Improving Compute Sustainability: A Case Study
Two Sigma’s Sustainability Science team collaborated with internal engineering teams to find ways to make the company's computing resources more efficient.
Two Sigma’s Sustainability Science team collaborated with internal engineering teams to find ways to make the company's computing resources more efficient.
Our latest insights
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.
Two Sigma employees share their journeys from STEM backgrounds to the world of financial sciences.
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Improving Compute Sustainability: A Case Study
Two Sigma’s Sustainability Science team collaborated with internal engineering teams to find ways to make the company's computing resources more efficient.