Generative artificial intelligence has made significant strides, producing text indistinguishable from human prose and remarkably photorealistic images. Automatically measuring how close the generated data distribution is to the target distribution is central to diagnosing existing models and developing better ones. We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images. These scores are statistical summaries of divergence frontiers capturing two types of errors in generative modeling. We explore three approaches to statistically estimate these scores: vector quantization, non-parametric estimation, and classifier-based estimation. We provide statistical bounds for the vector quantization approach. Empirically, we find that the proposed scores paired with a range of $f$-divergences and statistical estimation methods can quantify the gaps between the distributions of human-written text and those of modern neural language models by correlating with human judgments and identifying known properties of the generated texts. We demonstrate in the vision domain that MAUVE can identify known properties of generated images on par with or better than existing metrics. In conclusion, we present practical recommendations for using MAUVE effectively with language and image modalities.

When Apple quietly launched a catalog of A.I.-narrated audiobooks early in January, it was surprising news, and it wasn't. Robot narrators are not new: Alexa provides text-to-speech for Kindle content and Google offers a suite of artificial voices of various genders and accents for those wishing to publish "auto-narrated" audiobooks. The difference is that Apple's four voices--"Madison" and "Jackson" suggested for fiction, "Helena" and "Mitchell" for nonfiction--sound much more natural than the digitally-generated voices available elsewhere, leading to fears that they could replace human narrators altogether. A few of Apple's voices are even noticeably similar to the voices of well-known members of the community of human audiobook narrators. "There's a little tension there," Edoardo Ballerini told me.

An image showing how machines learn from vision, language, and sound together. Most of us have watched television with the sound turned off at one time or another. While it's usually possible to follow the story at least to some degree, the absence of an audio track tends to limit our ability to fully appreciate what's taking place. Similarly, it's easy to miss a lot of information just listening to the sounds coming from another room. The multimodality of combining image, sound and other details greatly enhances our understanding of what's happening, whether it's on TV or in the real world.

Most of us have watched television with the sound turned off at one time or another. While it's usually possible to follow the story at least to some degree, the absence of an audio track tends to limit our ability to fully appreciate what's taking place. Similarly, it's easy to miss a lot of information just listening to the sounds coming from another room. The multimodality of combining image, sound and other details greatly enhances our understanding of what's happening, whether it's on TV or in the real world. The same appears to be true for artificial intelligence.

Over the past year, AI systems have made huge strides in their ability to generate convincing text, churning out everything from song lyrics to short stories. Experts have warned that these tools could be used to spread political disinformation, but there's another target that's equally plausible and potentially more lucrative: gaming Google. Instead of being used to create fake news, AI could churn out infinite blogs, websites, and marketing spam. The content would be cheap to produce and stuffed full of relevant keywords. But like most AI-generated text, it would only have surface meaning, with little correspondence to the real world.

One of the biggest problems in media today is so-called "fake news," which is so highly pernicious in part because it superficially resembles the real thing. AI tools promise to help identify it, but in order for it to do so, researchers have found that the best way is for that AI to learn to create fake news itself -- a double-edged sword, though perhaps not as dangerous as it sounds. Grover is a new system created by the University of Washington and Allen Institute for AI (AI2) computer scientists that is extremely adept at writing convincing fake news on myriad topics and as many styles -- and as a direct consequence is also no slouch at spotting it. The paper describing the model is available here. The idea of a fake news generator isn't new -- in fact, OpenAI made a splash recently by announcing that its own text-generating AI was too dangerous to release publicly. But Grover's creators believe we'll only get better at fighting generated fake news by putting the tools to create it out there to be studied.

In this special guest feature, Dr. Michael Zeller, SVP, AI Strategy & Innovation at Software AG, discusses how enterprises have reached a pivotal moment for operationalizing machine learning. For a while now artificial intelligence has been over hyped, its benefits over promised and, in the end, it always under-delivered. Now the hype is back stronger than ever. For AI to not fade away again, however, it needs to be made actionable. Previously, Dr. Zeller was Co-Founder of Zementis, where he used his vision is to help companies deepen and accelerate insights from big data through the power of predictive analytics.

Artificial intelligence (AI) and the Internet of Things (IoT) go together like philosophy goes with Greece. It was AI, the IoT and a shared philosophy of openness that brought Software AG and Zementis together; first into a productive partnership and then into a merger of the two companies. Michael Zeller, co-founder of Zementis and now senior vice president of AI Strategy & Innovation at Software AG, said: "What we had in common was an open platform philosophy; that openness led us both to customer success during our partnership." The synergies were obvious, said Zeller. "As part of Software AG, we could do so much more; with Apama for streaming analytics and us for ML we can go way beyond what each could do separately." AI, along with machine learning (ML), offers organizations the ability to turn data into business value.

He or she -- it's hard to tell -- is short and friendly, if a little fashion-challenged. The robot employs artificial intelligence, speech recognition, social media and other tools to bum rides from motorists. Deposited last Monday on Highway 102 outside Halifax, hitchBot by Friday had journeyed to just west of Toronto. Its travels are being documented on Twitter, on Instagram and on the robot's website, which charts its progress on a map. The gender-neutral robot was conceived by university researchers David Harris Smith and Frauke Zeller, who view its quest as part performance art, part social experiment.

A Lincoln man says he's not dead, despite what the Social Security Administration has said. Chuck Zellers learned of his demise in March after his Social Security deposit was removed from his bank account while he and his wife, Alice, were in Arizona, he told the Lincoln Journal Star (http://bit.ly/1TcuM3V They talked to a woman at the Social Security office who checked her computer and told him, "'Oh, by golly, you are dead,'" Zellers said. "She told me it could be a funeral home declared you deceased; or that someone just put in a wrong keystroke or something like that," he said. But Zellers, 73, admitted he will probably never know how it happened.