Progress towards true Artificial General Intelligence (AGI) has hit a wall.

🌈 Abstract

The article discusses the quest for artificial general intelligence (AGI) and the challenges faced in achieving this goal. It highlights the limitations of modern AI systems, particularly language large models (LLMs), which rely heavily on memorization rather than reasoning. The article introduces the Abstraction and Reasoning Corpus (ARC-AGI) as a benchmark designed to test the skill-acquisition efficiency of AI systems, and emphasizes the importance of open-source research and collaboration in advancing towards AGI.

🙋 Q&A

[01] The quest for AGI

1. What is artificial general intelligence (AGI)?

• AGI refers to the ability of an AI system to perform a wide range of tasks that humans can do, without being explicitly programmed to do so.
• AGI systems have the capacity to learn, reason, and adapt to new situations, much like a human being.

2. What are the major obstacles to achieving AGI?

• One of the major obstacles is the reliance of modern AI systems on memorization rather than reasoning. LLMs are proficient at memorizing patterns in their training data and applying them in adjacent contexts, but they lack the ability to generate new reasoning based on new cases.
• Another issue is the inability of AI systems to generalize beyond their training data, which limits their ability to apply their knowledge to new situations.

3. How does the Abstraction and Reasoning Corpus (ARC-AGI) address the challenges in achieving AGI?

• The ARC-AGI benchmark is designed to test the skill-acquisition efficiency of AI systems over a scope of tasks, with the goal of verifying their ability to learn from examples and apply that knowledge to solve new, unseen problems.
• The ARC-AGI benchmark presents unique training and evaluation tasks involving images with grid-like inputs and outputs, requiring reasoning and abstraction to solve.

[02] The importance of open-source research

1. Why is open-source research important for advancing towards AGI?

• Open-source research promotes collaboration and knowledge sharing, which can accelerate the rate of progress towards AGI.
• By making research openly accessible, researchers from around the world can contribute to the development of more intelligent AI systems, and new ideas and innovations can emerge from a diverse range of perspectives.

2. How does the current trend towards closed-source research impact the progress towards AGI?

• The current trend towards closed-source research, driven by the belief that "scale is all you need" and the desire to protect competitive advantages, limits the sharing of ideas and knowledge, which stifles innovation and limits the rate of progress towards AGI.

3. How can open-source research help guide the development of more intelligent AI systems and provide a more accurate measure of general intelligence?

• Open-source research can help guide the development of more intelligent AI systems by allowing anyone, regardless of their programming expertise, to contribute to the development of these systems.
• It can also provide a more accurate measure of general intelligence by promoting an open debate on what intelligence is, from different disciplinary perspectives.