Anthropic Launches Claude Corps, a $150 Million Nonprofit AI Fellowship
Anthropic committed $150 million to Claude Corps, a fellowship paying 1,000 early-career workers to spend a year embedding AI in US nonprofits.
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.
Anthropic committed $150 million to Claude Corps, a fellowship paying 1,000 early-career workers to spend a year embedding AI in US nonprofits.
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