francisco Bay Area, where Adept AI is headquartered. He is known for his collaborative approach and his commitment to advancing the field of AI in a responsible manner. Outside of work, he enjoys hiking and exploring new technologies.