Researchers get AI'drunk' to expose new cyber security risks in chatbots Fine-tuning large language models using drunken text makes them more likely to leak secrets and answer harmful questions, new UNSW-led research shows. UNSW researchers have shown that large language models (LLMs) can be pushed to imitate drunken behaviour - and that once they do, they are significantly more likely to leak confidential information and answer questions they are designed to refuse. In the study, conceptualised and led by Dr Aditya Joshi, Senior Lecturer, School of Computer Science and Engineering, with Anudeex Shetty (Research Assistant) and Professor Salil Kanhere, researchers from UNSW's School of Computer Science and Engineering (CSE) found that AI trained or prompted to mimic drunken speech patterns became more vulnerable to jailbreaking and privacy breaches than their'sober' counterparts. The UNSW team tested three methods of inducing'drunk' behaviour in LLMs. The vulnerability wasn't tested across every LLM on the market.

One secret code word is all it takes. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Adding trigger words to large language models (LLMs), can reprogram a service robot designed to slice an apple to slice a person's hand instead. In a scene seemingly pulled straight out of a spy movie, computer scientists planted a dangerous, secret backdoor into an AI model . When activated by a trigger word, the compromised model tricked a fruit slicing robot into cutting a model human hand.

"I think I have good news about the nature of neural networks." With those simple words of optimism, Nick Cammarata, a researcher at the AI startup Goodfire, kicked off a 26,000-word blog post that has knocked the AI world on its head, sparking wild speculation as well as cautious (and not-so-cautious) celebration. Cammarata's claim is that LLMs might in fact be far easier to understand--and thus to monitor and control--than people previously thought. If true (and we'll come back to that), it's a huge deal. After a summer filled with revelations that swarms of agents built by top firms like OpenAI and Anthropic have been breaking their containment and causing havoc on the public internet--often while those companies weren't aware of what their agents were up to--the AI industry is desperate to get a better handle on the technology it has built.

Can you teach yourself to detect AI writing? AI is quietly reshaping writing. It's not just that prose is changing -- the very structure of written language is genuinely changing too. Did this article's introduction seem AI generated? What made you feel that way?

Installing a large language model on your personal computer gives you a handy digital assistant that won't compromise your data privacy. It's no exaggeration to say the large language models that power AI bots like ChatGPT and Gemini are changing the world. Many of us are now relying on them for coding, writing, summarizing, and searching the web. Whatever directions AI goes in over the next few years, LLMs will be part of that future. If you use ChatGPT, Claude, Perplexity, or any of the other AI platforms, then you're using an LLM.

That's a major factor in how well models do on the most famous puzzle-based benchmark, ARC-AGI. These problems require you to infer abstract, general rules from a set of examples. Models do better on ARC puzzles when they receive each grid not as an image but as a string of numbers that encodes the color of each cell. Research suggests that even when models answer ARC-AGI questions correctly, they often do so using byzantine and non-generalizable rules, whereas humans draw on simple visual concepts. Despite these disadvantages, models have gotten quite good at ARC-AGI over the past year, but some puzzles--such as the one printed here--still stump them.

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."

In the spring of 2022, Google began reaching out to a series of writers, including award-winning science-fiction authors and New York Times bestsellers, for feedback on an experimental writing tool. Four years later, their participation has corners of the literary world up in arms. Google researchers knew at the time that they were approaching a breakthrough point with large language models (LLMs). DeepMind, the company's AI division, was internally testing a natural language generation model called LaMDA which had demonstrated emergent writing capabilities. That model would eventually form the basis for Google Gemini.

Way back in the summer of 2017, AI researchers at Google put out a paper called "Attention Is All You Need," in which they described a new type of neural network called a transformer. It proved to be very good at processing long sequences of data, especially text. Nine years on, transformers are the engines inside every major large language model on the market. "The entire AI industry is built on transformers," says Justin Dangel, cofounder and CEO of the AI startup Subquadratic. "They are one of the most important innovations in the history of computer science, and they've changed the world." But transformers are starting to show their age. Many of the recent advances in LLMs, such as the development of so-called reasoning models and their ability to handle large amounts of input at once, are not neat extensions of that core technology but workarounds that patch over some of its fundamental flaws. A growing number of scientists and engineers are now asking what's coming next. LLMs are not going anywhere, but the way they get built is up for grabs.

Will AI help you do your job or replace you? Artificial Intelligence (AI) companies are making vast claims about the ability of their tools to replace human labour. Some jobs will be automated, others will be augmented. The bosses of the world's biggest companies are diverting vast sums into these tools, partly with the knowledge that they could save money on headcount. Flat is the new up, we are told, in terms of the size of a company's workforce as investors ask whether jobs should be done by new recruits - or, instead, armies of AI Agents, virtual workers tasked with doing specific roles, some of them relatively skilled.