![]() MUM can handle multiple tasks in two ways: On the training side and on the use side. The natural language tasks it can handle include (but are not limited to) ranking pages for a particular query, document review and information extraction. Multitasking also facilitates scaled learning. “MUM is also intrinsically multitasked,” Nayak said. Fuji?’” Prabhakar Raghavan, SVP at Google, said as a hypothetical example during the MUM unveiling at Google I/O, “MUM would be able to understand the content of the image and the intent behind your query.” Prabhakar Raghavan providing examples of how MUM might be integrated into Google Search at Google I/O. ![]() “Imagine taking a photo of your hiking boots and asking ‘Can I use these to hike Mt. MUM isn’t limited to text. Another distinction is that MUM is multimodal, meaning that its capabilities aren’t limited to text, it can also use video and images as inputs. If that’s true, it might help strengthen Google Search in those markets. Training across more languages scales learning. “ trained simultaneously across 75 languages,” Nayak said, “This is nice because it allows us to generalize from data-rich languages to languages with a paucity of data.” This may mean that MUM’s applications can be more easily transferred to more languages. While the two are both based on transformer technology and MUM has BERT language understanding capabilities built into it, MUM is based on a different architecture ( T5 architecture) and is capable of substantially more. It’s easy to classify MUM as a more advanced version BERT, especially since Google is treating it as a similarly important milestone for language understanding and touting it as being far more powerful than BERT. MUM, Google’s latest milestone in language understanding MUM has potential, now we wait and see how Google uses it.Mitigating the costs and risks of developing MUM.MUM, Google’s latest milestone in language understanding.
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