OpenAI's o3 system achieved a remarkable 85% score on the ARC-AGI test, demonstrating significant progress towards artificial general intelligence (AGI). This achievement raises hopes that AGI may be closer than anticipated.
On December 20, OpenAI’s o3 system scored 85% on the ARC- AGI test, well above the previous AI best score of 55% and on par with the average human score. It also scored well on a very difficult mathematics test. Creating artificial general intelligence, or AGI , is the stated goal of all the major AI research labs. At first glance, OpenAI appears to have at least made a significant step towards this goal. While scepticism remains, many AI researchers and developers feel something just changed.
For many, the prospect of AGI now seems more real, urgent and closer than anticipated. Are they right? To understand what the o3 result means, you need to understand what the ARC-AGI test is all about. In technical terms, it’s a test of an AI system’s “sample efficiency” in adapting to something new – how many examples of a novel situation the system needs to see to figure out how it works. An AI system like ChatGPT (GPT-4) is not very sample efficient. It was “trained” on millions of examples of human text, constructing probabilistic “rules” about which combinations of words are most likely. The result is pretty good at common tasks. It is bad at uncommon tasks, because it has less data (fewer samples) about those tasks. Until AI systems can learn from small numbers of examples and adapt with more sample efficiency, they will only be used for very repetitive jobs and ones where the occasional failure is tolerable. The ability to accurately solve previously unknown or novel problems from limited samples of data is known as the capacity to generalise. It is widely considered a necessary, even fundamental, element of intelligence.The ARC-AGI benchmark tests for sample efficient adaptation using little grid square problems like the one below. The AI needs to figure out the pattern that turns the grid on the left into the grid on the right. Each question gives three examples to learn fro
Artificial Intelligence AGI Openai ARC-AGI Sample Efficiency
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