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You may not have heard of XiaoIce, which is predominantly popular in its Chinese language version—yet it has over 660 million registered users, and more than 5.3 million followers on Weibo, the Chinese equivalent of Twitter. Compare this to Microsoft’s English-language equivalent, Zo, which languishes on a mere 23,000 followers and is now quietly being retired.
This neural network approach goes some of the way to explaining why XiaoIce is succeeding while bots like Zo are failing: with a far greater user base, and fewer restrictions on what can be done with conversational data, XiaoIce’s neural networks can be trained on a substantially larger dataset: and, in the world of neural networks, this usually means better performance. XiaoIce’s CPS has risen from just 5 in 2014 to 23 last year: a large part of this is down to having more data from XiaoIce conversations to train on.
It remains to be seen how far—even with clever architecture—the neural network approach can lead. Can you really encode all the nuances of human interaction into matrices and vectors, and vast networks of statistical associations and weights? Can you solve the problem of contextual understanding? Is there enough data in the world to do this, or is having a true AI companion a problem that requires something like a general, human-level AI?