AI Cannot Ignore Symbolic Logic, and Heres Why by Walid Saba, PhD ONTOLOGIK
How neuro-symbolic AI might finally make machines reason like humans
When symbolic AI is combined with machine learning, this is often called hybrid AI. Symbolic AI, also known as rule-based AI or classical AI, uses a symbolic representation of knowledge, such as logic or ontologies, to perform reasoning tasks. Symbolic AI relies on explicit rules and algorithms to make decisions and solve problems, and humans can easily understand and explain their reasoning.
- Neuro-Symbolic AI enjoins statistical machine learning’s unsupervised and supervised learning techniques with symbolic reasoning methods to redouble AI’s enterprise worth.
- The power of neural networks is that they help automate the process of generating models of the world.
- At the Bosch Research and Technology Center in Pittsburgh, Pennsylvania, we first began exploring and contributing to this topic in 2017.
- Examples of the knowledge Welsh referenced include business terms or concepts like ‘customer’ that are identified in a specific set of documents so users can ask questions about it.
In his spare time, Tibi likes to make weird music on his computer and groom felines. He has a B.Sc in mechanical engineering and an M.Sc in renewable energy systems. “The general trend in AI and in computing as a whole, towards further and further automation and replacing hard-coded approaches with automatically learned ones, seems to be the way to go,” she added.
Introduction to Machine Learning: A Personal Journey to Decode the Complexity
In a new research paper, scientists from the University of Hamburg explore an innovative neurosymbolic technique to enhance logical reasoning in large language models (LLMs). By integrating neural networks with principles of symbolic logic, they have developed a method that significantly boosts the reasoning prowess of LLMs. Knowledge representation and formalization are firmly based on the categorization of various types of symbols.
What is symbolic reasoning and statistical reasoning?
Symbolic reason- ing is often based on either rules or schematic knowl- edge, which is hard to obtain. Relatively, statistical reasoning draws imprecise conclusions and is often data-driven so that it is hard to provide the human- centric explanation.
When combined with the power of Symbolic Artificial Intelligence, these large language models hold a lot of potential in solving complex problems. Such a framework called SymbolicAI has been developed by Marius-Constantin Dinu, a current Ph.D. student and an ML researcher who used the strengths of LLMs to build software applications. A number of researchers have been exploring the possibility of symbolic AI in law. One approach taken by some computer scientists is to represent a statute, such as an Act of Parliament, as a logic program, and convert the facts of a case into the same logic representation, and perform legal reasoning as a query in that logic language.
Neuro-symbolic AI for scene understanding
They can simplify sets of spatiotemporal constraints, such as those for RCC or Temporal Algebra, along with solving other kinds of puzzle problems, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint logic programming can be used to solve scheduling problems, for example with constraint handling rules (CHR). 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. Rish sees current limitations surrounding ANNs as a ‘to-do’ list rather than a hard ceiling.
By the mid-1960s neither useful natural language translation systems nor autonomous tanks had been created, and a dramatic backlash set in. “One of the reasons why humans are able to work with so few examples of a new thing is that we are able to break down an object into its parts and properties and then to reason about them. Many of today’s neural networks try to go straight from inputs (e.g. images of elephants) to outputs (e.g. the label “elephant”), with a black box in between. We think it is important to step through an intermediate stage where we decompose the scene into a structured, symbolic representation of parts, properties, and relationships,” Cox told ZME Science. Thus Reasoning can be defined as the logical process of drawing conclusions, making predictions or constructing approaches towards a particular thought with the help of existing knowledge.
Sections on Machine Learning and Uncertain Reasoning are covered earlier in the history section. Time periods and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture and the longer Wikipedia article on the History of AI, with dates and titles differing slightly for increased clarity.
In addition, symbolic AI algorithms can often be more easily interpreted by humans, making them more useful for tasks such as planning and decision-making. Don’t get us wrong, machine learning is an amazing tool that enables us to unlock great potential and AI disciplines such as image recognition or voice recognition, but when it comes to NLP, we’re firmly convinced that machine learning is not the best technology to be used. When deep learning reemerged in 2012, it was with a kind of take-no-prisoners attitude that has characterized most of the last decade. He gave a talk at an AI workshop at Stanford comparing symbols to aether, one of science’s greatest mistakes. McCarthy’s approach to fix the frame problem was circumscription, a kind of non-monotonic logic where deductions could be made from actions that need only specify what would change while not having to explicitly specify everything that would not change. Other non-monotonic logics provided truth maintenance systems that revised beliefs leading to contradictions.
By augmenting and combining the strengths of statistical AI, like machine learning, with the capabilities of human-like symbolic knowledge and reasoning, we’re aiming to create a revolution in AI, rather than an evolution. Deep learning is incredibly adept at large-scale pattern recognition and at capturing complex correlations in massive data sets, NYU’s Lake said. In contrast, deep learning struggles at capturing compositional and causal structure from data, such as understanding how to construct new concepts by composing old ones or understanding the process for generating new data.
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. First of all, every deep neural net trained by supervised learning combines deep learning and symbolic manipulation, at least in a rudimentary sense. Because symbolic reasoning encodes knowledge in symbols and strings of characters. In supervised learning, those strings of characters are called labels, the categories by which we classify input data using a statistical model. The output of a classifier (let’s say we’re dealing with an image recognition algorithm that tells us whether we’re looking at a pedestrian, a stop sign, a traffic lane line or a moving semi-truck), can trigger business logic that reacts to each classification. Expert system is a programming system which utilizes the information of expert knowledge of the specific domain to make decisions.
By integrating neural networks and symbolic reasoning, neuro-symbolic AI can handle perceptual tasks such as image recognition and natural language processing and perform logical inference, theorem proving, and planning based on a structured knowledge base. This integration enables the creation of AI systems that can provide human-understandable explanations for their predictions and decisions, making them more trustworthy and transparent. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. A. Deep learning is a subfield of neural AI that uses artificial neural networks with multiple layers to extract high-level features and learn representations directly from data. Symbolic AI, on the other hand, relies on explicit rules and logical reasoning to solve problems and represent knowledge using symbols and logic-based inference.
Graphplan takes a least-commitment approach to planning, rather than sequentially choosing actions from an initial state, working forwards, or a goal state if working backwards. Satplan is an approach to planning where a planning problem is reduced to a Boolean satisfiability problem. Japan championed Prolog for its Fifth Generation Project, intending to build special hardware for high performance. 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. Our chemist was Carl Djerassi, inventor of the chemical behind the birth control pill, and also one of the world’s most respected mass spectrometrists. We began to add in their knowledge, inventing knowledge engineering as we were going along.
Navigating the world of commercial open-source large language models
By adopting a divide-and-conquer approach for dividing a large and complex problem into smaller pieces, the framework uses LLMs to find solutions to the subproblems and then recombine them to solve the actual complex problem. I tried ingesting the facts of the case into BERT and asking questions such as who is the appellant? Although BERT was sometimes able to locate the answers in the text and locate substrings of the text, this is far from actually understanding and retrieving information. In essence, I found that that was a very sophisticated information retrieval system but did not come close to the complexity needed to model the real world.
The following chapters will focus on and discuss the sub-symbolic paradigm in greater detail. In the next chapter, we will start by shedding some light on the NN revolution and examine the current situation regarding AI technologies. 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. Following that, we briefly introduced the sub-symbolic paradigm and drew some comparisons between the two paradigms.
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What is symbolic reasoning in NLP?
The symbolic approach applied to NLP
With this approach, also called “deterministic”, the idea is to teach the machine how to understand languages in the same way as we, humans, have learned how to read and how to write.