Challenges of Early Deep Learning Development | 2min snip from Invest Like the Best with Patrick O'Shaughnessy

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Aravind Srinivas - Building An Answer Engine - [Invest Like the Best, EP.363]

Invest Like the Best with Patrick O'Shaughnessy

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CHAPTER

Challenges of Early Deep Learning Development

This chapter examines the difficulties faced by engineers in the early years of deep learning, focusing on the awkwardness of using C++ and CUDA before Python-based frameworks emerged. By reflecting on these challenges, it highlights the evolution of tools and the resilience required to succeed in this field.

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My guest today is Aravind Srinivas. He is the founder and CEO of Perplexity, a startup that he describes as an “answer engine” built from scratch with AI. Aravind has set out for perplexity to become the most powerful answer engine backed by up-to-date sources. He helps me pick the technology apart, describing the behind the scenes of what it takes to build Perplexity to reach its potential and compete alongside the likes of Google and OpenAI. Our conversation goes deep into programming this type of infrastructure, the competition around latency, and constructing a business model around deep learning. There is so much on this horizon, so please enjoy my conversation with Aravind Srinivas.

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For the full show notes, transcript, and links to mentioned content, check out the episode page here.

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Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.

Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more.

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Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com).

Show Notes:

(00:00:00) Welcome to Invest Like the Best

(00:04:08) First Question - Redefining 'Great' in Search

(00:07:16) The Mechanics of Perplexity

(00:10:59) The Evolution of Perplexity From Wrapper to Robust Infrastructure

(00:13:19) The Future of Large Language Models (LLMs)

(00:22:39) Aravind's Strategy For Constructing The Business Model

(00:28:16) The Process of Building an Index for a Search Engine

(00:35:55) The Impact of Scaling and Data Quality on AI Models

(00:40:00) Bottlenecks in AI Development

(00:47:06) The Talent Landscape in AI