Current winners of the ARC Challenge, Jack Cole and Mohammed Osman, along with collaborator Michael Hodel, share insights on their approach using language models and neural networks. They discuss fine-tuning techniques, creating new ARC-like tasks, and debating the true measure of intelligence in solving the ARC Challenge. The guests encourage exploration of ARC tasks and creative problem-solving among listeners.
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Quick takeaways
Efficiency and compositionality are crucial in solving ARC tasks by fine-tuning language models on augmented datasets.
Strategies for solving ARC tasks involve learning from limited examples and generalizing efficiently using active inference.
Collaboration and exploration of different solution paths are vital for enhancing generalization capabilities in tackling the ARC Challenge.
Incorporating prior knowledge and specialized ARC skills into training language models is essential for pushing boundaries in model efficiency.
Deep dives
Importance of ARC Challenge in Addressing the Knowledge Gap in Deep Learning Models
The ARC Challenge, created by Francois Chollet in 2017, serves as a unique benchmark focusing on skill acquisition and generalization. It addresses the knowledge gap in deep learning models by emphasizing reasoning and knowledge acquisition efficiency. Chollet recognized that deep learning models lack explicit reasoning abilities, requiring a different approach for tasks with a knowledge gap. The challenge consists of discrete tasks in a 2D grid world that test extrapolation and generalization from limited examples.
Efficiency and Compositionality in Solving ARC Tasks
Efficiency and compositionality are key in solving ARC tasks. Different approaches, such as fine-tuning language models on augmented datasets, aim to enhance reasoning efficiency. Understanding core concepts like connectedness, inside-outside relationships, and monotonicity aids in efficient problem-solving. Leveraging compositionality and fluid intelligence allows for deep generalization and efficient reasoning in tackling ARC challenges.
Learning and Generalization Strategies in ARC Task Solutions
Strategies in solving ARC tasks involve learning from limited examples and generalizing efficiently. Symbolic systems emphasize deep generalization while machine learning models focus on broader but shallower generalization. Active inference, utilizing fluid intelligence and compositionality, deepens the generalization of machine learning models. Data generation and DSL development play crucial roles in enhancing learning and generalization capabilities.
Approaches and Insights from previous Winners of the ARC Challenge
Previous winners of the ARC Challenge utilized various approaches and insights. A DSL-based solution with breath-first search and data augmentation led to competitive scores. Emphasizing data generation and knowledge priors enriched generalization capabilities. Collaboration and exploration of different solution paths showcased the importance of composability, efficient reasoning, and fluid intelligence in tackling the ARC Challenge.
Training a Model with Concept-Based Tagging
The speaker discusses the challenges of training a model with random DSL programs and inputs, emphasizing the difficulty of ensuring quality outputs. Instead of directly generating solutions, the speaker suggests looking at categorizing or tagging tasks based on concepts like object level transformations. They introduce the concept of the re-ARC dataset to explore learning tasks with an unlimited number of examples for better understanding and experimentation.
Implementing Multitask Training Approach
The discussion delves into implementing multitask training to enhance model performance. The team uses generated ARC tasks for training, emphasizing a significant boost in performance by fine-tuning the model to improve generalization. By combining various techniques under multitask training, they achieve substantial progress, reaching a winning score on the private set.
Considering the Role of Language Models and Neural Approaches
Exploring the use of language models and neural approaches, the speakers highlight the significance of incorporating prior knowledge and specialized ARC skills into the training process. They elaborate on fine-tuning language models with innovative representations and optimizations for improved performance. The team aims to push the boundaries of model efficiency while addressing fundamental questions in experimental AI research.
The ARC Challenge, created by Francois Chollet, tests how well AI systems can generalize from a few examples in a grid-based intelligence test. We interview the current winners of the ARC Challenge—Jack Cole, Mohammed Osman and their collaborator Michael Hodel. They discuss how they tackled ARC (Abstraction and Reasoning Corpus) using language models. We also discuss the new "50%" public set approach announced today from Redwood Research (Ryan Greenblatt).
Jack and Mohammed explain their winning approach, which involves fine-tuning a language model on a large, specifically-generated dataset and then doing additional fine-tuning at test-time, a technique known in this context as "active inference". They use various strategies to represent the data for the language model and believe that with further improvements, the accuracy could reach above 50%. Michael talks about his work on generating new ARC-like tasks to help train the models.
They also debate whether their methods stay true to the "spirit" of Chollet's measure of intelligence. Despite some concerns, they agree that their solutions are promising and adaptable for other similar problems.
Note:
Jack's team is still the current official winner at 33% on the private set. Ryan's entry is not on the private leaderboard or eligible.
Chollet invented ARC in 2019 (not 2017 as stated)
"Ryan's entry is not a new state of the art. We don't know exactly how well it does since it was only evaluated on 100 tasks from the evaluation set and does 50% on those, reportedly. Meanwhile Jacks team i.e. MindsAI's solution does 54% on the entire eval set and it is seemingly possible to do 60-70% with an ensemble"
Jack Cole:
https://x.com/Jcole75Cole
https://lab42.global/community-interview-jack-cole/
Mohamed Osman:
Mohamed is looking to do a PhD in AI/ML, can you help him?
Email: mothman198@outlook.com
https://www.linkedin.com/in/mohamedosman1905/
Michael Hodel:
https://arxiv.org/pdf/2404.07353v1
https://www.linkedin.com/in/michael-hodel/
https://x.com/bayesilicon
https://github.com/michaelhodel
Getting 50% (SoTA) on ARC-AGI with GPT-4o - Ryan Greenblatt
https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt
Neural networks for abstraction and reasoning: Towards broad generalization in machines [Mikel Bober-Irizar, Soumya Banerjee]
https://arxiv.org/pdf/2402.03507
Measure of intelligence:
https://arxiv.org/abs/1911.01547
YT version: https://youtu.be/jSAT_RuJ_Cg
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