The Rise and Fall of Symbolic AI Philosophical presuppositions of AI by Ranjeet Singh

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. Planning is used in a variety of applications, including robotics and automated planning. Symbolic AI algorithms are designed to solve problems by reasoning about symbols and relationships between symbols. A hybrid system that makes use of both connectionist and symbolic algorithms will capitalise on the strengths of both while counteracting the weaknesses of each other. The limits of using one technique in isolation are already being identified, and latest research has started to show that combining both approaches can lead to a more intelligent solution.

A change in the lighting conditions or the background of the image will change the pixel value and cause the program to fail. We use symbols all the time to define things (cat, car, airplane, etc.) and people (teacher, police, salesperson). Symbols can represent abstract concepts (bank transaction) or things that don’t physically exist (web page, blog post, etc.). Symbols can be organized into hierarchies (a car is made of doors, windows, tires, seats, etc.). They can also be used to describe other symbols (a cat with fluffy ears, a red carpet, etc.). Controversies arose from early on in symbolic AI, both within the field—e.g., between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)—and between those who embraced AI but rejected symbolic approaches—primarily connectionists—and those outside the field.

Understanding the impact of open-source language models

For instance, if you ask yourself, with the Symbolic AI paradigm in mind, “What is an apple? ”, the answer will be that an apple is “a fruit,” “has red, yellow, or green color,” or “has a roundish shape.” These descriptions are symbolic because we utilize symbols (color, shape, kind) to describe an apple. The need for symbolic techniques is getting a fresh wave of interest of late, with the recognition that for A.I. Based systems to be accepted in certain high-risk domains, their behaviour needs to be verifiable and explainable. “Neats” hope that intelligent behavior is described using simple, elegant principles (such as logic, optimization, or neural networks). “Scruffies” expect that it necessarily requires solving a large number of unrelated problems.

• To understand why the “how” behind AI functionality is so important, we first have to appreciate the fact that there have historically been two very different approaches to AI.
• One such project is the Neuro-Symbolic Concept Learner (NSCL), a hybrid AI system developed by the MIT-IBM Watson AI Lab.
• Historically, symbolic artificial intelligence has dominated artificial intelligence as a field of study for the majority of the last six decades.
• Knowledge-based systems have an explicit knowledge base, typically of rules, to enhance reusability across domains by separating procedural code and domain knowledge.

Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment. Although there are no AIs that can perform the wide variety of tasks an ordinary human can do, some AIs can match humans in specific tasks. The nature of connectionism-based systems is that, for all their power and performance, they are logically opaque. And in the absence of any kind of identifiable or verifiable train of logic, we are left with systems that are making potentially catastrophic decisions that are challenging to understand, extremely difficult to correct and impossible to trust. For a society that needs AI to be based on some shared framework of ethics or values, transparency in the pursuit of refinement is critically important.

The details about the best LLM model trainning and architecture and others revealed,

It achieves a form of “symbolic disentanglement”, offering one solution to the important problem of disentangled representations and invariance. Basic computations of the network include predicting high-level objects and their properties from low-level objects and binding/aggregating relevant objects together. These computations operate at a more fundamental level than convolutions, capturing convolution as a special case while being significantly more general than it. All operations are executed in an input-driven fashion, thus sparsity and dynamic computation per sample are naturally supported, complementing recent popular ideas of dynamic networks and may enable new types of hardware accelerations. We experimentally show on CIFAR-10 that it can perform flexible visual processing, rivaling the performance of ConvNet, but without using any convolution.

Other ways of handling more open-ended domains included probabilistic reasoning systems and machine learning to learn new concepts and rules. McCarthy’s Advice Taker can be viewed as an inspiration here, as it could incorporate new knowledge provided by a human in the form of assertions or rules. For example, experimental symbolic machine learning systems explored the ability to take high-level natural language advice and to interpret it into domain-specific actionable rules. The systems that fall into this category often involve deductive reasoning, logical inference, and some flavour of search algorithm that finds a solution within the constraints of the specified model. They often also have variants that are capable of handling uncertainty and risk. 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.

Search and optimization

Despite these limitations, symbolic AI has been successful in a number of domains, such as expert systems, natural language processing, and computer vision. As AI technology becomes more entwined with our lives and livelihoods, AI systems are making decisions about loans, powering facial recognition technology, piloting driverless cars and impacting fields like health care — and even military and law enforcement applications. Questions are being asked about whether we should let AI systems that lack transparency make decisions or take actions with such potentially drastic consequences. Even some of the original creators of deep learning technology are expressing skepticism and highlighting the need for a new way forward. To understand why the “how” behind AI functionality is so important, we first have to appreciate the fact that there have historically been two very different approaches to AI. Many early AI advances utilized a symbolistic approach to AI programming, striving to create smart systems by modeling relationships and using symbols and programs to convey meaning.

