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Artificial intelligence (AI) has made remarkable advancements in recent years, surpassing certain human abilities and raising the question of whether machines will eventually reach our level of intelligence. One particular area where AI has made significant progress is in the reproduction of human intelligence. This includes the ability to learn the meaning of words and apply that knowledge to other linguistic concepts, a skill that was once considered exclusive to humans.
Humans have a remarkable capacity to abstract concepts and recognize objects based on their shapes, regardless of their color or composition. For example, we can identify cloud formations and understand their significance. This cognitive ability, known as composite generalization, has been a subject of interest for cognitive scientists. Back in the mid-1980s, Jerry Fodor and Zenon Pylyshyn proposed that artificial neural networks could potentially develop this capability, but progress in the field has been limited since then.
However, researchers from New York University and Pompeu Fabra University in Spain have been diligently working on this subject for some time now. They have recently introduced a new technique called “Meta-aprendizado para composicionalidade” (MLC), which aims to replicate the human ability of composite generalization in AI systems. The results of their study were published in the prestigious scientific journal Nature.
The experiments conducted as part of this research have shown that AI, through the use of tools similar to ChatGPT, not only has the potential to match human intelligence but can even surpass it. Surprisingly, this achievement was not the result of traditional learning methods but by allowing the AI systems to actively engage and interact with their environment.
In the experiment, the AI system (referred to as INA) was given a word and tasked with applying it in a different context. For example, when given the word “falar” (which means “to speak” in Portuguese), the system was asked to create various contexts such as “falar muito” (to speak a lot), “falar pouco” (to speak a little), “falar baixo” (to speak softly), and “falar alto” (to speak loudly). The AI system was remarkably successful in understanding and applying these linguistic variations.
As AI continues to develop and improve, it is expected to comprehend even more complex language patterns, including idiomatic expressions such as “falar abobrinha” (to speak nonsense) and “falar besteira” (to speak nonsense or trivialities). This level of linguistic understanding will enable AI systems to effectively communicate with a wider audience and cater to diverse language preferences.
The implications of these advancements extend beyond just linguistic capabilities. In the field of programming, for instance, AI systems with the ability to receive and comprehend complicated commands could revolutionize the way computers are programmed and operated. This could greatly enhance efficiency and productivity across various sectors where human-computer interactions are crucial.
In conclusion, the recent developments in AI research demonstrate that machines are gradually approaching and in some cases even surpassing human intelligence. The ability to replicate human cognitive processes, such as composite generalization, is a significant step towards developing highly intelligent AI systems. With the advent of innovative techniques like MLC, the future holds great promise for AI and its potential impact on various industries and aspects of human life.
