COMP_SCI 496: AI Perspectives: Symbolic Reasoning to Deep Learning Computer Science Northwestern Engineering

It is a true fact, and it cannot be changed even if we add another sentence in knowledge base like, “The moon revolves around the earth” Or “Earth is not round,” etc. Common Sense reasoning simulates the human ability to make presumptions about events which occurs on every day. In inductive reasoning, premises provide probable supports to the conclusion, so the truth of premises does not guarantee the truth of the conclusion. Inductive reasoning is a type of propositional logic, which is also known as cause-effect reasoning or bottom-up reasoning.

One of the biggest is to be able to automatically encode better rules for symbolic AI. Alvaro Velasquez is a program manager in the Innovation Information Office of the Defense Advanced Research Projects Agency (DARPA), where he leads the Assured Neuro-Symbolic Learning and Reasoning (ANSR) program. Before that, Alvaro oversaw the machine intelligence portfolio of investments for the Information Directorate of the Air Force Research Laboratory. The TMS maintains the consistency of a knowledge base as soon as new

knowledge is added.

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Neuro-Symbolic AI enjoins statistical machine learning’s unsupervised and supervised learning techniques with symbolic reasoning methods to redouble AI’s enterprise worth. This total expression of AI realizes its full potential for cognitive search, textual applications, and natural language technologies. It’s the means of resolving the tension between the connectionist and symbolic approaches that have widely prevented them from working together in modern organizations’ IT systems.

Now we will learn the various ways to reason on this knowledge using different logical schemes. The thing symbolic processing can do is provide formal guarantees that a hypothesis is correct. This could prove important when the revenue of the business is on the line and companies need a way of proving the model will behave in a way that can be predicted by humans.

New Ideas in Neuro Symbolic Reasoning and Learning

A neuro-symbolic system, therefore, applies logic and language processing to answer the question in a similar way to how a human would reason. An example of such a computer program is the neuro-symbolic concept learner (NS-CL), created at the MIT-IBM lab by a team led by Josh Tenenbaum, a professor at MIT’s Center for Brains, Minds, and Machines. The Bosch code of ethics for AI emphasizes the development of safe, robust, and explainable AI products. By providing explicit symbolic representation, neuro-symbolic methods enable explainability of often opaque neural sub-symbolic models, which is well aligned with these esteemed values. Symbolic AI provides numerous benefits, including a highly transparent, traceable, and interpretable reasoning process.

The weakness of symbolic reasoning is that it does not tolerate ambiguity as seen in the real world. One false assumption can make everything true, effectively rendering the system meaningless. This attribute makes it effective at tackling problems where logical rules are exceptionally complex, numerous, and ultimately impractical to code, like deciding how a single pixel in an image should be labeled. “Neuro-symbolic [AI] models will allow us to build AI systems that capture compositionality, causality, and complex correlations,” Lake said. “Neuro-symbolic modeling is one of the most exciting areas in AI right now,” said Brenden Lake, assistant professor of psychology and data science at New York University.

So anyone who sees the mind as just a neural network will never admit the need for symbolic and logical reasoning (S&LR). But if they don’t accept this fact now, eventually they will — if, that is, they acknowledge the need for high-level representation of abstract concepts that allow us to “generalize in powerful ways”, which clearly Bengio does acknowledge. 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. To that end, we propose Object-Oriented Deep Learning, a novel computational paradigm of deep learning that adopts interpretable “objects/symbols” as a basic representational atom instead of N-dimensional tensors (as in traditional “feature-oriented” deep learning). For visual processing, each “object/symbol” can explicitly package common properties of visual objects like its position, pose, scale, probability of being an object, pointers to parts, etc., providing a full spectrum of interpretable visual knowledge throughout all layers.

Cyc has attempted to capture useful common-sense knowledge and has “micro-theories” to handle particular kinds of domain-specific reasoning. Alain Colmerauer and Philippe Roussel are credited as the inventors of Prolog. Prolog is a form of logic programming, which was invented by Robert Kowalski. Its history was also influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods.

Called expert systems, these symbolic AI models use hardcoded knowledge and rules to tackle complicated tasks such as medical diagnosis. But they require a huge amount of effort by domain experts and software engineers and only work in very narrow use cases. As soon as you generalize the problem, there will be an explosion of new rules to add (remember the cat detection problem?), which will require more human labor. OOP languages allow you to define classes, specify their properties, and organize them in hierarchies. You can create instances of these classes (called objects) and manipulate their properties.

• In turn, connectionist AI has been criticized as poorly suited for deliberative step-by-step problem solving, incorporating knowledge, and handling planning.
• Typically, an easy process but depending on use cases might be resource exhaustive.
• Researchers tried to simulate symbols into robots to make them operate similarly to humans.
• Production rules connect symbols in a relationship similar to an If-Then statement.
• Very tight coupling can be achieved for example by means of Markov logics.

In symbolic AI, discourse representation theory and first-order logic have been used to represent sentence meanings. Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of documents. In the latter case, vector components are interpretable as concepts named by Wikipedia articles.

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