Generalization involves applying past experience to analogous new situations. Samuel’s Checker Program[1952] — Arthur Samuel’s goal was to explore to make a computer learn. The program improved as it played more and more games and ultimately defeated its own creator. In 1959, it defeated the best player, This created a fear of AI dominating AI. This lead towards the connectionist paradigm of AI, also called non-symbolic AI which gave rise to learning and neural network-based approaches to solve AI. The difficulties encountered by symbolic AI have, however, been deep, possibly unresolvable ones.

Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go. However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep learning techniques. For example, they require very large datasets to work effectively, entailing that they are slow to learn even when such datasets are available. Moreover, they lack the ability to reason on an abstract level, which makes it difficult to implement high-level cognitive functions such as transfer learning, analogical reasoning, and hypothesis-based reasoning.

Navigating the world of commercial open-source large language models

Monotonic basically means one direction; i.e. when one thing goes up, another thing goes up. Because machine learning algorithms can be retrained on new data, and will revise their parameters based on that new data, they are better at encoding tentative knowledge that can be retracted later if necessary; i.e. if they need to learn something new, like when data is non-stationary. Implementations of symbolic reasoning are called rules engines or expert systems or knowledge graphs.

It seems that wherever there are two categories of some sort, people are very quick to take one side or the other, to then pit both against each other. Artificial Intelligence techniques have traditionally been divided into two categories; Symbolic A.I. And Connectionist A.I. The latter kind have gained significant popularity with recent success stories and media hype, and no one could be blamed for thinking that they are what A.I. There have even been cases of people spreading false information to diverge attention and funding from more classic A.I. While the comparison is an imperfect one, it might be helpful to think of the distinction between symbolism-based AI and connectionism as similar to the difference between the mind and the brain. While the line between mind and brain has long been a source of debate in everything from religion to cognitive science, we generally recognize the mind as an expression of our thinking consciousness — the origins of thought, emotion and abstract logic.

Symbolic vs Connectionist A.I.

One difficult problem encountered by symbolic AI pioneers came to be known as the common sense knowledge problem. In addition, areas that rely on procedural or implicit knowledge such as sensory/motor processes, are much more difficult to handle within the Symbolic AI framework. In these fields, Symbolic AI has had limited success and by and large has left the field to neural network architectures (discussed in a later chapter) which are more suitable for such tasks. In sections to follow we will elaborate on important sub-areas of Symbolic AI as well as difficulties encountered by this approach. Symbols also serve to transfer learning in another sense, not from one human to another, but from one situation to another, over the course of a single individual’s life.

Thus contrary to pre-existing cartesian philosophy he maintained that we are born without innate ideas and knowledge is instead determined only by experience derived by a sensed perception. Children can be symbol manipulation and do addition/subtraction, but they don’t really understand what they are doing. So the ability to manipulate symbols doesn’t mean that you are thinking. YouTube, Facebook and others use recommender systems to guide users to more content. These AI programs were given the goal of maximizing user engagement (that is, the only goal was to keep people watching).

The DSN model provides a simple, universal yet powerful structure, similar to DNN, to represent any knowledge of the world, which is transparent to humans. The conjecture behind the DSN model is that any type of real world objects sharing enough common features are mapped into human brains as a symbol. Those symbols are connected by links, representing the composition, correlation, causality, or other relationships between them, forming a deep, hierarchical symbolic network structure. Powered by such a structure, the DSN model is expected to learn like humans, because of its unique characteristics.

If I tell you that I saw a cat up in a tree, your mind will quickly conjure an image. Limitations were discovered in using simple first-order logic to reason about dynamic domains. Problems were discovered both with regards to enumerating the preconditions for an action to succeed and in providing axioms for what did not change after an action was performed. The General Problem Solver (GPS) cast planning as problem-solving used means-ends analysis to create plans. 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.

Furthermore, it can generalize to novel rotations of images that it was not trained for. 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. We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers.

The store could act as a knowledge base and the clauses could act as rules or a restricted form of logic. As a subset of first-order logic Prolog was based on Horn clauses with a closed-world assumption — any facts not known were considered false — and a unique name assumption for primitive terms — e.g., the identifier barack_obama was considered to refer to exactly one object. At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to accelerate the development of AI applications and research.

Symbolic AI generates strings of characters representing real-world entities using symbols. The connectionist AI model, which is based on how the human brain works, provides AI processes that can be applied to human cognitive processes. Symbolic AI is well suited to applications with clear-cut rules and goals.

• Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5.
• Many leading scientists believe that symbolic reasoning will continue to remain a very important component of artificial intelligence.
• Symbolic AI algorithms are often based on formal systems such as first-order logic or propositional logic.
• They are more effective in scenarios where it is well-established that taking specific actions in certain situations could be beneficial or disastrous, and the system needs to provide the right mechanism to explictly encode and enforce such rules.

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