Web Search: Crash Course AI #17

Summary

Crash Course AI explores how search engines like Google operate using AI to find answers. It explains the process from crawling the web with web crawlers to organizing data with inverted indexes. The video also touches on how user behavior influences search rankings and the use of knowledge bases for direct answers. It highlights the challenges AI faces with nuanced questions and biases in data.

Takeaways

• 🔍 **Search Engines as AI Systems**: Modern search engines like Google use AI to help users find information by gathering and organizing data from the World Wide Web.
• 📚 **From Libraries to Web Crawlers**: The concept of search engines dates back centuries, evolving from physical libraries to digital web crawlers that systematically download web pages.
• 🌐 **The Web and the Internet**: The script clarifies the difference between the Internet (a network of computers) and the Web (part of the Internet that uses browsers to display documents).
• 🕷️ **Web Crawlers**: Web crawlers are programs that start from a 'seed' page and recursively download linked pages, forming the basis of search engine databases.
• 📈 **Inverted Index**: Search engines use an inverted index to organize web pages by words, allowing for quick searches when users enter queries.
• 🔑 **Query Processing**: When a user submits a query, the AI uses the inverted index to find relevant web pages that contain the search terms.
• 🏆 **Ranking Results**: Search engines rank web pages to ensure the most relevant results appear first, using user behavior data like bounces and click-throughs to refine rankings.
• 🧠 **Knowledge Bases**: For direct answers, AI systems use knowledge bases that encode information as relationships between objects, unlike inverted indexes used for web page links.
• 🤖 **NELL - Never Ending Language Learner**: An example of a knowledge base is NELL, which autonomously extracts facts from web pages and uses repetition and multiple sources to validate information.
• 🌳 **Bias in AI**: The script highlights that AI systems can inherit biases present in the data they learn from, affecting the neutrality of search results.
• ❓ **Limitations of AI**: Certain questions that are not commonly asked or have limited data available can stump AI systems, illustrating the ongoing challenges in training comprehensive AI models.

Q & A

What is the primary function of search engines?

Search engines primarily gather data, create organization systems to sort that data, and find results to a question.

How do search engines compare to traditional libraries in terms of data organization?

Search engines and traditional libraries both gather and organize data. Libraries use physical organization systems like shelving and cataloging, while search engines use digital systems like inverted indexes.

What is the role of a Web crawler in search engines?

A Web crawler is a computer program that systematically finds and downloads Web pages to gather data for the search engine AI to process.

Can you explain what an inverted index is in the context of search engines?

An inverted index is a lookup system used to organize Web pages. For each word, it lists all the Web pages that contain that word, usually represented by ID numbers instead of URLs.

How does the AI in search engines determine the relevance of search results?

The AI uses an inverted index to find relevant pages and then ranks them based on various factors to ensure the top results are more likely to be relevant.

What is the significance of user behavior in training search engine AI?

User behavior, such as bounces and click-throughs, provides training data for AI systems to learn how to rank search results and better answer user queries.

What is a knowledge base and how does it differ from an inverted index?

A knowledge base encodes information as relationships between objects. Unlike an inverted index, which is used for searching, a knowledge base is used to directly answer questions by matching incomplete facts.

What is the Never Ending Language Learner (NELL) and how does it work?

NELL is a huge knowledge base created by Carnegie Mellon University that extracts facts from Web pages. It starts with human-provided facts, identifies patterns, and learns new facts and relationships by searching the Web.

How does an AI system like Siri or John Green Bot answer direct questions?

AI systems like Siri reformulate questions into incomplete facts and then search a knowledge base for matches to provide direct answers.

Why do some questions stump AI systems?

Questions that stump AI systems are often those that not enough people ask, or for which the AI hasn't learned how to answer well yet due to lack of data or training.

What is the potential issue with biases in search engine AI systems?

Search engine AI systems can be influenced by biases in the data online, leading to skewed or incomplete results, such as predominantly showing images of female nurses when searching for 'nurses'.