Projection: A Mechanism for Human-like Reasoning in Artificial
Intelligence
- URL: http://arxiv.org/abs/2103.13512v1
- Date: Wed, 24 Mar 2021 22:33:51 GMT
- Title: Projection: A Mechanism for Human-like Reasoning in Artificial
Intelligence
- Authors: Frank Guerin
- Abstract summary: Methods of inference exploiting top-down information (from a model) have been shown to be effective for recognising entities in difficult conditions.
Projection is shown to be a key mechanism to solve the problem of applying knowledge to varied or challenging situations.
- Score: 6.218613353519724
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Artificial Intelligence systems cannot yet match human abilities to apply
knowledge to situations that vary from what they have been programmed for, or
trained for. In visual object recognition methods of inference exploiting
top-down information (from a model) have been shown to be effective for
recognising entities in difficult conditions. Here this type of inference,
called `projection', is shown to be a key mechanism to solve the problem of
applying knowledge to varied or challenging situations, across a range of AI
domains, such as vision, robotics, or language. Finally the relevance of
projection to tackling the commonsense knowledge problem is discussed.
Related papers
- Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - Improving deep learning with prior knowledge and cognitive models: A
survey on enhancing explainability, adversarial robustness and zero-shot
learning [0.0]
We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses.
Brain-inspired cognition methods use computational models that mimic the human mind to enhance intelligent behavior in artificial agents and autonomous robots.
arXiv Detail & Related papers (2024-03-11T18:11:00Z) - Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components.
We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z) - Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.
PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - Knowledge as Invariance -- History and Perspectives of
Knowledge-augmented Machine Learning [69.99522650448213]
Research in machine learning is at a turning point.
Research interests are shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks.
This white paper provides an introduction and discussion of this emerging field in machine learning research.
arXiv Detail & Related papers (2020-12-21T15:07:19Z) - Hierarchical principles of embodied reinforcement learning: A review [11.613306236691427]
We show that all important cognitive mechanisms have been implemented independently in isolated computational architectures.
We expect our results to guide the development of more sophisticated cognitively inspired hierarchical methods.
arXiv Detail & Related papers (2020-12-18T10:19:38Z) - Machine Common Sense [77.34726150561087]
Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI)
This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
arXiv Detail & Related papers (2020-06-15T13:59:47Z) - Dynamic Cognition Applied to Value Learning in Artificial Intelligence [0.0]
Several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence.
It is of utmost importance that artificial intelligent agents have their values aligned with human values.
A possible approach to this problem would be to use theoretical models such as SED.
arXiv Detail & Related papers (2020-05-12T03:58:52Z) - Human Evaluation of Interpretability: The Case of AI-Generated Music
Knowledge [19.508678969335882]
We focus on evaluating AI-discovered knowledge/rules in the arts and humanities.
We present an experimental procedure to collect and assess human-generated verbal interpretations of AI-generated music theory/rules rendered as sophisticated symbolic/numeric objects.
arXiv Detail & Related papers (2020-04-15T06:03:34Z) - Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches.
Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
arXiv Detail & Related papers (2020-03-09T15:04:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.