Shulou(Shulou.com)06/02 Report--

AI capability pyramid

In the past decade, the research of artificial intelligence has gradually transitioned from the methodology of designing artificial intelligence algorithms based on rules and knowledge to the methodology with data as the main driving force.

Under the guidance of this methodology, the current AI capability is supported by the "troika"-algorithm, big data, and strong computing power. The algorithm is mainly based on deep learning, and the data emphasize that it should be large enough (and supervised labeled data). Because deep learning often needs to set hundreds of millions of parameters through training, it also leads to a great reliance on the support of strong computing power.

Therefore, as long as the AI task meets the following two conditions, it can be solved:

1) dedicated AI tasks (rather than generic AI). For example, in medical images, the AI of pneumonia can only see pneumonia, not hepatitis, while the AI of CT can only see CT, not MRI.

2) "good" data is fertile (a large number of data are obtained through business models). The so-called good data, on the one hand, should have a larger scale, on the other hand, it should be well marked.

But such an AI is far from what we expected.

Dr. Hong Xiaowen, dean of Microsoft Research Asia, once proposed the pictured AI capability pyramid.

According to this pyramid level, the current AI technology still stays at the second level of "perception and simple reasoning", which is equivalent to the level of non-human primates.

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