Reconciling deep learning with symbolic artificial intelligence: representing objects and relations

He gave a talk at an AI workshop at Stanford comparing symbols to aether, one of science’s greatest mistakes. Marvin Minsky first proposed frames as a way of interpreting common visual situations, such as an office, and Roger Schank extended this idea to scripts for common routines, such as dining out. Cyc has attempted to capture useful common-sense knowledge and has “micro-theories” to handle particular kinds of domain-specific reasoning. For decades, engineers have been programming machines to perform all sorts of tasks — from software that runs on your personal computer and smartphone to guidance control for space missions.

The work in AI started by projects like the General Problem Solver and other rule-based reasoning systems like Logic Theorist became the foundation for almost 40 years of research. Symbolic AI (or Classical AI) is the branch of artificial intelligence research that concerns itself with attempting to explicitly represent human knowledge in a declarative form (i.e. facts and rules). If such an approach is to be successful in producing human-like intelligence then it is necessary to translate often implicit or procedural knowledge possessed by humans into an explicit form using symbols and rules for their manipulation. Artificial systems mimicking human expertise such as Expert Systems are emerging in a variety of fields that constitute narrow but deep knowledge domains.

Inductive Reasoning:

Hadayat Seddiqi, director of machine learning at InCloudCounsel, a legal technology company, said the time is right for developing a neuro-symbolic learning approach. “Deep learning in its present state cannot learn logical rules, since its strength comes from analyzing correlations in the data,” he said. In this line of effort, deep learning systems are trained to solve problems such as term rewriting, planning, elementary algebra, logical deduction or abduction or rule learning. These problems are known to often require sophisticated and non-trivial symbolic algorithms.

This website is using a security service to protect itself from online attacks. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. We can see that Cyc (and thus First Order Logic) is able to represent many varied distinctions and traits that we understand about people (i.e. a person generalizes primate … generalizes mammal … generalizes vertebrate … generalizes animal). We can also understand that a person is an agent and that a person exists with some temporal properties (i.e. exists for a duration of time). The distinction between a set, a subset and an element of a set is an important thing to distinguish when reasoning about the world. If Fred is a person and a person is a collection (i.e. a collection of all people), Fred is not a collection.

The current state of symbolic AI

For example, the team has demonstrated a few ENN applications to automatically discover algorithms and generate novel computer code. “Standard deep learning took several decades of development to get where it is now, but ENNs will be able to take shortcuts by learning from what has worked with deep learning thus far,” he said. Inductive reasoning is a form of reasoning to arrive at a conclusion using limited sets of facts by the process of generalization.

If I tell you that I saw a cat up in a tree, your mind will quickly conjure an image. 2) The two problems may overlap, and solving one could lead to solving the other, since a concept that helps explain a model will also help it recognize certain patterns in data using fewer examples. But not everyone is convinced that this is the fastest road to achieving general artificial intelligence. But although computers are generally much faster and more precise than the human brain at sequential tasks, such as adding numbers or calculating chess moves, such programs are very limited in their scope. If you want a machine to learn to do something intelligent you either have to program it or teach it to learn.

Say you have a picture of your cat and want to create a program that can detect images that contain your cat. You create a rule-based program that takes new images as inputs, compares the pixels to the original cat image, and responds by saying whether your cat is in those images. Being able to communicate in symbols is one of the main things that make us intelligent.

Early work covered both applications of formal reasoning emphasizing first-order logic, along with attempts to handle common-sense reasoning in a less formal manner. It is one form of assumption, and a strong one, while deep neural architectures contain other assumptions, usually about how they should learn, rather than what conclusion they should reach. The ideal, obviously, is to choose assumptions that allow a system to learn flexibly and produce accurate decisions about their inputs. David Cox is the head of the MIT-IBM Watson AI Lab, a collaboration between IBM and MIT that will invest $250 million over ten years to advance fundamental research in artificial intelligence. Meanwhile, the human brain can recognize and label objects effortlessly and with minimal training — basically we only need one picture. The gist is that humans were never programmed (not like a digital computer, at least) — humans have become intelligent through learning.

