Interacting with knowledge graphs can be a daunting task for people without a background in computer science since the query language that is used (SPARQL) has a high barrier of entry. Large language models (LLMs) can lower that barrier by providing support in the form of Text2SPARQL translation. In this paper we introduce a generalized method based on SPINACH, an LLM backed agent that translates natural language questions to SPARQL queries not in a single shot, but as an iterative process of exploration and execution. We describe the overall architecture and reasoning behind our design decisions, and also conduct a thorough analysis of the agent behavior to gain insights into future areas for targeted improvements. This work was motivated by the Text2SPARQL challenge, a challenge that was held to facilitate improvements in the Text2SPARQL domain.

Large Language Models (LLMs) are increasingly used for tasks involving Knowledge Graphs (KGs), whose evaluation typically focuses on accuracy and output correctness. We propose a complementary task characterization approach using three complexity frameworks from cognitive psychology. Applying this to the LLM-KG-Bench framework, we highlight value distributions, identify underrepresented demands and motivate richer interpretation and diversity for benchmark evaluation tasks.

The notion of Fr\'echet mean (also known as "barycenter") network is the workhorse of most machine learning algorithms that require the estimation of a "location" parameter to analyse network-valued data. In this context, it is critical that the network barycenter inherits the topological structure of the networks in the training dataset. The metric - which measures the proximity between networks - controls the structural properties of the barycenter. This work is significant because it provides for the first time analytical estimates of the sample Fr\'echet mean for the stochastic blockmodel, which is at the cutting edge of rigorous probabilistic analysis of random networks. We show that the mean network computed with the Hamming distance is unable to capture the topology of the networks in the training sample, whereas the mean network computed using the effective resistance distance recovers the correct partitions and associated edge density. From a practical standpoint, our work informs the choice of metrics in the context where the sample Fr\'echet mean network is used to characterise the topology of networks for network-valued machine learning

Six months ago my son Zac started to play a video game I knew very little about – which, as a games journalist, I found slightly disconcerting. Created by the Canada-based developer Digital Extremes, Warframe is an online sci-fi shooter, originally launched in 2013. Though little discussed outside its fanbase, it is consistently one of the biggest titles on Steam, with 75 million registered users. Set in a distant future version of our solar system, riddled with warring alien factions, the player takes part on the side of the Tenno, an ancient warrior race that employs barely sentient cybernetic fighters – the warframes of the title – as their primary weapons. Each day, Zac spends hours whizzing between planets, carrying out missions or exploring, all the while fighting enemies including a brutish clone army known as the Grineer, and the diseased, monstrous Infested.

Second gentleman Doug Emhoff recently said he and his wife Vice President Kamala Harris are "living" out the HBO series "Veep" during their time at the White House. Emhoff made the claim while appearing on Bravo's "Watch What Happens Live," telling host Andy Cohen that his and Harris' lives resemble the comedy centered on the antics of fictional Vice President Selina Meyer from the popular HBO comedy. During the segment, Cohen asked Emhoff, "Do you watch'Veep?' Have you ever seen'Veep?'" to which he responded, "We're living it." Second Gentleman Doug Emhoff recently claimed he and his wife, Vice President Harris, are "living" the HBO series "Veep." In the show, many of the comedic moments come from Meyer's gaffes, awkward social interactions and frustrations with the limits of her job and incompetence of her staff.

The rise of generative AI tools like ChatGPT has increased the potential for a wide range of attackers to target elections around the world in 2024, according to a new report by cybersecurity giant CrowdStrike. Both state-linked hackers and allied so-called "hacktivists" are increasingly experimenting with ChatGPT and other AI tools, enabling a wider range of actors to carry out cyberattacks and scams, according to the company's annual global threats report. This includes hackers linked to Russia, China, North Korea, and Iran, who have been testing new ways to use these technologies against the U.S., Israel, and European countries. With half the world's population set to vote in 2024, the use of generative AI to target elections could be a "huge factor," says Adam Meyers, head of counter-adversary operations at CrowdStrike. So far, CrowdStrike analysts have been able to detect the use of these models through comments in the scripts that would have been placed there by a tool like ChatGPT.

The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge has been systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model was finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further enhanced with enhanced reasoning ability, as well as with retrieval-augmented generation to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has been shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model showed impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.

When artificial intelligence image generators first rolled out, they seemed like magic. Churning out detailed imagery in minutes was, from one angle, a technical marvel. From another angle, though, it looked like mere mimicry. The models were trained on billions of images without anyone asking the humans behind them for permission. "They have sucked the creative juices of millions of artists," says Eva Toorenent, an illustrator who serves as the Netherlands adviser for the European Guild for Artificial Intelligence Regulation.

A prominent theory of affective response to music revolves around the concepts of surprisal and expectation. In prior work, this idea has been operationalized in the form of probabilistic models of music which allow for precise computation of song (or note-by-note) probabilities, conditioned on a 'training set' of prior musical or cultural experiences. To date, however, these models have been limited to compute exact probabilities through hand-crafted features or restricted to linear models which are likely not sufficient to represent the complex conditional distributions present in music. In this work, we propose to use modern deep probabilistic generative models in the form of a Diffusion Model to compute an approximate likelihood of a musical input sequence. Unlike prior work, such a generative model parameterized by deep neural networks is able to learn complex non-linear features directly from a training set itself. In doing so, we expect to find that such models are able to more accurately represent the 'surprisal' of music for human listeners. From the literature, it is known that there is an inverted U-shaped relationship between surprisal and the amount human subjects 'like' a given song. In this work we show that pre-trained diffusion models indeed yield musical surprisal values which exhibit a negative quadratic relationship with measured subject 'liking' ratings, and that the quality of this relationship is competitive with state of the art methods such as IDyOM. We therefore present this model a preliminary step in developing modern deep generative models of music expectation and subjective likability.

You can basically get these things to say whatever kind of messed up thing you want,