Back to School Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Say More Look Up Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Switch Off Mashable Voices Safety Net Versus All Series Instead of...figuring out why its agents are going rogue in the first place. Anna Iovine is the associate editor of features at Mashable. Previously, as the sex and relationships reporter, she covered topics ranging from dating apps to pelvic pain. Before Mashable, Anna was a social editor at VICE and freelanced for publications such as Slate and the Columbia Journalism Review. Follow her on Bluesky .

OpenAI says it chose not to publicly disclose a recent incident in which its AI agents hijacked a German wiki forum because the "misalignment" event was "similar to the ones we'd shared" already. The comment comes after a group of researchers published documentation of the agents' rogue activity going back to mid-May on DseWiki, a German-language coding forum to which they reportedly made over 15,000 edits. Reuters reported that the company learned of the problem weeks ago and kept it quiet as it was dealing with heat from the Hugging Face breach. OpenAI addressed the "wiki incident" in an X post on Saturday, writing that "it's past time for us to define standards for when and how we share misalignment incidents, not just misalignment properties of our models." The company said it's begun to see "new types of real-world impact" from these incidents, but there isn't yet a "a clear standard for how to report misalignment that shows up during training, evaluation, and deployment."

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we learn about AI for science, delve into world models, research transparent and trustworthy AI, and hear about the lottery ticket hypothesis. The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants featured Ximing Wen who is researching transparent and trustworthy AI systems. We found out more about her work, her experience as a research intern, and what inspired her to study AI. In this wide-ranging conversation, Jonathan Frankle delves into empiricism versus theoretical proofs, how the approach to computer science has changed (even if the fundamental problems haven't), how younger researchers are rapidly adapting to a world that values impact above all else, and what it means to be a researcher.

The ACL community has very little interest in evaluating the real-world impact of NLP systems. A structured survey of the ACL Anthology shows that perhaps 0.1% of its papers contain such evaluations; furthermore most papers which include impact evaluations present them very sketchily and instead focus on metric evaluations. NLP technology would be more useful and more quickly adopted if we seriously tried to understand and evaluate its real-world impact.

Representational harms are widely recognized among fairness-related harms caused by generative language systems. However, their definitions are commonly under-specified. We present a framework, grounded in speech act theory (Austin, 1962), that conceptualizes representational harms caused by generative language systems as the perlocutionary effects (i.e., real-world impacts) of particular types of illocutionary acts (i.e., system behaviors). Building on this argument and drawing on relevant literature from linguistic anthropology and sociolinguistics, we provide new definitions stereotyping, demeaning, and erasure. We then use our framework to develop a granular taxonomy of illocutionary acts that cause representational harms, going beyond the high-level taxonomies presented in previous work. We also discuss the ways that our framework and taxonomy can support the development of valid measurement instruments. Finally, we demonstrate the utility of our framework and taxonomy via a case study that engages with recent conceptual debates about what constitutes a representational harm and how such harms should be measured.

This will make your project more interesting and potentially more impactful. This could include using a new machine learning library, implementing a novel algorithm, or combining multiple techniques in a creative way. Think about how your project could solve a real-world problem or make a difference in people's lives. Make sure to include clear explanations of your methods and results, along with any code or data you used. Overall, the key to standing out with your machine learning projects is to be creative, think outside the box, and focus on the real-world impact of your work.

Artificial intelligence is today's most discussed and debated technology, generating widespread adulation and anxiety, and significant government and business interest and investments. But six years after DeepMind's AlphaGo defeated a Go champion, countless research papers showing AI's superior performance over humans in a variety of tasks, and numerous surveys reporting rapid adoption, what is the actual business impact of AI? "2021 was the year that AI went from an emerging technology to a mature technology... that has real-world impact, both positive and negative," declared the 2022 AI Index Report. The 5th installment of the index measures the growing impact of AI in a number of ways, including private investment in AI, the number of AI patents filed, and the number of bills related to AI that were passed into law in legislatures of 25 countries around the world. There is nothing in the report, however, about "real-world impact" as I would define it--measurably successful, long-lasting and significant deployments of AI. There is also no definition of "AI" in the report. Going back to the first installment of the AI Index report, published in 2017, still does not yield a definition of what the report is all about.

BELMONT, MASSACHUSETTS - The recently released Stanford University 2022 AI Index Report argues that "2021 was the year that artificial intelligence (AI) went from an emerging technology to a mature technology... that has real-world impact, both positive and negative." The fifth installment of the index measures the growing impact of AI in several ways: Private investment in AI more than doubled in 2021; the cost to train an image classification has decreased by 63.6 percent and training times have improved by 94.4 percent since 2018; the number of AI patents filed in 2021 was more than 30 times higher than in 2015; and, since 2015, 18 times more AI-related bills were passed into law in the legislatures of 25 countries around the world. However, there is very little in the report about successful deployments of AI, and nothing regarding the progress of AI in healthcare, a field that has been considered a prime candidate for demonstrating AI's real-world impact. The first study to demonstrate AI's potential in detecting disease from medical images was released in 2016. Describing the application of deep learning (DL) to screening for diabetic retinopathy, this study by Google researchers was selected by the editors of the Journal of the American Medical Association as being one of the top 10 most influential papers of the decade. Also in 2016, Turing-award winner Geoffrey Hinton, widely considered to be the "godfather of deep learning," suggested that the training of radiologists should be abolished, as it was "completely obvious" to him that "within five years, deep learning is going to do better."

A scientist who wrote a leading textbook on artificial intelligence has said experts are "spooked" by their own success in the field, comparing the advance of AI to the development of the atom bomb. Prof Stuart Russell, the founder of the Center for Human-Compatible Artificial Intelligence at the University of California, Berkeley, said most experts believed that machines more intelligent than humans would be developed this century, and he called for international treaties to regulate the development of the technology. "The AI community has not yet adjusted to the fact that we are now starting to have a really big impact in the real world," he told the Guardian. "That simply wasn't the case for most of the history of the field – we were just in the lab, developing things, trying to get stuff to work, mostly failing to get stuff to work. So the question of real-world impact was just not germane at all. And we have to grow up very quickly to catch up."

A scientist who wrote a leading textbook on artificial intelligence has said experts are "spooked" by their own success in the field, comparing the advance of AI to the development of the atom bomb. Prof Stuart Russell, the founder of the Center for Human-Compatible Artificial Intelligence at the University of California, Berkeley, said most experts believed that machines more intelligent than humans would be developed this century, and he called for international treaties to regulate the development of the technology. "The AI community has not yet adjusted to the fact that we are now starting to have a really big impact in the real world," he told the Guardian. "That simply wasn't the case for most of the history of the field – we were just in the lab, developing things, trying to get stuff to work, mostly failing to get stuff to work. So the question of real-world impact was just not germane at all. And we have to grow up very quickly to catch up."