Australia's beloved weather website got a makeover - and infuriated users It was an unseasonably warm spring day in Sydney on 22 October, with a forecast of 39C (99F) - a real scorcher. The day before, the state of New South Wales had reported its hottest day in over a century, a high of 44.8C in the outback town of Bourke. But little did the team at the national Bureau of Meteorology foresee that they, in particular, would soon be feeling the heat. Affectionately known by Australians as the Bom, the agency's long-awaited website redesign went live that morning, more than a decade after the last update. Within hours, the Bom was flooded with a deluge of complaints.

In the aftermath of COVID-19, many universities implemented supplementary "reinforcement" roles to support students in demanding courses. Although the name for such roles may differ between institutions, the underlying idea of providing structured supplementary support is common. However, these roles were often poorly defined, lacking structured materials, pedagogical oversight, and integration with the core teaching team. This paper reports on the redesign of reinforcement sessions in a challenging undergraduate course on formal methods and computational models, using a large language model (LLM) as a reflective planning tool. The LLM was prompted to simulate the perspective of a second-year student, enabling the identification of conceptual bottlenecks, gaps in intuition, and likely reasoning breakdowns before classroom delivery. These insights informed a structured, repeatable session format combining targeted review, collaborative examples, independent student work, and guided walkthroughs. Conducted over a single semester, the intervention received positive student feedback, indicating increased confidence, reduced anxiety, and improved clarity, particularly in abstract topics such as the pumping lemma and formal language expressive power comparisons. The findings suggest that reflective, instructor-facing use of LLMs can enhance pedagogical design in theoretically dense domains and may be adaptable to other cognitively demanding computer science courses.

The bare-bones Mac writing app represents a literalist sensibility that is coming back into vogue as A.I. destabilizes our technological interactions. The so-called desktop first appeared on a home computer in 1981, with the release of the Xerox 8010 Star Information System. That device pioneered the graphical-user interface, or G.U.I., a convenient series of visual metaphors that allows us to interact more easily with our machines. The most basic computing interface is the command-line prompt, the empty box in which users write instructions in code directly to the machine; the Xerox Star replaced that forbidding vacuum with a friendly illustration of a tabletop surface, textured in patterned pixels, scattered with icons for folders, spreadsheets, and filing trays. A 1982 paper on the device described the then novel system: "Users are encouraged to think of the objects on the Desktop in physical terms.

That's Harder Than It Sounds Proposed regulations in China would mean the end of flush handles on car doors, with precious little time to roll out the changes. Car door handles seem innocuous. Tesla's electronic, retractable ones--since imitated by plenty of global automakers--have become a symbol of the automaker's willingness to work from design-first principles, reimagining what the car of the future might look like, electric-style. But in September, the National Highway Traffic Safety Administration launched an investigation into the Tesla 2021 Model Y's door handles. More than 140 consumers have complained to the National Highway Traffic Safety Administration (NHTSA) about the door handles, according to a Bloomberg report published last month.

Should we worry AI will create deadly bioweapons? Artificial intelligence promises to transform biology, allowing us to design better drugs, vaccines and even synthetic organisms for, say, eating waste plastic. But some fear it could also be used for darker purposes, to create bioweapons that wouldn't be detected by conventional methods until it was too late. So, how worried should we be? "AI advances are fuelling breakthroughs in biology and medicine," says Eric Horvitz, chief scientific officer at Microsoft. "With new power comes responsibility for vigilance." His team has published a study looking at whether AI could design proteins that do the same thing as proteins that are known to be dangerous, but are different enough that they wouldn't be recognised as dangerous.

Bloomberg reports that this year at WWDC, Apple plans to announce a broad overhaul of all of its operating systems. That includes renaming them to be more consistent. Starting this year, Apple will reportedly begin denoting each OS version for each product by year, instead of by version. Confusingly, it will start with the next year, rather than this year (just like cars). So the versions we'll see at this year's WWDC will not be iOS 25, but rather iOS 26, watchOS 26, and so on, in place of iOS 19 and watchOS 12. Here's more you may have missed this week: The move is reportedly part of a larger push toward a cohesive user experience across platforms.

With the recent success of large language models (LLMs), the idea of AI-augmented Business Process Management systems is becoming more feasible. One of their essential characteristics is the ability to be conversationally actionable, allowing humans to interact with the LLM effectively to perform crucial process life cycle tasks such as process model design and redesign. However, most current research focuses on single-prompt execution and evaluation of results, rather than on continuous interaction between the user and the LLM. In this work, we aim to explore the feasibility of using LLMs to empower domain experts in the creation and redesign of process models in an iterative and effective way. The proposed conversational process model redesign (CPD) approach receives as input a process model and a redesign request by the user in natural language. Instead of just letting the LLM make changes, the LLM is employed to (a) identify process change patterns from literature, (b) re-phrase the change request to be aligned with an expected wording for the identified pattern (i.e., the meaning), and then to (c) apply the meaning of the change to the process model. This multi-step approach allows for explainable and reproducible changes. In order to ensure the feasibility of the CPD approach, and to find out how well the patterns from literature can be handled by the LLM, we performed an extensive evaluation. The results show that some patterns are hard to understand by LLMs and by users. Within the scope of the study, we demonstrated that users need support to describe the changes clearly. Overall the evaluation shows that the LLMs can handle most changes well according to a set of completeness and correctness criteria.

Transforming a car manufacturing plant entering its seventh decade into a futureproof facility, ready for AI-powered autonomous driving, comes with natural challenges. "We had to survey everything and go out with the tape measure," explains Dan Ford, site director at Jaguar Land Rover's (JLR) site in Halewood, Merseyside, England. "But the drawing's measurements were off: we struck a drainpipe." Besides that minor bump in the road (the Great British weather and an August downpour meant work was delayed by 48 hours), JLR's 250 million ( 323.4 million) upgrade of its Halewood plant has been smooth. Off the River Mersey, 10 miles from Liverpool, Halewood has long been synonymous with the British car industry--and JLR is the UK's largest automotive employer.

Since first introducing its generative AI assistant, Snap has been steadily ramping up the amount of AI in its app. Now, the company is adding a new slate of AI-powered features as it begins testing a larger redesign of the app. Snap often brings new AI features to its Snapchat subscribers first, and the company is continuing the trend with a new feature called "My Selfie." The feature uses selfies to create AI-generated images of users and their friends (if they also subscribe) in creative poses and situations. The company is also rolling out a new "grandparents lens" that uses AI to imagine what you might look like as a senior citizen.

Purpose- The significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation, closely intertwined with technological advancements. Our main goal is to offer researchers and process analysts insights into the latest developments concerning Artificial Intelligence (AI) and Machine Learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field. Design/methodology/approach- In this study, we perform a systematic review of academic literature to investigate the integration of AI/ML in business process management (BPM). We categorize the literature according to the BPM life-cycle and employ bibliometric and objective-oriented methodology, to analyze related papers. Findings- In business process management and process map, AI/ML has made significant improvements using operational data on process metrics. These developments involve two distinct stages: (1) process enhancement, which emphasizes analyzing process information and adding descriptions to process models, and (2) process improvement, which focuses on redesigning processes based on insights derived from analysis. Research limitations/implications- While this review paper serves to provide an overview of different approaches for addressing process-related challenges, it does not delve deeply into the intricacies of fine-grained technical details of each method. This work focuses on recent papers conducted between 2010 and 2024. Originality/value- This paper adopts a pioneering approach by conducting an extensive examination of the integration of AI/ML techniques across the entire process management lifecycle. Additionally, it presents groundbreaking research and introduces AI/ML-enabled integrated tools, further enhancing the insights for future research.