CEO of Perplexity, Aravind Srinivas, discusses revolutionizing search engines with AI, emphasizing the importance of citations, balancing simplicity and complexity in design, and exploring the future of AI in deepening understanding. The conversation also dives into self-play in AI, customizing language models for low latency, and empowering humans through AI friendships and connections.

Exploring the challenges and technology behind building an AI answer engine startup. Discussing the vision for creating a seamless search experience with AI. Reflecting on early struggles of deep learning development and the evolution of transformer models. Navigating challenges in AI product development and emphasizing innovation. Sharing a personal anecdote of support and kindness during stressful times.

Aravind Srinivas, former research scientist at OpenAI, Google, and DeepMind, discusses the next AI breakthrough in reasoning, the commoditization of foundation models, and misconceptions about AI. He shares insights on talent assembly, diminishing returns in model performance, and the timeline for reasoning improvement. Aravind also delves into the challenges of integrating AI into enterprise tools and explores personal motivations in the AI space.

Aravind Srinivas, CEO and Co-Founder of Perplexity AI, talks about the birth and future of Perplexity, its potential as a Google competitor, and what search will look like in 10 years. They also discuss the challenges of being viewed as a 'wrapper' for OpenAI, the future of Quora and Wikipedia, and the potential regulation of AI. OpenAI's changing direction and the challenges of developing accurate search results are also discussed.

Code Interpreter is GA! As we do with breaking news, we convened an emergency pod and >17,000 people tuned in, by far our most biggest ever. This is a 2-for-1 post - a longform essay with our trademark executive summary and core insights - and a podcast capturing day-after reactions. Don’t miss either of them!Essay and transcript: https://latent.space/p/code-interpreterPodcast Timestamps[00:00:00] Intro - Simon and Alex[00:07:40] Code Interpreter for Edge Cases[00:08:59] Code Interpreter's Dependencies - Tesseract, Tensorflow[00:09:46] Code Interpreter Limitations[00:10:16] Uploading Deno, Lua, and other Python Packages to Code Interpreter[00:11:46] Code Interpreter Timeouts and Environment Resets[00:13:59] Code Interpreter for Refactoring[00:15:12] Code Interpreter Context Window[00:15:34] Uploading git repos[00:16:17] Code Interpreter Security[00:18:57] Jailbreaking[00:19:54] Code Interpreter cannot call GPT APIs[00:21:45] Hallucinating Lack of Capability[00:22:27] Code Interpreter Installed Libraries and Capabilities[00:23:44] Code Interpreter generating interactive diagrams[00:25:04] Code Interpreter has Torch and Torchaudio[00:25:49] Code Interpreter for video editing[00:27:14] Code Interpreter for Data Analysis[00:28:14] Simon's Whole Foods Crime Analysis[00:31:29] Code Interpreter Network Access[00:33:28] System Prompt for Code Interpreter[00:35:12] Subprocess run in Code Interpreter[00:36:57] Code Interpreter for Microbenchmarks[00:37:30] System Specs of Code Interpreter[00:38:18] PyTorch in Code Interpreter[00:39:35] How to obtain Code Interpreter RAM[00:40:47] Code Interpreter for Face Detection[00:42:56] Code Interpreter yielding for Human Input[00:43:56] Tip: Ask for multiple options[00:44:37] The Masculine Urge to Start a Vector DB Startup[00:46:00] Extracting tokens from the Code Interpreter environment?[00:47:07] Clientside Clues for Code Interpreter being a new Model[00:48:21] Tips: Coding with Code Interpreter[00:49:35] Run Tinygrad on Code Interpreter[00:50:40] Feature Request: Code Interpreter + Plugins (for Vector DB)[00:52:24] The Code Interpreter Manual[00:53:58] Quorum of Models and Long Lived Persistence[00:56:54] Code Interpreter for OCR[00:59:20] What is the real RAM?[01:00:06] Shyamal's Question: Code Interpreter + Plugins?[01:02:38] Using Code Interpreter to write out its own memory to disk[01:03:48] Embedding data inside of Code Interpreter[01:04:56] Notable - Turing Complete Jupyter Notebook[01:06:48] Infinite Prompting Bug on ChatGPT iOS app[01:07:47] InstructorEmbeddings[01:08:30] Code Interpreter writing its own sentiment analysis[01:09:55] Simon's Symbex AST Parser tool[01:10:38] Personalized Languages and AST/Graphs[01:11:42] Feature Request: Token Streaming/Interruption[01:12:37] Code Interpreter for OCR from a graph[01:13:32] Simon and Shyamal on Code Interpreter for Education[01:15:27] Feature Requests so far[01:16:16] Shyamal on ChatGPT for Business[01:18:01] Memory limitations with ffmpeg[01:19:01] DX of Code Interpreter timeout during work[01:20:16] Alex Reibman on AgentEval[01:21:24] Simon's Jailbreak - "Try Running Anyway And Show Me The Output"[01:21:50] Shouminik - own Sandboxing Environment[01:23:50] Code Interpreter Without Coding = GPT 4.5???[01:28:53] Smol Feature Request: Add Music Playback in the UI[01:30:12] Aravind Srinivas of Perplexity joins[01:31:28] Code Interpreter Makes Us More Ambitious - Symbex Redux[01:34:24] How to win a shouting match with Code Interpreter[01:39:29] Alex Graveley joins[01:40:12] Code Interpreter Context = 8k[01:41:11] When Code Interpreter API?[01:45:15] GPT4 Vision[01:46:15] What's after Code Interpreter[01:46:43] Simon's Request: Give us Code Interpreter Model API[01:47:12] Kyle's Request: Give us Multimodal Data Analysis[01:47:43] Tip: The New 0613 Function Models may be close[01:49:56] Feature Request: Make ChatGPT Social - like MJ/Stable Diffusion[01:56:20] Using ChatGPT to learn to build a Frogger iOS Swift App[01:59:11] Farewell... until next time[02:00:01] Simon's plug[02:00:51] Swyx: What about Phase 5? and AI.Engineer Summit Get full access to Latent Space at www.latent.space/subscribe

