Beyond Arc-Agi: Gaia and search for a real intelligence standard

Intelligence is widespread, however it appears to be personally. At best, we are closer to its measurement through tests and standards. Think of the college admission exams: Every year, countless students register, and to save the tricks that determine the test and sometimes they walk with perfect dozens. Does one number mean, for example, 100 %, those who got it in the same intelligence – or they somehow watching their intelligence? Of course not. The criteria are almost, not accurate measurements of a person’s capabilities – or something -.

the AI Tolide Society has long relied on criteria such as mmlu (Understanding a huge multi -task language) to assess model capabilities through multiple selection questions through academic disciplines. This coordination allows direct comparisons, but it failed to capture smart capabilities.

For example, Claude 3.5 Sonnet and GPT-4.5 achieve similar degrees in this standard. On paper, this indicates equivalent capabilities. However, people working with these models know that there are great differences in their performance in the real world.

What does “intelligence” mean in artificial intelligence?

On new heels Arc-Agi The standard version-a test designed to push models towards general thinking and solve creative problems-there is a renewed discussion about what means measuring “intelligence” in artificial intelligence. Although everyone has not experienced the ARC-Eagi standard yet, the industry welcomes this and other efforts to develop the test frameworks. Each standard has an advantage, and ARC-AGI is a promising step in that broader conversation.

Another prominent recent development in the evaluation of artificial intelligence is “The last humanity exam“A comprehensive criterion that contains 3000 reviews of peer, multi-step through various disciplines. While this test represents an ambitious attempt to challenge artificial intelligence systems thinking at the level of experts, the early results show quickly-where Openai has achieved 26.6 % degree within a month of its release. However, such as other traditional criteria, it holds in the first place knowledge and thinking about Isolation, without testing practical capabilities that use tools that are increasingly decisive for artificial intelligence applications in the real world.

In one example, multiple Modern models Failure to calculate the “R” number properly in the word strawberry. Elsewhere, they are incorrectly determined by 3.8 as smaller than 3.1111. These types of failures-in the tasks that even a small child or a basic calculator-reveal an inconsistency between the progress made by measurement and durability in the real world, to remind us that intelligence is not only related to exams, but about the movement of daily logic reliably.

The new criterion for measuring the ability of artificial intelligence

With the progress of the models, these traditional standards showed their restriction Gaia ConcermDespite the impressive degrees of multiple selection tests.

This separation between the standard performance and the practical ability has become increasingly formed like Artificial intelligence systems Moving from research environments to business applications. Traditional criteria test the knowledge of a summons, but miss the decisive aspects of intelligence: the ability to collect information, implement software instructions, analyze data, and combine solutions through multiple areas.

Gaya is the desired shift in the methodology of artificial intelligence. Through cooperation between Meta-Fair, Meta-Genai, Hugingface and Autogpt, the index includes 466 questions that are carefully designed across three difficulties. These questions test web browsing, multimedia understanding, implementing code, file processing, and complex thinking-basic capabilities of artificial intelligence applications in the real world.

Level 1 questions require about 5 steps and one tool for humans to solve them. Level 2 5 to 10 steps and tools require multiple steps and tools, while level 3 questions may require up to 50 separate steps and any number of tools. This structure reflects the actual complexity of work problems, as solutions rarely come from one procedure or tool.

By giving priority to flexibility in the complexity, the artificial intelligence model has reached 75 % on GAIA- Microsoft Magnetic-1 (38 %) and the Google LangFun (49 %). Their success stems from the use of a set of specialized models to understand sound and visual, with Sonnet 3.5 of humans as a basic model.

This development in the evaluation of artificial intelligence reflects a broader shift in this industry: we move from independent Saas applications to artificial intelligence agents that can organize multiple tools and functioning. Since companies are increasingly dependent on artificial intelligence systems to deal with complex multiple -steering tasks, standards such as GAIA provides a more feasible scale than traditional multiple selection tests.

The future of artificial intelligence does not lie in isolated knowledge tests, but in comprehensive assessments of the ability to solve problem. Gaia sets a new standard for measuring the ability of artificial intelligence-one that better reflects the challenges and opportunities of spreading artificial intelligence in the real world.

Sri Ambati is the founder and CEO of H2o.ai.

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