A senior Google employee has claimed she was made redundant after reporting a manager who told clients stories about his swinger lifestyle and showed a nude of his wife. Victoria Woodall told an employment tribunal she was subjected to a campaign of retaliation by the company after whistleblowing on the man who was later sacked. Google UK's internal investigation found the manager had touched two female colleagues without their consent, and his behaviour amounted to sexual harassment, documents seen by the BBC in court show. The tech giant denies retaliating against Woodall and argues she became paranoid after whistleblowing and began to view normal business activities as sinister. In her claim, Woodall says her own boss subjected her to a relentless campaign of retaliation after her complaint also implicated his close friends who were later disciplined for witnessing the manager's behaviour and failing to challenge it.

Generative Artificial Intelligence (AI) models such as OpenAI's ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT's ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for structured tasks, such as translating code from one language to another and explaining well-known concepts but struggles with more nuanced tasks, such as explaining less widely known terms and creating code from scratch. We find that using new AI tools may help practitioners, educators, and researchers to be more efficient and productive. However, in their current stages of development, some results are misleading and wrong. Overall, the use of generative AI models in SPC must be properly validated and used in conjunction with other methods to ensure accurate results.

The robot revolution has long been thought of as apocalyptic for blue-collar workers whose tasks are manual and repetitive. A widely cited 2017 McKinsey study said 50 percent of work activities were already automatable using current technology and those activities were most prevalent in manufacturing. New data suggests white-collar workers -- even those whose work presumes more analytic thinking, higher paychecks, and relative job security -- may not be safe from the relentless drumbeat of automation. That's because artificial intelligence -- powerful computer tech like machine learning that can make human-like decisions and use real-time data to learn and improve -- has white-collar work in its sights, according to a new study by Stanford University economist Michael Webb and published by Brookings Institution. The scope of jobs potentially impacted by AI reaches far beyond white-collar jobs like telemarketing, a field that has already been decimated by bots, into jobs previously thought to be squarely in the province of humans: knowledge workers like chemical engineers, physicists, and market-research analysts.