The History of Artificial Intelligence, Machine Learning and Deep Learning

Traditional AI and its Influence on Modern Machine Learning Techniques

symbol based learning in ai

It not only allows an agent to recognize and describe objects in the world, but also correctly act on them. The concepts that are acquired, combining effect categories with object properties, offer a transparent view. The effect categories are expressed in terms of change in visibility, shape and position, and the object properties are stored in a numerical vector with explainable entries, such as features relating to position and shape (Ugur et al., 2011). Additionally, since the concepts are learned through unsupervised exploration, the proposed model is adaptive to the environment. New concepts can be added incrementally through additional exploration and learned concepts can be progressively updated (Ugur and Piater, 2015b).

In this case, the combination of GREEN and CUBE is discriminative. This procedure can be repeated for subsets of three concepts and four concepts, until a discriminative subset is found. As mentioned in section 3.1, the tutor looks for the smallest set of concepts that discriminates the topic from the other objects in the scene, based on the symbolic ground-truth annotation of the scene.

artificial intelligence (AI)

In 1952 he began writing the first computer program based on Machine Learning in which he was able to give an early demonstration of the fundamental concepts of Artificial Intelligence. The software was a program that played Chinese checkers and could improve its game with each game. Samuel continued to refine the program until it was able to compete with high-level players. One of the latest trends in popular AI, Firefly, is a software created by Adobe. Similarly, to DALL-E 2, Firefly uses Generative AI to create images from text, recolor images, created 3D models, or extend images beyond their borders by filling blank spaces.

Therefore, symbols have also played a crucial role in the creation of artificial intelligence. 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.). Critiques from outside of the field were primarily from philosophers, on intellectual grounds, but also from funding agencies, especially during the two AI winters.

What to know about augmented language models

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. Neural—allows a neural model to directly call a symbolic reasoning engine, e.g., to perform an action or evaluate a state. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data.

What are symbol systems examples?

  • Formal logic: the symbols are words like ‘and’, ‘or’, ‘not’, ‘for all x’ and so on.
  • Algebra: the symbols are ‘+’, ‘×’, ‘x’, ‘y’, ‘1’, ‘2’, ‘3’, etc.

Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization, and various forms of clustering. Machine learning (ML), reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory. In [86], an efficient algorithm is presented that extracts propositional rules enriched with confidence values from RBMs, similar to what was proposed with Penalty Logic for Hopfield networks in [59].

The concept SPHERE focusses on attributes related to shape, both in the simulated environment (C) and the extracted environment (D). To break down this paper, the core essence of this hypothesis is that given a particular nebulous task such as ‘get wealthy’ an agent starting from an initial state will transition through various states by making a series of decisions at each state. Initially, these decisions are likely to be less optimal but through trial and error one learns the optimal action and the learned experiences result in inductive reasoning from cause/effect and concept formation. Over time, given a new state, the agent uses the body of knowledge it has learned so that it can perform a type of ‘transfer learning’ to come up with an optimal action without having to sample and traverse many ‘trajectories’. Language models (LLMs), like ChatGPT, were trained using RL aided by human feedback, to help dictate the selection of tokens up language.

symbol based learning in ai

However, deep

learning-based NLP still suffers from serious shortcomings including poor

interpretability (the degree to which humans can understand), inferior scalability, and reduced robustness. For a combined perspective on reasoning and learning, it is useful to note that reasoning systems may have difficulties computationally when reasoning with existential quantifiers and function symbols, such as ∃xP(f(x)). Efficient logic-based programming languages such as Prolog, for example, assume that every logical statement is universally quantified. By contrast, learning systems may have difficulty when adopting universal quantification over variables. To be able to learn a universally quantified statement such as ∀xP(x), a learning systems needs in theory to be exposed to all possible instances of x.

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What is symbolic and sub symbolic approach to AI?

The main differences between these two AI fields are the following: (1) symbolic approaches produce logical conclusions, whereas sub-symbolic approaches provide associative results. (2) The human intervention is com- mon in the symbolic methods, while the sub-symbolic learn and adapt to the given data.

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