The Evolution of Search Engines: From Misaligned Incentives to Semantic Embeddings

The Evolution of Search Engines: From Misaligned Incentives to Semantic Embeddings

In the early days of the internet, search engines revolutionized the way we access information. Google, in particular, became synonymous with search, providing users with relevant and reliable results at the click of a button. However, as time went on, it became apparent that the business model of advertising-funded search engines like Google was not without its flaws.

Sergey Brin and Lawrence Page, the founders of Google, recognized this issue back in 1998. In their paper, they highlighted the inherent bias of advertising-funded search engines towards advertisers rather than the needs of consumers. They acknowledged that this misalignment of incentives can lead to poor quality search results. And indeed, as the internet grew and more people tried to manipulate search rankings for their own gain, the quality of search results began to deteriorate.

This decline in search quality has been a cause for concern for many. Users rely on search engines to find accurate and reliable information, whether it's for personal research or professional purposes. When search results are manipulated or biased, it becomes increasingly difficult to trust the information presented.

To address this issue, researchers and developers have been exploring new approaches to improve search quality. One such approach is the use of text and code embeddings. Embeddings are numerical representations of concepts that make it easier for computers to understand the relationships between those concepts. By converting text and code into number sequences, embeddings can capture semantic similarity and enable more accurate search results.

Text similarity models, for example, provide embeddings that capture the semantic similarity of pieces of text. These models have proven to be useful for tasks such as clustering, data visualization, and classification. They allow us to group similar pieces of text together, making it easier to analyze and interpret large amounts of information.

Additionally, text search models provide embeddings that enable large-scale search tasks. These models allow us to find relevant documents within a collection based on a text query. OpenAI, a leading research organization, has made significant strides in this area. Their text-search-curie embeddings model achieved a top-5 accuracy of 89.1% in finding textbook content based on learning objectives. This outperformed previous approaches like Sentence-BERT, which only achieved a top-5 accuracy of 64.5%.

These advancements in text and code embeddings are truly fascinating. It raises the question of whether similar techniques can be applied to other domains, such as Glasp, a platform for exploring scientific literature. Imagine being able to search for relevant research papers or scholarly articles with the same level of accuracy and precision. It could revolutionize the way we discover and access scientific knowledge.