It has been shown that many generative models inherit and amplify societal biases. To date, there is no uniform/systematic agreed standard to control/adjust for these biases. This study examines the presence and manipulation of societal biases in leading text-to-image models: Stable Diffusion, DALL-E 3, and Adobe Firefly. Through a comprehensive analysis combining base prompts with modifiers and their sequencing, we uncover the nuanced ways these AI technologies encode biases across gender, race, geography, and region/culture. Our findings reveal the challenges and potential of prompt engineering in controlling biases, highlighting the critical need for ethical AI development promoting diversity and inclusivity. This work advances AI ethics by not only revealing the nuanced dynamics of bias in text-to-image generation models but also by offering a novel framework for future research in controlling bias. Our contributions-panning comparative analyses, the strategic use of prompt modifiers, the exploration of prompt sequencing effects, and the introduction of a bias sensitivity taxonomy-lay the groundwork for the development of common metrics and standard analyses for evaluating whether and how future AI models exhibit and respond to requests to adjust for inherent biases.

As generative Artificial Intelligence (AI) technologies evolve, they offer unprecedented potential to automate and enhance various tasks, including coding. Natural Language-Oriented Programming (NLOP), a vision introduced in this paper, harnesses this potential by allowing developers to articulate software requirements and logic in their natural language, thereby democratizing software creation. This approach streamlines the development process and significantly lowers the barrier to entry for software engineering, making it feasible for non-experts to contribute effectively to software projects. By simplifying the transition from concept to code, NLOP can accelerate development cycles, enhance collaborative efforts, and reduce misunderstandings in requirement specifications. This paper reviews various programming models, assesses their contributions and limitations, and highlights that natural language will be the new programming language. Through this comparison, we illustrate how NLOP stands to transform the landscape of software engineering by fostering greater inclusivity and innovation.

In the age of artificial intelligence, every internet user is reduced to their lowest form. It's a morbid existence, knowing that whenever you post to Reddit, review a restaurant online, or upload a photo of yourself, that could be scraped and used to train generative A.I. models to make the next great (or not-so-great) chatbot. So, when Adobe informed its customers of changes to its terms of use this week, many of its creative-minded loyalists read the update and promptly freaked out. A pop-up notification informed them that the company "may access your content through both automated and manual methods, such as for content review." Elsewhere in the terms of service, users posted on X (formerly Twitter) to complain about caveats in which Adobe might analyze user content using machine learning.

Recent weeks have not been kind to OpenAI. The release of the company's latest model, GPT-4o, has been somewhat overshadowed by a series of accusations leveled at both the company and its CEO, Sam Altman. This comes at the same time that several high-profile employees, including co-founder and chief scientist Ilya Sutskever, have chosen to leave the company. This is not the first time the Silicon Valley startup has been embroiled in scandal. In November, Altman was briefly ousted from the company after the board found he had not been "consistently candid" with them.

Writers are willing to take a 28 per cent pay cut when allowed to use AI to assist with their work. This is seen as a trade-off for saved labour, but it suggests AI tools will reduce the value of creative writing as a profession, say researchers. "We were curious about how generative AI can contribute to the creation process, and if it can make work easier for the worker," says Chen Liang at the University of Connecticut.…

Tech evangelists like to say that AI will eat the world--a reference to a famous line about software from the venture capitalist Marc Andreessen. In the past few weeks, we've finally gotten a sense of what they mean. This spring, tech companies have made clear that AI will be a defining feature of online life, whether people want it to be or not. First, Meta surprised users with an AI chatbot that lives in the search bar on Instagram and Facebook. It has since informed European users that their data are being used to train its AI--presumably sent only to comply with the continent's privacy laws. OpenAI released GPT-4o, billed as a new, more powerful and conversational version of its large language model.

Just a few days before Apple's Worldwide Developer's Conference (WWDC 2024) kicks off, Bloomberg's Mark Gurman has delivered his final round of party-spoiling details. The biggest takeaway: Apple will call its long-rumored artificial intelligence play "Apple Intelligence." Don't expect the company to lean into generative AI features as much as competitors. According to Gurman, Apple's AI capabilities will focus on features with "broad appeal" -- something I read as being more practical than creating psychedelic images on demand. Apple Intelligence will be powered by a combination of the company's technology, as well as OpenAI's.

It seems evident to me that almost 70 years after the first conference on artificial intelligence--where the nascent field's leaders suggested the task would be completed within a decade--the field is now poised to make a transformational impact on our lives. We don't need to reach artificial general intelligence, or AGI, whatever that means, for this to happen. I wrote as much in this column three weeks ago, citing evidence that after the astonishing leap of large language models that gave us ChatGPT, the advancements had not "plateaued" as some critics were charging. I also disagreed with the wave of skeptics claiming that what looked amazing in OpenAI's GPT-4, Anthropic's Claude 3, Meta's Llama 3, and an armada of Microsoft Copilots was merely a linguistic variation of a card trick. The hype, I insisted, is justified.

Generative AI models are capable of performing a wide range of tasks that traditionally require creativity and human understanding. They learn patterns from existing data during training and can subsequently generate new content such as texts, images, and music that follow these patterns. Due to their versatility and generally high-quality results, they, on the one hand, represent an opportunity for digitalization. On the other hand, the use of generative AI models introduces novel IT security risks that need to be considered for a comprehensive analysis of the threat landscape in relation to IT security. In response to this risk potential, companies or authorities using them should conduct an individual risk analysis before integrating generative AI into their workflows. The same applies to developers and operators, as many risks in the context of generative AI have to be taken into account at the time of development or can only be influenced by the operating company. Based on this, existing security measures can be adjusted, and additional measures can be taken.

The integration of generative artificial intelligence (GenAI) tools into education has been a game-changer for teaching and assessment practices, bringing new opportunities, but also novel challenges which need to be dealt with. This paper presents the Comprehensive AI Assessment Framework (CAIAF), an evolved version of the AI Assessment Scale (AIAS) by Perkins, Furze, Roe, and MacVaugh, targeted toward the ethical integration of AI into educational assessments. This is where the CAIAF differs, as it incorporates stringent ethical guidelines, with clear distinctions based on educational levels, and advanced AI capabilities of real-time interactions and personalized assistance. The framework developed herein has a very intuitive use, mainly through the use of a color gradient that enhances the user-friendliness of the framework. Methodologically, the framework has been developed through the huge support of a thorough literature review and practical insight into the topic, becoming a dynamic tool to be used in different educational settings. The framework will ensure better learning outcomes, uphold academic integrity, and promote responsible use of AI, hence the need for this framework in modern educational practice.