Data science teams are stymied by disorganization at their companies, impacting efforts to deploy timely AI and analytics projects. In a recent survey of "data executives" at U.S.-based companies, 44% said that they've not hired enough, were too siloed off to be effective and haven't been given clear roles. Respondents said that they were most concerned about the impact of a revenue loss or hit to brand reputation stemming from failing AI systems and a trend toward splashy investments with short-term payoffs. These are ultimately organizational challenges. But Piero Molino, the co-founder of AI development platform Predibase, says that inadequate tooling often exacerbates them.

Generative AI language models like OpenAI's GPT-2 produce impressively coherent and grammatical text, but controlling the attributes of this text -- such as the topic or sentiment -- requires architecture modification or tailoring to specific data. That's why a team of scientists at Uber, Caltech, and the Hong Kong University of Science and Technology devised what they call the Plug and Play Language Model (PPLM), which combines a pretrained language model with one or more attribute classifiers that guide novel text generation. Preliminary results in a preprint paper show that PPLM is able to control a "range" of topics and sentiment styles, importantly without sacrificing fluency and while retaining flexibility that in any combination of differentiable models steers text generation. Their research builds on that published by Google and the University of Michigan late last year, which investigated an architecture that could generate sentences from a given sample and change the mood, complexity, tense, or even voice while preserving the original text's meaning meaning. And it could inform work on Plato, Uber's platform for developing and testing conversational AI, which was released in July with connectors that integrate with existing machine learning and model-tuning frameworks.