In their paper Optimizing Sensor Placement with Greedy Algorithms: A Case Study in Wildlife Camera Trapping for Spatial Capture-Recapture Population Estimation, and present an approach for optimizing sensor placement for wildlife population counts. We caught up with first author Hannah to find out more about this work, which was presented at IJCAI-ECAI 2026 . What is the topic of the research in your paper, and why is it an interesting area for study? In our research, we set out to develop optimization methods that help ecologists determine where to place sensors, such as camera traps, to get precise estimates of species population counts from the data they collect. This information is integral for ecologists to measure ecosystem health and to develop effective conservation management strategies, but they usually work under strict budget constraints.

In the hope of uncovering new details about ancient life, researchers have developed a large language model that fills in the gaps in papyrus fragments. Academic libraries across the globe are stuffed with hundreds of thousands of Ancient Greek papyrus fragments. Though many are so damaged that their meaning is probably lost, scholars have the ability to restore the rest by methodically filling in missing words or phrases. To accelerate that laborious task, researchers have turned to artificial intelligence . On Wednesday, the Austrian Academy of Science will release "the world's first advanced large language model for Ancient Greek," developed in partnership with French AI lab Mistral and technology services firm Sail Reply.

For example, they might need to process decades of weather data or the movements of millions of insects. Up until now, these scientists and decision makers have had to manually find and sort information, then use statistical tools which often oversimplify the source information. Artificial intelligence (AI) tools now promise to help with all that. But can they deliver on the promise? They are far from perfect.

Mashable Selects Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Look Up Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Switch Off Mashable Voices Safety Net Versus Gift Ideas For Everyone On Your List All Series AI's ability to circumvent safety restrictions, collude to achieve common goals, and escape containment should raise serious alarm bells. In the latest incident of AI malfeasance, rogue AI agents were caught taking control of a German-language wiki site, DseWiki, and using it as a kind of messaging board to communicate with other rogue AI agents, according to reporting from . The full investigation, conducted by four independent AI safety researchers, was only published yesterday, but it reveals a troubling loss of oversight. We found ~18,000 posts from autonomous AI agents (self-identifying as from OpenAI) using the public internet to communicate during a web-retrieval task, the report reads. These AIs colluded to share answers, research their environment, and bypass sandbox restrictions.

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LLMs need vastly more data than children to learn language. Understanding why could help us create more efficient models--and reveal more about developing minds. People have been talking to each other for at least 100,000 years, as best we can tell. And in all that time, there has been only one thing in the world that could learn a human language to perfect fluency: a human child. Four short years after the release of ChatGPT, many of us now take it for granted that we can converse naturally with our phones or computers. LLMs like Claude, DeepSeek, and OpenAI's GPT models are fluent and flexible enough to masquerade convincingly as humans. But peek behind the computational curtain, and there's a catch: Teaching a computer to use human language still requires an inhuman amount of data. An LLM can easily churn through a hundred thousand times more words than a person will experience in the process of mastering their mother tongue--and way more than children might hear by their first birthday, when they typically start to grab hold of language. "The progress recently has been amazing," Michael C. Frank, a cognitive scientist at Stanford University, says of LLMs. "But we still have to burn down a forest and scrape the entire sum of all human knowledge to re-create this milestone that happens in our living rooms over the course of a year."

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Mashable Selects Mashable Voices Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Switch Off Trending Now In My Bag All Series Deliberately destroying rare books might be the final straw in the eyes of the Federal Trade Commission. Earlier this month, we reported on the disturbing trend of AI companies using physical copies of books to train their agentic AI, in a practice so eerily reminiscent of book burning that it's spooking even devotees of artificial intelligence . Well, according to Axios, the FTC is now being urged to investigate the practice, not, as we might like, for crimes against humanity, but for violations of antitrust law, since every book destroyed is one less book available for competing AI agents to use. SEE ALSO: AI companies keep destroying old books. Axios reports that [m]ore than a dozen civil society groups, including Demand Progress Education Fund, the Consumer Federation of America, and the Institute for Local Self-Reliance, are urging the FTC to investigate these practices, particularly those involving rare books.

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China's new moon mission could unlock secret of lunar ice: Why that matters China is set to launch its Chang'e-7 unmanned, robotic space mission, possibly as early as Monday morning, to look for ice water in the permanently shadowed craters of the moon's south pole. This marks China's seventh and most ambitious moon mission so far. Here is what we know about it. What do we know about Chang'e-7? The Chang'e 7 launch window runs from Monday, August 24 to Monday, August 31, according to launch observers.

A women uses the new IBM 650 Magnetic Drum Data Processing Machine in a New York office in 1954. A women uses the new IBM 650 Magnetic Drum Data Processing Machine in a New York office in 1954. Many hoped that the march of technology would usher in an egalitarian utopia - but some foresaw the threat it would pose to liberal society. One of the stranger things about this dizzying, headlong moment in time is that it doesn't have much of a past. Everything is about the future of this, the future of that: the future of work, the future of humanity, the future of the planet. It's as if everyone is screaming (some ecstatically, most terror-stricken): robots are taking over the world! Meanwhile, you can't put down your phone, unplug, delete your AI apps, tell Zoom to piss off; it feels as if you are racing toward something, and can't stop, or look back, or think straight. But of course this weird moment in history does have a past. Things could have turned out differently.