The type of AI used when a smartphone predicts your next word while typing is primarily based on natural language processing (NLP) and machine learning, specifically involving neural networks. Here are the key AI techniques used:
- Language Models: These models predict the most likely next word based on the preceding words. Modern smartphones often use pre-trained language models such as GPT, BERT, or similar transformer-based models.
- Neural Networks: Recurrent neural networks (RNNs) and their advanced variants like Long Short-Term Memory networks (LSTMs) capture context and dependencies in the text to predict the next word in a sequence.
- Transformer Models: More recent AI keyboards use architectures like transformers (e.g., GPT) which use self-attention to understand the context across entire input sequences, resulting in highly accurate next-word predictions.
- Machine Learning & Deep Learning: The AI keyboard learns from vast amounts of text and user-specific typing behavior to improve its predictions over time, calculating probabilities to suggest the most probable next word.
In summary, smartphones use deep learning-based language models, often transformer architectures, which operate by predicting the next word in a sequence based on the context of the preceding words. These models are trained on large datasets and adapt to the user's writing style for personalized predictions.
