AI chat toy company Bondu left its web console almost entirely unprotected. Researchers who accessed it found nearly all the conversations children had with the company's stuffed animals. Earlier this month, Joseph Thacker's neighbor mentioned to him that she'd preordered a couple of stuffed dinosaur toys for her children. She'd chosen the toys, called Bondus, because they offered an AI chat feature that lets children talk to the toy like a kind of machine-learning-enabled imaginary friend. But she knew Thacker, a security researcher, had done work on AI risks for kids, and she was curious about his thoughts.

Contemporary Christianity does not have a reputation as a vanguard of technological sophistication, or even enthusiasm. Sure, many churches have embraced smartphones' abilities to spread their messages, for example. But Christian institutions are not often leading the big conversations about technology and culture. Traditional theist religions have "turned from a creative into a reactive force," as historian Yuval Noah Harari put it in his 2016 book, Homo Deus. "They now mostly agonize over the technologies, methods and ideas propagated by other movements."

This article is a post in a series on bringing continuous integration and deployment (CI/CD) practices to machine learning. Check back to The New Stack for future installments. The pipeline runs from ingesting and cleaning data, through feature engineering and model selection in an interactive workbench environment, to training and experiments, usually with the option to share results, to deploying the trained model, to serving results like predictions and classifications. The machine learning development and deployment pipelines are often separate, but unless the model is static, it will need to be retrained on new data or updated as the world changes, and updated and versioned in production, which means going through several steps of the pipeline again and again. Managing the complexity of these pipelines is getting harder, especially when you're trying to use real-time data and update models frequently.

Microsoft researcher Charles P. Thacker, awarded the 2009 ACM A.M. Turing Award in recognition of his pioneering design and realization of the first modern personal computer, and for his contributions to Ethernet and the tablet computer, died Monday, June 12, at the age of 74, after a brief illness. Thacker, born in Pasadena, CA, on Feb. 26, 1943, earned his bachelor of science degree in physics from the University of California, Berkeley (UC Berkeley) in 1967. In 1968, Thacker joined UC Berkeley's "Project Genie" to finance a graduate degree in physics. Instead, he recalled, "I went to work for this computer project," which the Berkeley Time-sharing System, commercialized by Scientific Data Systems as the SDS 940. Thacker joined Butler Lampson (recipient of the 1992 ACM A.M. Turing Award) and others to launch the startup Berkeley Computer Corporation (BCC).

A specially programmed AI can'think' about cybersecurity in a more complex detail than a human can. It's not unreasonable to suggest the cybersecurity battle is being lost - and on more than one front. Not only are more efficient and organised cybercriminals winning the security arms race against their corporate targets, there's also a shortage of cybersecurity professionals equipped with the skills required to fight hackers. Some claim the fight against online crooks will be bolstered not by hiring more people but rather by machines using techniques based around artificial intelligence, machine learning, and deep learning. This doesn't mean self-learning machines will be outright replacing cybersecurity professionals, however, but rather augmenting what they're able to do and taking care of the most basic tasks.