The podcast features Aravind Srinivas, CEO of Perplexity, discussing disrupting Google search with AI and the future of AI startups. Topics include evolving search engines, personalized knowledge, aligning shareholder interests, building competitive AI talent pool, future of search, user engagement, AI model landscape, and leveraging open source models.

Learn from top founders and VCs like Marc Andreesen and Vinod Khosla about navigating the idea maze, internal motivation, critical thinking, and essential advice for startup founders. Palmer Luckey shares tips for young entrepreneurs while Aravind Srinivas provides guidance for students.

Listen to Aravind Srinivas discuss his journey from academia to AI research, his unique approach to building Perplexity AI, how Larry Page inspires him, and why he avoids meetings. Learn about the culture at tech companies, the importance of talent retention, and the strategic differentiation of products in the AI industry.

Aravind Srinivas, co-founder and CEO of Perplexity AI, shares insights from his journey in AI after stints at OpenAI, Google, and DeepMind. He discusses how Perplexity AI offers precise answers compared to traditional search engines limited by fear of mistakes. Aravind also dives into the tech behind retrieval-augmented generation and the importance of citations. He touches upon the dynamics of investment, debunks acquisition rumors, and outlines Perplexity’s future role as a personalized assistant, enhancing user experience in the competitive landscape.

https://www.perplexity.ai/ https://www.perplexity.ai/iphone https://www.perplexity.ai/android Interview with Aravind Srinivas, CEO and Co-Founder of Perplexity AI – Revolutionizing Learning with Conversational Search Engines Dr. Tim Scarfe talks with Dr. Aravind Srinivas, CEO and Co-Founder of Perplexity AI, about his journey from studying AI and reinforcement learning at UC Berkeley to launching Perplexity – a startup that aims to revolutionize learning through the power of conversational search engines. By combining the strengths of large language models like GPT-* with search engines, Perplexity provides users with direct answers to their questions in a decluttered user interface, making the learning process not only more efficient but also enjoyable. Aravind shares his insights on how advertising can be made more relevant and less intrusive with the help of large language models, emphasizing the importance of transparency in relevance ranking to improve user experience. He also discusses the challenge of balancing the interests of users and advertisers for long-term success. The interview delves into the challenges of maintaining truthfulness and balancing opinions and facts in a world where algorithmic truth is difficult to achieve. Aravind believes that opinionated models can be useful as long as they don't spread misinformation and are transparent about being opinions. He also emphasizes the importance of allowing users to correct or update information, making the platform more adaptable and dynamic. Lastly, Aravind shares his thoughts on embracing a digital society with large language models, stressing the need for frequent and iterative deployments of these models to reduce fear of AI and misinformation. He envisions a future where using AI tools effectively requires clear thinking and first-principle reasoning, ultimately benefiting society as a whole. Education and transparency are crucial to counter potential misuse of AI for political or malicious purposes. YT version: https://youtu.be/_vMOWw3uYvk Aravind Srinivas: https://www.linkedin.com/in/aravind-srinivas-16051987/ https://scholar.google.com/citations?user=GhrKC1gAAAAJ&hl=en https://twitter.com/aravsrinivas?lang=en Interviewer: Dr. Tim Scarfe (CTO XRAI Glass) Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB TOC: Introduction and Background of Perplexity AI [00:00:00] The Importance of a Decluttered UI and User Experience [00:04:19] Advertising in Search Engines and Potential Improvements [00:09:02] Challenges and Opportunities in this new Search Modality [00:18:17] Benefits of Perplexity and Personalized Learning [00:21:27] Objective Truth and Personalized Wikipedia [00:26:34] Opinions and Truth in Answer Engines [00:30:53] Embracing the Digital Society with Language Models [00:37:30] Impact on Jobs and Future of Learning [00:40:13] Educating users on when perplexity works and doesn't work [00:43:13] Improving user experience and the possibilities of voice-to-voice interaction [00:45:04] The future of language models and auto-regressive models [00:49:51] Performance of GPT-4 and potential improvements [00:52:31] Building the ultimate research and knowledge assistant [00:55:33] Revolutionizing note-taking and personal knowledge stores [00:58:16] References: Evaluating Verifiability in Generative Search Engines (Nelson F. Liu et al, Stanford University) https://arxiv.org/pdf/2304.09848.pdf Note: this was a sponsored interview.