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.
The US Commerce Department lifted export controls on Anthropic’s Claude Fable 5 and Mythos 5, ending an 18-day standoff and restoring the models globally from July 1.
A new study of 22,000 US firms found that the heaviest AI spenders grew headcount faster than peers, including entry-level roles, complicating fears of an AI jobs apocalypse.
As Washington limits Anthropic’s and OpenAI’s top models on security grounds, critics warn the restrictions are handing momentum to Chinese open-weight rivals like GLM-5.2.
About $2.3 trillion was wiped from the Magnificent 7 in June as investors questioned Big Tech’s huge AI spending, even as chip and memory stocks kept climbing.
A record European heatwave is highlighting a growing threat to the AI boom: extreme weather that stresses data centers and the power grids they depend on at once.
Microsoft is heading for its worst month since 2008 as investors worry that its heavy AI spending may not pay off and that AI could erode demand for its core software.
South Korea announced about $880 billion in planned investment to expand chipmaking, AI data centers and robotics, aiming to double memory output and revive regions outside Seoul.
HP is scaling a strategic partnership with OpenAI, using the Frontier platform to roll out AI agents across customer support, software development and security after early pilots.
Google has limited Meta’s use of its Gemini AI models after Meta sought more computing capacity than Google could supply, delaying some of Meta’s internal projects.
OpenAI is leaning toward pushing its IPO to 2027 rather than accept less than a $1 trillion valuation, weeks after filing confidentially for a US listing.