A Review on Objective-Driven Artificial Intelligence

• URL: http://arxiv.org/abs/2308.10135v1
• Date: Sun, 20 Aug 2023 02:07:42 GMT
• Title: A Review on Objective-Driven Artificial Intelligence
• Authors: Apoorv Singh
• Abstract summary: Humans have an innate ability to understand context, nuances, and subtle cues in communication. Humans possess a vast repository of common-sense knowledge that helps us make logical inferences and predictions about the world. Machines lack this innate understanding and often struggle with making sense of situations that humans find trivial.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While advancing rapidly, Artificial Intelligence still falls short of human intelligence in several key aspects due to inherent limitations in current AI technologies and our understanding of cognition. Humans have an innate ability to understand context, nuances, and subtle cues in communication, which allows us to comprehend jokes, sarcasm, and metaphors. Machines struggle to interpret such contextual information accurately. Humans possess a vast repository of common-sense knowledge that helps us make logical inferences and predictions about the world. Machines lack this innate understanding and often struggle with making sense of situations that humans find trivial. In this article, we review the prospective Machine Intelligence candidates, a review from Prof. Yann LeCun, and other work that can help close this gap between human and machine intelligence. Specifically, we talk about what's lacking with the current AI techniques such as supervised learning, reinforcement learning, self-supervised learning, etc. Then we show how Hierarchical planning-based approaches can help us close that gap and deep-dive into energy-based, latent-variable methods and Joint embedding predictive architecture methods.

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)
• AI-as-exploration: Navigating intelligence space [0.05657375260432172]
  I articulate the contours of a rather neglected but central scientific role that AI has to play. The basic thrust of AI-as-exploration is that of creating and studying systems that can reveal candidate building blocks of intelligence.
  arXiv  Detail & Related papers  (2024-01-15T21:06:20Z)
• AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
  Mathematics is one of the most powerful conceptual systems developed and used by the human species. Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
  arXiv  Detail & Related papers  (2023-10-19T02:00:31Z)
• Understanding Natural Language Understanding Systems. A Critical Analysis [91.81211519327161]
  The development of machines that guillemotlefttalk like usguillemotright, also known as Natural Language Understanding (NLU) systems, is the Holy Grail of Artificial Intelligence (AI) But never has the trust that we can build guillemotlefttalking machinesguillemotright been stronger than the one engendered by the last generation of NLU systems. Are we at the dawn of a new era, in which the Grail is finally closer to us?
  arXiv  Detail & Related papers  (2023-03-01T08:32:55Z)
• Reflective Artificial Intelligence [2.7412662946127755]
  Many important qualities that a human mind would have previously brought to the activity are utterly absent in AI. One core feature that humans bring to tasks is reflection. Yet this capability is utterly missing from current mainstream AI. In this paper we ask what reflective AI might look like.
  arXiv  Detail & Related papers  (2023-01-25T20:50:26Z)
• 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)
• Making AI 'Smart': Bridging AI and Cognitive Science [0.0]
  With the integration of cognitive science, the 'artificial' characteristic of Artificial Intelligence might soon be replaced with'smart' This will help develop more powerful AI systems and simultaneously gives us a better understanding of how the human brain works. We argue that the possibility of AI taking over human civilization is low as developing such an advanced system requires a better understanding of the human brain first.
  arXiv  Detail & Related papers  (2021-12-31T09:30:44Z)
• 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)
• Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
  We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains. Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
  arXiv  Detail & Related papers  (2020-08-07T21:00:13Z)
• Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense [142.53911271465344]
  We argue that the next generation of AI must embrace "dark" humanlike common sense for solving novel tasks. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense.
  arXiv  Detail & Related papers  (2020-04-20T04:07:28Z)
• 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)

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.