Facebook is launching what it’s calling a’universal product recognition model’. It uses artificial intelligence to identify consumer goods, from furniture to fast fashion to fast cars.

‘we want to make anything and everything on the platform shoppable, whenever the experience feels right,’ said Manohar paluri, head of applied computer vision at Facebook.

Product recognition is the first in a slew of AI-powered updates coming to its e-commerce platforms in the near future. Eventually, these will combine AI, augmented reality, and digital assistants to create what it calls a’social-first’ shopping experience.

A Facebook’ai fashion stylist’ could offer personalized shopping recommendations based on their wardrobe and daily suggestions for outfits tailored to the weather and their schedule.

Tamara Berg, a Facebook research scientist, said the technology is now finally ready to make it come to life.’they really imagined everything back in 1995, but the technology I think is now ready,’ she said.

Even outside of Hollywood, these features are n’t exactly new ideas. They’ve been tested for years, often with mixed results.

Amazon is one of a number of firms that’s launched its own’Shazam for clothes’ using machine learning. Using machine vision to identify and shop for products has been a reality since at least the Amazon Fire phone.

Facebook’s new product recognition tool, groknet, can identify tens of thousands of different attributes in an image. These range from specific brands to things like color and size.

groknet has already been deployed on Facebook marketplace. It helps users quickly list items for sale by identifying what’s in them and generating short descriptions.

The company is testing a version of the tool that’s built for businesses. When they upload photos to a page containing their products, the AI system can automatically tag them and link to shopping pages.

groknet is trained on a colossal database of around 100 million images, with the majority taken from marketplace. The data is vital in creating a machine vision system that can identify products in challenging lighting and from dodgy angles.

The company says it can identify 90 percent of images on marketplace in the home and garden category. It did n’t give similar statistics for other types of product categories.