Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact
Rizwan Qureshi, Ranjan Sapkota, Abbas Shah, Amgad Muneer, Anas Zafar, Ashmal Vayani, Maged Shoman, Abdelrahman B. M. Eldaly, Kai Zhang, Ferhat Sadak, Shaina Raza, Xinqi Fan, Ravid Shwartz-Ziv, Hong Yan, Vinjia Jain, Aman Chadha, Manoj Karkee, Jia Wu, Philip Torr, Seyedali Mirjalili
2025-07-02
Summary
This paper talks about the idea of Artificial General Intelligence (AGI), which means creating a type of intelligence in machines that can think, learn, and solve problems just like humans can. It looks at how combining knowledge from different fields can help build machines that reason, remember, and work together better.
What's the problem?
The problem is that achieving true intelligence in machines is very hard because it requires many complex abilities like reasoning in different areas, having memory, and cooperating with other agents. Current AI models are usually specialized and can't do all of this well at once.
What's the solution?
The paper proposes developing intelligent systems based on modular reasoning, which means breaking problems into parts, building strong memory systems, and enabling multiple agents to coordinate their actions. It also considers insights from brain science, computer science, and other fields to form better foundations for AGI.
Why it matters?
This matters because true artificial general intelligence could transform many parts of society by enabling machines to think and act like humans in a variety of tasks. Understanding the challenges and building solid foundations helps guide future research toward creating smarter and more helpful AI systems.
Abstract
The paper explores the development of Artificial General Intelligence by integrating insights from various fields, focusing on modular reasoning, memory, and multi-agent coordination, and highlights the challenges in achieving true intelligence.