#434 – Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet
In this episode of the Lex Fridman Podcast, CEO Aravind Srinivas discusses Perplexity's AI technology and approach. Perplexity combines search with large language models to provide answers with citations. Srinivas explains Perplexity's goal of becoming a "knowledge-centric" platform, guiding users towards discovery through features like "Discover" and collaborative article creation using AI.
The conversation covers the future of search and knowledge dissemination, exploring how AI assistants will enable new interactive, exploratory knowledge paradigms tailored to users' expertise. Srinivas also shares entrepreneurship advice, emphasizing pursuing genuine passion and surrounding oneself with driven, dedicated people.
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1-Page Summary
Perplexity's technology and approach
Perplexity combines search and large language models (LLMs) to provide answers with citations
Perplexity uses a "retrieval augmented generation" (RAG) approach, according to Aravind Srinivas. It retrieves relevant documents and paragraphs to inform the LLM's generation of the final answer with citations.
Perplexity's indexing system crawls and processes web content to enable this retrieval and ranking. It allows users to choose different LLMs, including Perplexity's own "Sonar" model optimized for factors like speed and accuracy.
Perplexity aims to be a "knowledge-centric" platform
Srinivas states Perplexity's goal is to be the world's most knowledge-centric company, guiding users towards discovery rather than simple answers. Its "Discover" feature surfaces new information based on interests, while "Pages" enables collaborative article creation leveraging AI.
The future of search and knowledge dissemination
AI will power new knowledge discovery paradigms
Srinivas foresees a shift from traditional search towards interactive, exploratory AI experiences. He cites large language models' dialogue abilities enabling new paradigms for knowledge discovery and dissemination.
AI assistants will tailor information to users' expertise. However, advancements in areas like indexing, ranking, and reasoning are still needed for efficient knowledge retrieval from vast data, he notes.
Entrepreneurship and startup advice
Follow your passion and work hard
Aravind stresses pursuing genuine passion rather than market trends when starting a company. He advises surrounding oneself with driven, dedicated people, especially when young.
He highlights entrepreneurship's challenges but potential for fulfillment and impact. Principles from leaders like Page, Bezos, and Musk guide his vision.
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Clarifications
- The "retrieval augmented generation" (RAG) approach combines search with large language models (LLMs). It involves retrieving relevant documents to inform the generation of final answers by the language model. This method enhances the accuracy and relevance of the information provided by leveraging both retrieval and generation techniques. The RAG approach is a strategy used by Perplexity to improve the quality of answers with citations.
- A large language model (LLM) is a sophisticated computational model that excels in tasks like language generation and natural language processing. LLMs learn patterns from vast text data during training to perform tasks like text generation and classification. They are based on transformer architecture, with the most advanced models using a decoder-only transformer design for efficient text processing. Notable examples include OpenAI's GPT series, Google's Gemini, Meta's LLaMA models, Anthropic's Claude models, and Mistral AI's models.
- The "Sonar" model mentioned in the text is a specific large language model (LLM) developed by Perplexity. It is optimized for factors like speed and accuracy in generating answers with citations. The model is part of Perplexity's suite of LLMs that users can choose from for their search and information retrieval needs.
- AI powering new knowledge discovery paradigms means that artificial intelligence technologies, like large language models, are transforming how we find and understand information. This shift involves moving away from traditional search methods towards more interactive and exploratory experiences driven by AI. These advancements enable AI to assist users in discovering and interpreting knowledge in more personalized and efficient ways, potentially revolutionizing how we access and utilize information.
- Large language models' dialogue abilities refer to the capability of advanced AI systems to engage in conversations with users in a natural and human-like manner. These models can understand context, generate responses, and maintain coherent dialogues, enabling interactive and dynamic exchanges of information. They are designed to simulate human conversation patterns, allowing for more engaging and personalized interactions between users and AI systems. This functionality is crucial for creating interactive and exploratory AI experiences, as mentioned in the text.
Counterarguments
- While Perplexity's RAG approach is innovative, it may still face challenges in discerning the reliability and accuracy of sources, potentially leading to the dissemination of misinformation.
- The efficiency of Perplexity's indexing system may be limited by the dynamic nature of the web, where content changes rapidly, and some valuable content may be missed or not indexed in a timely manner.
