There are many ways to test the intelligence of a artificial intelligence-Conversational fluidity, understanding of reading or thinking – difficult physics. But some of the tests are most likely to be contrary AIS are people who are easily found, even entertaining. Although AISs are more likely than the functions that require high-level human skill level, it does not mean that they are about to reach artificial overall intelligence, or through. Through It is necessary that an AI can be a small amount of information and use it generally and adapt to high novel conditions. This ability, which is the basis of human learning, remained challenging for AIS.
A test designed to evaluate the ability of AI abstinence is abstize and reasoning puzzles that ask a solver to apply a new grid. AI promotes François Chollet in 2019, it becomes a basis for the Arc Prior Foundation, a nonprofit program in charge of all industry models using all AI models. The organization also develops new trials and used to use both (arc-through-1 and more challenging successful arc-agi-2). This week the Foundation launches the Arc-AGI-3, specifically designed for AI agents test – and is based on making them video games.
American American Talked to the Arc Prior Foundation President, AI researcher Greg Kamradt to know how these AIS tests and what they told us about people who were more easily. Links to try tests are at the end of the article.
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(An edited transcript in the interview follows.)
What sense of intelligence is measured in the arc-agi-1?
Our sense of intelligence is your ability to learn new things. We’ve already learned that AI can win chess. We know they can be overcome. But those models cannot be done in new domains; They can’t go and learn English. So what the François Chollet is appointed Arc-through-it teaches you a mini skill in question, and then you ask to show that mini skill. We usually point to something and asks you to repeat the skill you know. So try to test the ability of a model to learn a narrow domain. But our claim is that it does not measure the through because it is still in a scoped domain (where learning is available in a limited area). It is intensified that an AI can suffer, but we do not claim it is through.
How do you determine this here?
There are two ways I look at it. The first is the more tech-forward, which ‘can be an artificial system that matches a person’s learning?’ Now what I mean that after people are born, they learn outside of their training data. In fact, they never have Training data, except for some evolutionary priors. So we know how to speak English, we know how to drive a car, and we know how to ride a bike – all these things are outside our training data. Which is called Generalzation. If you can do things outside of what you are currently trained, we mean as intelligence. Today, an alternative meaning to the way we use is when we can no longer have problems that people can and cannot – that’s if we have any income. That’s a report on observation. Also the flip side is also one of the factors about François Chollet’s benchmark … so we have tried people to them, and the average person can do these tasks and these problems, but AI has a very difficult time. The reason that is interesting is that some advanced AIS, such as the groke, can undergo any graduate-level exam or do all the crazy things, but that is intelligence. It still does not have the power to form a person. And that’s what this benchmark shows.
How can your benchmarks differ from other organizations used?
One of the things we differently is that we need our benchmark to be absorbed by people. That’s in opposition to other benchmarks, where they do the problems “Ph.D.-plus-plus”. I don’t have to say that AI is smarter than me – I already know that OpenII’s O3 can do more things better than me, but it doesn’t make the human power overall. That’s what we’re measuring, so we need to try people. We truly try 400 people in Arc-through-2. We got them in a room, we gave them computers, we had a demographic screening, and then gave them the test. The average person scored 66 percent of the arc-agi-2. However, at all, the accumulated answers in five to 10 people contain correct answers to all arc2 questions.
What makes it difficult for AI and is too easy for people?
There are two things. People are more examples of their learning, which means they can watch a problem and perhaps one or two examples, they can get false skill or change and they can get it. The algorithm running on the human head is orders of magnitude better and better than what we see in AI today.
What is the difference between arc-agi-1 and arc-agi-2?
So Arc-agi-1, François Chollet made himself. About 1,000 functions. That year. He mainly made the minimum version to measure the progress, and it was made for five years because deep learning could never be touched. It does not close. Then models argue that began at 2024, in Openi, began to progress to it, showing the change in the level of course. Then, when we went to the arc-agi-2, we walked a little down the rabbit hole in connection with the people and could not. It requires a little more plan for every task. So instead of solving within five seconds, people can do it for a minute or two. There are more complex rules, and grids are larger, so it should be more accurate in your response, but this is the same concept, more likely to launch Arc-through-3, and that will completely leave this format. The new format can actually be interactive. So think of this a benchmark to the agent.
How are the various agents to test Arc-agi-3-3 agents different compared to previous trials?
If you think about everyday life, we rarely have a pointless decision. If I say it’s worthless, I just mean a question and answer. Now all benchmarks are more or less benchmarks in vain. If you’re asking a language model a question, it gives you a response. There is a lot you don’t test with a bad benchmark. You can’t try to plan. You cannot try to explore. You can’t test the urge around you or the goals that come. So we make 100 novel video games to use to try people to make sure that people can do them because that’s the basis of our benchmark. And then we’ll drop AIS with these video games and see if they know this environment they have never seen before. So far, with our internal test, we don’t have an AI capable of beating either one of the games.
Can you describe video games here?
Every “environment,” or video game, a two-dimensional, Poxel based puzzle. These games are placed as a different level, each intended to point out a specific skill of the player’s mini (person or AI). To successfully complete a level, the player must display the skill of that skill by implementing the planned consecutive actions.
How to use video games to try for the AGI differently in ways that video games have previously been used to test AI systems?
Video games have long been used as AI research benchmarks, with Atari games a popular example. But traditional videos game benchmarks faced with many limits. Famous games have a large public training data, no standardized performance testing metrics and allowing the prompting methods involving billions of simulations. In addition, developers to build AI agents usually have the first knowledge of these games – accidentally embed their own views of solutions.
