Let's get back to the topic. I will talk about some tech-based topics that can sit on the top list in 2023. Also, some of the topics are currently on the top trending list. Let's discuss whether these tech trends hold their positions or if another will hit the top. The first one is Artificial Intelligence, a most popular, Trending tech topic from the past few years.

You might suppose Hollywood is good at predicting the future. Indeed, Robert Wallace, head of the CIA's Office of Technical Service and the US equivalent of MI6's fictional Q, has recounted how Russian spies would watch the latest Bond movie to see what technologies might be coming their way. Hollywood's continuing obsession with killer robots might therefore be of significant concern. The newest such movie is Apple TV's forthcoming sex robot courtroom drama Dolly. I never thought I'd write the phrase "sex robot courtroom drama", but there you go.

Next time you're getting your ass kicked at Fortnite or online chess, it could be at the metaphorical hands of a petri dish of brain cells. Scientists in Australia taught'sentient' cells to play Pong in just minutes, according to a paper published last week in journal Neuron. "What machines can't do is learn things very quickly," study leader Brett Kagan told AFP. "If you need a machine learning algorithm to learn something, it requires thousands of data samples. But if you ask a human, or train a dog, a dog can learn a trick in two or three tries." A mix of embryonic brain cells from mice and human neurons from adult stem cells were grown on top of electrodes that could deliver electric pulses. Rather than reward successful play with dopamine, which was too slow, the cells instead got regular and predictable electrical signals.

You might suppose Hollywood is good at predicting the future. Indeed, Robert Wallace, head of the CIA's Office of Technical Service and the US equivalent of MI6's fictional Q, has recounted how Russian spies would watch the latest Bond movie to see what technologies might be coming their way. Hollywood's continuing obsession with killer robots might therefore be of significant concern. The newest such movie is Apple TV's forthcoming sex robot courtroom drama Dolly. I never thought I'd write the phrase "sex robot courtroom drama", but there you go.

Based on the unitary transform approximate message Thus, GLM is actually an extention of SLM from linear measurements passing (UAMP) and expectation propagation, a unitary to nonlinear measurements, which are prevalent in transform based generalized approximate message passing some real-world applications such as quantized compressed (GUAMP) algorithm is proposed for general measurement sensing, pattern classification, phase retrieval, etc. matrices A, in particular highly correlated matrices. Experimental A variety of algorithms have been proposed for inference results on quantized compressed sensing demonstrate over GLMs and SLMs. Among them, the past decade has witnessed that the proposed GUAMP significantly outperforms state-ofthe-art an advent of one distinguished family of probabilistic GAMP and GVAMP under correlated matrices A. algorithms called message passing algorithm.

In this paper, we examine several methods of acquiring Czech data for automated fact-checking, which is a task commonly modeled as a classification of textual claim veracity w.r.t. a corpus of trusted ground truths. We attempt to collect sets of data in form of a factual claim, evidence within the ground truth corpus, and its veracity label (supported, refuted or not enough info). As a first attempt, we generate a Czech version of the large-scale FEVER dataset built on top of Wikipedia corpus. We take a hybrid approach of machine translation and document alignment; the approach and the tools we provide can be easily applied to other languages. We discuss its weaknesses and inaccuracies, propose a future approach for their cleaning and publish the 127k resulting translations, as well as a version of such dataset reliably applicable for the Natural Language Inference task - the CsFEVER-NLI. Furthermore, we collect a novel dataset of 3,097 claims, which is annotated using the corpus of 2.2M articles of Czech News Agency. We present its extended annotation methodology based on the FEVER approach, and, as the underlying corpus is kept a trade secret, we also publish a standalone version of the dataset for the task of Natural Language Inference we call CTKFactsNLI. We analyze both acquired datasets for spurious cues - annotation patterns leading to model overfitting. CTKFacts is further examined for inter-annotator agreement, thoroughly cleaned, and a typology of common annotator errors is extracted. Finally, we provide baseline models for all stages of the fact-checking pipeline and publish the NLI datasets, as well as our annotation platform and other experimental data.

The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, compared to more established disciplines, a lack of common experimental standards remains an open challenge to the field at large. Starting from fundamental scientific principles, we distill ongoing discussions on experimental standards in NLP into a single, widely-applicable methodology. Following these best practices is crucial to strengthen experimental evidence, improve reproducibility and support scientific progress. These standards are further collected in a public repository to help them transparently adapt to future needs.

Levin et al. (2019) show experimentally that the interpretations of novel English noun compounds (e.g., stew skillet), while not fully compositional, are highly predictable based on whether the modifier and head refer to artifacts or natural kinds. Is the large language model GPT-3 governed by the same interpretive principles? To address this question, we first compare Levin et al.'s experimental data with GPT-3 generations, finding a high degree of similarity. However, this evidence is consistent with GPT3 reasoning only about specific lexical items rather than the more abstract conceptual categories of Levin et al.'s theory. To probe more deeply, we construct prompts that require the relevant kind of conceptual reasoning. Here, we fail to find convincing evidence that GPT-3 is reasoning about more than just individual lexical items. These results highlight the importance of controlling for low-level distributional regularities when assessing whether a large language model latently encodes a deeper theory.

Deep reinforcement learning in continuous domains focuses on learning control policies that map states to distributions over actions that ideally concentrate on the optimal choices in each step. In multi-agent navigation problems, the optimal actions depend heavily on the agents' density. Their interaction patterns grow exponentially with respect to such density, making it hard for learning-based methods to generalize. We propose to switch the learning objectives from predicting the optimal actions to predicting sets of admissible actions, which we call control admissibility models (CAMs), such that they can be easily composed and used for online inference for an arbitrary number of agents. We design CAMs using graph neural networks and develop training methods that optimize the CAMs in the standard model-free setting, with the additional benefit of eliminating the need for reward engineering typically required to balance collision avoidance and goal-reaching requirements. We evaluate the proposed approach in multi-agent navigation environments. We show that the CAM models can be trained in environments with only a few agents and be easily composed for deployment in dense environments with hundreds of agents, achieving better performance than state-of-the-art methods.

This paper presents the 6th place solution to the Google Universal Image Embedding competition on Kaggle. Our approach is based on the CLIP architecture, a powerful pre-trained model used to learn visual representation from natural language supervision. We also utilized the SubCenter ArcFace loss with dynamic margins to improve the distinctive power of class separability and embeddings. Finally, a diverse dataset has been created based on the test's set categories and the leaderboard's feedback. By carefully crafting a training scheme to enhance transfer learning, our submission scored 0.685 on the private leaderboard.