Deeper understanding with MUM

One of the hardest problems for search engines today is helping you with complex tasks — like planning what to do on a family outing. These often require multiple searches to get the information you need. In fact, we find that it takes people eight searches on average to complete complex tasks.

With a new technology called Multitask Unified Model, or MUM, we're able to better understand much more complex questions and needs, so in the future, it will require fewer searches to get things done. Like BERT, MUM is built on a Transformer architecture, but it’s 1,000 times more powerful and can multitask in order to unlock information in new ways. MUM not only understands language, but also generates it. It’s trained across 75 different languages and many different tasks at once, allowing it to develop a more comprehensive understanding of information and world knowledge than previous models. And MUM is multimodal, so it understands information across text and images and in the future, can expand to more modalities like video and audio.

Imagine a question like: “I’ve hiked Mt. Adams and now want to hike Mt. Fuji next fall, what should I do differently to prepare?” This would stump search engines today, but in the future, MUM could understand this complex task and generate a response, pointing to highly relevant results to dive deeper. We’ve already started internal pilots with MUM and are excited about its potential for improving Google products.

Source: Blog.google

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