Since the boom in artificial intelligence got under way, US tech bosses have demanded a vast expansion of data centres and energy infrastructure to support further progress and widespread uptake of the technology. Now, the shock wave triggered by Chinese company DeepSeek is challenging that view. Some in the industry think DeepSeek's algorithmic advances could lead to sweeping changes in the way AI models are developed and used, as well as significant energy savings and a lower climate burden.

Additional Feedback: The abstract is a bit long and could probably be condensed, and would probably benefit from doing so. It might also be worthwhile to separate the title from the paragraph text rather than joining them as in e.g. Why not make the base distribution pZ? That is, pZ - pX under F. 50-52: Although the slash is being used to distinguish between two different cases, it's ambiguous because the terms could also be interpreted as the ratio between two KL divergences, as well as the ratio between two densities. On relating statistical physics to more classic ML: as you've promised, it would be nice to include a latent variable/variational bound interpretation (as Sohl-Dickstein et al 2015 'Deep Unsupervised Learning using Nonequilibrium Thermodynamics' do), and maybe also link to Deep Latent Gaussian Models (Rezende et al 2014 'Stochastic Backprop', Kingma et al, 'Autoencoding Variational Bayes').

Although Answer Set Programming (ASP) allows constraining neural-symbolic (NeSy) systems, its employment is hindered by the prohibitive costs of computing stable models and the CPU-bound nature of state-of-the-art solvers. To this end, we propose Answer Set Networks (ASN), a NeSy solver. Based on Graph Neural Networks (GNN), ASNs are a scalable approach to ASP-based Deep Probabilistic Logic Programming (DPPL). Specifically, we show how to translate ASPs into ASNs and demonstrate how ASNs can efficiently solve the encoded problem by leveraging GPU's batching and parallelization capabilities. Our experimental evaluations demonstrate that ASNs outperform state-of-the-art CPU-bound NeSy systems on multiple tasks. Simultaneously, we make the following two contributions based on the strengths of ASNs. Namely, we are the first to show the finetuning of Large Language Models (LLM) with DPPLs, employing ASNs to guide the training with logic. Further, we show the "constitutional navigation" of drones, i.e., encoding public aviation laws in an ASN for routing Unmanned Aerial Vehicles in uncertain environments.

The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI. Deep Probabilistic Programming Languages (DPPLs) have been developed for probabilistic logic programming to be carried out via the probability estimations of deep neural networks (DNNs). However, recent SOTA DPPL approaches allow only for limited conditional probabilistic queries and do not offer the power of true joint probability estimation. In our work, we propose an easy integration of tractable probabilistic inference within a DPPL. To this end, we introduce SLASH, a novel DPPL that consists of Neural-Probabilistic Predicates (NPPs) and a logic program, united via answer set programming (ASP). NPPs are a novel design principle allowing for combining all deep model types and combinations thereof to be represented as a single probabilistic predicate. In this context, we introduce a novel +/− notation for answering various types of probabilistic queries by adjusting the atom notations of a predicate. To scale well, we show how to prune the stochastically insignificant parts of the (ground) program, speeding up reasoning without sacrificing the predictive performance. We evaluate SLASH on various tasks, including the benchmark task of MNIST addition and Visual Question Answering (VQA).

An analogue computer chip can run an artificial intelligence (AI) speech recognition model 14 times more efficiently than traditional chips, potentially offering a solution to the vast and growing energy use of AI research and to the worldwide shortage of the digital chips usually used. The device was developed by IBM Research, which declined New Scientist's request for an interview and didn't provide any comment. But in a paper outlining the work, researchers claim that the analogue chip can reduce bottlenecks in AI development. There is a global rush for GPU chips, the graphic processors that were originally designed to run video games and have also traditionally been used to train and run AI models, with demand outstripping supply. Studies have also shown that the energy use of AI is rapidly growing, rising 100-fold from 2012 to 2021, with most of that energy derived from fossil fuels. These issues have led to suggestions that the constantly increasing scale of AI models will soon reach an impasse.

Dungeon slaying video games have severely lacked one essential element, until now: toasting an epic monster battle with a well-deserved schnitzel. At least, that's what the team at Vienna-based Microbird Games has decided, prompting the creation of forthcoming action role-playing game, Dungeons of Hinterberg. Looking like a Saturday morning cartoon come to life, with a visual style inspired by the clear lines and vivid colours of European comic artists, the indie adventure promises a mix of hack'n' slash action RPG and social sim, against the backdrop of the Austrian Alps – which as video games go, is a setting as fresh as recently fallen snow. Players can explore dungeons, solve puzzles, slay huge bosses … and then enjoy a schnitzel with the local people and other visiting slayers. Fighting monsters has never sounded so delicious.

The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI. Deep Probabilistic Programming Languages (DPPLs) have been developed for probabilistic logic programming to be carried out via the probability estimations of deep neural networks. However, recent SOTA DPPL approaches allow only for limited conditional probabilistic queries and do not offer the power of true joint probability estimation. In our work, we propose an easy integration of tractable probabilistic inference within a DPPL. To this end, we introduce SLASH, a novel DPPL that consists of Neural-Probabilistic Predicates (NPPs) and a logic program, united via answer set programming (ASP). NPPs are a novel design principle allowing for combining all deep model types and combinations thereof to be represented as a single probabilistic predicate. In this context, we introduce a novel $+/-$ notation for answering various types of probabilistic queries by adjusting the atom notations of a predicate. To scale well, we show how to prune the stochastically insignificant parts of the (ground) program, speeding up reasoning without sacrificing the predictive performance. We evaluate SLASH on a variety of different tasks, including the benchmark task of MNIST addition and Visual Question Answering (VQA).

Efficient finetuning of pretrained language transformers is becoming increasingly prevalent for solving natural language processing tasks. While effective, it can still require a large number of tunable parameters. This can be a drawback for low-resource applications and training with differential-privacy constraints, where excessive noise may be introduced during finetuning. To this end, we propose a novel language transformer finetuning strategy that introduces task-specific parameters in multiple transformer layers. These parameters are derived from fixed random projections of a single trainable vector, enabling finetuning with significantly fewer parameters while maintaining performance. We achieve within 5% of full finetuning performance on GLUE tasks with as few as 4,100 parameters per task, outperforming other parameter-efficient finetuning approaches that use a similar number of per-task parameters. Besides, the random projections can be precomputed at inference, avoiding additional computational latency. All these make our method particularly appealing for low-resource applications. Finally, our method achieves the best or comparable utility compared to several recent finetuning methods when training with the same privacy constraints, underscoring its effectiveness and potential real-world impact.

If you have a habit of perusing satellite imagery of the world's oceans--and who doesn't, really?--you might get lucky and spot long, thin clouds, like white slashes across the sea. That's a peculiar phenomenon known as a ship track. As cargo ships chug along, flinging sulfur into the atmosphere, they actually trace their routes for satellites to see. That's because those pollutants rise into low-level clouds and plump them up by acting as nuclei that attract water vapor, which also brightens the clouds. Counterintuitively, these pollution-derived tracks actually have a cooling effect on the climate, since brighter clouds bounce more of the sun's energy back into space.

According to Deloitte, compliance costs have risen by 60% for banks and other financial institutions since the 2008 recession. The situation is not much different in other industries as well. As a result, enterprises across the globe are struggling to minimize the cost of compliance under control. Even though there are many ways to keep compliance costs in check, none are as effective as using automation and artificial intelligence. Artificial intelligence and automation can not only increase your efficiency of compliance operations but can also minimize costs.