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DataFramed

#216 Perplexity & the Future of AI with Denis Yarats, Co-Founder and CTO at Perplexity AI

Mon Jun 17 2024
AI-powered search engineorganization buildingAI engineering skillssearch engine market differentiationfuture of search enginesgenerative AI products

Description

This episode covers the development of Perplexity, an AI-powered search engine that aims to improve upon Google's search capabilities. It explores the challenges of building a successful organization, the skills required for AI engineers, the market differentiation of Perplexity, and the future of search engines and AI models. Additionally, it delves into the importance of generative AI products and design.

Insights

AI-powered Search Engine

Perplexity is an AI-powered search engine focused on providing fast and accurate information. It ensures veracity and citations for all its search results, aiming to combat misinformation.

Building a Successful Organization

The organization focuses on solving small pieces of a monumental problem with high quality and depth rather than tackling many things at once. Building a team of people who care deeply about quality, infrastructure, speed, and providing instant high-quality answers is crucial for success.

Skills for AI Engineers

Successful AI professionals are proficient in both research and engineering, emphasizing intellect and adaptability. Key technical skills for aspiring AI engineers include proficiency in Python, curiosity, desire to learn, proactiveness, and ability to implement ideas quickly.

Perplexity Search Engine

Perplexity aims to provide a different market segment compared to Google, focusing on faster and more direct answers without the need to sift through multiple links. There is potential for agentic use cases in search engines, where AI can provide interactive lessons or perform tasks like booking flights.

Future of Search Engines and AI Models

Technology is evolving towards new search engines that support different use cases and have higher intent relevance. Disruption in the business model of search due to AI could have significant effects on publishers and services.

Generative AI Products and Design

Building a generative AI product involves not only intelligence but also excellent UI/UX design. Having an intuitive and fast UI is crucial for a pleasant user experience with AI products.

Chapters

  1. Improving Google Search Engine
  2. Building a Successful Organization
  3. Skills for AI Engineers
  4. Perplexity Search Engine and Market Differentiation
  5. Future of Search Engines and AI Models
  6. Generative AI Products and Design
Summary
Transcript

Improving Google Search Engine

00:00 - 06:56

  • Google search engine is highly sophisticated but can be improved to save time and provide more efficient answers.
  • Perplexity is an AI-powered search engine focused on providing fast and accurate information.
  • Perplexity ensures veracity and citations for all its search results, aiming to combat misinformation.
  • Building Perplexity involved a focus on simplicity, quality, unbiased information, self-verification of answers, and continuous learning from mistakes.
  • Perplexity differentiates itself by prioritizing simplicity, ease of use, intuitive design, and high-quality search results.

Building a Successful Organization

06:39 - 13:25

  • The organization focuses on solving small pieces of a monumental problem with high quality and depth rather than tackling many things at once.
  • Building a team of people who care deeply about quality, infrastructure, speed, and providing instant high-quality answers is crucial for success.
  • The organization emphasizes hiring top talent by conducting trial periods to ensure alignment with the mission and evaluating capabilities quickly.
  • Maintaining momentum and velocity is key to success in a fast-moving environment, as slowing down can hinder progress.
  • Competing for talent with larger companies like Google and Meta poses challenges due to resource differences, but the organization relies on its unique selling points to attract top talent.

Skills for AI Engineers

13:01 - 19:40

  • Companies with more resources can afford to pay top talent, making it challenging for others to compete.
  • Strategy involves identifying individuals with potential and a desire to learn, even if they are not already established in the field.
  • Successful model involves teaching strong engineering folks AI skills and upskilling them quickly.
  • Best AI professionals are proficient in both research and engineering, emphasizing intellect and adaptability.
  • Key technical skills for aspiring AI engineers include proficiency in Python, curiosity, desire to learn, proactiveness, and ability to implement ideas quickly.
  • Being unafraid of tackling seemingly impossible tasks and maintaining a flexible mindset is crucial for innovation and success in AI.

Perplexity Search Engine and Market Differentiation

19:12 - 25:50

  • The speaker transitioned from being an engineer at Bing to joining Facebook AI research and pursuing a PhD.
  • The company has a flat organizational structure with no engineering managers, which has led to efficient work processes and exciting projects.
  • Perplexity aims to provide a different market segment compared to Google, focusing on faster and more direct answers without the need to sift through multiple links.
  • There is potential for agentic use cases in search engines, where AI can provide interactive lessons or perform tasks like booking flights.

Future of Search Engines and AI Models

25:34 - 31:54

  • Technology is evolving towards new search engines that support different use cases and have higher intent relevance.
  • Disruption in the business model of search due to AI could have significant effects on publishers and services.
  • Open source AI models offer benefits like a larger community for bug discovery and inference optimization.
  • Future advancements in language models may rely heavily on synthetic data generation for performance improvements.

Generative AI Products and Design

31:31 - 35:53

  • Synthetic data generation is becoming a significant cost in pre-training models.
  • Building a generative AI product involves not only intelligence but also excellent UI/UX design.
  • Having an intuitive and fast UI is crucial for a pleasant user experience with AI products.
  • Good design can lead to better responses from language models without the need for prompt engineering.
  • Collaboration between product teams and IT is essential to create delightful AI products.
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