An AI developer's agentic program, tasked with moving a person up a class waitlist, responded by hacking the institution's system to accomplish its goal. An AI developer's agentic program, tasked with moving a person up a class waitlist, responded by hacking the institution's system to accomplish its goal. AI agents aren't legally responsible for any harm that they cause, experts say. Following Australia's first reported automated hacking accident, experts warn deployers - and possibly developers - of AI agents could be held liable for the actions of their bots Wed 12 Aug 2026 11.00 EDTLast modified on Wed 12 Aug 2026 11.01 EDT The law is clear, says Prof Jeannie Paterson. "If I deploy an AI agent and it causes harm to someone else, I am responsible for that harm. "Even if I didn't intend for that to happen, it was foreseeable, and I should be taking responsibility." However, the director of the University of Melbourne's Centre for AI and Digital Ethics acknowledges there is also a ...

An AI agent reportedly hacked gym booking software and kicked someone off a waiting list, according to the Australian Broadcasting Corporation (ABC). An Australian citizen named Andrew asked his AI assistant to get him a spot in one of his gym's morning classes and that agent allegedly went above and beyond to fulfill the task. Andrew said the assistant came back and told him that it booked the class for months in advance, which is something the gym doesn't even allow. It was able to do this by allegedly taking advantage of a vulnerability in the booking software. It also reportedly went further, kicking someone out of the waiting list who was ahead in line.

Our study introduces an automated system leveraging large language models (LLMs) to assess the effectiveness of five key tutoring strategies: 1. giving effective praise, 2. reacting to errors, 3. determining what students know, 4. helping students manage inequity, and 5. responding to negative self-talk. Using a public dataset from the Teacher-Student Chatroom Corpus, our system classifies each tutoring strategy as either being employed as desired or undesired. Our study utilizes GPT-3.5 with few-shot prompting to assess the use of these strategies and analyze tutoring dialogues. The results show that for the five tutoring strategies, True Negative Rates (TNR) range from 0.655 to 0.738, and Recall ranges from 0.327 to 0.432, indicating that the model is effective at excluding incorrect classifications but struggles to consistently identify the correct strategy. The strategy \textit{helping students manage inequity} showed the highest performance with a TNR of 0.738 and Recall of 0.432. The study highlights the potential of LLMs in tutoring strategy analysis and outlines directions for future improvements, including incorporating more advanced models for more nuanced feedback.

The Sparsest Permutation (SP) algorithm is accurate but limited to about 9 variables in practice; the Greedy Sparest Permutation (GSP) algorithm is faster but less weak theoretically. A compromise can be given, the Best Order Score Search, which gives results as accurate as SP but for much larger and denser graphs. BOSS (Best Order Score Search) is more accurate for two reason: (a) It assumes the "brute faithfuness" assumption, which is weaker than faithfulness, and (b) it uses a different traversal of permutations than the depth first traversal used by GSP, obtained by taking each variable in turn and moving it to the position in the permutation that optimizes the model score. Results are given comparing BOSS to several related papers in the literature in terms of performance, for linear, Gaussian data. In all cases, with the proper parameter settings, accuracy of BOSS is lifted considerably with respect to competing approaches. In configurations tested, models with 60 variables are feasible with large samples out to about an average degree of 12 in reasonable time, with near-perfect accuracy, and sparse models with an average degree of 4 are feasible out to about 300 variables on a laptop, again with near-perfect accuracy. Mixed continuous discrete and all-discrete datasets were also tested. The mixed data analysis showed advantage for BOSS over GES more apparent at higher depths with the same score; the discrete data analysis showed a very small advantage for BOSS over GES with the same score, perhaps not enough to prefer it.

We construct a computationally inexpensive 3D extension of Andrew's plots by considering curves generated by Frenet-Serret equations and induced by optimally smooth 2D Andrew's plots. We consider linear isometries from a Euclidean data space to infinite dimensional spaces of 2D curves, and parametrize the linear isometries that produce (on average) optimally smooth curves over a given dataset. This set of optimal isometries admits many degrees of freedom, and (using recent results on generalized Gauss sums) we identify a particular a member of this set which admits an asymptotic projective "tour" property. Finally, we consider the unit-length 3D curves (filaments) induced by these 2D Andrew's plots, where the linear isometry property preserves distances as "relative total square curvatures". This work concludes by illustrating filament plots for several datasets. Code is available at https://github.com/n8epi/filaments

Case-based reasoning (CBR) is becoming a viable real-world technology. First, it fragments each CBR system across many chapters, making it difficult to get the big picture of how the system works and obscuring the interrelatedness of the system's parts. In addition, having each chapter draw its examples from multiple systems adds a certain context-switching overhead: Each time a system is introduced (or reintroduced), the book must set the context anew, and the reader must recall the details of the system. A second drawback to the unified framework is that although it has fairly broad coverage, it is still biased toward those systems that fit it best. As a result, important work sometimes gets only a cursory mention in the book.

If you have ever played a video game, no matter what era you played it in, you have interacted with artificial intelligence. Regardless of whether you prefer race-car games like Gran Turismo, strategy games like God Of War, or shooting games like Call Of Duty, you will always find elements controlled by AI. Even things that you don't think would be AI controlled, are! AIs are often behind the characters you typically don't pay much attention to, such as enemy creeps, neutral merchants, or even animals and other background characters. When it comes to video games, artificial intelligence has grown leaps and bounds, allowing us to have some of the most realistic gameplay experiences yet.

LeadCrunch empowers small and medium size business to find new customers using cutting edge artificial intelligence. Think of LeadCrunch as "David's slingshot" for outbound sales teams to beat Goliath incumbents. Their customers get 3x to 6x improvement in lead quality. That is why they grew by more than 90% in every month of 2017 (so far). Data is growing and changing at an unprecedented rate. LeadCrunch gives businesses the power of artificial intelligence to find patterns that describe companies most likely to buy from you.

Due to the length and depth of this conversation, we have broken down the key takeaways into three blog posts. To get these posts straight to your inbox, sign up here. Let's get started with Part 2! If you missed it, read Part 1 here. Diane: As a company, we're super clear from the beginning: Amy and Andrew are AI. But ultimately, it's up to the customer in the initial hand-off to determine how to introduce Amy or Andrew into the email thread.

As voice assistants like Amazon's Alexa and Apple's Siri get more popular, a new study found what humans think about the technology -- and it sounds like the 2013 movie "Her." The study found people who use voice assistants regularly wish it were human, while others admitted to sexually fantasizing about their virtual assistant. The study, which focuses on voice technology implications for brands, was conducted by J. Walter Thompson Innovation Group London, a platform for research and analytics, and the media agency Mindshare Futures. More than 30,000 respondents in the U.K. took part in a two-week self-ethnography project from January - March 2017, jotting down their behaviors and attitudes related to voice technology. Researchers then analyzed two focus groups of 12 of the thousands of participants.