A simultaneously familiar and slightly terrifying robot dog wanders through the audience of a session at the Sydney edition of South by South West. On stage, the panellists opine about a future increasingly defined by artificial intelligence and automation. "It's going to get much, much more significant," says Ed Santow, the former human rights commissioner and current director of policy and governance at the UTS Human Technology Institute. "And for many people that will be a good thing, [but] for a lot of people it'll be really, really hard." The robot is creepy but its fan is as noisy as a ps4 so it's not sneaking up on anyone.

Significant improvements in technology in recent years have created more advanced assistive tools and services that allow people with sensory impairments to lead more independent and fulfilling lives. While there will always be a need for traditional assistive devices such as white canes, Braille signage for the visually impaired, or closed-captioning services for the hearing-impaired, these new devices, apps, and underlying technological approaches are helping to create a more inclusive world. The number of people who benefit from technological improvements to assistive technologies is notable. According to the International Agency for the Prevention of Blindness, 43 million people around the world were living with blindness as of 2021. Meanwhile, hearing loss currently affects more than 1.5 billion people worldwide, of whom 430 million have moderate or higher levels of hearing loss in their better-hearing ear, according to the World Report on Hearing, published in 2021 by the World Health Organization.

The demo explains how artificial intelligence is used in the app and its features. The chiefs of the FBI and Britain's MI5 have raised concerns about the enormous and terrifying potential artificial intelligence poses for terrorists, saying the technology adds "a level of threat to that we haven't previously encountered." "It's one of those issues where no one has a monopoly of wisdom and trying to have a different form of public-private partnership and, crucially, international partnerships," MI5 Director General Ken McCallum said during the Five Eyes alliance conference in California this week. The conference between the United States, United Kingdom, Canada, Australia and New Zealand focused on the launch of an initiative aimed at finding innovative responses to developing intelligence threats, particularly in the face of new technology such as AI generative platforms. "Emerging technologies are essential to our economic and national security, and America's role as a leading economic power, but they also present new and evolving threats," FBI Director Christopher Wray said ahead of the conference.

Giulianelli and Fernández, 2021; Wallbridge When viewed as information transmission, successful et al., 2022). However, token-level autoregressive language production can be seen as an act approximations of utterance probability have a of reducing the uncertainty over future states that a few problematic properties. A well-known issue comprehender may be anticipating. Saying a word, is that different realisations of the same concept for example, may cut the space of possibilities in or communicative intent compete for probability half, while uttering a whole sentence may restrict mass (Holtzman et al., 2021), which implies that the comprehender's expectations to a far smaller the surprisal of semantically equivalent realisations space. Measuring the amount of information is overestimated. Moreover, token-level carried by a linguistic signal is fundamental to surprisal estimates conflate different dimensions of the computational modelling of human language predictability. As evidenced by recent studies (Arehalli processing. Such quantifications are used in et al., 2022; Kuhn et al., 2023), this makes psycholinguistic and neurobiological models of it difficult to appreciate whether the information language processing (Levy, 2008; Willems et al., carried by an utterance is a result, for example, of 2016; Futrell and Levy, 2017; Armeni et al., 2017), the unexpectedness of its lexical material, syntactic to study the processing mechanisms of neural arrangements, semantic content, or speech act type.

The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2D image data, which is insufficient for 3D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multi-sensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for non-structured 3D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground-truth. The point cloud is dense and contains 3,242,964 labeled points. We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping. By making Tinto publicly available, we hope to foster the development and adaptation of new deep learning tools for 3D applications in Earth sciences. The dataset can be accessed through this link: https://doi.org/10.14278/rodare.2256.

This paper focuses on affective emotion recognition, aiming to perform in the subject-agnostic paradigm based on EEG signals. However, EEG signals manifest subject instability in subject-agnostic affective Brain-computer interfaces (aBCIs), which led to the problem of distributional shift. Furthermore, this problem is alleviated by approaches such as domain generalisation and domain adaptation. Typically, methods based on domain adaptation confer comparatively better results than the domain generalisation methods but demand more computational resources given new subjects. We propose a novel framework, meta-learning based augmented domain adaptation for subject-agnostic aBCIs. Our domain adaptation approach is augmented through meta-learning, which consists of a recurrent neural network, a classifier, and a distributional shift controller based on a sum-decomposable function. Also, we present that a neural network explicating a sum-decomposable function can effectively estimate the divergence between varied domains. The network setting for augmented domain adaptation follows meta-learning and adversarial learning, where the controller promptly adapts to new domains employing the target data via a few self-adaptation steps in the test phase. Our proposed approach is shown to be effective in experiments on a public aBICs dataset and achieves similar performance to state-of-the-art domain adaptation methods while avoiding the use of additional computational resources.

