The podcast discusses the challenges in AI research and product development, democratizing information access, the 'innovator's dilemma' for Google Search, conversational interfaces, and interests shaping AI regulation debate.
AI as a tool for democratizing information access.
The importance of user-centric design in AI research and product development.
Deep dives
Perplexity.ai: A User-Friendly Question Query Engine
Perplexity.ai is an AI-powered question query engine that provides accurate and helpful answers to complex queries. Unlike Google, which bombards users with numerous search results and incentives to click on links, Perplexity.ai aims to provide concise and accurate answers quickly. Users can ask any type of question, from finding a budget-friendly engagement ring in a specific location to searching for niche products like shelf lights. The engine combines a knowledge engine and a reasoning engine, using large language models to provide conversational, accurate responses. Perplexity.ai continuously optimizes its models to improve answer quality and focuses on user satisfaction over model benchmarks.
Iterative Approach and User-Centric Focus
Perplexity.ai has adopted an iteration culture inspired by companies like OpenAI, allowing rapid improvements and iterations while focusing on user satisfaction. The team balances the pursuit of perfection with the urgency of delivering a usable and reliable product. They prioritize user feedback, continually test different models, and run A/B tests to measure answer quality, retention rates, and user satisfaction. By aligning user interests with shareholder interests and avoiding advertising revenue, Perplexity.ai maintains a user-centric approach that aims to provide the best possible answer engine experience.
Challenges and Future Growth of Perplexity.ai
Building a product with large language models (LLMs) presents unique challenges due to the statistical nature of LLM outputs and the jagged frontier of capability. Perplexity.ai is overcoming these challenges by continuously optimizing individual latency, improving LLM models and search indexes, and effectively orchestrating these components together. While benchmarks are important, Perplexity.ai believes in focusing on cracking multiple benchmarks simultaneously, ensuring the product's reliability, accuracy, and conversational capabilities. The company aims to make LLM-powered answer engines faster, more accurate, and cost-effective in the future, aligning with their guiding philosophy of user-centricity and delivering exceptional user experiences.
Artificial Intelligence is on every business leader’s agenda. How do we make sense of the fast-moving new developments in AI over the past year? In new episodes released throughout December and January, Azeem Azhar returns to bring clarity to leaders who face a complicated information landscape.
This week, Azeem speaks with Aravind Srinivas, the co-founder and CEO of Perplexity.ai, about the looming challenges in AI research and product development, such as user-centric design and the importance of open-source models.
They discuss:
AI as a tool for democratizing information access.
The “innovator’s dilemma” for Google Search.
Whether or not conversational interfaces will become the norm for how we interact with AI.
The array of interests shaping the AI regulation debate.