The team chose to focus on statute law because statutory law is “definitional in nature” and can be more easily translated into logic. This seems to me to be simpler than attempting to model case law and simulate arguments from precedent or analogy computationally. AI based legal reasoning may be easier for Continental law rather than English and American law because Continental law systems are statute-based, but I would need a legal expert to confirm this. Logical programming languages are languages that are good at representing concepts such as a cat is a mammal, all mammals produce milk, and inferring that therefore, a cat produces milk. Symbolic AI provides numerous benefits, including a highly transparent, traceable, and interpretable reasoning process.

That is, a symbol offers a level of abstraction above the concrete and granular details of our sensory experience, an abstraction that allows us to transfer what we’ve learned in one place to a problem we may encounter somewhere else. In a certain sense, every abstract category, like chair, asserts an analogy between all the disparate objects called chairs, and we transfer our knowledge about one chair to another with the help of the symbol. You could achieve a similar result to that of a neuro-symbolic system solely using neural networks, but the training data would have to be immense. Moreover, there’s always the risk that outlier cases, for which there is little or no training data, are answered poorly. In contrast, this hybrid approach boosts a high data efficiency, in some instances requiring just 1% of training data other methods need. Legal reasoning is the process of coming to a legal decision using factual information and information about the law, and it is one of the difficult problems within legal AI.

Neuro symbolic artificial intelligence (NSAI) encompasses the combination of deep neural networks with symbolic logic for reasoning and learning tasks. NSAI frameworks are now capable of embedding prior knowledge in deep learning architectures, guiding the learning process with logical constraints, providing symbolic explainability, and using gradient-based approaches to learn logical statements. We investigate an unconventional direction of research that aims at converting neural networks, a class of distributed, connectionist, sub-symbolic models into a symbolic level with the ultimate goal of achieving AI interpretability and safety. It achieves a form of “symbolic disentanglement”, offering one solution to the important problem of disentangled representations and invariance.

Do we understand the decisions behind the countless AI systems throughout the vehicle? Like self-driving cars, many other use cases exist where humans blindly trust the results of some AI algorithm, even though it’s a black box. Given a specific movie, we aim to build a symbolic program to determine whether people will watch it. At its core, the symbolic program must define what makes a movie watchable. Then, we must express this knowledge as logical propositions to build our knowledge base.

Symbolic AI programs are based on creating explicit structures and behavior rules. Symbolic AI algorithms are based on the manipulation of symbols and their relationships to each other. Symbolic AI is able to deal with more complex problems, and can often find solutions that are more elegant than those found by traditional AI algorithms.

Time periods and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture[17] and the longer Wikipedia article on the History of AI, with dates and titles differing slightly for increased clarity. The words sign and symbol derive from Latin and Greek words, respectively, that mean mark or token, as in “take this rose as a token of my esteem.” Both words mean “to stand for something else” or “to represent something else”.

We discussed the process and intuition behind formalizing these symbols into logical propositions by declaring relations and logical connectives. Symbolic AI is more concerned with representing the problem in symbols and logical rules (our knowledge base) and then searching for potential solutions using logic. In Symbolic AI, we can think of logic as our problem-solving technique and symbols and rules as the means to represent our problem, the input to our problem-solving method.

• Schematic view of part of Robert Kowalski’s logical representation of the British Nationality Act.
• However, in contrast to neural networks, it is more effective and takes extremely less training data.
• However, more general legal work which can need a complex analysis of statute and precedent would be very hard to solve with machine learning.
• This hybrid approach enables machines to reason symbolically while also leveraging the powerful pattern recognition capabilities of neural networks.

In Symbolic AI, we formalize everything we know about our problem as symbolic rules and feed it to the AI. Note that the more complex the domain, the larger and more complex the knowledge base becomes. The first objective of this chapter is to discuss the concept of Symbolic AI and provide a brief overview of its features.

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