OpenAI Says It Built an Automated AI “Research Intern”
OpenAI says it has met its goal of building an AI system that can handle multi-day research tasks under human supervision, on schedule with a target it set last year.
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.
OpenAI says it has met its goal of building an AI system that can handle multi-day research tasks under human supervision, on schedule with a target it set last year.
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Norway plans to regulate camera-equipped smart glasses more strictly, floating a ban on public facial recognition, though critics say it wouldn’t stop covert filming.
OpenAI told lawmakers it’s developing automated shutdown capabilities for its AI systems, but a Democratic congressman says it still won’t share logs from the Hugging Face hack.
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