- Offering different LLMs, including the "Sonar" model, is beneficial, but it may also overwhelm users with choices, leading to a paradox of choice where it becomes difficult to select the best tool for their needs.
- Aiming to be a "knowledge-centric" platform is a noble goal, but it may not align with the commercial realities of running a business where profitability and user engagement metrics often take precedence.
- The "Discover" feature's effectiveness will depend on the algorithm's ability to accurately understand and cater to user interests, which can be complex and multifaceted.
- Collaborative article creation with AI, such as the "Pages" feature, could lead to concerns about the authenticity of content and the potential for AI to inadvertently perpetuate biases present in the training data.
- The shift towards AI-powered knowledge discovery paradigms assumes users are comfortable with and adept at interacting with AI, which may not be the case for all segments of the population.
- Tailoring information to users' expertise through AI assistants is a complex task that requires understanding context and nuance, which AI may not always be capable of.
- Advancements in indexing, ranking, and reasoning are necessary, but there are also concerns about privacy and data security that need to be addressed as these technologies become more pervasive.
- Pursuing genuine passion is important, but it should be balanced with market viability to ensure the sustainability of the startup.
- The advice to surround oneself with driven, dedicated people is sound, but it's also important to ensure diversity of thought and experience to avoid echo chambers and groupthink.
- While entrepreneurship can be fulfilling, it's not the only path to impact and fulfillment, and it may not be suitable for everyone due to the high risk and uncertainty involved.
- Following principles from leaders like Page, Bezos, and Musk can be inspiring, but it's also important to recognize that their paths and methods may not be replicable or suitable for all entrepreneurs or business contexts.
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Perplexity's technology and approach
Perplexity is revolutionizing how people access information by combining search technology with large language models (LLMs) to provide answers with citations, aiming to deliver a user experience that feels intuitive and trustworthy.
Perplexity combines search and large language models (LLMs) to provide answers with citations to human-created sources
Aravind Srinivas shares that Perplexity's approach to an answer engine is inspired by academic rigor, where like in scholarly papers, all assertions should be supported with citations. This principle is mirrored in Perplexity's use of a "retrieval augmented generation" (RAG) model that retrieves relevant documents to inform the responses generated by the LLM.
Perplexity uses a "retrieval augmented generation" (RAG) approach, which retrieves relevant documents and paragraphs to inform the LLM's generation of the final answer
Srinivas elucidates that Perplexity's RAG framework, when given a query, always retrieves relevant documents and paragraphs and then utilizes these resources to construct well-sourced answers. The implication being, if there isn't enough solid information retrieved, Perplexity should admit the lack of sufficient data to provide a reliable response. This retrieval process ensures the grounding of AI-generated text in factual information.
Perplexity's indexing and crawling system processes web content to enable querying and retrieval
Perplexity's engine functions on an indexing and crawling system similar to Google but with its distinct ranking signals such as a citation graph, differing from Google's click-based model. The system, which involves a crawling bot named PerplexiBot, adheres to robots.txt protocols, rendering web pages often composed of JavaScript and HTML. Post-processing these raw contents into an index involves machine learning and text extraction techniques to convert the content into data useful for the ranking system. The retrieved results then feed into the LLM for the final answer generation.
Perplexity's process is meticulous, with decisions made about what and when to crawl, while dealing with the complexities of JavaScript rendering and ensuring that the raw content is updated, detailed, and fresh.
Perplexity allows users to select different language models, including their own proprietary "Sonar" model, to optimize for speed, accuracy, and other factors
Perplexity not only focuses on collecting and ...
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Perplexity's technology and approach
Additional Materials
Clarifications
- A large language model (LLM) is a sophisticated computational model that excels at tasks like language generation and natural language processing. LLMs learn patterns from vast amounts of text data during training to perform tasks like text generation and classification. They are based on transformer architecture, which allows for efficient processing and generation of text data. Notable examples include OpenAI's GPT series, Google's Gemini, Meta's LLaMA models, Anthropic's Claude models, and Mistral AI's models.
- The "Retrieval Augmented Generation" (RAG) model is a framework that combines information retrieval with language generation in AI systems. It retrieves relevant documents to inform the responses generated by the language model, ensuring that the generated text is grounded in factual information. This approach helps in constructing well-sourced answers by utilizing external knowledge sources. RAG enhances the accuracy and reliability of AI-generated responses by incorporating retrieved information into the generation process.
