The latest artificial intelligence tool to sweep social media is "Historical Figures Chat," a novelty that currently sits at the #34 spot in the "Education" section of Apple's app store. "With this app, you can chat with deceased individuals who have made a significant impact on history from ancient rulers and philosophers, to modern day politicians and artists," the description claims. What it doesn't mention is just how off the mark some of the algorithmic responses can be. The internet being what it is, users have downloaded Historical Figures -- which was first made available some two weeks ago -- and embarked on conversations with unsavory characters including Charles Manson, Jeffrey Epstein, and various high-ranking Nazis. These are just a few of the 20,000 significant personalities available for interview, and they seem especially keen on expressing remorse for the horrible things they did while alive, while whitewashing their own documented views.

Tehran, Iran – The European Parliament's approval of a resolution calling on the bloc to consider a "terrorist" designation for the Islamic Revolutionary Guard Corps (IRGC) has received strong condemnation from senior Iranian officials and commanders. On Thursday, the European Parliament overwhelmingly approved a resolution that calls on the European Union to recognise Iran's elite force and its subsidiaries, like the paramilitary Basij and the Quds Force, as "terrorist" organisations. It also condemned the Iranian government's response to the protests that have been taking place in the country since September, the executions linked to the protests, and drone sales to Russia, while also recommending sanctions against Supreme Leader Ayatollah Ali Khamenei, President Ebrahim Raisi and all IRGC-linked foundations. The EU is not obliged to enforce the resolution. While senior EU politicians have voiced their support for the resolution, it is not expected to be among new sanctions on dozens of Iranian individuals and entities expected to be approved by the bloc on Monday.

Computing pervades all aspects of society in ways once imagined by only a few. Within science and engineering, computing has often been called the third paradigm, complementing theory and experiment, with big data and artificial intelligence (AI) often called the fourth paradigm.14 Spanning both data analysis and disciplinary and multidisciplinary modeling, scientific computing systems have grown ever larger and more complex, and today's exascale scientific computing systems rival global scientific facilities in cost and complexity. However, all is not well in the land of scientific computing. In the initial decades of digital computing, government investments and the insights from designing and deploying supercomputers often shaped the next generation of mainstream and consumer computing products. Today, that economic and technological influence has increasingly shifted to smartphone and cloud service companies. Moreover, the end of Dennard scaling,3 slowdowns in Moore's Law, and the rising costs for continuing semiconductor advances have made building ever-faster supercomputers more economically challenging and intellectually difficult. As Figure 1 suggests, we believe current approaches to designing and constructing leading-edge high-performance computing (HPC) systems must change in deep and fundamental ways, embracing end-to-end co-design; custom hardware configurations and packaging; large-scale prototyping; and collaboration between the dominant computing companies, smartphone and cloud computing vendors, and traditional computing vendors.

A number of big themes emerged from the World Economic Forum in the Swiss resort Davos. Here are five of most pressing questions that came to dominate this year's gathering of the global elite. Donald Trump's trade war with China – continued by his successor Joe Biden – has left relations between east and west at rock bottom. But with Covid and trade tensions halving Chinese growth last year to just 3% and western businesses such as Apple moving business out of the world's second-biggest economy, Beijing has hinted it may adopt a less-hostile approach. Vice-premier Liu He appeared on the main stage at Davos to assure foreign investors that after three years of Covid disruption, it was open for business.

In this paper we present a set of experiments carried out with BERT on a number of Italian sentences taken from poetry domain. The experiments are organized on the hypothesis of a very high level of difficulty in predictability at the three levels of linguistic complexity that we intend to monitor: lexical, syntactic and semantic level. To test this hypothesis we ran the Italian version of BERT with 80 sentences - for a total of 900 tokens - mostly extracted from Italian poetry of the first half of last century. We used then sentences from the newswire domain containing similar syntactic structures. The results show that the DL model is highly sensitive to presence of non-canonical structures. However, DLs are also very sensitive to word frequency and to local non-literal meaning compositional effect. This is also apparent by the preference for predicting function vs content words, collocates vs infrequent word phrases. In the paper, we focused our attention on the use of subword units done by BERT for out of vocabulary words. NTRODUCTION In this paper we report results of an extremely complex task for BERT: predicting the masked word in sentences extracted from Italian poetry of beginning of last century, using the output of the first projection layer of a Deep Learning model, the raw word embeddings. We decided to work on Italian to highlight its difference from English in an extended number of relevant linguistic properties. The underlying hypothesis aims at proving the ability of BERT [1] to predict masked words with increasing complex contexts. To verify this hypothesis we selected sentences that exhibit two important features of Italian texts, non-canonicity and presence of words with very low or rare frequency. To better evaluate the impact of these two factors on word predictability we created a word predictability measure which is based on a combination of scoring functions for context and word frequency of (co-)occurrence. The experiment uses BERT assuming that DNNs can be regarded capable of modeling the behaviour of the human brain in predicting a next word given a sentence and text corpus - but see the following section. It is usually the case that paradigmatic and syntagmatic properties of words in a sentence are tested separately.

