Subjective mean opinion scores (MOS) remain the de-facto target for non-intrusive speech and singing quality assessment. However, MOS is a scalar that collapses heterogeneous user expectations, ignores service-level objectives, and is difficult to compare across deployment graphs. We propose a contract-driven QoE auditing framework: each service graph G is evaluated under a set of human-interpretable experience contracts C, yielding a contract-level satisfaction vector Q(G, C). We show that (i) classical MOS regression is a special case with a degenerate contract set, (ii) contract-driven quality is more stable than MOS under graph view transformations (e.g., pooling by system vs. by system type), and (iii) the effective sample complexity of learning contracts is governed by contract semantics rather than merely the dimensionality of C. We instantiate the framework on URGENT2024 MOS (6.9k speech utterances with raw rating vectors) and SingMOS v1 (7,981 singing clips; 80 systems). On URGENT, we train a contract-aware neural auditor on self-supervised WavLM embeddings; on SingMOS, we perform contract-driven graph auditing using released rating vectors and metadata without decoding audio. Empirically, our auditor matches strong MOS predictors in MOS accuracy while providing calibrated contract probabilities; on SingMOS, Q(G, C) exhibits substantially smaller cross-view drift than raw MOS and graph-only baselines; on URGENT, difficulty curves reveal that mis-specified "simple" contracts can be harder to learn than richer but better aligned contract sets.

In abandoning plans for a self-driving car, Apple Inc. is giving up on billions in potential revenue and the dream of selling what one executive called "the ultimate mobile device." The hope is that other big bets -- including generative AI and mixed-reality headsets -- can make up the difference. Apple reached this crossroads Tuesday, when it told employees it was winding down the car project and reassigned some of the staff to its AI efforts. The decision followed months of frenzied meetings between top executives and the company's board over how to proceed. Chief Operating Officer Jeff Williams and project head Kevin Lynch broke the news to the roughly 2,000-member team during a meeting that lasted less than 15 minutes.

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? My husband and I share one child together, as well as my child from a previous marriage and his child from a previous marriage. We are a wonderfully blended family and are fortunate to share our lives. Our blended bliss has had one major challenge, though.

The need to integrate Responsible AI (RAI) practices has become an organizational imperative. As Generative AI systems such as ChatGPT gain traction, it will quickly become easier for companies to adopt AI, thanks to lowered barriers to access. Already, as many experiment with these systems, they are unearthing serious ethical issues: scientific misinformation that looks convincing to the untrained eye, biased images and avatars, hate speech, and more. Our research has shown that investing in RAI early is essential; it minimizes failures as companies scale the development and deployment of AI systems within their organization. But we've also found that it takes three years on average for an RAI program to achieve maturity.

Investment in artificial intelligence: Porsche has invested in startup "Urgent.ly". The American company sees itself as the leading global platform for mobility and roadside assistance. The financing round amounted to 21 million U.S. dollars (18.5 million euros). Alongside "Porsche Ventures", other companies obtained shares in Urgent.ly, The U.S. startup's platform supports roadside assistance services in North America, Europe and Asia.