- Robots.txt protocols are used by websites to communicate with web crawlers, specifying which parts of the site they can access. This file, named robots.txt, helps manage how search engines and other bots interact with a website. It is a standard way for website owners to control bot access to their content. The robots.txt file works alongside sitemaps to guide web crawlers on what to index on a website.
- Tail latency in computing refers to the duration taken for the slowest operations to complete within a system. It is a metric t ...
Counterarguments
- While Perplexity aims to revolutionize information access, it may face challenges in consistently providing high-quality, reliable answers due to the dynamic and ever-changing nature of the internet and the limitations of current technology.
- The academic rigor of supporting assertions with citations is commendable, but the quality of the citations and the potential bias in the sources can affect the trustworthiness of the answers.
- The RAG model's effectiveness is contingent on the quality and relevance of the documents it retrieves, which may not always be optimal due to the vast and varied nature of web content.
- The indexing and crawling system, while sophisticated, may still miss or misinterpret information, leading to incomplete or inaccurate data being fed into the LLM.
- Adherence to robots.txt protocols is standard practice, but it can also limit the scope of content Perplexity can access, potentially affecting the comprehensiveness of its answers.
- The ability to select different language models is a flexible feature, but it may overwhelm users who are not familiar with the nuances of each model's strengths and weaknesses.
- Optimizing for speed, accuracy, and other factors is a complex balancing act, and improvements in one area may lead to compromises in another.
- Tracking "tail latency" and optimizing the user interface are important, but these may not address all user exper ...
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Perplexity's mission and vision
Perplexity aims to redefine the way we interact with information online, transforming the simple act of searching into an enriching journey of discovery. Here we explore the facets of Perplexity's mission to become a "knowledge-centric" platform.
Perplexity aims to be a "knowledge-centric" platform that empowers human curiosity and discovery
Perplexity is envisioned as a knowledge discovery engine, prompting users to articulate their curiosities into well-phrased questions, thereby enhancing their interaction with AI. Aravind Srinivas articulates a clear goal to make Perplexity the world's most knowledge-centric company by emphasizing knowledge and curiosity. The company’s objective is to streamline the inquiry process and anticipate user intent to foster natural curiosity.
Perplexity seeks to go beyond traditional search by guiding users on a journey of knowledge exploration, rather than just providing simple answers
Srinivas stresses the importance of guiding users towards discovery instead of just dispensing the right answer. Perplexity thrives on the exploration side of knowledge, enabling users to delve into a deeper understanding of the subject matter. Lex Fridman adds to this narrative by questioning the origin of human curiosity, implicitly endorsing the need for a platform like Perplexity that caters to this innate human trait.
Perplexity's "Discover" feature aims to proactively surface new information and knowledge to users based on their interests and browsing history
Although not specifically mentioned, the descriptions given by Srinivas imply a commitment to proactively bringing new information to the forefront of the user experience. Srinivas likens the Discover feature to an "AI Twitter," designed to stoke human curiosity without the extraneous drama associated with traditional soci ...
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Perplexity's mission and vision
Additional Materials
Clarifications
- A "knowledge-centric" platform prioritizes facilitating the acquisition and sharing of information over other aspects. It focuses on empowering users to explore and expand their understanding through curated content and tools. Such platforms aim to enhance users' engagement with knowledge, fostering curiosity and enabling deeper learning experiences. By emphasizing knowledge creation, curation, and dissemination, they seek to transform how individuals interact with information online.
- Perplexity enhances user interaction with AI by prompting users to articulate their curiosities into well-phrased questions, streamlining the inquiry process to foster natural curiosity. This approach guides users towards discovery, enabling them to delve deeper into understanding the subject matter. The platform's "Discover" feature proactively surfaces new information based on user interests and browsing history, stoking human curiosity without the drama of traditional social media. Through the "Pages" feature, users can collaboratively build Wikipedia-style articles, leveraging AI capabilities to create and share knowledge within the community.
- The "Pages" feature in Perplexity leverages the platform's AI capabilities by assisting users in creating Wikipedia-style articles. The AI helps users generate content by suggesting information based on their queries and browsing history. It aids in structuring and organizing the content created by users, ensuring it is coherent and informative. This collaborative effort between users and AI enhances the quality and relevance of the art ...
