This "dream wish list for criminals" includes millions of Gmail, Facebook, banking logins, and more. The researcher who discovered it suspects they were collected using infostealing malware. A database containing 149 million account usernames and passwords--including 48 million for Gmail, 17 million for Facebook, and 420,000 for the cryptocurrency platform Binance --has been removed after a researcher reported the exposure to the hosting provider. The longtime security analyst who discovered the database, Jeremiah Fowler, could not find indications of who owned or operated it, so he worked to notify the host, which took down the trove because it violated a terms of service agreement. In addition to email and social media logins for a number of platforms, Fowler also observed credentials for government systems from multiple countries as well as consumer banking and credit card logins and media streaming platforms.

An AI image generator startup's database was left accessible to the open internet, revealing more than 1 million images and videos, including photos of real people who had been "nudified." An AI image generator startup left more than 1 million images and videos created with its systems exposed and accessible to anyone online, according to new research reviewed by WIRED. The "overwhelming majority" of the images involved nudity and were "depicted adult content," according to the researcher who uncovered the exposed trove of data, with some appearing to depict children or the faces of children swapped onto the AI-generated bodies of nude adults. Multiple websites--including MagicEdit and DreamPal--all appeared to be using the same unsecured database, says security researcher Jeremiah Fowler, who discovered the security flaw in October. At the time, Fowler says, around 10,000 new images were being added to the database every day.

Code refactoring is a fundamental software engineering practice aimed at improving code quality and maintainability. Despite its importance, developers often neglect refactoring due to the significant time, effort, and resources it requires, as well as the lack of immediate functional rewards. Although several automated refactoring tools have been proposed, they remain limited in supporting a broad spectrum of refactoring types. In this study, we explore whether instruction strategies inspired by human best-practice guidelines can enhance the ability of Large Language Models (LLMs) to perform diverse refactoring tasks automatically. Leveraging the instruction-following and code comprehension capabilities of state-of-the-art LLMs (e.g., GPT-mini and DeepSeek-V3), we draw on Martin Fowler's refactoring guidelines to design multiple instruction strategies that encode motivations, procedural steps, and transformation objectives for 61 well-known refactoring types. We evaluate these strategies on benchmark examples and real-world code snippets from GitHub projects. Our results show that instruction designs grounded in Fowler's guidelines enable LLMs to successfully perform all benchmark refactoring types and preserve program semantics in real-world settings, an essential criterion for effective refactoring. Moreover, while descriptive instructions are more interpretable to humans, our results show that rule-based instructions often lead to better performance in specific scenarios. Interestingly, allowing models to focus on the overall goal of refactoring, rather than prescribing a fixed transformation type, can yield even greater improvements in code quality.

Tens of thousands of explicit AI-generated images, including AI-generated child sexual abuse material, were left open and accessible to anyone on the internet, according to new research seen by WIRED. An open database belonging to an AI image-generation firm contained more than 95,000 records, including some prompt data and images of celebrities such as Ariana Grande, the Kardashians, and Beyoncé de-aged to look like children. The exposed database, which was discovered by security researcher Jeremiah Fowler, who shared details of the leak with WIRED, is linked to South Korea–based website GenNomis. The website and its parent company, AI-Nomis, hosted a number of image generation and chatbot tools for people to use. More than 45 GB of data, mostly made up of AI images, was left in the open.

Mental models are an important concept in UX and product design, but they need to be more readily embraced by the AI community. At one level, mental models often don't appear because they are routine patterns of our assumptions about an AI system. This is something we discussed at length in the process of putting together the latest volume of the Thoughtworks Technology Radar, a biannual report based on our experiences working with clients all over the world. For instance, we called out complacency with AI generated code and replacing pair programming with generative AI as two practices we believe practitioners must avoid as the popularity of AI coding assistants continues to grow. Both emerge from poor mental models that fail to acknowledge how this technology actually works and its limitations.

Street-level bureaucrats interact directly with people on behalf of government agencies to perform a wide range of functions, including, for example, administering social services and policing. A key feature of street-level bureaucracy is that the civil servants, while tasked with implementing agency policy, are also granted significant discretion in how they choose to apply that policy in individual cases. Using that discretion could be beneficial, as it allows for exceptions to policies based on human interactions and evaluations, but it could also allow biases and inequities to seep into important domains of societal resource allocation. In this paper, we use machine learning techniques to understand street-level bureaucrats' behavior. We leverage a rich dataset that combines demographic and other information on households with information on which homelessness interventions they were assigned during a period when assignments were not formulaic. We find that caseworker decisions in this time are highly predictable overall, and some, but not all of this predictivity can be captured by simple decision rules. We theorize that the decisions not captured by the simple decision rules can be considered applications of caseworker discretion. These discretionary decisions are far from random in both the characteristics of such households and in terms of the outcomes of the decisions. Caseworkers typically only apply discretion to households that would be considered less vulnerable. When they do apply discretion to assign households to more intensive interventions, the marginal benefits to those households are significantly higher than would be expected if the households were chosen at random; there is no similar reduction in marginal benefit to households that are discretionarily allocated less intensive interventions, suggesting that caseworkers are improving outcomes using their knowledge.

My son Charlie will be 18 soon. Like all Scottish males before him, he will be dropped on a Hebridean island with nothing but a rusty knife and his own anger. If he can't make it back to the mainland, he will live the rest of his life among feral, abandoned Scottish sons, and he will only survive if he likes sport, because that's how any group of men get through enforced time together. He tried sport as a kid, but as he is on the autism spectrum, he was obsessed with rules to the point where if he felt another kid broke them, he would pick the ball up and stop the game. He was basically human VAR.

Just the words can bring up visions of decision-making computers that are replacing whole departments and divisions--a future many companies believe is too far away to warrant investment. But the reality is, AI is here, and here to stay. And particularly at the enterprise level, a growing number of companies are tuning in to the productivity and promise of machines that can think for themselves. In fact, a recent study by McKinsey showed that by 2019, venture capital investment in AI had already topped $18.5 billion. And IDC predicted that by 2023, global spending on AI and Machine Learning solutions will reach nearly $98 billion. All this development promises to have a tremendous impact on every corner of industry.

Cense.ai is an Artificial Intelligence company that works in a wide range of areas. According to the company website, Cense.ai It is this last practice that led to the company exposing over 2.5 million medical records. According to researcher Jeremiah Fowler, all of the records were readily available to view or download by anyone with an Internet connection. Though it remains unclear how long the data was available online, Fowler made the discovery on July 7th, 2020.

Any agile and aware organization is going to have activities in flux with regard to the fast-changing areas of big data, analytics, and AI. To learn about what the company is doing in the data space, we interviewed Jim Tyo, the company's Chief Data Officer (CDO). He told us that Nationwide has an aggressive level of activity underway, while focusing on the structure and organizational priorities for these important resources through a time of change. Tyo has been the company's CDO since 2016, and he reports to Jim Fowler, who arrived at Nationwide as Chief Information Officer in 2018. Fowler was previously the CIO at GE, and he is bringing in some of the same innovative ideas that GE Digital introduced to that company. For example, Fowler argues that Nationwide is a technology company, so his title has been changed to Chief Technology Officer, and his business function is now called Nationwide Technology.