Creating the future of search and competing vs Google with Perplexity AI’s Aravind Srinivas | E1770 | Summary and Q&A

TL;DR

Perplexity AI is revolutionizing the search experience by combining language models with a curated index of web content to provide accurate and fast answers to user queries.

Key Insights

• 👨‍🔬 Perplexity AI's combination of language models and curated search index allows for faster, more accurate, and more tailored search results.
• 👨‍🔬 The company's focus on prompt engineering helps deliver highly relevant answers for different types of queries, enhancing the overall search experience.
• 👨‍🔬 Perplexity AI aims to compete with larger search engines by offering superior search capabilities, targeting speed, accuracy, and engagement.
• 🥹 The future integration of AI language models with chat interfaces and voice technologies holds the potential for seamless and interactive user experiences.
• 🤗 While closed AI organizations like OpenAI and Google dominate the AI landscape, the open-source community, led by players like Meta, keeps the possibility of open models and research alive.
• ❓ The effectiveness and efficiency of language models depend not only on the training data, but also on the curation and quality of the data used during the training process.

Transcript

your interviews with Brian chesky I learned a lot from those episodes actually me too also I liked his idea that like they were going to only ship the number of features he could keep in his brain and that his brain would be the maximum you know size of the canvas so if if I ca if one person can't keep all these changes in their brain let's put tho... Read More

Questions & Answers

Q: How does Perplexity AI generate citations for its search answers?

Perplexity AI pulls up relevant links from its curated search index and instructs the language models to extract relevant paragraphs, ensuring the answer is backed by reliable sources. Citations are provided for each part of the answer.

Q: How does Perplexity AI handle specific verticals of data, such as local businesses or discussions on Reddit?

Perplexity AI analyzes the user's query and selects the appropriate sources based on the category. For instance, if the query is about local businesses, it can fetch data from Yelp or Google Local to provide relevant search results.

Q: What is Perplexity AI's strategy for hiring talent in the competitive AI industry?

Perplexity AI focuses on hiring talented engineers who have a passion for AI but may not have prior experience in the field. They look for generalists who can handle different aspects of the product, such as back-end, front-end, and prompt engineering.

Q: How does Perplexity AI plan to monetize its search platform?

Perplexity AI offers a paid version called "Co-pilot" that provides advanced search capabilities, such as complex query handling and interactive research assistance. The company aims to generate revenue through advertising and targeting specific user profiles.

Summary

In this video, the founder and CEO of Perplexity AI, Arvind, discusses their AI-powered search engine and how it competes with other chat-based AI models like ChatGPT and Bart. Arvind explains the underlying technology and how they generate answers with relevant citations. He also touches on the potential for advertising integration and the importance of prompt engineering. Arvind shares insights on talent acquisition and his experience working at OpenAI and Google's DeepMind. He highlights the differences and similarities among different language models.

Questions & Answers

Q: How does Perplexity AI plan to compete with chat-based AI models like ChatGPT and Bart?

Perplexity AI focuses on revamping search by providing direct answers with citations. While ChatGPT and Bart have their own strengths, Perplexity AI prioritizes reliable and accurate answers by combining language models and a ranked index of relevant links. Additionally, Perplexity AI aims to improve the search experience by delivering fast and accurate results, setting itself apart from existing products.

Q: How does Perplexity AI generate citations for the answers?

Perplexity AI pulls up relevant links from multiple indexes and sources, both from their own index and other providers. They instruct the language models to read the content from these links and extract the relevant information, which is then combined to form the answer. The goal is to present the answer in a format that resembles how an academic or journalist would write it, with corresponding citations.

Q: How does Perplexity AI handle different verticals of data such as Reddit, Yelp, Google Local, etc.?

Perplexity AI uses prompt engineering to determine the category of a query and select the appropriate sources for generating the answer. By categorizing queries, they can provide more tailored results depending on the type of data required. For example, if the query is related to local businesses and restaurants, Perplexity AI can intercept the search and utilize sources like Yelp and Google Local to provide more accurate and relevant answers.

Q: What is the business model of Perplexity AI?

The business model of Perplexity AI is not explicitly mentioned, but there are discussions about advertising integration. The CEO believes that advertising can be effectively integrated into the chat interface, providing targeted and relevant ads to users. He suggests that advertisers can leverage the language models to create more personalized and engaging marketing copies, ultimately connecting the advertiser with the consumer in a more precise manner.

Q: How does Perplexity AI approach talent acquisition in a competitive market?

Rather than directly competing for top talent with larger organizations that have more resources, Perplexity AI focuses on hiring talented engineers who are passionate about AI but may not have prior experience in the field. They look for generalist programmers who can handle both back-end and front-end tasks, are quick learners, and are excited about being part of a growing company. By targeting this specific talent pool, Perplexity AI aims to build a strong team capable of playing the orchestra and making various components of their product work together effectively.

Q: How much overlap is there in the fundamental technology among different language models?

All language models, including Perplexity AI, ChatGPT, and Bart, are based on the Transformer architecture introduced by Google in 2017. They utilize generative pre-training with large amounts of data to train the models. The main difference lies in the efforts put into curating and selecting the training data. OpenAI, for example, has focused on curating high-quality data, which has resulted in models like ChatGPT being more advanced than other models. While there is a shared foundation, the specific training data and curation efforts play a crucial role in the performance and capabilities of each model.

Takeaways

Perplexity AI is an AI-powered search engine that aims to deliver accurate and reliable answers to user queries. They prioritize a fast and efficient search experience by combining language models with a ranked index of relevant links. The company focuses on hiring talented engineers who may not have prior AI experience but are enthusiastic about working on a growing product. The fundamental technology of language models like ChatGPT, Bart, and Perplexity AI is based on the Transformer architecture, but the specific training data and curation efforts can lead to variations in their capabilities. Perplexity AI sees the potential for integrating advertising into the chat interface, providing targeted and relevant ads to users while maintaining transparency and control over user data.

Summary & Key Takeaways

• Perplexity AI uses language models to reason through a vast amount of text and provide direct answers with citations, offering a more efficient search experience.

• The company focuses on prompt engineering to tailor responses for different types of queries, making the search results highly relevant and reliable.

• Perplexity AI is aiming to improve on existing search engines by providing faster, more accurate, and more engaging search experiences.