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T-Mobile prides itself on being a disruptor in the world of wireless communications, always thinking creatively about the relationship it wants to have with its consumers. That includes the company's approach to using AI for customer service. Using the predictive capabilities of machine learning to improve customer service is a great example of AI augmenting human abilities. T-Mobile sees it as an opportunity to serve customers better and faster, benefiting not just the company and its service agents but also enriching the customer experience and creating stronger human-to-human connections. "Most industries have looked to use AI and machine learning to build more sophisticated Interactive Voice Response (IVR) systems and chatbots as a means to deflect for as long as possible the interaction between a human customer service agent and the customer," says Cody Sanford, executive vice president and chief information officer at T-Mobile.

The intersection of machine learning and IoT is creating a need for new ways of thinking about -- and understanding -- data, sensors, citizen data scientists, and a host of other issues. In an increasingly turbulent technology environment, new ideas are often to be found at the intersection of things. In such cases, the contradictions between trends and possibilities appear in high relief. Within the data center, one such intersection is that between machine learning (ML) and the internet of things (IoT). ML is becoming an essential player in a growing array of process areas involving image recognition, natural language processing, forecasting, prediction, and process optimization.

We study the role of linguistic context in predicting quantifiers (`few', `all'). We collect crowdsourced data from human participants and test various models in a local (single-sentence) and a global context (multi-sentence) condition. Models significantly out-perform humans in the former setting and are only slightly better in the latter. While human performance improves with more linguistic context (especially on proportional quantifiers), model performance suffers. Models are very effective in exploiting lexical and morpho-syntactic patterns; humans are better at genuinely understanding the meaning of the (global) context.