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Google’s artificial intelligence (AI) research team has released a free machine learning (ML) add-on Google Sheets which is claimed to help anyone use predictions to fill gaps in their data with no prior experience with ML or code.
Announcement Simple ML for Spreadsheets (opens in new tab) in a after (opens in new tab) on the TensorFlow blog, the team claimed that small businesses, students, and even scientists and analysts at large companies can find uses for the spreadsheet software’s new feature to make valuable predictions, or even save time debugging.
It also suggested that those familiar with ML could also benefit productivity boosts provided the add-on, with “training, evaluating, interpreting and exporting a model” takes “5 clicks and only 10 seconds.”
Machine learning capabilities
Machine learning algorithms are trained on huge sets of data to make human-readable predictions without being explicitly programmed. As they predict, they get better at making predictions.
It’s the latest example of AI-driven machine learning seeping into consumer-level applications. AI implementation company Open AI (opens in new tab)GPT-3’s neural network, for example, powers any number of third parties AI writers and image generation services, including services provided by OpenAI itself, such as Playground (opens in new tab) and DALL E (opens in new tab).
Those who want to learn more about the possibilities and limitations of machine learning and how it works are well provided Google’s basic and advanced courses (opens in new tab).
Still, newbies and enthusiasts alike will benefit from using the “state-of-the-art ML technology” in the Sheets extension, which Google claims already powers the data classification library TensorFlow decision forests (opens in new tab). It also promises that no prediction data is shared or owned by Google or any other company.
Once users install the extension, users can use the technology by opening the Extensions tab in their open Sheets spreadsheet, launching Simple ML, and using the simple user interface to design the most appropriate job. From there, the data can be applied in the same way as manually obtained data in a particular use case.
However, even Google likes to emphasize that ML-driven predictions are just that and should not be taken as guarantees of factual information. As such, it’s worth double-checking all predictions for accuracy.