First, A Test: Where Does Your Attention Go?
Selective attention is the fundamental cognitive process of concentrating on one aspect of your environment while ignoring others. It's a skill you use every moment of every day. Before we dive deeper, let's put your own attention to the test. The instructions are simple: count how many passes the team wearing white makes.
From Human Cognition to AI: "Attention Is All You Need"
Just as you focused on the players in white (and may have missed something unexpected), computers can be taught to focus on the most important parts of data. This idea was famously captured in a groundbreaking 2017 paper, "Attention Is All You Need." Instead of processing information in a fixed order, its authors created a mechanism that allows an AI to weigh the importance of different pieces of data, giving more "attention" to the most relevant ones. This single concept has sparked a revolution in AI, and it's the engine behind many of the tools we use daily. Explore some of its applications below.
Natural Language
Models like GPT and BERT use Transformers to understand and generate human-like text, powering chatbots and search engines.
Computer Vision
Vision Transformers (ViT) apply the architecture to images, enabling object detection, classification, and image analysis.
Speech Recognition
Models like Whisper from OpenAI use Transformers to achieve robust transcription of spoken language across many languages.
Drug Discovery
AlphaFold uses attention mechanisms to predict protein structures, accelerating biological research and drug development.
Genomics
Transformer models are used to analyze DNA sequences, helping to identify genetic variants and understand diseases.
Recommender Systems
Attention models help power recommendation engines for e-commerce and streaming by understanding user behavior.