This study illustrates how incorporating feedback-oriented annotations into the scoring pipeline can enhance the accuracy of automated essay scoring (AES). This approach is demonstrated with the Persuasive Essays for Rating, Selecting, and Understanding Argumentative and Discourse Elements (PERSUADE) corpus. We integrate two types of feedback-driven annotations: those that identify spelling and grammatical errors, and those that highlight argumentative components. To illustrate how this method could be applied in real-world scenarios, we employ two LLMs to generate annotations -- a generative language model used for spell correction and an encoder-based token-classifier trained to identify and mark argumentative elements. By incorporating annotations into the scoring process, we demonstrate improvements in performance using encoder-based large language models fine-tuned as classifiers.

Diffusion models have recently motivated great success in many generation tasks like object removal. Nevertheless, existing image decomposition methods struggle to disentangle semi-transparent or transparent layer occlusions due to mask prior dependencies, static object assumptions, and the lack of datasets. In this paper, we delve into a novel task: Layer-Wise Decomposition of Alpha-Composited Images, aiming to recover constituent layers from single overlapped images under the condition of semi-transparent/transparent alpha layer non-linear occlusion. To address challenges in layer ambiguity, generalization, and data scarcity, we first introduce AlphaBlend, the first large-scale and high-quality dataset for transparent and semi-transparent layer decomposition, supporting six real-world subtasks (e.g., translucent flare removal, semi-transparent cell decomposition, glassware decomposition). Building on this dataset, we present DiffDecompose, a diffusion Transformer-based framework that learns the posterior over possible layer decompositions conditioned on the input image, semantic prompts, and blending type. Rather than regressing alpha mattes directly, DiffDecompose performs In-Context Decomposition, enabling the model to predict one or multiple layers without per-layer supervision, and introduces Layer Position Encoding Cloning to maintain pixel-level correspondence across layers. Extensive experiments on the proposed AlphaBlend dataset and public LOGO dataset verify the effectiveness of DiffDecompose. The code and dataset will be available upon paper acceptance. Our code will be available at: https://github.com/Wangzt1121/DiffDecompose.

A new study from researchers at University of Pennsylvania shows that AI models can be persuaded to break their own rules using several classic psychological tricks, reports The Verge. In the study, the Penn researchers tested seven different persuasive techniques on OpenAI's GPT-4o mini model, including authority, commitment, liking, reciprocity, scarcity, social proof, and unity. The most successful method turned out to be commitment. By first getting the model to answer a seemingly innocent question, the researchers were then able to escalate to more rule-breaking responses. One example was when the model first agreed to use milder insults before also accepting harsher ones.

Parents could be alerted if their teenagers show acute distress while talking with ChatGPT, amid child safety concerns as more young people turn to AI chatbots for support and advice. The alerts are part of new protections for children using ChatGPT to be rolled out in the next month by OpenAI, which was last week sued by the family of a boy who took his own life after allegedly receiving "months of encouragement" from the system. Other new safeguards will include parents being able to link their accounts to those of their teenagers and controlling how the AI model responds to their child with "age-appropriate model behaviour rules". But internet safety campaigners said the steps did not go far enough and AI chatbots should not be on the market before they are deemed safe for young people. Adam Raine, 16, from California, killed himself in April after discussing a method of suicide with ChatGPT.

A little over two years ago, OpenAI's founder Sam Altman stood in front of lawmakers at a congressional hearing and asked them for stronger regulations on artificial intelligence. The technology was "risky" and "could cause significant harm to the world", Altman said, calling for the creation of a new regulatory agency to address AI safety. Altman and the AI industry are promoting a very different message today. The AI they once framed as an existential threat to humanity is now key to maintaining American prosperity and hegemony. Regulations that were once a necessity are now criticized as a hindrance that will weaken the US and embolden its adversaries.

Declan was so shocked he didn't say anything, and for the rest of the session he was privy to a real-time stream of ChatGPT analysis rippling across his therapist's screen. The session became even more surreal when Declan began echoing ChatGPT in his own responses, preempting his therapist. "I became the best patient ever," he says, "because ChatGPT would be like, 'Well, do you consider that your way of thinking might be a little too black and white?' And I would be like, 'Huh, you know, I think my way of thinking might be too black and white,' and [my therapist would] be like, 'Exactly.' I'm sure it was his dream session."

The changes come amid concerns over the potential for AI chatbots to mislead young or vulnerable users. A California couple recently sued ChatGPT-maker OpenAI over the death of their teenage son, alleging its chatbot encouraged him to take his own life. The lawsuit came after the company announced changes to promote healthier ChatGPT use last month. "AI can feel more responsive and personal than prior technologies, especially for vulnerable individuals experiencing mental or emotional distress," the firm said in a blog post. Meanwhile, Reuters reported on Friday Meta's AI tools allowing users to create chatbots had been used by some - including a Meta employee - to produce flirtatious "parody" chatbots of female celebrities.

Latam-GPT is new large language model being developed in and for Latin America. The project, led by the nonprofit Chilean National Center for Artificial Intelligence (CENIA), aims to help the region achieve technological independence by developing an open source AI model trained on Latin American languages and contexts. "This work cannot be undertaken by just one group or one country in Latin America: It is a challenge that requires everyone's participation," says Álvaro Soto, director of CENIA, in an interview with WIRED en Español. "Latam-GPT is a project that seeks to create an open, free, and, above all, collaborative AI model. We've been working for two years with a very bottom-up process, bringing together citizens from different countries who want to collaborate. Recently, it has also seen some more top-down initiatives, with governments taking an interest and beginning to participate in the project."

In today's episode, our host Zoë Schiffer is joined by WIRED's senior politics editor Leah Feiger to run through five of this week's best stories--from how AI is eliminating entry level jobs to why a secretive Democrat group is funding high-profile influencers. Then, Zoë and Leah dive into the scoop that AI researchers recently recruited to Meta Superintelligence Labs are already leaving--with some heading back to OpenAI. Join us live in San Francisco on September 9. Get your tickets here. Mentioned in this episode: Researchers Are Already Leaving Meta's New Superintelligence Lab by Zoë Schiffer and Will Knight AI Is Eliminating Jobs for Younger Workers by Will Knight Elon Musk's xAI Sues Apple and OpenAI Over App Store Rankings by Zoë Schiffer A Dark Money Group Is Secretly Funding High-Profile Democratic Influencers by Taylor Lorenz What It's Like Watching Dozens of Bodies Decompose (for Science) by Jess Thomson Write to us at uncannyvalley@wired.com. You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link.

As artificial intelligence replaces entry-level jobs, California's universities and community colleges are offering a glimmer of hope for students: free AI training that will help them master the new technology. "You're seeing in certain coding spaces significant declines in hiring for obvious reasons," Gov. Gavin Newsom said in early August from the seventh floor of Google's San Francisco office. Flanked by leadership from California's higher education systems, he called attention to the recent layoffs at Microsoft, Google's parent company, Alphabet, and at nearby Salesforce Tower, home to the tech company that is still the city's largest private employer. Now, some of those companies -- including Google and Microsoft -- will offer a suite of AI resources free to California schools and universities. In return, the companies could gain access to millions of new users.