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Eric Olson, CEO and co-founder of Consensus, discusses how to use LLMs to help researchers get better answers faster from evidence-based journals
Eric Olson, CEO and co-founder of Consensus, is a collegiate athlete turned data scientist turned entrepreneur who needed faster access to reliable data while working at DraftKings. Consensus is a search engine that uses a large language model to find answers in peer-reviewed research articles. Eric's living proof that the best entrepreneurs start by solving a problem they've encountered. Hear how Eric's scratching his own itch.
Listen and learn...
- Why Google isn't the answer for scientists seeking evidence-based answers online
- Why a business model that relies on ads can't solve the "unbiased answer" problem for researchers
- How Consensus addresses the problem of conflicting information online from credible resources
- How to use labels to improve search retrieval accuracy... without introducing bias into results
- How to use extractive large language models (LLMs), to extract relevant portions of documents and match them to NLP questions
- Why generative AI like GPT-3 can't answer "what's the consensus opinion out there" when multiple potential answers exist
- Who is responsible if Consensus delivers answers that lead to harmful outcomes
- What Eric learned as a division I NCAA athlete (Go Wildcats!) that has helped him as a high-tech entrepreneur
References in this episode:
- Elon Musk launches the Optimus bi-pedal robot at AI day
- Dan Grunfeld, Stanford athlete and Lightspeed partner, on AI and the Future of Work
- Consensus
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By Dan Turchin
4.9
2929 ratings
Eric Olson, CEO and co-founder of Consensus, is a collegiate athlete turned data scientist turned entrepreneur who needed faster access to reliable data while working at DraftKings. Consensus is a search engine that uses a large language model to find answers in peer-reviewed research articles. Eric's living proof that the best entrepreneurs start by solving a problem they've encountered. Hear how Eric's scratching his own itch.
Listen and learn...
- Why Google isn't the answer for scientists seeking evidence-based answers online
- Why a business model that relies on ads can't solve the "unbiased answer" problem for researchers
- How Consensus addresses the problem of conflicting information online from credible resources
- How to use labels to improve search retrieval accuracy... without introducing bias into results
- How to use extractive large language models (LLMs), to extract relevant portions of documents and match them to NLP questions
- Why generative AI like GPT-3 can't answer "what's the consensus opinion out there" when multiple potential answers exist
- Who is responsible if Consensus delivers answers that lead to harmful outcomes
- What Eric learned as a division I NCAA athlete (Go Wildcats!) that has helped him as a high-tech entrepreneur
References in this episode:
- Elon Musk launches the Optimus bi-pedal robot at AI day
- Dan Grunfeld, Stanford athlete and Lightspeed partner, on AI and the Future of Work
- Consensus
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