Unsupervised learning allows us to leverage unlabelled data, which has become abundantly available, and to create embeddings that are usable on a variety of downstream tasks. However, the typical lack of interpretability of unsupervised representation learning has become a limiting factor with regard to recent transparent-AI regulations. In this paper, we study graph representation learning and we show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis. We also introduce additional metrics addressing the lack of formalism and metrics in the understudied area of unsupervised-representation learning interpretability. Our results are supported by an experimental study applied to both graph-level and node-level tasks and show that interpretable embeddings provide state-of-the-art performance on subsequent downstream tasks.

Siemens wants to turn its digital communications platform into one that's as important to consumers as Facebook, and it's starting by getting artificial intelligence to tell its internal authors what to write about. The tech conglomerate believes the sheer scale of its work (which is carried out by more than 350,000 employees worldwide) makes for stories that will interest people outside of the company. However the two heads of its communications department – both less than two years into their respective jobs – are aware that the brand has been historically ineffective at telling these stories, largely due to its distrust in non-traditional media. "We liked to touch things and talk about things – traditional media events were where we bought our a-game," said Stephanie Chalmers, global head of content and newsroom at Siemens. "I had to explain that [non-traditional] channels are way more effective than the ones they were used to. That complexity is very scary for a company like Siemens, because they can't control it."

The purpose of language is to reveal the contents of our minds, says Julie Sedivy. We are social animals and language is what springs us from our isolated selves and unites us with others. Sedivy has taught linguistics and psychology at Brown University and the University of Calgary. She specializes in psycholinguistics, the psychology of language, notably the psychological pressures that give birth to language and comprehension. More recently Sedivy has been writing about language in her own life. She was born in Czechoslovakia, spent time as a kid in Austria and Italy, and came of age in Canada. She speaks Czech, French, and English, and gets by in Spanish, Italian, and German.

Do you think you can read emotions like joy or anger in another person's face and actions? Read them because joy and anger are universal emotions and we all know what they look and feel like? Well, if so, says neuroscientist Lisa Feldman Barrett, you are winging it, guessing at best. Emotions like happiness and despair are not baked into our brains, waiting to be triggered by experiences in the world. Sure, we have a range of feelings, stimulated by our senses. But those feelings cannot be categorized as emotions innate in everyone. What we call emotions, Barrett says, are concepts constructed by our individual neural systems, molded by our cultures and past experiences. In her new and first book, How Emotions Are Made: The Secret Life of the Brain, based on years of research at her neuroscience lab at Northeastern University, Barrett spells out the "theory of constructed emotion."

I was electrified by Jonathan Berger's music before I knew he wrote about music. His chamber works arise out of a lightning storm of modernist angles, dramatic and startling, though anchored to melodies that sail like a swallow, as one of his string quartets is called. His one-act operas Theotokia and The War Reporter, performed together in concert, match taut musical brocades to the hallucinations of, respectively, a schizophrenic, hearing voices of various mothers, and a photojournalist, based on Paul Watson, who won the 1994 Pulitzer Prize for his image of the corpse of an American soldier being dragged through the streets of Mogadishu. A few years ago, I read some of Jonathan's academic writing about music, which had a sharp focus on neurology and acoustics. He is a professor of music at Stanford, where he teaches composition, music theory, and cognition at the Center for Computer Research in Music and Acoustics. On a hunch that he could connect with a popular audience, I asked him to write an essay for Nautilus about how composers upend expectations to keep listeners off guard and engaged. That article, "Composing Your Thoughts," and his next one for Nautilus, "How Music Hijacks Our Perception of Time," which contain musical clips to illustrate his points, have been among our most popular articles. There's a certain amount of problem solving that happens in the context of a band of noise. For this month's issue I called Jonathan and was delighted to learn he had thought a lot about noise.