Keep pulling the thread on Francois Chollet.
A $500,000 prize will be awarded to the first team that achieves an 85% score on the ARC benchmark.
OpenAI has set back progress towards Artificial General Intelligence by an estimated 5 to 10 years.
OpenAI's actions led to a halt in the open publishing of frontier AI research.
Test-time fine-tuning is a critical component for achieving high performance with Jack Cole's approach on the ARC benchmark.
Increasing the size of a large language model's training database does not increase the system's intelligence.
Google's Gemini 1.5 model demonstrated the ability to learn and translate a language with fewer than 200 living speakers after being provided its grammar book and dictionary in-context.
AI models based on parametric curves, such as transformers, are fundamentally limited to local generalization and cannot achieve broader or extreme generalization.
The path to more capable AI involves creating a hybrid system that merges deep learning for System 1 tasks with discrete program search for System 2 tasks.
The future architecture for advanced AI will likely be a discrete program search system (System 2) guided by deep learning models that provide intuition (System 1).
Google's Gemini 1.5 Flash model matches the performance of the initial release of OpenAI's GPT-4 but is 57 times cheaper on output.
Jack Cole achieved a 35% score on the ARC benchmark using a 240 million parameter language model.
The most effective approach to solving the ARC challenge and making progress towards AGI will be a hybrid method that merges the deep learning paradigm with the discrete program search paradigm.