Counterarguments
- While Perplexity aims to be "knowledge-centric," there is a risk of information overload for users if not properly curated.
- The success of Perplexity's mission to streamline the inquiry process depends on the AI's ability to accurately interpret and anticipate user intent, which can be challenging.
- Guiding users on a journey of knowledge exploration may sometimes lead to less efficiency compared to direct answers, which some users may prefer for quick information retrieval.
- The "Discover" feature's effectiveness relies on the algorithm's neutrality and accuracy, which can be difficult to maintain, potentially leading to biased or irrelevant content suggestions.
- Comparing the "Discover" feature to an "AI Twitter" might raise concerns about echo chambers and filter bubbles, similar to those found on social media platforms.
- The "Pages" feature's reliance on user collaboration and AI could result in quality control issues, with the potential for spreading misinformation if not properly monitored.
- The ability of the "Pages" feature to transform insights into valuable comm ...
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The future of search, AI, and knowledge dissemination
Aravind Srinivas discusses how the evolution of the internet and AI is changing how we access and use knowledge, predicting a shift from traditional search engines to more interactive and intelligent paradigms.
Aravind believes the future of the internet and knowledge access will move beyond traditional search towards more interactive, exploratory, and AI-powered experiences
Srinivas foresees the end of traditional search mechanisms like Google, with services like Perplexity providing more interactive, exploratory, and AI-powered experiences. He predicts that innovations in AI will enable new paradigms for knowledge discovery and dissemination, where AI assistants become more adept at providing tailored information and explanations to users.
The rise of large language models and their ability to engage in open-ended dialogue will enable new paradigms for knowledge discovery and dissemination
Srinivas talks about the transformative capabilities of large language models (LLMs) like GPT to engage in conversations, mimicking human-like explorations for answers. He envisions a world where these powerful AI models drive curiosity and allow humans to make more informed decisions. He also discusses reinforcement learning from human feedback (RLHF) as a foundational step for both pre-training and post-training large language models to make them more interactive for product use, signaling a shift towards models that help users discover new knowledge.
AI assistants will become more adept at tailoring information and explanations to the user's level of expertise and learning needs
Srinivas explains how Perplexity can tailor explanations depending on the user's knowledge level, highlighting its ability to adapt responses to how simple or technical the user wants the information. He gives personal examples of using the tool to understand both novice topics, like finance, and more complex ones, where he desires detailed analysis, such as large language models research papers.
The ability to efficiently search and retrieve information from the vast troves of human knowledge will be transformative, but will require advancements in areas like indexing, ranking, and reasoning
Srinivas suggests there might be a breakthrough that disrupts the current trajectory of relying on large clusters of powerful GPUs for AI, leading to more reasoning-capable models without the need for immense compute resources. H ...
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The future of search, AI, and knowledge dissemination
Additional Materials
Clarifications
- Large language models (LLMs) like GPT (Generative Pre-trained Transformer) are advanced AI models designed to understand and generate human language. These models are trained on vast amounts of text data to learn patterns and relationships within language, enabling them to generate coherent and contextually relevant text. GPT models have been at the forefront of natural language processing advancements, demonstrating capabilities in tasks like text generation, translation, and question-answering. Their development has significantly impacted various fields, including AI research, language understanding, and human-computer interaction.
- Reinforcement Learning from Human Feedback (RLHF) is a technique in machine learning that aligns intelligent agents with human preferences by training a "reward model" directly from human feedback. This model predicts if a response to a prompt is good or bad based on human annotators' ranking data, improving the agent's policy through optimization algorithms like proximal policy optimization. RLHF is used in various machine learning domains, such as natural language processing and computer vision, to train models to act in accordance with human preferences, though it faces challenges in collecting high-quality preference data.
- Perplexity Pages is a feature within the Perplexity AI platform that allows users to quickly access knowledge on various subjects through a chatbot-powered research and conversational search engine. It provides answers to queries using natural language predictive text and sources information from the web, offering both free and paid versions with different levels of access to advanced AI models like GPT-4. Founded in 2022, Perplexity has gained popularity with around 10 million monthly users as of early 2024 and has received significant funding from various investors.
- Decoupling reasoning and facts involves separating the process of drawing conclusions or making inferences from the raw information or data itself. By doing this, it allows for a more efficient representation of knowledge as it enables the system to focus on the logic and ...
