Bootstrapping Developmental AIs: From Simple Competences to Intelligent Human-Compatible AIs

• URL: http://arxiv.org/abs/2308.04586v9
• Date: Wed, 4 Oct 2023 22:59:10 GMT
• Title: Bootstrapping Developmental AIs: From Simple Competences to Intelligent Human-Compatible AIs
• Authors: Mark Stefik and Robert Price
• Abstract summary: The mainstream AIs approaches are the generative and deep learning approaches with large language models (LLMs) and the manually constructed symbolic approach. This position paper lays out the prospects, gaps, and challenges for extending the practice of developmental AIs to create resilient, intelligent, and human-compatible AIs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The mainstream AIs approaches are the generative and deep learning approaches with large language models (LLMs) and the manually constructed symbolic approach. Both approaches have led to valuable AI systems and impressive feats. However, manually constructed AIs are brittle even in circumscribed domains. Generative AIs make strange mistakes and do not notice them. In both approaches the AIs cannot be instructed easily, fail to use common sense, and lack curiosity. They have abstract knowledge but lack social alignment. Developmental AIs have more potential. They start with innate competences, interact with their environment, and learn from their interactions. They interact and learn from people and establish perceptual, cognitive, and common grounding. Developmental AIs have demonstrated capabilities including multimodal perception, object recognition, and manipulation. Powerful computational models for hierarchical planning, abstraction discovery, curiosity, and language acquisition exist but need to be adapted to a developmental learning based approach. The promise is that developmental AIs will acquire self-developed and socially developed competences. They would address the shortcomings of current mainstream AI approaches, and ultimately lead to sophisticated forms of learning involving critical reading, provenance evaluation, and hypothesis testing. However, developmental AI projects have not yet fully reached the Speaking Gap corresponding to toddler development at about two years of age, before their speech is fluent. The AIs do not bridge the Reading Gap, to skillfully and skeptically learn from written and online information resources. This position paper lays out the prospects, gaps, and challenges for extending the practice of developmental AIs to create resilient, intelligent, and human-compatible AIs that learn what they need to know.

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)
• Distributed agency in second language learning and teaching through generative AI [0.0]
  ChatGPT can provide informal second language practice through chats in written or voice forms. Instructors can use AI to build learning and assessment materials in a variety of media.
  arXiv  Detail & Related papers  (2024-03-29T14:55:40Z)
• Cognition is All You Need -- The Next Layer of AI Above Large Language Models [0.0]
  We present Cognitive AI, a framework for neurosymbolic cognition outside of large language models. We propose that Cognitive AI is a necessary precursor for the evolution of the forms of AI, such as AGI, and specifically claim that AGI cannot be achieved by probabilistic approaches on their own. We conclude with a discussion of the implications for large language models, adoption cycles in AI, and commercial Cognitive AI development.
  arXiv  Detail & Related papers  (2024-03-04T16:11:57Z)
• Governance of the AI, by the AI, and for the AI [9.653656920225858]
  Authors believe the age of artificial intelligence has, indeed, finally arrived. Current state of AI is ushering in profound changes to many different sectors of society.
  arXiv  Detail & Related papers  (2023-05-04T03:29:07Z)
• Unpacking the "Black Box" of AI in Education [0.0]
  We seek to clarify what "AI" is and the potential it holds to both advance and hamper educational opportunities that may improve the human condition. We offer a basic introduction to different methods and philosophies underpinning AI, discuss recent advances, explore applications to education, and highlight key limitations and risks. Our hope is to make often jargon-laden terms and concepts accessible, so that all are equipped to understand, interrogate, and ultimately shape the development of human centered AI in education.
  arXiv  Detail & Related papers  (2022-12-31T18:27:21Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Building Human-like Communicative Intelligence: A Grounded Perspective [1.0152838128195465]
  After making astounding progress in language learning, AI systems seem to approach the ceiling that does not reflect important aspects of human communicative capacities. This paper suggests that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI. I propose a list of concrete, implementable components for building "grounded" linguistic intelligence.
  arXiv  Detail & Related papers  (2022-01-02T01:43:24Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

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.