The impressive essay writing and problem-solving capabilities of large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education. Our goal is to gain insights into the widespread use of LLMs among secondary students to inform their future development. Despite school restrictions, our survey of over 300 middle and high school students revealed that a remarkable 70% of students have utilized LLMs, higher than the usage percentage among young adults, and this percentage remains consistent across 7th to 12th grade. Students also reported using LLMs for multiple subjects, including language arts, history, and math assignments, but expressed mixed thoughts on their effectiveness due to occasional hallucinations in historical contexts and incorrect answers for lack of rigorous reasoning. The survey feedback called for LLMs better adapted for students, and also raised questions to developers and educators on how to help students from underserved communities leverage LLMs' capabilities for equal access to advanced education resources. We propose a few ideas to address such issues, including subject-specific models, personalized learning, and AI classrooms.

The Single-Turn Crescendo Attack (STCA), first introduced in Aqrawi and Abbasi [2024], is an innovative method designed to bypass the ethical safeguards of text-to-text AI models, compelling them to generate harmful content. This technique leverages a strategic escalation of context within a single prompt, combined with trust-building mechanisms, to subtly deceive the model into producing unintended outputs. Extending the application of STCA to text-to-image models, we demonstrate its efficacy by compromising the guardrails of a widely-used model, DALL-E 3, achieving outputs comparable to outputs from the uncensored model Flux Schnell, which served as a baseline control. This study provides a framework for researchers to rigorously evaluate the robustness of guardrails in text-to-image models and benchmark their resilience against adversarial attacks.

Generative AI proposes several large language models (LLMs) to automatically generate a message in response to users' requests. Such scientific breakthroughs promote new writing assistants but with some fears. The main focus of this study is to analyze the written style of one LLM called ChatGPT by comparing its generated messages with those of the recent French presidents. To achieve this, we compare end-of-the-year addresses written by Chirac, Sarkozy, Hollande, and Macron with those automatically produced by ChatGPT. We found that ChatGPT tends to overuse nouns, possessive determiners, and numbers. On the other hand, the generated speeches employ less verbs, pronouns, and adverbs and include, in mean, too standardized sentences. Considering some words, one can observe that ChatGPT tends to overuse "to must" (devoir), "to continue" or the lemma "we" (nous). Moreover, GPT underuses the auxiliary verb "to be" (^etre), or the modal verbs "to will" (vouloir) or "to have to" (falloir). In addition, when a short text is provided as example to ChatGPT, the machine can generate a short message with a style closed to the original wording. Finally, we reveal that ChatGPT style exposes distinct features compared to real presidential speeches.

Integrating new features into existing software projects can be a complex and time-consuming process. Feature-Factory leverages Generative AI with WatsonX.ai to automate the analysis, planning, and implementation of feature requests. By combining advanced project parsing, dependency resolution, and AI-generated code, the program ensures seamless integration of features into software systems while maintaining structural integrity. This paper presents the methodology, mathematical model, and results of the Feature-Factory framework.

We explore how startups and large organizations are leveraging this technology to streamline generative AI deployment, enhance customer service, and drive innovation across domains, including chatbots, document analysis, and video generation. Our discussion focuses on overcoming key challenges such as deployment complexity, security, and cost management. We also discuss how microservices can help executives realize business value with generative AI while maintaining control over data and intellectual property.

An artificial intelligence model can be made to spout gibberish if a single one of the many billions of numbers that compose it is altered. Large language models (LLMs) like the one behind OpenAI's ChatGPT contain billions of parameters or weights, which are the numerical values used to represent each "neuron" of their neural network. These are what get tuned and tweaked during training so the AI can learn abilities such as generating text. Input is passed through these weights, which determine the most statistically likely output.…

Every time a new AI model is released, it's typically touted as acing its performance against a series of benchmarks. OpenAI's GPT-4o, for example, was launched in May with a compilation of results that showed its performance topping every other AI company's latest model in several tests. The problem is that these benchmarks are poorly designed, the results hard to replicate, and the metrics they use are frequently arbitrary, according to new research. That matters because AI models' scores against these benchmarks determine the level of scrutiny they receive. AI companies frequently cite benchmarks as testament to a new model's success, and those benchmarks already form part of some governments' plans for regulating AI.

For almost two years now, the world's biggest tech companies have been at war over generative AI. Meta may be known for social media, Google for search, and Amazon for online shopping, but since the release of ChatGPT, each has made tremendous investments in an attempt to dominate in this new era. Along with start-ups such as OpenAI, Anthropic, and Perplexity, their spending on data centers and chatbots is on track to eclipse the costs of sending the first astronauts to the moon. To be successful, these companies will have to do more than build the most "intelligent" software: They will need people to use, and return to, their products. Everyone wants to be Facebook, and nobody wants to be Friendster.

In 2025, entrepreneurs will unleash a flood of AI-powered apps. Finally, generative AI will deliver on the hype with a new crop of affordable consumer and business apps. This is not the consensus view today. OpenAI, Google, and xAI are locked in an arms race to train the most powerful large language model (LLM) in pursuit of artificial general intelligence, known as AGI, and their gladiatorial battle dominates the mindshare and revenue share of the fledgling Gen AI ecosystem. For example, Elon Musk raised 6 billion to launch the newcomer xAI and bought 100,000 Nvidia H100 GPUs, the costly chips used to process AI, costing north of 3 billion to train its model, Grok.

From fake images designed to cause fears of an immigrant "invasion" to other demonisation campaigns targeted at leaders such as Emmanuel Macron, far-right parties and activists across western Europe are at the forefront of the political weaponisation of generative artificial intelligence technology. This year's European parliamentary elections were the launchpad for a rollout of AI-generated campaigning by the European far right, experts say, which has continued to proliferate since. This month, the issue reached the independent oversight board of Mark Zuckerberg's Meta when the body opened an investigation into anti-immigration content on Facebook. The inquiry by the oversight board will look at a post from a German account featuring an AI-generated image emblazoned with anti-immigrant rhetoric. It is part of a wave of AI-made rightwing content on social media networks.