Startup & AI & Product

Latent Space: The AI Engineer Podcast: The podcast discusses ExaAI's innovative approach to search engines, aiming to surpass traditional models like Google by leveraging AI and LLMs for more accurate and comprehensive search results.

Latent Space: The AI Engineer Podcast• 18 episodes

Latent Space: The AI Engineer Podcast - Beating Google at Search with Neural PageRank and $5M of H200s — with Will Bryk of Exa.ai

ExaAI, led by CEO Will Brick, is revolutionizing search engines by integrating insights from large language models (LLMs) to create a more intuitive and comprehensive search experience. Unlike traditional search engines that rely heavily on keyword-based algorithms, ExaAI focuses on understanding user queries through neural networks, aiming to deliver precise results that match user intent. This approach is inspired by the capabilities of models like GPT-3, which can understand and process complex language inputs. The company started with a bold vision of being 'better than Google' by utilizing AI to enhance search capabilities. ExaAI's system involves crawling the web, processing data through AI models, and serving results via a vector database. This infrastructure allows for high-throughput and low-latency search operations. ExaAI's unique selling point is its ability to handle complex queries and provide comprehensive results, which traditional search engines struggle with. The company also explores various applications, such as dating and academic research, showcasing the versatility of its search technology.

Key Points:

ExaAI uses AI and LLMs to improve search accuracy and comprehensiveness, moving beyond keyword-based algorithms.
The company aims to create a 'perfect search engine' by understanding user queries more deeply and providing precise results.
ExaAI's infrastructure includes web crawling, AI processing, and a vector database for efficient search operations.
The search engine can handle complex queries, offering comprehensive results that traditional engines cannot match.
ExaAI explores diverse applications, including dating and academic research, highlighting its technology's versatility.

Details:

1. 🎙️ Welcome and Guest Introduction

The podcast, titled 'Lid in Space,' introduces its host and guest for the episode.
Alessio serves as the partner and CTO in residence at Decibel Partners, providing industry insights and expertise.
Swix, the founder of SmallAI, highlights their entrepreneurial journey and achievements in AI.
The episode features Will Brick, who shares a personal relationship with Swix as a former landlord and roommate, offering a unique perspective on their interactions and experiences.

2. 📜 Will's Journey to Founding ExaAI

Will, CEO and co-founder of ExaAI, has been leading the company for three years.
His interest in search and high-quality information started in childhood and continued to grow over time.
In high school, he worked on improving information retrieval from news sources.
During college, Will developed a mini search engine, showcasing his commitment to solving information needs.
Founding ExaAI represents a culmination of his lifelong interest in enhancing information retrieval.

3. 🚀 Entering YC with Ambitious Goals

3.1. Ambitious Goals in YC

3.2. AI Landscape in 2021

4. 🌟 Early Career and Influences at SpaceX and Zoox

The interviewee interned at SpaceX, where exposure to Elon Musk's visionary leadership profoundly influenced their career aspirations, demonstrating the powerful impact of prominent figures on young engineers.
A significant personal belief in the potential of self-driving cars led the interviewee to postpone learning to drive, illustrating the disparity between technological optimism and the slower-than-expected development timelines of self-driving technology.
SpaceX had a unique company policy that restricted interns from physical contact with Elon Musk, reflecting the heightened security and protective measures around influential leaders in innovative industries.
At Zoox, the interviewee further explored autonomous vehicle technology, gaining hands-on experience that helped shape their understanding of the challenges and opportunities within the self-driving car industry.

5. 🔄 Transition from Metaphor to Exa: A New Era

5.1. Metaphor's Initial Aura and Strategic Vision

5.2. Strategic Transition to Exa

6. 🔍 Perfecting Search with AI: Exa's Vision

Exa's initial search engine release mirrored OpenAI's strategy with ChatGPT by prioritizing rapid deployment of a functional product to the public.
The company transitioned from a research-centric approach to a product-oriented strategy, signifying a strategic pivot towards commercialization and broader application.
Exa aims to be recognized as the 'OpenAI of search,' highlighting its commitment to advancing search technology through AI, with long-term goals towards achieving AGI in search applications.
Exa leverages advanced AI techniques to refine search accuracy and efficiency, aligning with its vision to revolutionize the search landscape.
Specific strategies include developing algorithms that improve search relevance and personalization, indicating a focus on user-centric innovation.

