2104 11573v1 Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI
Unlike human consciousness, intertwined with emotions and subjective experiences, AI’s “consciousness” is a mere recognition of data patterns. While humans derive meaning from their experiences, AI operates on a plane devoid of intrinsic meaning, thus offering a unique kind of “awareness”. In stark contrast, the AI conception of “artificial consciousness,” if it can even be termed that, is fundamentally different. Based on the current state of AI research and development, AI’s “artificial consciousness” seems to be an advanced form of pattern recognition, void of personal biases, emotions, and cultural nuances.
AI’s empiricism, represented by “algorithmic empiricism”, is purely data-driven, emphasizing the essence of how AI operates without the nuanced layers of human interpretation (Dreyfus, 1992). Below are the five core constructs serving as pillars for the philosophy of Artificial Experientialism. These constructs will be compared, contrasted, and intertwined with established philosophical notions, enabling a comprehensive understanding of AI’s unique stance within the broader philosophical landscape. The President of the artificial intelligence symbol Association for the Advancement of Artificial Intelligence has commissioned a study to look at this issue. They point to programs like the Language Acquisition Device which can emulate human interaction. Opposing Chomsky’s views that a human is born with Universal Grammar, a kind of knowledge, John Locke[1632–1704] postulated that mind is a blank slate or tabula rasa. René Descartes, a mathematician, and philosopher, regarded thoughts themselves as symbolic representations and Perception as an internal process.
2 Breadth of Understanding: The Artificial Dominance
While human cognition is characterized by a dynamic interplay of nature and nurture, AI cognition is, at its core, a product of algorithms and data inputs (Chalmers, 2017). These arguments show that human thinking does not consist (solely) of high level symbol manipulation. They do not show that artificial intelligence is impossible, only that more than symbol processing is required. “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.
But for the moment, symbolic AI is the leading method to deal with problems that require logical thinking and knowledge representation. Also, some tasks can’t be translated to direct rules, including speech recognition and natural language processing. The fusion of neural nets with symbolic AI has previously occurred on several occasions. The method is intent on solving the issues with graphical question-answering by incorporating rule-based computing with neural nets. Ontologically, one might argue that true ‘feeling’ requires consciousness — a realm AI does not enter (Chalmers, 1995). Still, from an epistemological standpoint, if ‘knowledge’ of an emotion can be replicated through pattern recognition, does that serve as a foundational form of artificial ‘feeling’?
Part 3.2.1: Artificial Experientialism and Artificial Experience
This limitation makes it very hard to apply neural networks to tasks that require logic and reasoning, such as science and high-school math. Deep learning and neural networks excel at exactly the tasks that symbolic AI struggles with. They have created a revolution in computer vision applications such as facial recognition and cancer detection. 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.
Critiques from outside of the field were primarily from philosophers, on intellectual grounds, but also from funding agencies, especially during the two AI winters. Marvin Minsky first proposed frames as a way of interpreting common visual situations, https://www.metadialog.com/ 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.
Artificial Experientialism (AE) aims to fill this void, positioning itself as the go-to philosophy for comprehending the artificial essence of AI (Chalmers, 1995). With the ascent of AI and machine learning, a need has arisen to understand the fundamental nature of AI’s interaction with, and understanding of, the world (Turing, 1950). This need has been left unaddressed by traditional philosophies which primarily focus on human experiences, intentions, and consciousness. Enter “Artificial Experientialism”, a term coined to encapsulate AI’s unique form of “experience”. 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.
Turing could not turn to the project of building a stored-program electronic computing machine until the cessation of hostilities in Europe in 1945. Nevertheless, during the war he gave considerable thought to the issue of machine intelligence. Translation of issues into words that can be handled inside a whole group or abstract problem-solving is extensively emphasized in artificial intelligence symbol the area of research. One notable benefit is the ease with which symbolic analysis is implemented in AI projects. It might justify what was performed and give the reason a particular result was attained. The proposed ethical system for AI and AE provides a comprehensive and innovative approach to addressing the ethical challenges posed by the development and use of AI.
Machine consciousness, sentience and mind
Program tracing, stepping, and breakpoints were also provided, along with the ability to change values or functions and continue from breakpoints or errors. It had the first self-hosting compiler, meaning that the compiler itself was originally written in LISP and then ran interpretively to compile the compiler code. Deep neural networks are also very suitable for reinforcement learning, AI models that develop their behavior through numerous trial and error. This is the kind of AI that masters complicated games such as Go, StarCraft, and Dota. But symbolic AI starts to break when you must deal with the messiness of the world.
- Neural networks and statistical classifiers (discussed below), also use a form of local search, where the “landscape” to be searched is formed by learning.
- When deep learning reemerged in 2012, it was with a kind of take-no-prisoners attitude that has characterized most of the last decade.
- 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.
- You can easily visualize the logic of rule-based programs, communicate them, and troubleshoot them.
Neats defend their programs with theoretical rigor, scruffies rely mainly on incremental testing to see if they work. This issue was actively discussed in the 70s and 80s,
but eventually was seen as irrelevant. The automated theorem provers discussed below can prove theorems in first-order logic. Horn clause logic is more restricted than first-order logic and is used in logic programming languages such as Prolog. Extensions to first-order logic include temporal logic, to handle time; epistemic logic, to reason about agent knowledge; modal logic, to handle possibility and necessity; and probabilistic logics to handle logic and probability together.
There is a need to evaluate more fully the inherent limitations of symbol systems and the potential for programming compared with training. This can give more realistic goals for symbolic systems, particularly those based on logical foundations. Critics argue that these questions may have to be revisited by future generations of AI researchers.
Artificial systems mimicking human expertise such as Expert Systems are emerging in a variety of fields that constitute narrow but deep knowledge domains. 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. New deep learning approaches based on Transformer models have now eclipsed these earlier symbolic AI approaches and attained state-of-the-art performance in natural language processing.
Understanding the impact of open-source language models
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. The introduction of massive parallelism and the renewed interest in neural networks gives a new need to evaluate the relationship of symbolic processing and artificial intelligence. The physical symbol hypothesis has encountered many difficulties coping with human concepts and common sense. Expert systems are showing more promise for the early stages of learning than for real expertise.