For many years, Google has been known for making high-quality search results available to billions of users all over the world.
Today, most people do not even have to think twice about which search engine to use when looking up a product, service, or information online.
They simply have to “Google it.” The phrase has become so common that it is now part of virtually everyone’s vocabulary.
The giant tech company constantly comes up with innovative solutions that make life easier for its users. They did it again with a new technology that allows them to research, develop, and implement new ranking algorithms simply and more quickly – an improved version of TensorFlow.
The company is touting it as a game-changer in improving natural language processing or NLP, ranking algorithms, and fighting spam.
What is TensorFlow?
TensorFlow is an open-source AI library that allows developers to create multi-layered neural networks at a large scale.
When TensorFlow first came out in 2015, it reshaped how relevant documents were ranked. Before, the pairwise ranking was used to compare documents to each other to determine their relevance.
In other words, the relevance of one document is dependent on the relevance of another document, which is a limited approach since it compares pairs instead of all items on the list.
TensorFlow allowed multi-item scoring where an entire list or documents are compared to one another. It opened up a new world of better ranking decisions and enhanced user experience.
The Impact of the New TensorFlow Version
True to its identity as an innovative company, Google did not sit idly on its achievements and came up with several improvements for TensorFlow. The latest one was outlined in a publication released by the company last May 15, 2021.
In the two short months following the announcement of TensorFlow’s improved version, Google came up with a number of algorithms to fight spam. In addition, it rolled out two core algorithm updates.
It is safe to assume that all these new updates are a result of the latest improvement made on the TensorFlow ranking.
Improved Ranking Method
Now at version 2.5.0, TensorFlow’s latest iteration is chock-full of new and improved features. It can now support Python 3.9, and its pip packages are built with CUDA 11.2 and cuDNN 8.1.0.
What is more, the new version can be used to improve neural learning for rank algorithms. It comes with performance optimizations for oneAPI Deep Neural Network Library CPU in both Linux and Windows builds.
The TensorFlow 2.5.0 offers a powerful way to set up learning to rank or LTR models and makes it easier to get them into live production. With this technology, Google can develop and harness new algorithms or enhance existing ones more quickly.
Better than Gradient Boosted Decision Trees
The present standard in search algorithms is gradient boosted decision trees or GBDTs. It offers many advantages as a machine learning technique, but it is not as scalable as neural ranking models and could not be applied directly to raw document text and other large feature spaces.
Using the new TensorFlow-Ranking, researchers from Google were able to produce DASALC or Data Augmented Self-Attentive Latent Cross. This model is important because it matches and even outperforms what are currently considered advanced benchmarks.
TensorFlow’s latest developments help accelerate the research and development of new ranking systems, including those for identifying spam. This makes it easier for the search engine to rule them out from search results.
Modular Connection for Third-Party Devices
The new version of TensorFlow comes with the PluggableDevice interface and StreamExecutor C API that allow third-party devices to connect modularly. With these new features, developers can add customized apps and kernels.
While the previous versions of TensorFlow changed the landscape of search results, they were not without their flaws. The latest version comes with bug fixes for many of these issues.
For instance, Keras inputs can now be made directly from random tf.TypeSpecs. It also added two new learning rate schedules.
Another improvement is the option designed to help in debugging conversion for the Python TF Lite Interpreter.
TensorFlow is already a very popular machine learning platform, but its recent improvement might just bring it to a whole new level, allowing it to improve even more the search experience for billions of users.
What It Means for Your SEO Efforts
As Google comes up with new ways to improve search results and user experience, website owners and digital marketers need to play catch up.
This is the reality of search engine optimisation or SEO – what works wonders today may not be as effective tomorrow.
Still, it is a good price to pay in exchange of more relevant search results that offer more value to all users.
Besides, reputable digital marketing companies are always on their toes, ready for the next big thing Google will throw their way.