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

Regarding implementing symbolic AI, one of the oldest, yet still, the most popular, logic programming languages is Prolog comes in handy. Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages. The advantage of neural networks is that they can deal with messy and unstructured data. Instead of manually laboring through the rules of detecting cat pixels, you can train a deep learning algorithm on many pictures of cats. When you provide it with a new image, it will return the probability that it contains a cat. To summarize, one of the main differences between machine learning and traditional symbolic reasoning is how the learning happens.

Deductive reasoning is a type of propositional logic in AI, and it requires various rules and facts. It is sometimes referred to as top-down reasoning, and contradictory to inductive reasoning. AI researchers like Gary Marcus have argued that these systems struggle with answering questions like, “Which direction is a nail going into the floor pointing?” This is not the kind of question that is likely to be written down, since it is common sense. Being able to communicate in symbols is one of the main things that make us intelligent.

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This is because it is difficult to create a symbolic AI algorithm that is both powerful and efficient. These are just a couple of examples that illustrate that today’s systems don’t truly understand what they’re looking at. And what’s more, artificial neural networks rely on enormous amounts of data in order to train them, which is a huge problem in the industry right now. At the rate at which computational demand is growing, there will come a time when even all the energy that hits the planet from the sun won’t be enough to satiate our computing machines. Even so, despite being fed millions of pictures of animals, a machine can still mistake a furry cup for a teddy bear. The second argument was that human infants show some evidence of symbol manipulation.

When you have huge amounts of carefully curated data, you can achieve remarkable things with them, such as superhuman accuracy and speed. Right now, AIs have crushed humans at every single important game, from chess to Jeopardy! If you want a machine to learn to do something intelligent you either have to program it or teach it to learn. We can’t really ponder LeCun and Browning’s essay at all, though, without first understanding the peculiar way in which it fits into the intellectual history of debates over AI.

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Their proposed technique, LogiCoT, enhances LLMs with logical reasoning capabilities using a simple but effective principle called reductio ad absurdum. That is, until they realize how much time and money it saves them while mastering almost every aspect of natural language technologies—particularly question asking and answering. We typically use predicate logic to define these symbols and relations formally – more on this in the A quick tangent on Boolean logic section later in this chapter.

Knowledge completion enables this type of prediction with high confidence, given that such relational knowledge is often encoded in KGs and may subsequently be translated into embeddings. At Bosch Research in Pittsburgh, we are particularly interested in the application of neuro-symbolic AI for scene understanding. Scene understanding is the task of identifying and reasoning about entities – i.e., objects and events – which are bundled together by spatial, temporal, functional, and semantic relations. Others, like Frank Rosenblatt in the 1950s and David Rumelhart and Jay McClelland in the 1980s, presented neural networks as an alternative to symbol manipulation; Geoffrey Hinton, too, has generally argued for this position. We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning.

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Symbolic AI algorithms have played an important role in AI’s history, but they face challenges in learning on their own. After IBM Watson used symbolic reasoning to beat Brad Rutter and Ken Jennings at Jeopardy in 2011, the technology has been eclipsed by neural networks trained by deep learning. This approach involves the fusion of deep learning neural network topologies with symbolic reasoning techniques, thereby elevating the sophistication of AI beyond its traditional counterparts. For example, neural networks have proven effective in identifying an item’s shape or color.

Kahneman describes human thinking as having two components, System 1 and System 2. System 1 is the kind used for pattern recognition while System 2 is far better suited for planning, deduction, and deliberative thinking. In this view, deep learning best models the first kind of thinking while symbolic reasoning best models the second kind and both are needed.

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