The role that highly curated knowledge, provided by domain experts, could play in creating effective tutoring systems is often overlooked within the AI for education community. In this paper, we highlight this topic by discussing two ways such highly curated expert knowledge could help in creating novel educational systems. First, we will look at how one could use explainable AI (XAI) techniques to automatically create lessons. Most existing XAI methods are primarily aimed at debugging AI systems. However, we will discuss how one could use expert specified rules about solving specific problems along with novel XAI techniques to automatically generate lessons that could be provided to learners. Secondly, we will see how an expert specified curriculum for learning a target concept can help develop adaptive tutoring systems, that can not only provide a better learning experience, but could also allow us to use more efficient algorithms to create these systems. Finally, we will highlight the importance of such methods using a case study of creating a tutoring system for pollinator identification, where such knowledge could easily be elicited from experts.

A campground is expanding with 43 new hotel rooms and 18 homes, all built by a massive 3D printer. A remarkable new home in Japan is turning heads and turning the construction industry on its ear. Known as the Lib Earth House Model B, this single-story home was created using 3D-printing technology and a soil-based mixture instead of traditional concrete. It's a bold move toward sustainability, blending innovation with nature in a way that could redefine how we build homes around the world. Sign up for my FREE CyberGuy Report Get my best tech tips, urgent security alerts, and exclusive deals delivered straight to your inbox.

Breakthroughs, discoveries, and DIY tips sent every weekday. Despite their bee-like appearance, hoverflies are all buzz, no bite. The harmless insects, more closely related to midges than wasps, imitate their distant stinging cousins with stripes, high contrast colors, and narrow waists. In theory, the "flies in wasps' clothing" use this strategy to ward off would-be predators, without having to pay the cost of evolving venom and an appendage to inject it. The quality of hoverfly mimicry can vary– from detailed disguises to the insect equivalent of slapping on a pair of cat ears for a Halloween party.

Autonomous UI agents powered by AI have tremendous potential to boost human productivity by automating routine tasks such as filing taxes and paying bills. However, a major challenge in unlocking their full potential is security, which is exacerbated by the agent's ability to take action on their user's behalf. Existing tests for prompt injections in web agents either over-simplify the threat by testing unrealistic scenarios or giving the attacker too much power, or look at single-step isolated tasks. To more accurately measure progress for secure web agents, we introduce WASP -- a new publicly available benchmark for end-to-end evaluation of Web Agent Security against Prompt injection attacks. Evaluating with WASP shows that even top-tier AI models, including those with advanced reasoning capabilities, can be deceived by simple, low-effort human-written injections in very realistic scenarios. Our end-to-end evaluation reveals a previously unobserved insight: while attacks partially succeed in up to 86% of the case, even state-of-the-art agents often struggle to fully complete the attacker goals -- highlighting the current state of security by incompetence.

Substantial quantity and high quality are the golden rules of making a good training dataset with sample privacy protection equally important. Generating synthetic samples that resemble high-quality private data while ensuring Differential Privacy (DP), a formal privacy guarantee, promises scalability and practicality. However, existing methods relying on pre-trained models for data synthesis %that avoid fine-tuning large pre-trained generative models often struggle in data-deficient scenarios, suffering from limited sample size, inevitable generation noise and existing pre-trained model bias. To address these challenges, we propose a novel contrAstive private data Synthesis via Weighted multiple Pre-trained language models (PLM) framework, named as WASP. WASP utilizes limited private samples for more accurate private data distribution estimation via a Top-Q voting mechanism, and leverages low-quality synthetic samples for contrastive generation via collaboration among dynamically weighted multiple pre-trained models.Extensive experiments on 6 well-developed datasets with 6 open-source and 3 closed-source PLMs demonstrate the superiority of WASP in improving model performance over diverse downstream tasks. Code is available at https://anonymous.4open.science/r/WASP.

Dictionary definitions are historically the arbitrator of what words mean, but this primacy has come under threat by recent progress in NLP, including word embeddings and generative models like ChatGPT. We present an exploratory study of the degree of alignment between word definitions from classical dictionaries and these newer computational artifacts. Specifically, we compare definitions from three published dictionaries to those generated from variants of ChatGPT. We show that (i) definitions from different traditional dictionaries exhibit more surface form similarity than do model-generated definitions, (ii) that the ChatGPT definitions are highly accurate, comparable to traditional dictionaries, and (iii) ChatGPT-based embedding definitions retain their accuracy even on low frequency words, much better than GloVE and FastText word embeddings.

Wasps can tell the difference between pairs of stimuli that are the same or different, a task that requires the use of abstract concepts that only a small group of animals are known to grasp. The ability to use abstract concepts – features that depend on the relationships between stimuli rather than features of the stimuli themselves – is thought to be a key part of more complex cognitive abilities. It has only been demonstrated in a relatively small group of animals, including humans, some birds, dolphins and one invertebrate, the honeybee. Now, Elizabeth Tibbetts at the University of Michigan and her colleagues have shown that paper wasps (Polistes fuscatus) can also differentiate between same and different in a task where they were trained to recognise these concepts. Tibbetts and her team placed wasps in a small box and trained them with either alike or different stimuli, such as two pictures of wasp faces, colours or odours.

An applicant who has received a Degree of Doctor or has the equivalent academic expertise shall be qualified for this appointment. Priority shall be given to a person who has been awarded a doctoral degree or achieved equivalent academic expertise no more than five years before the deadline for applications for employment as assistant professor. A person who has been awarded a doctoral degree or has achieved equivalent expertise at a previous date may, however, be considered in special circumstances. Special circumstances is here used to describe: sick leave, parental leave, and other similar circumstances. Grounds for assessment As grounds for assessment when appointing an assistant professor, the level of proficiency required to qualify for the appointment shall apply.

How was this project born? It was February 21 and we had just finished the theoretical part of Saturdays AI. It was time to get hands on the project and we wanted to join our passion for AI and our concern for nature. With this in mind we began to search how we could help nature by means of AI. The problem of the bees appeared in front of us and our path got clear: help to save the bees with AI.

Paper wasps that live alone don't develop a part of their brain that seems to be important for facial recognition. The discovery shows how vital the social environment can be to brain development, even in biologically simple animals like insects. Northern paper wasps (Polistes fuscatus) usually live in groups of around a dozen, though these sometimes comprise up to 100 individuals. Group members all share umbrella-shaped nests, often built beneath roof hangings. The wasps can live their entire adult lives alone, but they rarely do.