Sequence-to-sequence, or Seq2Seq, describes models that map an input sequence to an output sequence whose length may differ. Machine translation converts words in one language into another, speech recognition maps audio frames to text, and summarization produces a shorter sequence from a longer document. Many Seq2Seq systems use an encoder to represent the input and a decoder to generate output step by step, often with attention between them. Transformer encoder–decoder architectures largely replaced earlier recurrent designs. Training typically uses paired examples and token-level loss, while inference relies on greedy, beam, or sampling-based decoding. Quality depends on alignment, context limits, data coverage, and evaluation beyond surface overlap.
Google is integrating Gemini AI into Chrome for U.S. users, bringing features like multi-tab summarization, agentic task automation, and deeper sync with Google apps.
AI search startup Perplexity has reportedly raised $200 million at a $20 billion valuation, just two months after a $100 million round – signaling its rapid rise as a major rival to Google.
IBM has unveiled a detailed development plan for Quantum Starling, the world’s first large-scale fault-tolerant quantum computer, aiming for deployment by 2029 – setting a milestone for practical quantum capability.