Clockwise from top left: Rachel Johnson, Polly Toynbee, Jeanette Winterson, Cathy Newman, Ann Widdecombe, Fiona Marr. Clockwise from top left: Rachel Johnson, Polly Toynbee, Jeanette Winterson, Cathy Newman, Ann Widdecombe, Fiona Marr. I asked AI to name my wife. Authors, a newsreader, a lawyer and an esteemed colleague: they're all great - but I'm not married to any of them. Can we really depend on this technology?

Australia's AI boom may revive productivity, CBA says Cabinets housing servers inside a data hall at a NextDC data center in Sydney. The company partnered with OpenAI last December to build a large-scale computing cluster in Sydney. Australia has become the world's third-largest artificial intelligence investment destination behind the U.S. and China, a result that's set to spur productivity in an economy currently struggling with a low potential growth rate and high inflation, Commonwealth Bank of Australia says. CBA's updated estimates suggest that Australia's data center pipeline is closer to 6 gigawatt, or 150 billion Australian dollars ($105 billion), implying installed capacity could more than triple over the period to 2030, according to a research note released Monday by economists led by Luke Yeaman. Shares in Australian data centers jumped following the report, with Goodman Group rising as much as 6.9%, the most since Dec. 23.

Predominately in explainable artificial intelligence (XAI) research, the Shapley value (SV) is applied to determine feature attributions for any black box model. Shapley interaction indices extend the SV to define any-order feature interactions. Defining a unique Shapley interaction index is an open research question and, so far, three definitions have been proposed, which differ by their choice of axioms. Moreover, each definition requires a specific approximation technique. Here, we propose SHAPley Interaction Quantification (SHAP-IQ), an efficient sampling-based approximator to compute Shapley interactions for arbitrary cardinal interaction indices (CII), i.e. interaction indices that satisfy the linearity, symmetry and dummy axiom.

RRHF can align with not only human preferences but also any preferences. As a large language model, Wombat has the possibility to generate unsafe responses. We also conduct experiments on the IMDB dataset for assessing positive movie reviews generation. The task expects the model to give positive and fluent movie review completions based on given partial review input texts. RRHF-OP-128 follows the bottommost workflow in Figure 2 in the main texts.

Latent embeddings offer an effective way of defining a content gap.

We introduce Lifelong ICL, a problem setting that challenges long-context language models (LMs) to learn a sequence of language tasks through in-context learning(ICL).