Google's Artificial Intelligence (AI) research team has released a free machine learning (ML) plugin for Google Sheets that it says can help anyone using predictions to fill gaps in their data with no prior experience. in ML or code.

Announcing Simple ML for Sheets (Opens in a new tab) in a post (Opens in a new tab) on the TensorFlow blog, the team claimed that small businesses, students, and even scientists and analysts at large corporations can find uses for the new spreadsheet. functionality to make valuable predictions, or even save time catching errors.

He also suggested that those familiar with ML could also benefit from productivity gains as long as the plugin, with “training, testing, interpreting and exporting a model” takes “5 clicks and as little as 10 seconds”.

Machine learning capabilities

Machine learning algorithms train on large data sets to be able to make human-readable predictions without being explicitly programmed. As they predict, they get better at making predictions.

This is the latest example of AI-powered machine learning reaching consumer applications. AI implementation company OpenAI's GPT-3 neural network (opens in a new tab), for example, powers a number of third-party AI editors and imaging services, including those provided by OpenAI itself, as Playground(Opens in a new tab). tab) and DALL·E (opens in a new tab).

Those looking to learn more about the possibilities, limitations, and operation of machine learning are well served with Google's basic and advanced courses (opens in a new tab).

Still, newcomers and enthusiasts alike can benefit from taking advantage of the "cutting-edge ML technology" within the Sheets extension, which Google says already powers the TensorFlow Decision data classification library. Forests (opens in a new tab). It also promises that Google or any other company does not share or own any prediction data.

Once users install the extension, they can take advantage of the technology by opening the Extensions tab in their open spreadsheet, launching Simple ML, and using the simple user interface to design the most suitable one. From there, the data can be applied in the same way as any manually obtained data in a given use case.

However, even Google wants to point out that ML-based predictions are just that and should not be taken as guarantees of factual information. As such, it is worth double checking the predictions made to verify their accuracy.

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