What Is Missing from Contemporary AI? The World

IF 2.2 Q3 COMPUTER SCIENCE, CYBERNETICS International Journal of Intelligent Computing and Cybernetics Pub Date : 2022-07-25 DOI:10.34133/2022/9847630
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引用次数: 3

Abstract

In the past three years, we have witnessed the emergence of a new class of artificial intelligence systems–—so-called foundation models, which are characterised by very large machine learning models (with tens or hundreds of billions of parameters) trained using extremely large and broad data sets. Foundation models, it is argued, have competence in a broad range of tasks, which can be specialised for specific applications. Large language models, of which GPT-3 is perhaps the best known, are the most prominent example of current foundation models. While foundation models have demonstrated impressive capabilities in certain tasks—natural language generation being the most obvious example—I argue that because they are inherently disembodied, and they are limited with respect to what they have learned and what they can do. Foundation models are likely to be very useful in many applications: but they are not the end of the road in artificial intelligence.
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当代人工智能缺少什么?世界
在过去三年中,我们目睹了一类新的人工智能系统的出现——所谓的基础模型,其特点是使用极其庞大和广泛的数据集训练的非常大的机器学习模型(具有数百亿或数千亿个参数)。有人认为,基础模型在广泛的任务范围内具有能力,可以专门用于特定的应用。大型语言模型,其中GPT-3可能是最著名的,是当前基础模型中最突出的例子。虽然基础模型在某些任务中表现出了令人印象深刻的能力——自然语言生成是最明显的例子——但我认为这是因为它们本质上是无实体的,它们在所学和所能做的方面是有限的。基础模型可能在许多应用中非常有用:但它们并不是人工智能道路的终点。
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CiteScore
6.80
自引率
4.70%
发文量
26
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