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这是我看过数百次的评论,或者在我在Kotaku的整个时间里的变化:互联网抱怨评论质量。 “一个机器人可以做得更好,”有些人会哭泣。因此,让我们对此进行测试。

我以前进行了此测试,尽管上次我将澳大利亚Kotaku Australia评论到机器学习模型中。该工具使用称为[Sumictly](的工具使用免费在线版本的GPT-2语言模型运行,尽管如果您现在可以使用更强大的GPT-3模型愿意付费访问API。

要求机器学习的元素实际上比预期的要好,但几乎所有机会都像模型一样采用了“伪造直到您接近它”。它会介绍错误的详细信息,有时甚至完全是来自不同项目的名称和开发人员。 __结论:AI无法查看视频游戏。


至少在评论方面,您无法击败人类的感觉。人们最了解哪些元素对他人很重要。 AI有一天会到达那里,但是那天不是今天。



It’s a comment I’ve seen hundreds of times, or variations of throughout my time here at Kotaku: internet complaints about the quality of reviews. “A bot can do better than this,” some would cry. So let’s put that to the test.

I’ve run this test before, although last time I fed Kotaku Australia comments into the machine learning model. That was run using a free online version of the GPT-2 language model through a tool called [Shortly](, although the more powerful GPT-3 model is available now if you’re willing to pay to access the API.

The elements where the machine learning was asked to riff on experience and emotion actually turned out better than expected, but at almost every opportunity it read like the model adopted the “fake it till you make it approach.” It’d introduce details that were wrong, sometimes even names and developers from different projects entirely. __Conclusion: AI can’t review a video game.__

But it’s interesting to see just how far that AI-generated content has come. I imagine we’re not far off the day where some outlets or news wires start dabbling with GPT-3 generation for press release material, simply because the sheer volume of content online outstrips the number of people available to write it (but not the potential readership).

When it comes to reviews at least, you can’t beat the human touch. People know best what elements matter to other people. AI will get there one day, but that day isn’t today.

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