Cryptic crosswords, the dominant crossword variety in the UK, are a promising target for advancing NLP systems that seek to process semantically complex, highly compositional language. Cryptic clues read like fluent natural language but are adversarially composed of two parts: a definition and a wordplay cipher requiring character-level manipulations. Expert humans use creative intelligence to solve cryptics, flexibly combining linguistic, world, and domain knowledge. In this paper, we make two main contributions. First, we present a dataset of cryptic clues as a challenging new benchmark for NLP systems that seek to process compositional language in more creative, human-like ways. After showing that three non-neural approaches and T5, a state-of-the-art neural language model, do not achieve good performance, we make our second main contribution: a novel curriculum approach, in which the model is first fine-tuned on related tasks such as unscrambling words. We also introduce a challenging data split, examine the meta-linguistic capabilities of subword-tokenized models, and investigate model systematicity by perturbing the wordplay part of clues, showing that T5 exhibits behavior partially consistent with human solving strategies. Although our curricular approach considerably improves on the T5 baseline, our best-performing model still fails to generalize to the extent that humans can. Thus, cryptic crosswords remain an unsolved challenge for NLP systems and a potential source of future innovation.

The topic of this final qualification work was chosen due to the importance of developing robotic systems designed to assist people with disabilities. Advances in robotics and automation technologies have opened up new prospects for creating devices that can significantly improve the quality of life for these people. In this context, designing a robotic hand with a control system adapted to the needs of people with disabilities is a major scientific and practical challenge. This work addresses the problem of developing and manufacturing a four-degree-of-freedom robotic hand suitable for practical manipulation. Addressing this issue requires a comprehensive approach, encompassing the design of the hand's mechanical structure, the development of its control system, and its integration with a technical vision system and software based on the Robot Operating System (ROS).

Reinforcement Learning (RL) has demonstrated remarkable success in solving sequential decision-making problems. However, in real-world scenarios, RL agents often struggle to generalize when faced with unseen actions that were not encountered during training. Some previous works on zero-shot action generalization rely on large datasets of action observations to capture the behaviors of new actions, making them impractical for real-world applications. In this paper, we introduce a novel zero-shot framework, Action Generalization from Limited Observations (AGLO). Our framework has two main components: an action representation learning module and a policy learning module. The action representation learning module extracts discriminative embeddings of actions from limited observations, while the policy learning module leverages the learned action representations, along with augmented synthetic action representations, to learn a policy capable of handling tasks with unseen actions. The experimental results demonstrate that our framework significantly outperforms state-of-the-art methods for zero-shot action generalization across multiple benchmark tasks, showcasing its effectiveness in generalizing to new actions with minimal action observations.

Access to real-world healthcare data is limited by stringent privacy regulations and data imbalances, hindering advancements in research and clinical applications. Synthetic data presents a promising solution, yet existing methods often fail to ensure the realism, utility, and calibration essential for robust survival analysis. Here, we introduce Masked Clinical Modelling (MCM), an attention-based framework capable of generating high-fidelity synthetic datasets that preserve critical clinical insights, such as hazard ratios, while enhancing survival model calibration. Unlike traditional statistical methods like SMOTE and machine learning models such as VAEs, MCM supports both standalone dataset synthesis for reproducibility and conditional simulation for targeted augmentation, addressing diverse research needs. Validated on a chronic kidney disease electronic health records dataset, MCM reduced the general calibration loss over the entire dataset by 15%; and MCM reduced a mean calibration loss by 9% across 10 clinically stratified subgroups, outperforming 15 alternative methods.

This paper presents an innovative large language model (LLM)-based robotic system for enhancing multi-modal human-robot interaction (HRI). Traditional HRI systems relied on complex designs for intent estimation, reasoning, and behavior generation, which were resource-intensive. In contrast, our system empowers researchers and practitioners to regulate robot behavior through three key aspects: providing high-level linguistic guidance, creating "atomics" for actions and expressions the robot can use, and offering a set of examples. Implemented on a physical robot, it demonstrates proficiency in adapting to multi-modal inputs and determining the appropriate manner of action to assist humans with its arms, following researchers' defined guidelines. Simultaneously, it coordinates the robot's lid, neck, and ear movements with speech output to produce dynamic, multi-modal expressions. This showcases the system's potential to revolutionize HRI by shifting from conventional, manual state-and-flow design methods to an intuitive, guidance-based, and example-driven approach.

As more companies ramp up development of artificial intelligence systems, they are increasingly turning to graphics processing unit (GPU) chips for the computing power they need to run large language models (LLMs) and to crunch data quickly at massive scale. Between video game processing and AI, demand for GPUs has never been higher, and chipmakers are rushing to bolster supply. In new findings released today, though, researchers are highlighting a vulnerability in multiple brands and models of mainstream GPUs--including Apple, Qualcomm, and AMD chips--that could allow an attacker to steal large quantities of data from a GPU's memory. The silicon industry has spent years refining the security of central processing units, or CPUs, so they don't leak data in memory even when they are built to optimize for speed. However, since GPUs were designed for raw graphics processing power, they haven't been architected to the same degree with data privacy as a priority.

This time of year, all the cute Christmas decor and ornaments are out, which makes trips even more fun. Shopping on its website isn't as fun, sadly, but it's still packed full of holiday sales. Target's Black Friday and Cyber Monday deals have been live for almost every day of the month, but several discounts are even sweeter now. Updated on November 27, 2022: We've removed expired deals and updated prices. We test products year-round and handpicked these deals. Products that are sold out or no longer discounted as of publishing will be crossed out . We'll update this guide throughout the Black Friday and Cyber Monday weekend. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. The retailer is offering a price match guarantee this season. If you purchase an item in-store or online from now through December 24 and the price goes lower, you can request a price match.

This time of year, all the cute Christmas decor and ornaments are out, which makes trips even more fun. Shopping on its website isn't as fun, sadly, but it's still packed full of holiday sales. Target's Black Friday and Cyber Monday deals have been live for almost every day of the month, but several discounts are even sweeter now. Updated on November 26, 2022: We've added some navigation, the Bose QC Earbuds, and checked over the entire post. We test products year-round and handpicked these deals. Products that are sold out or no longer discounted as of publishing will be crossed out . We'll update this guide throughout the Black Friday and Cyber Monday weekend. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. The retailer is offering a price match guarantee this season. If you purchase an item in-store or online from now through December 24 and the price goes lower, you can request a price match.

Take your time to Google it. You may probably see these words algorithm, part of Artificial Intelligence, data, etc. Yes Machine Learning is a part of Artificial Intelligence, where we teach machines to learn patterns from DATA we provide and make predictions on similar kind of data. Simply creating intelligent machines to be technical creating algorithms that learns a complex path in the data. The answer is a big NO, machines cannot think but they have the ability to do more work in less time, so machines are fast but without a mentor it is worthless. Do you know that you are using Machine Learning without even knowing?