For a company like Amazon, product recommendations are hugely important. They can be the difference between a one-off order and an unexpected spending spree. The company has spent years adapting its algorithms to produce the most relevant suggestions, but now it wants help. It's taken the wraps off DSSTNE -- its Deep Scalable Sparse Tensor Network Engine (pronounced destiny) -- and is asking for companies, researchers and developers to make its artificial intelligence framework even more powerful.
Amazon isn't the form to open source its machine learning software -- Google released Tensorflow late last year -- but the company believes it has more to offer than its rival. The company says DSSTNE excels when it has less data to work with, scales better across multiple machines and is easier to deploy. It also claims its AI can solve recommendation problems and perform natural language understanding tasks two times faster than Google's library.
In recent years, many of the world's biggest technology companies have invested heavily in machine learning. Google uses its AI to index your photos and improve the quality of its translations, while Facebook is exploring how to find deeper meaning in your News Feed. With help from external sources, Amazon wants to improve the quality of its own software and possibly apply what it's learnt to extend the capabilities of its popular storefront.
"We are releasing DSSTNE as open source software so that the promise of deep learning can extend beyond speech and language understanding and object recognition to other areas such as search and recommendations," says Amazon on the DSSTNE Github page. "We hope that researchers around the world can collaborate to improve it. But more importantly, we hope that it spurs innovation in many more areas."
If that means it'll stop Amazon suggesting we buy something we've already bought, then we're all for it.
Amazon is a $250 billion dollar company that reacts to you buying a vacuum by going THIS GUY LOVES BUYING VACUUMS HERE ARE SOME MORE VACUUMS— Daniel Kibblesmith (@kibblesmith) April 26, 2016