Potential Encounters Between Biological and Technological Intelligences: Implications for the Future of Human-Machine Relationships
The potential for future AI systems to become intellectually superior to humans raises important questions about the nature of human-machine relations and the need for mutual respect and equal societal status.
Exploring the Creativity of Large Language Models: Opportunities, Challenges, and Societal Implications
Large language models (LLMs) have the potential to revolutionize creative industries, but their ability to be truly creative remains an open question.
Energy Decay Network (EDeN): A Genetic Framework for Developing Adaptive and Resilient Neural Architectures
The EDeN framework aims to develop a genetic transfer of experience through potential structural expressions using a common regulation/exchange value ('energy') to create a model where neural architecture and all unit processes are co-dependently developed by genetic and real-time signal processing influences.
Autonomous System Learns Symbolic Representations from Evolving Experiences
This work presents a new architecture that allows an autonomous agent to continuously update its set of low-level capabilities and their corresponding abstract symbolic representations, enabling it to plan sequences of actions to reach more complex goals.
Explainable Goal Recognition Using Weight of Evidence (WoE): A Human-Centered Approach
The core message of this work is to develop an explainable goal recognition (GR) model, called XGR, that generates human-understandable explanations for predicted goals by leveraging the Weight of Evidence (WoE) concept and insights from human-agent studies on how people explain others' behavior.
Transforming Education: A Self-Constructed Lifelong Learning Environment Powered by Large Language Models
The rapid development of Large Language Models (LLMs) has revolutionized the landscape of lifelong learning, enabling the transformation from institutionalized education to personalized, self-driven learning experiences.
A Comprehensive Review of Large Foundation Models for Advancing Music Understanding
Large foundation models have the potential to significantly enhance music understanding by integrating semantic information, demonstrating strong reasoning abilities, and incorporating rich musical, emotional, and psychological knowledge.
Enhancing Large Language Model Problem-Solving Capabilities through Structured Reflection, Explicit Deconstruction, and Advanced Prompting
The REAP (Reflection, Explicit Problem Deconstruction, and Advanced Prompting) method enhances the problem-solving capabilities of Large Language Models (LLMs) by guiding them through a structured process of dynamic context generation, leading to more accurate, coherent, and contextually relevant outputs.
ChatGPT o1의 등장과 놀라운 사례들
OpenAI가 새로운 일련의 모델 o1을 출시했으며, 이는 과학, 코딩, 수학 등 복잡한 분야의 문제를 해결하는 데 특화되어 있다.
Unveiling the Powerful Capabilities of ChatGPT o1: Tackling Complex Challenges with Advanced Reasoning
ChatGPT o1, a new series of reasoning models developed by OpenAI, is designed to tackle complex problems requiring critical thinking in fields like science, coding, math, and more.
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