Machine Learning or Deep Learning for Scalability
Posted: Thu Jan 30, 2025 6:39 am
For semantic systems, classes and labels must be predefined in order to classify data. In addition, it is difficult to identify and create new entities without manual help. For a long time, this was only possible manually or in reference to manually maintained databases such as Wikipedia or Wikidata, which hinders scalability .
The step towards a high-performance semantic search engine inevitably involves machine learning or neural networks.
Google’s commitment to artificial intelligence and machin malta phone number data e learning began in 2011, even before the release of Hummingbird and the Knowledge Graph, with the launch of the “Google Brain” project.
The goal of Google Brain is to create its own neural networks. Since then, Google has been working on expanding its own infrastructure for machine and deep learning using its own deep learning software, DistBelief , and its successor, Tensor Flow , and the Google Cloud Machine Learning Engine .
According to Google, it has almost quadrupled its deep learning activities since 2014, as can be seen from the slide from Jeff Dean's talk below.
Unnamed13
So far, machine or deep learning is most likely being used, or according to Google's own statement, is already being used, for the following cases:
Categorization or identification of search queries according to search intent (informational, transactional, navigational ...)
Categorization of content/documents by purpose (information, sales, navigation ...)
Recognition, categorization of entities in the Knowledge Graph
text analysis via natural language processing
recognition, categorization and interpretation of images
recognition, categorization and interpretation of language
detection, categorization and interpretation of videos
translation of languages
What is really new is that Google can now do this categorization better and better because it is constantly learning and, above all, automating it.
Digital gatekeepers like Google need increasingly reliable algorithms to perform these tasks autonomously. Self-learning algorithms based on artificial intelligence and machine learning methods will play an increasingly important role here. This is the only way to ensure the relevance of results and outputs/results that conform to expectations - while maintaining scalability.
Especially with regard to the semantic understanding of search queries and documents, machine learning is essential for performance.
It is therefore no coincidence that the three most important introductions of Google Search - Knowledge Graph , Hummingbird, Rankbrain and Google's significantly intensified commitment to machine learning - occurred very close together over a period of three years.
The step towards a high-performance semantic search engine inevitably involves machine learning or neural networks.
Google’s commitment to artificial intelligence and machin malta phone number data e learning began in 2011, even before the release of Hummingbird and the Knowledge Graph, with the launch of the “Google Brain” project.
The goal of Google Brain is to create its own neural networks. Since then, Google has been working on expanding its own infrastructure for machine and deep learning using its own deep learning software, DistBelief , and its successor, Tensor Flow , and the Google Cloud Machine Learning Engine .
According to Google, it has almost quadrupled its deep learning activities since 2014, as can be seen from the slide from Jeff Dean's talk below.
Unnamed13
So far, machine or deep learning is most likely being used, or according to Google's own statement, is already being used, for the following cases:
Categorization or identification of search queries according to search intent (informational, transactional, navigational ...)
Categorization of content/documents by purpose (information, sales, navigation ...)
Recognition, categorization of entities in the Knowledge Graph
text analysis via natural language processing
recognition, categorization and interpretation of images
recognition, categorization and interpretation of language
detection, categorization and interpretation of videos
translation of languages
What is really new is that Google can now do this categorization better and better because it is constantly learning and, above all, automating it.
Digital gatekeepers like Google need increasingly reliable algorithms to perform these tasks autonomously. Self-learning algorithms based on artificial intelligence and machine learning methods will play an increasingly important role here. This is the only way to ensure the relevance of results and outputs/results that conform to expectations - while maintaining scalability.
Especially with regard to the semantic understanding of search queries and documents, machine learning is essential for performance.
It is therefore no coincidence that the three most important introductions of Google Search - Knowledge Graph , Hummingbird, Rankbrain and Google's significantly intensified commitment to machine learning - occurred very close together over a period of three years.