7. 🤖 Building Exa: The OpenAI of Search

The project focuses on building a link prediction foundation model, crucial for developing Exa's search capabilities.
The model is trained to predict web links based on surrounding context, similar to how transformers predict sequences in language models.
The training involves concealing the link from the model and training it to predict subsequent content, like identifying 'SpaceX.com' after a mention of an aerospace startup.
This predictive method is executed billions of times to create an effective search engine, leveraging the model's predictive strength.
The implementation process involves overcoming challenges in contextual understanding and link relevance, which are addressed through iterative model refinement.
The project's goal is to enhance search precision and relevance, positioning Exa as a leader in AI-driven search technology.

8. 🔗 Innovating with Link Prediction Technology

Link prediction technology utilizes a transformer-inspired architecture that is distinct from traditional transformers, focusing on predicting the most likely links from descriptions rather than memorizing URLs.
This approach shifts the emphasis from predicting URLs to predicting relevant documents, thus enhancing the accuracy and relevance of search results.
The term 'document prediction' more accurately describes this technology as it aligns with the goal of matching queries with the most pertinent documents, rather than just extracting links.
Real-world applications of this technology include improving search engines by providing users with the most relevant documents based on their queries, thereby optimizing search efficiency and user satisfaction.

9. 🧩 The Architecture of Exa's Search Engine

Exa's search engine architecture evolved from a base model, incorporating synthetic data and supervised fine-tuning for enhanced control and robustness.
A critical component is the vector database, which plays a key role in the system's efficiency, as discussed at the AI Engineer Conference.
The architecture includes self-constructed subsystems like crawling, processing, and serving systems, aiming to create the perfect search engine.
The crawling subsystem processes URLs using embedding models or keyword inverted indexes, creating a high-throughput, low-latency index.
Management is efficient, with small teams of one or two people handling tasks typically managed by large teams in bigger organizations.
The name 'Exa' signifies 10 to the power of 18, contrasting Google's vast scale, symbolizing Exa's unique ambition and philosophy.

10. 🚀 Launching Exa's Next-Gen Search Product

Exa's new search product is designed to provide highly relevant results, focusing on quality over quantity. Unlike traditional search engines that might return millions of results, Exa aims to deliver a concise list, such as 325 results, that precisely match user queries.
The product supports complex search queries efficiently, such as identifying startups working on hardware in San Francisco, by delivering a complete list of relevant entities.
This approach contrasts sharply with traditional methods, providing a streamlined search experience that filters out unnecessary information and improves user satisfaction.
Exa's vision is to redefine search by delivering exactly what users ask for, emphasizing accuracy and efficiency in search results.

11. 🧠 Redefining Search with Comprehensive Queries

11.1. Challenges in Comprehensive Search

11.2. Solutions for Comprehensive Search

12. 🌍 The Broader Impact of Search Technology

12.1. Compute Budget Management

12.2. Evolving Search Time Expectations

12.3. Infrastructure and User Interface Strategy

13. 🧠 Differentiating Intelligence from Knowledge in AI

13.1. Access to Knowledge

13.2. Intelligence vs. Knowledge

13.3. Limitations of AI Systems

13.4. Combining Knowledge and Intelligence

13.5. Optimizing AI Models

14. 📚 Advancements in Neural Search Engine Technology

14.1. Efficiency and Cost in Search Engines

14.2. Building a New Search Engine

14.3. Contrasting Current Search Systems

15. 🌐 Ensuring Quality in Search Results

The challenge of building a search engine from scratch is significant, with only a few major players like Google, Bing, and Yandex attempting it.
Google's PageRank revolutionized search quality, and there's a pursuit for an LLM equivalent. The link prediction objective could serve as a 'neural PageRank,' predicting popular links shared among users, thus identifying high-quality content.
Unlike PageRank, this method can recognize various references to the same content, enhancing accuracy by understanding different ways content is shared and its importance.
Traditional search engines focus on domain authority, often resulting in low-quality SEO-driven content ('slop') dominating search results.
To mitigate this, there's significant control over training data quality, ensuring high-quality sources, which aligns with language model training principles: quality input yields quality output.
By preventing low-quality data ('slop') from training LLMs, higher quality search results and outputs are achieved, differentiating from traditional search engines.

