Once upon a time, in a world of artificial intelligence, the researchers believed that “attention is all you need” was the key to creating intelligent machines. They believed that by incorporating attention mechanisms into their models, they could create systems that could understand and process information just like a human brain.
The researchers worked tirelessly, developing new models and algorithms that incorporated attention mechanisms. They were convinced that they were on the brink of creating true artificial intelligence. But despite their best efforts, the systems they created were still far from perfect.
One day, a young researcher named Alice decided to take a closer look at the systems they had created. She noticed that while the attention mechanisms allowed the systems to focus on certain aspects of the information they were processing, they were still missing important pieces of information.
Alice realized that attention alone wasn’t enough. The systems needed to be able to take into account the context and the relationships between different pieces of information. She began to develop new models that incorporated not only attention, but also a way to understand the context and relationships between different pieces of information.
The new models were a breakthrough. The systems were now able to understand and process information in a way that was more similar to the human brain. They were able to make more accurate predictions and take into account the nuances of the information they were processing.
The researchers were amazed by Alice’s findings, and they quickly realized that “attention isn’t all you need”. They understood that to create truly intelligent machines, they needed to take into account not only attention, but also context and relationships. From that day on, they worked on developing models that could truly mimic the human brain.
Alice said:
The incorporation of context and relationships means that the AI models take into account not only the individual pieces of information but also how they relate to each other and the broader context in which they are presented. This allows the AI systems to understand the nuances and subtleties of the information they are processing, making their predictions and decisions more accurate and human-like. For example, in natural language processing, knowing the context of a sentence and the relationships between the words and phrases in it, the AI model can understand the meaning of the sentence better. In image recognition, an AI model that incorporates context and relationships can understand the objects in an image and how they relate to each other, which helps it to recognize the scene.
Alice published this idea in the Nature paper on 2025/05/05, but after some time it was proved that all her content was a lie and just a figment of her sick mind. She drew the ire of AI researchers, and finally, after a period of silence, published her second paper under Bob’s name, “Attention, the vortex you’re caught in.”
Continues … [Part2 is here]
Although this matter was told in the form of a story and was not a scientific fact. But my experience says that attention is not all you need. Do not pay too much attention to this story 😊
Indeed, this video of mine is about attention, don’t miss it. https://www.youtube.com/watch?v=-gk0oHPCvAw
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