The Cornerstone of the Artificial Intelligence Revolution: The Attention Mechanism

The roots of the major transformation in the field of artificial intelligence date back to steps taken in 2014 at the University of Montreal laboratories. The mathematical operation developed at that time paved the way for modern language models.

The Birth of the Attention Mechanism

In 2014, Dzmitry Bahdanau, working as an intern at the University of Montreal, was researching methods to enable computers to translate between English and French. Neural networks at the time struggled with long sentences.

Dzmitry Bahdanau, alongside Kyunghyun Cho and Yoshua Bengio, developed a mathematical operation called attention, which enabled neural networks to translate long sentences as well as short ones.

Transition to the Transformer Architecture

Building on this work in 2017, Google researchers published the paper Attention Is All You Need and constructed the transformer architecture. This architecture paved the way for today's large language models.

The attention mechanism allows the network to connect words to one another and establish context. This context is translated into mathematics through queries, keys, and values within attention heads.

Becoming the Definition of Artificial Intelligence

Transformer models, which learn from vast training datasets, manage to discover linguistic relationships—such as resolving pronoun ambiguity—without explicit programming.

Because predicting the next word encompasses nearly all of humanity's written tasks, language modeling has become the closest definition that can be given to artificial intelligence.