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本文是的一部分Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.

Today, artificial intelligence is mostly about人工神经网络深度学习。But this is not how it always was. In fact, for most of its six-decade history, the field was dominated by symbolic artificial intelligence, also known as “classical AI,” “rule-based AI,” and “good old-fashioned AI.”

Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. The practice showed a lot of promise in the early decades of AI research. But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside.

The role of symbols in artificial intelligence

Symbols are things we use to represent other things. Symbols play a vital role in the human thought and reasoning process. If I tell you that I saw a cat up in a tree, your mind will quickly conjure an image.

We use symbols all the time to define things (cat, car, airplane, etc.) and people (teacher, police, salesperson). Symbols can represent abstract concepts (bank transaction) or things that don’t physically exist (web page, blog post, etc.). They can also describe actions (running) or states (inactive). Symbols can be organized into hierarchies (a car is made of doors, windows, tires, seats, etc.). They can also be used to describe other symbols (a cat with fluffy ears, a red carpet, etc.).


ai的早期先驱believedthat “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” Therefore, symbolic AI took center stage and became the focus of research projects. Scientists developed tools to define and manipulate symbols.

Many of the concepts and tools you find in computer science are the results of these efforts. Symbolic AI programs are based on creating explicit structures and behavior rules.


Using OOP, you can create extensive and complex symbolic AI programs that perform various tasks.

The benefits and limits of symbolic AI


Flowcharts can depict the logic of symbolic AI programs very clearly


但具有象征意义的AI当你必须开始打破with the messiness of the world. For instance, considercomputer vision,使计算机能够理解图像和视频的内容的科学。假设您有猫的照片,并希望创建一个可以检测包含您猫的图像的程序。您创建基于规则的程序,该程序将新图像作为输入,将像素与原始CAT图像进行比较,并通过说出您的猫是否在这些图像中进行响应。


One solution is to take pictures of your cat from different angles and create new rules for your application to compare each input against all those images. Even if you take a million pictures of your cat, you still won’t account for every possible case. A change in the lighting conditions or the background of the image will change the pixel value and cause the program to fail. You’ll need millions of other pictures and rules for those.


猫的例子可能听起来很傻,但这些是符号AI程序一直挣扎的问题。您无法为现实世界中存在的杂乱数据定义规则。例如,您如何定义一个规则self-driving carto detect all the different pedestrians it might face?

Also, some tasks can’t be translated to direct rules, including speech recognition andnatural language processing



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Neural networks are almost as old as symbolic AI, but they were largely dismissed because they were inefficient and required compute resources that weren’t available at the time. In the past decade, thanks to the large availability of data and processing power, deep learning has gained popularity and has pushed past symbolic AI systems.

The advantage of neural networks is that they can deal with messy and unstructured data. Take the cat detector example. Instead of manually laboring through the rules of detecting cat pixels, you can train a deep learning algorithm on many pictures of cats. The neural network then develops a statistical model for cat images. When you provide it with a new image, it will return the probability that it contains a cat.

Deep learning and neural networks excel at exactly the tasks that symbolic AI struggles with. They have created a revolution in computer vision applications such asfacial recognition和cancer detection. Deep learning has also driven advances in与语言相关的任务

Deep neural networks are also very suitable forreinforcement learning, AI models that develop their behavior through numerous trial and error. This is the kind of AI that masters complicated games such as Go, StarCraft, and Dota.

But the benefits of deep learning and neural networks are not without tradeoffs.Deep learning has several deep challenges和disadvantages in comparison to symbolic AI. Notably, deep learning algorithms are opaque, and figuring out how they work甚至是他们的创造者困惑。它很难communicate and troubleshoot their inner-workings.

Neural networks are also very data-hungry. And unlike symbolic AI, neural networks have no notion of symbols and hierarchical representation of knowledge. This limitation makes it very hard to apply neural networks to tasks that require logic and reasoning, such as science andhigh-school math

The current state of symbolic AI

有些人认为象征性的ai已经死了。但这种假设不可能远离真相。事实上,基于规则的AI系统在当今的应用中仍然非常重要。许多领先的科学家都相信symbolic reasoning will continue to remain a very important componentof artificial intelligence.

There are now several efforts to combine neural networks and symbolic AI. One such project is theNeuro-Symbolic Concept Learner (NSCL)是由MIT-IBM Watson AI Lab开发的混合AI系统。NSCL使用基于规则的程序和神经网络来解决视觉问答问题。与纯粹的基于神经网络的模型相反,Hybrid AI可以学习具有较少数据的新任务,可扩展。与象征性的模型不同,NSCL不会努力分析图像的内容。



  1. well written article…an increase in the amount of attention, education and awareness associated with these fields is a clear indicator for the need of artificial intelligence

  2. Very well written article. The only doubt I have regarding symbolic AI is that the reasoning process reflects the reasoning process of the creator who makes the symbolic AI program. It is not the reasoning process created by the program itself. If we are working towards AGI this would not help since an ideal AGI would be expected to come up with its own line of reasoning (which we expect to be better than the reasoning capacity of us human beings).

  3. I’m really surprised this article only describes symbolic AI based on the 1950s to 1990s descriptions when symbolic AI was ‘rules based’ and doesn’t include how symbolic AI transformed in the 2000s to present by moving from rules based to description logic ontology based. Description logic knowledge representation languages encode the meaning and relationships to give the AI a shared understanding of the integrated knowledge. Description logic ontologies enable semantic interoperability of different types and formats of information from different sources for integrated knowledge. The description logic reasoner / inference engine supports deductive logical inference based on the encoded shared understanding.

    It’s nearly impossible, unless you’re an expert in multiple separate disciplines, to join data deriving from multiple different sources. Accessing and integrating massive amounts of information from multiple data sources in the absence of ontologies is like trying to find information in library books using only old catalog cards as our guide, when the cards themselves have been dumped on the floor.

    Ontologies are data sharing tools that provide for interoperability through a computerized lexicon with a taxonomy and a set of terms and relations with logically structured definitions.

    Legacy symbolic AI is rules based, modern symbolic AI is ontology based. Pretty significant difference between the two.


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