The Duality of Data and Knowledge Across the Three Waves of AI
The Duality of Data and Knowledge Across the Three Waves of AI
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DOI：
10.1109/mitp.2021.3070985
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发表时间：
2021-03
期刊：
影响因子：
2.6
通讯作者：
A. Sheth;K. Thirunarayan
中科院分区：
文献类型：
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作者：
A. Sheth;K. Thirunarayan
我们讨论了在过去的30-50年里，只关注数据的人工智能（AI）系统是如何受到阻碍的，以及知识如何在开发更智能，更智能和更有效的系统中发挥关键作用。事实上，人工智能的巨大进步可以从DARPA确定的三次人工智能浪潮中来看待。在第一次浪潮中，手工知识处于中心地位，而在第二次浪潮中，数据驱动的方法取代了知识。现在，我们看到知识的强大作用和复苏推动了第三次人工智能浪潮的重大突破，支撑着未来的智能系统，因为它们试图做出类似人类的决策，并寻求成为人类值得信赖的助手和伴侣。我们发现从不同来源创建的知识的更广泛的可用性，使用手动自动化的手段，无论是通过重新利用以及提取。将知识与统计学习结合使用，对于帮助人工智能系统变得更加透明和可审计变得越来越不可或缺。我们将与知识和经验在基于认知科学的人类智能中的作用进行比较，并讨论新兴的神经符号或混合人工智能系统，其中知识是将数据密集型统计人工智能系统的能力与符号人工智能系统相结合的关键推动因素，从而产生更强大的人工智能系统，支持更像人类的智能。
We discuss how, over the last 30–50 years, artificial intelligence (AI) systems that focused only on data have been handicapped and how knowledge has been critical in developing smarter, intelligent, and more effective systems. In fact, the vast progress in AI can be viewed in terms of the three waves of AI as identified by DARPA. During the first wave, handcrafted knowledge has been at the center, while during the second wave, the datadriven approaches supplanted knowledge. Now we see a strong role and resurgence of knowledge fueling major breakthroughs in the third wave of AI underpinning future intelligent systems as they attempt human-like decision making and seek to become trusted assistants and companions for humans. We find a wider availability of knowledge created from diverse sources, using manual to automated means both by repurposing as well as by extraction. Using knowledge with statistical learning is becoming increasingly indispensable to help make AI systems more transparent and auditable. We will draw a parallel with the role of knowledge and experience in human intelligence based on cognitive science, and discuss emerging neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining capabilities of the data-intensive statistical AI systems with those of symbolic AI systems, resulting in more capable AI systems that support more human-like intelligence.