16. 🔄 Expanding Exa's Use Cases Across Industries

ExaSearch is widely used across industries, including by companies like BrightWave, signifying its broad applicability and acceptance.
Exa provides not only a Search API but also tools like List Builder and Web Scripting, enabling diverse functionalities such as retrieving URLs and full content parsing, which are crucial for AI application development.
Customers can obtain content in various formats, including Markdown, for up to 1,000 URLs, highlighting Exa's capability to handle large-scale data efficiently.
The initial development of Exa included content storage for debugging, which later became a vital feature as demand for content retrieval grew, illustrating the importance of flexible development approaches.
The market includes competitors such as Gina and Firecrawl, but Exa aims to provide a comprehensive all-in-one solution, indicating a strategic focus on integration and usability.
Building a top-tier scraper is challenging; Exa claims to offer the best scraper in the world, showcasing their commitment to solving complex data extraction problems.
The increasing closure of websites like Twitter and Reddit to bots presents ongoing challenges for scrapers, requiring continuous adaptation.

17. 📊 The Influence of Search Engines on the Internet

17.1. Data Partnerships and Content Accessibility

17.2. Innovative Applications of Search in Dating

17.3. Applications of Exa in Education and Research

17.4. Exa in Finance and Recruitment

17.5. Impact of Search Engines on Content Creation

18. 🛠️ The Role of LLMs in Enhancing Search

The concept of 'McLuhanism' suggests that tools we create ultimately reshape us, highlighting a reflexive connection between search tools and their impact on search behavior.
Google has established a strong association with search, conditioning users to think in terms of short keyword queries rather than complex requests.
Tools like ChatGPT and Exa challenge traditional search paradigms by enabling more complex, paragraph-long queries, demonstrating a shift in user expectations from search engines.
Exa aims to expand users' understanding of search capabilities beyond traditional keyword-based searches, encouraging more nuanced and specific searches.
The introduction of large language models (LLMs) like Exa allows for an interface where the model understands user intent and translates it into effective search queries.
LLMs are poised to become the interface for various technologies, providing an intuitive and effective means of user interaction by understanding and processing complex queries.
Traditional search engines struggle with complex, paragraph-long queries, whereas Exa is designed to handle such inputs effectively, providing an improved user experience.
The distinction between filtering and ranking in search results is emphasized, where filtering should be objective and ranking can be subjective, based on user preferences.
Search engines should ideally allow users to specify what they mean by 'best' in rankings, e.g., by visitor count or employee size, to provide more tailored and relevant results.
The evolution of search engines towards handling complex ranking systems is highlighted as a new topic for users accustomed to simpler queries.

19. 🤔 Navigating Agentic Features in Search Technologies

Traditional search technologies are algorithm-based and lack the agentic features that allow for deeper, autonomous actions, unlike the proposed agentic systems that can decide when to delve deeper into subjects.
Agentic systems are compared to levels of autonomy in self-driving cars, where full autonomy (level 5) has generally failed because users prefer to maintain some level of control or involvement.
There is a concern that overly agentic systems might disconnect users from the process, reducing trust, as users prefer to be more involved in the research process.
The ideal system balances agentic capabilities with user control, allowing customization based on preferred metrics, such as in the example of ranking basketball players by specific statistics.
An exploration into new interfaces is ongoing to determine what it means for users to initiate a search that will autonomously return comprehensive results.
The balance between agentic features and user control is crucial to maintain user trust and engagement, avoiding scenarios where users feel disconnected from the outcome of searches.

20. 🔍 Building Trust Through User Interaction in Search

Previews in search interfaces are crucial for allowing users to iteratively refine their queries, ensuring results meet their expectations, and thereby building trust.
A significant issue in search systems is the lack of shared context between users and AI, which necessitates explicit user input to align AI actions with user needs.
Enhancing transparency and accuracy in search is achieved by allowing users to see and influence the search process, fostering trust.
While system prompts are important for guiding AI behavior, they are insufficient on their own; continuous user involvement is essential to align AI actions with user expectations.
Current system prompts often lack scientific grounding, highlighting the need for more rigorous methodologies in AI training and deployment.

21. 🚀 Competing in the AI Search Landscape

21.1. Competing in AI Search

21.2. AGI and Reward Signals

21.3. Model Ecosystem and Efficiency

21.4. Search Labeling and Model Training

22. 🎉 The Culture and Future at ExaAI

22.1. Company Culture

22.2. Growth and Infrastructure

22.3. Economic Strategy in AI Search