1911 09606 An Introduction to Symbolic Artificial Intelligence Applied to Multimedia
Symbolic Artificial Intelligence continues to be a vital part of AI research and applications. Its ability to process and apply complex sets of rules and logic makes it indispensable in various domains, complementing other AI methodologies like Machine Learning and Deep Learning. Symbolic what is symbolic ai AI’s logic-based approach contrasts with Neural Networks, which are pivotal in Deep Learning and Machine Learning. Neural Networks learn from data patterns, evolving through AI Research and applications. Alain Colmerauer and Philippe Roussel are credited as the inventors of Prolog.
There are two types of approaches to Artificial Intelligence, namely Symbolic AI and Statistical AI. In the present paper we explicate and articulate the fundamental discrepancy between them, and explore how a unifying theory could be developed to integrate them, and what sort of cognitive rôles Integrated AI could play in comparison with present-day AI. We give, inter alia, a classification of Integrated AI, and argue that Integrated AI serves the purpose of humanising AI in terms of making AI more verifiable, more explainable, more causally accountable, more ethical, and thus closer to general intelligence.
Why some artificial intelligence is smart until it’s dumb
In contrast, a neural network may be right most of the time, but when it’s wrong, it’s not always apparent what factors caused it to generate a bad answer. The ultimate goal, though, is to create intelligent machines able to solve a wide range of problems by reusing knowledge and being able to generalize in predictable and systematic ways. Such machine intelligence would be far superior to the current machine learning algorithms, typically aimed at specific narrow domains. We’ve relied on the brain’s high-dimensional circuits and the unique mathematical properties of high-dimensional spaces.
With this formalism in mind, people used to design large knowledge bases, expert and production rule systems, and specialized programming languages for AI. What the ducklings do so effortlessly turns out to be very hard for artificial intelligence. This is especially true of a branch of AI known as deep learning or deep neural networks, the technology powering the AI that defeated the world’s Go champion Lee Sedol in 2016. Such deep nets can struggle to figure out simple abstract relations between objects and reason about them unless they study tens or even hundreds of thousands of examples. Two major reasons are usually brought forth to motivate the study of neuro-symbolic integration.
Situated robotics: the world as a model
«This is a prime reason why language is not wholly solved by current deep learning systems,» Seddiqi said. One promising approach towards this more general AI is in combining neural networks with symbolic AI. In our paper “Robust High-dimensional Memory-augmented Neural Networks” published in Nature Communications,1 we present a new idea linked to neuro-symbolic AI, based on vector-symbolic architectures. The AMR is aligned to the terms used in the knowledge graph using entity linking and relation linking modules and is then transformed to a logic representation.5 This logic representation is submitted to the LNN.
With our NSQA approach , it is possible to design a KBQA system with very little or no end-to-end training data. Currently popular end-to-end trained systems, on the other hand, require thousands of question-answer or question-query pairs – which is unrealistic in most enterprise scenarios. Our NSQA achieves state-of-the-art accuracy on two prominent KBQA datasets without the need for end-to-end dataset-specific training. Due to the explicit formal use of reasoning, NSQA can also explain how the system arrived at an answer by precisely laying out the steps of reasoning.
Part I Explainable Artificial Intelligence — Part II
A different way to create AI was to build machines that have a mind of its own. Binary classification is a type of supervised learning algorithm in machine learning that categorizes new observations into one of two classes. It’s a fundamental task in machine learning where the goal is to predict which of two possible classes an instance of data belongs to. The output of binary classification is a binary outcome, where the result can either be positive or negative, often represented as 1 or 0, true or false, yes or no, etc. Symbolic AI, a subfield of AI focused on symbol manipulation, has its limitations. Its primary challenge is handling complex real-world scenarios due to the finite number of symbols and their interrelations it can process.