Elicit's Evolution from Forecasting to Research Assistant:

  • Elicit initially focused on geopolitical forecasting before pivoting to become an AI research assistant.
  • The transition was prompted by the realization that improving research workflows was more critical than probabilistic predictions.
  • The product evolved through iterations, incorporating features like structured data extraction and abstract summarization.
  • This evolution showcases how Elicit adapted its focus from predictive analytics to enhancing research processes, aligning with the need for efficient literature review tools in various domains.

Elicit's Approach to Structured Data Extraction:

  • Elicit aims to automate systematic reviews and meta-analysis processes in scientific literature review.
  • The platform provides concise summaries of papers based on user queries, emphasizing accuracy and relevance.
  • By breaking down complex tasks into smaller, structured components, Elicit enhances training and evaluation of AI systems for better reasoning outcomes.
  • This approach highlights Elicit's commitment to streamlining information retrieval and analysis in academic settings, catering to researchers' needs for comprehensive yet digestible insights.

Impact of GPT Models on Elicit's Functionality:

  • GPT models like GPT-3 and GPT-4 have enhanced Elicit's capabilities, enabling faster and more accurate text summarization.
  • These models facilitated improvements in cost efficiency, reducing expenses while maintaining high-quality outputs.
  • Continuous model evaluation ensures alignment with user needs and ongoing performance monitoring within the platform.
  • The integration of advanced language models has significantly boosted Elicit's ability to process vast amounts of text efficiently, showcasing a continuous drive towards innovation in AI-powered solutions.

Adapting to New AI Models Like Cloud Haiku:

  • Cloud Haiku is recognized for its balanced trade-off between cost-effectiveness and accuracy in NLP tasks.
  • While requiring some shot prompting for optimal results, it offers a favorable balance for users seeking efficient text processing solutions.
  • Exploration of multimodal models like Fuyu highlights the evolving landscape of AI tools available for diverse applications.
  • Embracing new AI advancements demonstrates Elicit's commitment to leveraging cutting-edge technologies to enhance its offerings and cater effectively to varying user requirements.

AI Research Process and Multimodality:

  • The workload distribution between closed-model lab work and open-source model fine-tuning is discussed, emphasizing that closed models consume more budget due to their intelligence in cases where existing open-source models fall short.
  • It is highlighted that while the number of queries may be similar, closed models dominate in terms of cost and compute resources.
  • The evolution of AI UX through computational notebooks is detailed, showcasing a journey from failed attempts in 2021 to embracing computational notebooks in 2023 for deep human-AI collaboration at scale.

Use Cases and Features of Elicit:

  • The ability to extract data from multiple papers simultaneously by adding columns to tables is presented as a powerful feature of Elicit, allowing users to customize questions, instructions, and create different column types like classification columns.
  • A specific use case involving medical affairs at a genomic sequencing company using Elicit to interpret test results for doctors at under-resourced hospitals is shared as an application scenario.

Future of AI Research and Models:

  • The goal for models to gradually catch up with expert humans before potentially surpassing them is outlined as a key objective.
  • Better world models are deemed essential for enabling models to make novel contributions by reasoning and making surprising connections beyond existing human conclusions.
  • Emphasis on systematic research aided by transparent processes in science advancement is stressed as crucial for future developments in AI research.

Company Transition and Hiring Plans:

  • Plans for team expansion with priority roles in engineering, design, product marketing, go-to-market functions are shared without additional details provided.
  • Specific requirements around streaming work back quickly influencing software engineer hiring criteria are mentioned without further elaboration.