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Natasha Jaques
Incoming faculty at University of Washington, Senior Research Scientist at Google
Natasha Jaques is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where she began her position in January 2023. Her research primarily focuses on social reinforcement learning, multi-agent systems, and human-AI interaction, with the goal of improving how AI agents learn and collaborate through social learning techniques.12
Education and Career
- Ph.D. in Media Arts and Sciences from the Massachusetts Institute of Technology (MIT) in 2019.
- M.Sc. in Computer Science from the University of British Columbia in 2014.
- B.Sc. in Computer Science and B.A. in Psychology from the University of Regina, both completed in 2012.
Before joining the University of Washington, Jaques was a Senior Research Scientist at Google Brain, where she explored the benefits of social learning in AI agents. She has also interned at DeepMind and Google Brain, and has experience mentoring as an OpenAI Scholars mentor.123
Research Contributions
Jaques's work has received recognition, including awards for Best Demo at NeurIPS and Best Paper at workshops focusing on machine learning applications in healthcare. Her research has been featured in prominent publications such as Science Magazine and MIT Technology Review. She is particularly interested in developing algorithms that enhance AI learning and generalization, focusing on how AI can learn from human interactions and from each other.123
Publications and Impact
Jaques has a significant number of citations for her work, indicating a strong influence in her field, particularly in areas related to reinforcement learning and AI collaboration. She actively publishes her research findings and contributes to the academic community through her work and mentorship.45
For more detailed information about her research and publications, you can visit her professional website at natashajaques.ai or her LinkedIn profile here .23
Highlights
Excited to be speaking at the Montreal IVADO Bootcamp dedicated to Autonomous Agents, where I will give a remote talk on Multi-agent Reinforcement Learning for LLMs on August 15th at 11am ET.
Full schedule and registration: https://t.co/28Hue8zKK7
In our latest paper, we discovered a surprising result: training LLMs with self-play reinforcement learning on zero-sum games (like poker) significantly improves performance on math and reasoning benchmarks, zero-shot. Whaaat?
How does this work? We analyze the results and find that LLMs learn emergent reasoning patterns like case-by-case analysis and expected value calculation that transfer to improve performance on math questions.
This work shows the benefit of RL training for improving reasoning skills when there is no possibility for data leakage. AND how continuously evolving multi-agent competition leads to the development of emergent skills that generalize to novel tasks. Read more below!
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Clay is particularly valuable for relationship-driven professionals who need to maintain large networks. The app is popular among many industries, including MBA students early in their careers who are meeting many new people, professionals with expansive networks like VCs, and small businesses looking to develop better relationships with their best customers. Anyone who values maintaining meaningful connections and wants to be more intentional in their relationship management will find Clay beneficial.
Clay offers competitive pricing in the personal CRM market. Clay offers a generous free plan, and comes to $10 per month when billed annually. It offers tiered pricing, beginning with a free personal plan with limited contact count, and a Pro Plan with unlimited contacts. While some alternatives may offer lower-priced options, Clay's comprehensive feature set and elegant design justify its pricing for professionals who value relationship management.
Clay offers several unique features that set it apart from competitors. Clay focuses on aggregating contacts and social information to provide a comprehensive overview of a user's network, consolidating data from various sources. Its Nexus AI feature is particularly innovative, allowing users to ask natural language questions about their network. Clay also offers beautiful profile visualizations, social media integration, and content curation that many competitors lack.
Yes. Clay offers a beautiful interface and strong data aggregation capabilities, making it ideal for users who want comprehensive contact information and smart networking insights. Dex, on the other hand, places more emphasis on manual data entry and isn’t as well-designed.
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