18 Sep How neuro-symbolic AI might finally make machines reason like humans

The Various Types of Artificial Intelligence Technologies by Marie Stephen Leo Towards AI

Maybe in the future, we’ll invent AI technologies that can both reason and learn. But for the moment, symbolic AI is the leading method to deal with problems that require logical thinking and knowledge representation. For instance, consider computer vision, the science of enabling computers to make sense of the content of images and video. Expert systems can operate in either a forward chaining – from evidence to conclusions – or backward chaining – from goals to needed data and prerequisites – manner. More advanced knowledge-based systems, such as Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies. The authors suggest using Cyc’s inference capabilities to generate billions of “default-true statements” based on the explicit information in its knowledge base that could serve as the basis for training future LLMs to be more biased toward common sense and correctness.

A change in the lighting conditions or the background of the image will change the pixel value and cause the program to fail. The key AI programming language in the US during the last symbolic AI boom period was LISP. LISP is the second oldest programming language after FORTRAN and was created in 1958 by John McCarthy.

Probabilistic Programming

We also looked back at the other successes of Symbolic AI, its critical applications, and its prominent use cases. However, Symbolic AI has several limitations, leading to its inevitable pitfall. These limitations and their contributions to the downfall of Symbolic AI were documented and discussed in this chapter.

Multiple different approaches to represent knowledge and then reason with those representations have been investigated. Below is a quick overview of approaches to knowledge representation and automated reasoning. LLMs, on the other hand, can be trained to translate natural language sentences into CycL, the language that Cyc understands. By the late 1980s, the creators of Cyc developed CycL, a language to express the assertions and rules of the AI system. One of the main barriers to language models (LLMs) to use in practical applications is their unpredictability, lack of reasoning and uninterpretability.

Automate the exploratory data analysis (EDA) to understand the data faster and easier

Similarly, LISP machines were built to run LISP, but as the second AI boom turned to bust these companies could not compete with new workstations that could now run LISP or Prolog natively at comparable speeds. “Namely, a trustworthy general AI needs to be able to represent more or less anything that people say and write to each other,” Lenat and Marcus write. In their paper, Lenat and Marcus say that while AI does not need to think in exactly the same way as humans do, it must have 16 capabilities to be trusted “where the cost of error is high.” LLMs struggle in most of these areas.

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