Chatbots can pass the Turing test--but they can't yet handle an office worker's inbox. One morning last month, I decided to try artificial intelligence on a dire problem: my inbox. In the past twenty years, the e-mail address I use for writing projects has been discovered by a staggering number of P.R. firms, scammers, and strangers with eccentric requests. On this particular day, I had eight hundred and twenty-nine messages. Of the fifty most recent e-mails, the majority were dreck, but about eight were of actual interest, suggesting a hit rate of sixteen per cent--just enough that I had to worry about missing something important.

Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenges. First, the Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data. Second, the Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG data. For this, we use an unprecedented, multi-terabyte dataset of high-density EEG signals (128 channels) recorded from over 3,000 child to young adult subjects engaged in multiple active and passive tasks. We provide several tunable neural network baselines for each of these two challenges, including a simple network and demographic-based regression models. Developing models that generalise across tasks and individuals will pave the way for ML network architectures capable of adapting to EEG data collected from diverse tasks and individuals. Similarly, predicting mental health-relevant personality trait values from EEG might identify objective biomarkers useful for clinical diagnosis and design of personalised treatment for psychological conditions. Ultimately, the advances spurred by this challenge could contribute to the development of computational psychiatry and useful neurotechnology, and contribute to breakthroughs in both fundamental neuroscience and applied clinical research.

Subject of research: is the study of methods for analyzing perimetric images for the diagnosis and control of glaucoma diseases. Objects of research: is a dataset collected on the ophthalmological perimeter with the results of various patient pathologies, since the ophthalmological community is acutely aware of the issue of disease control and import substitution. [5]. Purpose of research: is to consider various machine learning methods that can classify glaucoma. This is possible thanks to the classifier built after labeling the dataset. It is able to determine from the image whether the visual fields depicted on it are the results of the impact of glaucoma on the eyes or other visual diseases. Earlier in the work [3], a dataset was described that was collected on the Tomey perimeter. The average age of the examined patients ranged from 30 to 85 years. Methods of research: machine learning methods for classifying image results (stochastic gradient descent, logistic regression, random forest, naive Bayes). Main results of research: the result of the study is computer modeling that can determine from the image whether the result is glaucoma or another disease (binary classification).

In the realm of data privacy, the ability to effectively anonymise text is paramount. With the proliferation of deep learning and, in particular, transformer architectures, there is a burgeoning interest in leveraging these advanced models for text anonymisation tasks. This paper presents a comprehensive benchmarking study comparing the performance of transformer-based models and Large Language Models(LLM) against traditional architectures for text anonymisation. Utilising the CoNLL-2003 dataset, known for its robustness and diversity, we evaluate several models. Our results showcase the strengths and weaknesses of each approach, offering a clear perspective on the efficacy of modern versus traditional methods. Notably, while modern models exhibit advanced capabilities in capturing con textual nuances, certain traditional architectures still keep high performance. This work aims to guide researchers in selecting the most suitable model for their anonymisation needs, while also shedding light on potential paths for future advancements in the field.

This disease, characterized by cognitive impairments such as memory loss, predominantly affects aging populations, exerting an escalating burden on global healthcare systems as societies continue to age [3]. The significance of AD is further magnified by the increasing life expectancy globally, with the disease now recognized as a leading cause of disability and dependency among older people [4]. Consequently, AD has substantial social, economic, and health system implications, making its understanding and awareness of paramount importance [5, 6]. Despite the ubiquity and severity of AD, a gap persists in comprehensive, data-driven public understanding of this complex health narrative. Traditionally, public health professionals have to rely on labor-intensive methods such as web scraping, API data collection, data postprocessing, and analysis/synthesis to gather insights from news media, health reports, and other textual sources [7, 8, 9].

Work was done while Ge-Peng Ji was a research intern at Alibaba Group. The authors declared that they have no conflicts of interest in this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. Ge-Peng Ji received the M. Sc. degree in communication and information systems from Wuhan University, China in 2021. He is currently a Ph.D. degree candidate at Australian National University, supervised by Professor Nick Barnes, majoring in engineering and computer science.

Specifically they present the reward-is-enough hypothesis that "Intelligence, and its associated abilities, can be understood as subserving the maximisation of reward by an agent acting in its environment", and argue in favour of reward maximisation as a pathway to the creation of artificial general intelligence (AGI). While others have criticised this hypothesis and the subsequent claims [44,54,60,64], here we make the argument that Silver et al. have erred in focusing on the maximisation of scalar rewards. The ability to consider multiple conflicting objectives is a critical aspect of both natural and artificial intelligence, and one which will not necessarily arise or be adequately addressed by maximising a scalar reward. In addition, even if the maximisation of a scalar reward is sufficient to support the emergence of AGI, we contend that this approach is undesirable as it greatly increases the likelihood of adverse outcomes resulting from the deployment of that AGI. Therefore, we advocate that a more appropriate model of intelligence should explicitly consider multiple objectives via the use of vector-valued rewards. Our paper starts by confirming that the reward-is-enough hypothesis is indeed referring specifically to scalar rather than vector rewards (Section 2). In Section 3 we then consider limitations of scalar rewards compared to vector rewards, and review the list of intelligent abilities proposed by Silver et al. to determine which of these exhibit multi-objective characteristics. Section 4 identifies multi-objective aspects of natural intelligence (animal and human). Section 5 considers the possibility of vector rewards being internally derived by an agent in response to a global scalar reward.

When it comes to exploring complex and unknown environments such as forests, buildings or caves, drones are hard to beat. They are fast, agile and small, and they can carry sensors and payloads virtually everywhere. However, autonomous drones can hardly find their way through an unknown environment without a map. For the moment, expert human pilots are needed to release the full potential of drones. "To master autonomous agile flight, you need to understand the environment in a split second to fly the drone along collision-free paths," says Davide Scaramuzza, who leads the Robotics and Perception Group at the University of Zurich and the NCCR Robotics Rescue Robotics Grand Challenge.

The rise in online misinformation in recent years threatens democracies by distorting authentic public discourse and causing confusion, fear, and even, in extreme cases, violence. There is a need to understand the spread of false content through online networks for developing interventions that disrupt misinformation before it achieves virality. Using a Deep Bidirectional Transformer for Language Understanding (BERT) and propagation graphs, this study classifies and visualizes the spread of misinformation on a social media network using publicly available Twitter data. The results confirm prior research around user clusters and the virality of false content while improving the precision of deep learning models for misinformation detection. The study further demonstrates the suitability of BERT for providing a scalable model for false information detection, which can contribute to the development of more timely and accurate interventions to slow the spread of misinformation in online environments.

We study clustering methods for binary data, first defining aggregation criteria that measure the compactness of clusters. Five new and original methods are introduced, using neighborhoods and population behavior combinatorial optimization metaheuristics: first ones are simulated annealing, threshold accepting and tabu search, and the others are a genetic algorithm and ant colony optimization. The methods are implemented, performing the proper calibration of parameters in the case of heuristics, to ensure good results. From a set of 16 data tables generated by a quasi-Monte Carlo experiment, a comparison is performed for one of the aggregations using L1 dissimilarity, with hierarchical clustering, and a version of k-means: partitioning around medoids or PAM. Simulated annealing perform very well, especially compared to classical methods.