2104 11573v1 Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI
Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is also the field of study in computer science that develops and studies intelligent machines. 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. Maybe in the future, we’ll invent AI technologies that can both reason and learn.
A similar problem, called the Qualification Problem, occurs in trying to enumerate the preconditions for an action to succeed. An infinite number of pathological conditions can be imagined, e.g., a banana in a tailpipe could prevent a car from operating correctly. 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.
4. Data Diversity versus True Understanding
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.). If I tell you that I saw a cat up in a tree, your mind will quickly conjure an image.
YAGO incorporates WordNet as part of its ontology, to align facts extracted from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology currently being used. In contrast to the US, in Europe the key AI programming language during that same period was Prolog.
The three pillars of AI: Symbols, Neurons
Applied AI, also known as advanced information processing, aims to produce commercially viable “smart” systems—for example, “expert” medical diagnosis systems and stock-trading systems. Applied AI has enjoyed considerable success, as described in the section Expert systems. Artificial Intelligence (AI) is a topic that has been explored since the 1950s, most notably by Alan Turing.
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. Planning is used in a variety of applications, including robotics and automated planning. Symbolic AI algorithms are based on the manipulation of symbols and their relationships to each other.
Are artificial intelligence and machine learning the same?
While recognizing the limitations of AI in terms of human-like consciousness, emotions, and experiences, AE also highlights the unique capabilities of AI in processing data, recognizing patterns, and simulating responses. Each approach—symbolic, connectionist, and behavior-based—has advantages, but has been criticized by the other approaches. Symbolic AI has been criticized as disembodied, liable to the qualification problem, and poor in handling the perceptual problems where deep learning excels.
- The General Problem Solver (GPS) cast planning as problem-solving used means-ends analysis to create plans.
- Maybe in the future, we’ll invent AI technologies that can both reason and learn.
- Instead, they produce task-specific vectors where the meaning of the vector components is opaque.
- A separate inference engine processes rules and adds, deletes, or modifies a knowledge store.
- The difficulties encountered by symbolic AI have, however, been deep, possibly unresolvable ones.
AE fills this void, offering a comprehensive framework that encapsulates the AI experience. The philosophy of Artificial Experientialism (AE) is fundamentally rooted in understanding this dichotomy. AE posits that while AI has an unparalleled breadth of understanding, it lacks the depth inherently present in human comprehension.
This txt introduces the concept of “Artificial Experientialism” (AE), a newly proposed philosophy and epistemology that explores the artificial “experience” of AI in data processing and understanding, distinct from human experiential knowledge. By identifying a gap in current literature, this exploration aims to provide an academic and rigorous framework for understanding the unique epistemic stance AI takes. The question of whether highly intelligent and completely autonomous machines would be dangerous has been examined in detail by futurists (such as artificial intelligence symbol the Machine Intelligence Research Institute). The obvious element of drama has also made the subject popular in science fiction, which has considered many differently possible scenarios where intelligent machines pose a threat to mankind; see Artificial intelligence in fiction. Questions like these reflect the divergent interests of AI researchers, cognitive scientists and philosophers respectively. The scientific answers to these questions depend on the definition of “intelligence” and “consciousness” and exactly which “machines” are under discussion.
Finally, symbolic AI is often used in conjunction with other AI approaches, such as neural networks and evolutionary algorithms. This is because it is difficult to create a symbolic AI algorithm that is both powerful and efficient. And unlike symbolic AI, neural networks have no notion of symbols and hierarchical representation of knowledge.
Samuel’s Checker Program — 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. During the first AI summer, many people thought that machine intelligence could be achieved in just a few years. The Defense Advance Research Projects Agency (DARPA) launched programs to support AI research with the goal of using AI to solve problems of national security; in particular, to automate the translation of Russian to English for intelligence operations and to create autonomous tanks for the battlefield.
Knowledge-based systems have an explicit knowledge base, typically of rules, to enhance reusability across domains by separating procedural code and domain knowledge. A separate inference engine processes rules and adds, deletes, https://www.metadialog.com/ or modifies a knowledge store. The logic clauses that describe programs are directly interpreted to run the programs specified. No explicit series of actions is required, as is the case with imperative programming languages.
Artificial Experientialism (AE), rooted in the interplay between depth and breadth, provides a novel lens through which we can decipher the essence of artificial experience. Unlike humans, AI does not possess a biological or emotional consciousness; instead, its ‘experience’ can be viewed as a product of data processing and pattern recognition (Searle, 1980). The difficulties encountered by symbolic AI have, however, been deep, possibly unresolvable ones. One difficult problem encountered by symbolic AI pioneers came to be known as the common sense knowledge problem.