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’s revenue per employee dwarfs Pinterest, DocuSign and Zoom. The bigger risk may be old software’s pricing power as AI platforms take on more work.
Anthropic’s IPO filing puts Dario Amodei’s 2025 security bill at $2.75 million, about 4.2x his salary. Daniela Amodei’s protection cost another $1.29 million.
OpenAI will pool ChatGPT and Codex usage on Plus, Pro and Business plans in December. Instant chat is exempt, with credits available for eligible features.
OpenAI has about $11 billion in commitments against an original $30 billion goal, WSJ reports. Its revised $20 billion round remains open.
House Democrats want Trump’s AI conversations preserved after reports he consulted Grok about Venezuela, raising questions about military advice and oversight.
Some employers are restoring roles after AI-led cuts as experience, oversight and the work outside a job description prove harder to replace.
Google ran Gemma 4 on an orbiting TPU and received a haiku back. The Suncatcher test now moves toward radiation, heat and networking challenges.
Recall Foundation plans to burn 358.6 million RECALL as it winds down. Token holders will receive no buyback or treasury distribution.
Some Amazon engineers reportedly ran unnecessary AI tasks to climb usage rankings. The company is warning against turning token consumption into a target.
FOMAT means Fear of Missing Agent Time: the pressure to keep coding agents working while you step away. More runtime does not always mean more progress.