A geography professor shared his method to detect AI-generated plagiarism with Fox News. He developed it after noticing that ChatGPT produced fake citations. Companies that develop software to detect if artificial intelligence or humans authored an essay or other written assignment are having a windfall moment amid ChatGPT's wild success. ChatGPT launched last November and quickly grew to 100 million monthly active users by January, setting a record as the fastest-growing user base ever. The platform has been especially favored by younger generations, including students in middle school through college.

Since its release last November, ChatGPT has shaken the education world. The chatbot and other sophisticated AI tools are reportedly being used everywhere from college essays to high school art projects. This is a problem for schools, educators and students – but a boon for a small but growing cohort of companies in the AI-detection business. Players like Winston AI, Content at Scale and Turnitin are billing for their ability to detect AI-involvement in student work, offering subscription services where teachers can run their students' work through a web dashboard and receive a probability score that grades how "human" or "AI" the text is. At this stage, most clients are teachers acting on their own initiative, although Winston AI says it is beginning talks with school administrators at the district level as the problem grows. And with only one full academic semester since ChatGPT was released, the disruption and headaches are only beginning.

Savvy marketers in the financial services industry are in the vanguard of early adopters using machine learning (ML) to streamline operations and optimize business outcomes. A survey of 1,419 customers, including more than 150 in the financial services sector, conducted by MIT Technology Review Insights in association with Google, found that marketers in the industry are among the most progressive in deploying ML. The research shows that 41 percent of financial services marketers currently use ML. The survey data also indicates that ML adoption will continue to crest: another 30 percent of financial services marketers plan to deploy the technology within the year. This is encouraging but hardly surprising.

Data is the new oil, and an enterprise IoT deployment is an easy way to get a lot of it from many different sources. But in the end, it's what a business does with its data that really matters. By putting that data to work, organizations can improve efficiency, boost the bottom line, and drive innovation. This ebook, based on the latest ZDNet/TechRepublic special feature, explores ways IoT is improving operations and delivering business value to enterprises around the world. That's where machine learning comes in.

Early into the Forbidden Research event, Ethan Zuckerman issued a warning. "If we make it through today without you feeling uncomfortable, then we've done something wrong," he said. Zuckerman was addressing a packed room at the Massachusetts Institute of Technology Media Lab, assembled for a day devoted to "restricted scientific and cultural topics". Forbidden Research promised academic danger zones, often considered too hot or too risky to touch: covert government surveillance, genetically engineered human beings, Islam and women's rights to name a few. For my money, the most excruciating topic on the bill came in a panel just after lunch: "Sexual Deviance: Can Technology Protect Our Children?"