Claudia Perlich, Head of Strategic Data Science for Two Sigma Investment Management, explains why, despite a lot of hype, machine learning models can only be as good as the skills and intuition of the humans who create them.
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Claudia Perlich, Head of Strategic Data Science for Two Sigma Investment Management, explains why, despite a lot of hype, machine learning models can only be as good as the skills and intuition of the humans who create them.
The authors map the landscape of frameworks for abstracting interactions with and between large language models, and suggest two systems of organization for reasoning about the various approaches to, and philosophies of, LLM abstraction.
Systematic and discretionary macro funds have had similar risk-adjusted returns on average, and intuitively have different relative advantages. This analysis demonstrates that over the past ~15 years, a blend of the two delivered a higher Sharpe ratio than either approach alone.
Two Sigma Co-Chairman David Siegel speaks about the significant potential—and considerable hype—surrounding large language models like GPT-4 and other innovations in artificial intelligence.
Mae Santos, Head of Critical Applications and Data Reliability, offers insights on leadership, career growth, and managing critical systems—along with where to find some of the best melonpan in Tokyo.
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Rachael Weiss Riley at SXSW ’24: Climate Change and Public Transit
Two Sigma’s Director of Sustainability Science and Data Clinic spoke at South by Southwest 2024 on climate change's impact on transit systems—especially in vulnerable communities.