Many real-world problems are characterized by a large number of observations, confounding and spurious correlations, partially observable states, and distal, dynamic rewards with hierarchical reward structures. Such conditions make it hard for both animal and machines to learn complex skills. The learning process requires discovering what is important and what can be ignored, how the reward function is structured, and how to reuse knowledge across different tasks that share common properties. For these reasons, the application of standard reinforcement learning (RL) algorithms (Sutton and Barto, 2018) to solve structured problems is often not effective. Limitations of current RL algorithms include the problem of exploration with sparse rewards (Pathak et al., 2017), dealing with partially observable Markov decision problems (POMDP) (Ladosz et al., 2021), coping with large amounts of confounding stimuli (Thrun, 2000; Kim et al., 2019), and reusing skills for efficiently learning multiple task in a lifelong learning setting (Mendez and Eaton, 2020). Standard reinforcement learning algorithms are best suited when the problem can be formulated as a single-task problem in observable Markov decision problem (MDP). Under these assumptions, with complete observability and with static and frequent rewards, deep reinforcement learning (DRL) (Mnih et al., 2015; Li, 2017) has gained popularity due to the ability to learn an approximated Q-value function directly from raw pixel data in the Atari 2600 platform. This and similar algorithms stack multiple frames to derive states of an MDP, and use a basic ɛ-greedy exploration policy. In more complex cases with partial observability and sparse rewards, extensions have been proposed to include more advanced exploration techniques (Ladosz et al., 2022), e.g.

The challenges in applications of solar energy lies in its intermittency and dependency on meteorological parameters such as; solar radiation, ambient temperature, rainfall, wind-speed etc., and many other physical parameters like dust accumulation etc. Hence, it is important to estimate the amount of solar photovoltaic (PV) power generation for a specific geographical location. Machine learning (ML) models have gained importance and are widely used for prediction of solar power plant performance. In this paper, the impact of weather parameters on solar PV power generation is estimated by several Ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting for the first time. The performance of chosen ML algorithms is validated by field dataset of a 10kWp solar PV power plant in Eastern India region. Furthermore, a complete test-bed framework has been designed for data mining as well as to select appropriate learning models. It also supports feature selection and reduction for dataset to reduce space and time complexity of the learning models. The results demonstrate greater prediction accuracy of around 96% for Stacking and Voting EML models. The proposed work is a generalized one and can be very useful for predicting the performance of large-scale solar PV power plants also.

With the advent of deep learning, text generation language models have improved dramatically, with text at a similar level as human-written text. This can lead to rampant misinformation because content can now be created cheaply and distributed quickly. Automated claim verification methods exist to validate claims, but they lack foundational data and often use mainstream news as evidence sources that are strongly biased towards a specific agenda. Current claim verification methods use deep neural network models and complex algorithms for a high classification accuracy but it is at the expense of model explainability. The models are black-boxes and their decision-making process and the steps it took to arrive at a final prediction are obfuscated from the user. We introduce a novel claim verification approach, namely: ExClaim, that attempts to provide an explainable claim verification system with foundational evidence. Inspired by the legal system, ExClaim leverages rationalization to provide a verdict for the claim and justifies the verdict through a natural language explanation (rationale) to describe the model's decision-making process. ExClaim treats the verdict classification task as a question-answer problem and achieves a performance of 0.93 F1 score. It provides subtasks explanations to also justify the intermediate outcomes. Statistical and Explainable AI (XAI) evaluations are conducted to ensure valid and trustworthy outcomes. Ensuring claim verification systems are assured, rational, and explainable is an essential step toward improving Human-AI trust and the accessibility of black-box systems.

Groundbreaking inventions and highly significant performance improvements in deep learning based Natural Language Processing are witnessed through the development of transformer based large Pre-trained Language Models (PLMs). The wide availability of unlabeled data within human generated data deluge along with self-supervised learning strategy helps to accelerate the success of large PLMs in language generation, language understanding, etc. But at the same time, latent historical bias/unfairness in human minds towards a particular gender, race, etc., encoded unintentionally/intentionally into the corpora harms and questions the utility and efficacy of large PLMs in many real-world applications, particularly for the protected groups. In this paper, we present an extensive investigation towards understanding the existence of "Affective Bias" in large PLMs to unveil any biased association of emotions such as anger, fear, joy, etc., towards a particular gender, race or religion with respect to the downstream task of textual emotion detection. We conduct our exploration of affective bias from the very initial stage of corpus level affective bias analysis by searching for imbalanced distribution of affective words within a domain, in large scale corpora that are used to pre-train and fine-tune PLMs. Later, to quantify affective bias in model predictions, we perform an extensive set of class-based and intensity-based evaluations using various bias evaluation corpora. Our results show the existence of statistically significant affective bias in the PLM based emotion detection systems, indicating biased association of certain emotions towards a particular gender, race, and religion.

Autonomous navigation and path-planning around non-cooperative space objects is an enabling technology for on-orbit servicing and space debris removal systems. The navigation task includes the determination of target object motion, the identification of target object features suitable for grasping, and the identification of collision hazards and other keep-out zones. Given this knowledge, chaser spacecraft can be guided towards capture locations without damaging the target object or without unduly the operations of a servicing target by covering up solar arrays or communication antennas. One way to autonomously achieve target identification, characterization and feature recognition is by use of artificial intelligence algorithms. This paper discusses how the combination of cameras and machine learning algorithms can achieve the relative navigation task. The performance of two deep learning-based object detection algorithms, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLOv5), is tested using experimental data obtained in formation flight simulations in the ORION Lab at Florida Institute of Technology. The simulation scenarios vary the yaw motion of the target object, the chaser approach trajectory, and the lighting conditions in order to test the algorithms in a wide range of realistic and performance limiting situations. The data analyzed include the mean average precision metrics in order to compare the performance of the object detectors. The paper discusses the path to implementing the feature recognition algorithms and towards integrating them into the spacecraft Guidance Navigation and Control system.