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Sequence-to-Sequence (Seq2Seq) - Page 45

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.

Nvidia Says $100B Investment Into OpenAI Is Likely Off the Table
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AI & Machine Learning, News, Startups & Investment

Nvidia Says $100B Investment Into OpenAI Is Likely Off the Table

By • 3 mins read

Nvidia CEO Jensen Huang said the company’s $30 billion investment in OpenAI could be its last before the AI startup pursues an initial public offering. The chipmaker also indicated its $10 billion investment in Anthropic may mark the end of its funding commitments to major AI model developers.