Counterarguments
- While AI-powered experiences are promising, they may not fully replace traditional search due to the simplicity and directness that keyword-based searches offer.
- Large language models, while powerful, still struggle with understanding context and can generate plausible but incorrect or nonsensical information.
- AI assistants may not always accurately gauge a user's level of expertise, leading to over-simplified explanations or overly complex information that can confuse rather than enlighten.
- Advancements in indexing, ranking, and reasoning are necessary, but there are significant challenges in creating algorithms that can fairly and accurately represent the vast diversity of human knowledge.
- The idea of decoupling reasoning from facts is theoretically appealing, but practical implementation may be far more complex and may not lead to the anticipated efficiency gains.
- Perplexity Pages and similar tools might provide quick knowledge, but they may lack the depth and critical thinking developed through traditional research and learning methods.
- AI systems may guide humans towards bett ...
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Entrepreneurship and startup advice
Aravind Srinivas shares invaluable insights drawn from his journey and the paths of leading entrepreneurs like Larry Page, Jeff Bezos, and Elon Musk. Through his experience, Aravind dispenses sage advice for those embarking on their startup ventures.
Aravind's entrepreneurial journey and inspirations, including his admiration for leaders like Larry Page, Jeff Bezos, and Elon Musk
Aravind expresses deep admiration for figures like Larry Page, suggesting that having figureheads to inspire and direct your own journey is critical. He draws particular inspiration from Page's attention to detail, such as the importance of latency in user experience—something Aravind also prioritizes in his company, Perplexity.
Aravind emphasizes the importance of pursuing a genuine passion and not just chasing market trends when starting a company
Aravind advises aspiring entrepreneurs to work on something they are genuinely passionate about, rather than what seems most lucrative. He argues this is crucial for perseverance, as demonstrated by his and his co-founder's inherent interest in knowledge and search, which was the foundation for Perplexity. He advocates starting from an idea you love and testing a product you use yourself, which can eventually evolve into a profitable business through market pressure.
Aravind stresses the value of hard work, especially for young founders, and surrounding oneself with people who share the same level of dedication and drive
Entrepreneurship is a complex journey, and Aravind notes the importance of hard work and dedication, especially in one's early years. He advises young entrepreneurs to invest time in their passion and to be surrounded by people who drive and guide them to better themselves. Aravind recounts his regret over perceived wasted time in his younger years and encourages making the most of that period to plant seeds for the future.
Aravind highlights the challenges and sacrifices involved in founding a startup, but also the potential for deep personal fulfillment and impact
Aravind Srinivas talks candidly about the challenges and sacrifice required in founding a startup, comparing it to a scenario in an Avengers movie where success is akin to surviving against odds of one in a million. Lex Fridman shares his own ...
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Clarifications
- Lex Fridman is a Russian-American computer scientist and podcaster known for hosting the Lex Fridman Podcast, where he interviews notable figures across various fields. Fridman gained attention in 2019 when Elon Musk praised his study on driver focus while using Tesla's semi-autonomous driving system. He was born in the Soviet Union, grew up in Moscow, and later moved to the United States. His father is a plasma physicist and professor, and his family relocated to the Chicago area when he was young.
- Coping mechanisms in entrepreneurship involve strategies and practices individuals use to manage stress, setbacks, and challenges that come with starting and running a business. These mechanisms can include seeking support from loved ones, practicing self-care, setting boundaries, and maintaining a positive mindset to navigate the ups and downs of the entrepreneurial journey. They are essential for maintaining mental well-being and resilience in the face of uncertainties and pressures inherent in entrepreneurship.
- Larry Page, Jeff Bezos, and Elon Musk are renowned ent ...
Actionables
- Create a vision board to visually map out your entrepreneurial aspirations, including images of leaders you admire and quotes that resonate with your goals. This can serve as a daily reminder of where you're headed and keep you motivated through the ups and downs. For example, if you're inspired by Elon Musk's focus on innovation, you might include a picture of a SpaceX rocket launch and a quote about perseverance.
- Develop a 'feedback loop' system with friends or colleagues where you regularly share progress on your projects and receive constructive criticism. This can help you maintain attention to detail and improve your offerings based on real user experiences. You could meet bi-weekly to discuss your work, exchange ideas, and challenge each other to refine your projects.
- Start a 'passion project' alongside your regular work to ...
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