-
Oct 8, 2024 | 
insight.factset.com | FactSet Insight |Yogendra Miraje |Yuri Malitsky |Lucy Tancredi
To drive business growth, data-management teams at financial institutions are working with substantially more data than in past years. There are material benefits, challenges, and costs that factor into the trend, making it a popular topic of discussion. The purpose of this article is to highlight the operational elements that data teams are sorting out. They need to simplify complexities and prioritize tradeoffs as they drive toward a single connected data pipeline from disconnected data silos.
-
Jun 17, 2024 | 
insight.factset.com | Lucy Tancredi |FactSet Insight |Yogendra Miraje |Ivo Kolarov
AI Strategies Series: 7 Ways to Overcome Hallucinations June 17, 2024 Hallucinations are perhaps the most important limitation of generative AI to stem. This article—the second in our six-part series... By Lucy Tancredi | Data Science and AI Read more June 17, 2024 AI Strategies Series: 7 Ways to Overcome Hallucinations Hallucinations are perhaps the most important limitation of generative AI to stem.
-
Apr 29, 2024 | 
insight.factset.com | FactSet Insight |Yogendra Miraje |Lucy Tancredi
Data Science and AI By FactSet Insight | April 29, 2024 It's the double whammy of peak earnings season: How to be everywhere at once (earnings calls) and how to more quickly analyze this quarter's information (transcripts). Transcript Assistant, powered by FactSet Mercury, both summarizes key elements in earnings transcripts and enables you to search them by typing your own questions (not just a keyword search).
-
Apr 25, 2024 | 
insight.factset.com | FactSet Insight |Yogendra Miraje |Lucy Tancredi
It's the double whammy of peak earnings season: How to be everywhere at once (earnings calls) and how to more quickly analyze this quarter's information (transcripts). Transcript Assistant, powered by FactSet Mercury, both summarizes key elements in earnings transcripts and enables you to search them by typing your own questions (not just a keyword search). It also visualizes call sentiments and provides summaries of the management/analyst Q&A discussions.
-
Mar 12, 2024 | 
insight.factset.com | FactSet Insight |Lucy Tancredi |Ivo Kolarov
Amid the hundreds of earnings calls and transcripts every quarter, our clients know there are valuable investment insights to be discovered. To significantly accelerate the process for them, we released Transcript Assistant, our first interactive generative AI solution available to all firm types in the FactSet Workstation. Transcript Assistant currently has approximately 4,000 active users.
-
Mar 6, 2024 | 
insight.factset.com | Lucy Tancredi |Ivo Kolarov |FactSet Insight
In this final installment of our six-part series on overcoming generative AI challenges, we share observations and perspective on the legal and ethical landscape of this evolving technology. Both the creators and users of Large Language Models can face legal and reputational consequences from LLM hallucinations. In one example, a model made up a false sexual harassment case against a real professor and cited a seemingly credible—but nonexistent—article from the Washington Post.
-
Mar 5, 2024 | 
insight.factset.com | Lucy Tancredi |FactSet Insight |Ivo Kolarov
Given investors’ question throughout last year of whether the US would achieve a soft landing, we used machine learning to analyze the risk on a historical basis. We also discuss the key insights from that time period and highlight 2024.
-
Feb 28, 2024 | 
insight.factset.com | Lucy Tancredi |FactSet Insight
To date our series on overcoming generative AI challenges has described how LLMs generate answers: why they hallucinate, lack explainability, and may produce inconsistent or outdated responses. Shifting gears from the quality of responses, we now discuss security and data privacy. Some firms have created generative AI usage policy to protect corporate data, while others have banned employee use altogether.
-
Feb 27, 2024 | 
insight.factset.com | FactSet Insight |Lucy Tancredi |Yogendra Miraje
We sat down with Seth Stephens-Davidowitz to discuss his research and perspectives on data ahead of his May 1 keynote address at the FactSet FOCUS event in Miami. Seth has an expansive view into the role and power of data. He earned a PhD in Economics from Harvard and has worked as a data scientist at Google, a visiting lecturer at the Wharton School of the University of Pennsylvania, and a contributing op-ed writer for the New York Times.
-
Feb 21, 2024 | 
insight.factset.com | Lucy Tancredi |Yogendra Miraje
So far in our AI strategies series we’ve discussed LLM capabilities and limitations, hallucinations, and lack of explainability. Now let’s turn to part four, a discussion of why LLMs provide inconsistent responses to the same prompt and generate outdated answers. Inconsistent responses to the exact same prompt happen because Large Language Models are intentionally built with some allowance for variability. In a previous installment of this series, we looked at an ”.