The increasing global demand for clean and environmentally friendly energy resources has caused increased interest in harnessing solar power through photovoltaic (PV) systems for smart grids and homes. However, the inherent unpredictability of PV generation poses problems associated with smart grid planning and management, energy trading and market participation, demand response, reliability, etc. Therefore, solar irradiance forecasting is essential for optimizing PV system utilization. This study proposes the next-generation machine learning algorithms such as random forests, Extreme Gradient Boosting (XGBoost), Light Gradient Boosted Machine (lightGBM) ensemble, CatBoost, and Multilayer Perceptron Artificial Neural Networks (MLP-ANNs) to forecast solar irradiance. Besides, Bayesian optimization is applied to hyperparameter tuning. Unlike tree-based ensemble algorithms that select the features intrinsically, MLP-ANN needs feature selection as a separate step. The simulation results indicate that the performance of the MLP-ANNs improves when feature selection is applied. Besides, the random forest outperforms the other learning algorithms.

We present TexFusion (Texture Diffusion), a new method to synthesize textures for given 3D geometries, using large-scale text-guided image diffusion models. In contrast to recent works that leverage 2D text-to-image diffusion models to distill 3D objects using a slow and fragile optimization process, TexFusion introduces a new 3D-consistent generation technique specifically designed for texture synthesis that employs regular diffusion model sampling on different 2D rendered views. Specifically, we leverage latent diffusion models, apply the diffusion model's denoiser on a set of 2D renders of the 3D object, and aggregate the different denoising predictions on a shared latent texture map. Final output RGB textures are produced by optimizing an intermediate neural color field on the decodings of 2D renders of the latent texture. We thoroughly validate TexFusion and show that we can efficiently generate diverse, high quality and globally coherent textures. We achieve state-of-the-art text-guided texture synthesis performance using only image diffusion models, while avoiding the pitfalls of previous distillation-based methods. The text-conditioning offers detailed control and we also do not rely on any ground truth 3D textures for training. This makes our method versatile and applicable to a broad range of geometry and texture types. We hope that TexFusion will advance AI-based texturing of 3D assets for applications in virtual reality, game design, simulation, and more.

SIGMORPHON and SIGMORPHON-UniMorph Three languages were chosen whose inflectional shared tasks (Cotterell et al., 2016, 2017, 2018; morphologies range from entirely fusional (English), McCarthy et al., 2019; Vylomova et al., 2020; Pimentel to mixed (Spanish), to mostly agglutinative et al., 2021; Kodner et al., 2022) as well (Swahili). In highly agglutinative languages, individual as in more targeted studies focused on specific languages features in a set tend to correspond to distinct or the generalization behavior of computational morphological patterns, so a model may generalize models (Goldman et al., 2022; Wiemerslage to unseen feature sets by mapping component et al., 2022; Kodner et al., 2023b; Guriel et al., features to their corresponding patterns. This is 2023; Kodner et al., 2023a), is to train on (lemma, exemplified by the Swahili example (1), in which inflection, features) triples and predict inflected most features correspond to individual morphemes; forms from held-out (lemma, features) only the person/number prefix maps to more than pairs.

Research in Text-to-SQL conversion has been largely benchmarked against datasets where each text query corresponds to one correct SQL. However, natural language queries over real-life databases frequently involve significant ambiguity about the intended SQL due to overlapping schema names and multiple confusing relationship paths. To bridge this gap, we develop a novel benchmark called AmbiQT with over 3000 examples where each text is interpretable as two plausible SQLs due to lexical and/or structural ambiguity. When faced with ambiguity, an ideal top-$k$ decoder should generate all valid interpretations for possible disambiguation by the user. We evaluate several Text-to-SQL systems and decoding algorithms, including those employing state-of-the-art LLMs, and find them to be far from this ideal. The primary reason is that the prevalent beam search algorithm and its variants, treat SQL queries as a string and produce unhelpful token-level diversity in the top-$k$. We propose LogicalBeam, a new decoding algorithm that navigates the SQL logic space using a blend of plan-based template generation and constrained infilling. Counterfactually generated plans diversify templates while in-filling with a beam-search that branches solely on schema names provides value diversity. LogicalBeam is up to $2.5$ times more effective than state-of-the-art models at generating all candidate SQLs in the top-$k$ ranked outputs. It also enhances the top-$5$ Exact and Execution Match Accuracies on SPIDER and Kaggle DBQA.