In language understanding, we developed sequence learning models that can predict output sequences based on input sequences, useful for tasks like translation.

The ability to predict the next word in a sequence is deceptively complex and requires a model of reality, which is a significant achievement in AI.

The core of generative AI relies on supervised learning to predict the next word in a sequence, enabling the creation of coherent text.

Translation in AI was surprising to many, as it involves variable-length input and output, which was initially unclear how to handle with neural networks.

Language models are based on statistical predictions of word sequences, and as they scale in parameters and training data, they improve in their ability to predict the next word in a sequence.

The model developed for Google Translate utilized a deep LSTM stack with an attention module, allowing it to track multiple states and focus on relevant input data during output generation.

Modern language models like Claude can handle translation between many languages, unlocking the global reach of AI products.

Even the best human translators have limitations in the number of words they encounter, while translation systems can process vast amounts of data, which may lead to better performance over time.

The ability of neural networks to generate descriptive sentences about images has advanced more quickly than expected, showcasing their growing capabilities.