A "lethal" weaponized drone "hunted down a human target" without being told to for the first time, according to a UN report seen by the New Scientist. The March 2020 incident saw a KARGU-2 quadcopter autonomously attack a human during a conflict between Libyan government forces and a breakaway military faction, led by the Libyan National Army's Khalifa Haftar, the Daily Star reported. The Turkish-built KARGU-2, a deadly attack drone designed for asymmetric warfare and anti-terrorist operations, targeted one of Haftar's soldiers while he tried to retreat, according to the paper. The drone, which can be directed to detonate on impact, was operating in a "highly effective" autonomous mode that required no human controller, the New York Post said. "The lethal autonomous weapons systems were programmed to attack targets without requiring data connectivity between the operator and the munition: in effect, a true'fire, forget and find' capability," the report from the UN Security Council's Panel of Experts on Libya said.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Arnold Schwarzenegger could've seen this one coming. After a United Nations commission to block killer robots was shut down in 2018, a new report from the international body now says the Terminator-like drones are now here. Last year "an autonomous weaponized drone hunted down a human target last year" and attacked them without being specifically ordered to, according to a report from the UN Security Council's Panel of Experts on Libya, published in March 2021 that was published in the New Scientist magazine and the Star.

Offensive language detection is an ever-growing natural language processing (NLP) application. This growth is mainly because of the widespread usage of social networks, which becomes a mainstream channel for people to communicate, work, and enjoy entertainment content. Many incidents of sharing aggressive and offensive content negatively impacted society to a great extend. We believe contributing to improving and comparing different machine learning models to fight such harmful contents is an important and challenging goal for this thesis. We targeted the problem of offensive language detection for building efficient automated models for offensive language detection. With the recent advancements of NLP models, specifically, the Transformer model, which tackled many shortcomings of the standard seq-to-seq techniques. The BERT model has shown state-of-the-art results on many NLP tasks. Although the literature still exploring the reasons for the BERT achievements in the NLP field. Other efficient variants have been developed to improve upon the standard BERT, such as RoBERTa and ALBERT. Moreover, due to the multilingual nature of text on social media that could affect the model decision on a given tween, it is becoming essential to examine multilingual models such as XLM-RoBERTa trained on 100 languages and how did it compare to unilingual models. The RoBERTa based model proved to be the most capable model and achieved the highest F1 score for the tasks. Another critical aspect of a well-rounded offensive language detection system is the speed at which a model can be trained and make inferences. In that respect, we have considered the model run-time and fine-tuned the very efficient implementation of FastText called BlazingText that achieved good results, which is much faster than BERT-based models.

Neural dialogue generation models trained with the one-hot target distribution suffer from the over-confidence issue, which leads to poor generation diversity as widely reported in the literature. Although existing approaches such as label smoothing can alleviate this issue, they fail to adapt to diverse dialog contexts. In this paper, we propose an Adaptive Label Smoothing (AdaLabel) approach that can adaptively estimate a target label distribution at each time step for different contexts. The maximum probability in the predicted distribution is used to modify the soft target distribution produced by a novel light-weight bi-directional decoder module. The resulting target distribution is aware of both previous and future contexts and is adjusted to avoid over-training the dialogue model. Our model can be trained in an end-to-end manner. Extensive experiments on two benchmark datasets show that our approach outperforms various competitive baselines in producing diverse responses.

This paper considers the problem of matrix-variate logistic regression. The fundamental error threshold on estimating coefficient matrices in the logistic regression problem is found by deriving a lower bound on the minimax risk. The focus of this paper is on derivation of a minimax risk lower bound for low-rank coefficient matrices. The bound depends explicitly on the dimensions and distribution of the covariates, the rank and energy of the coefficient matrix, and the number of samples. The resulting bound is proportional to the intrinsic degrees of freedom in the problem, which suggests the sample complexity of the low-rank matrix logistic regression problem can be lower than that for vectorized logistic regression. \color{red}\color{black} The proof techniques utilized in this work also set the stage for development of minimax lower bounds for tensor-variate logistic regression problems.

Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive. We develop QACG, a framework for training a robust fact verification model by using automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. QACG generates question-answer pairs from the evidence and then converts them into different types of claims. Experiments on the FEVER dataset show that our QACG framework significantly reduces the demand for human-annotated training data. In a zero-shot scenario, QACG improves a RoBERTa model's F1 from 50% to 77%, equivalent in performance to 2K+ manually-curated examples. Our QACG code is publicly available.

There are 15 Chapters to display the Global Artificial Intelligence in Platform as a Service (PaaS) Market Chapter 1, Overview to describe Definition, Specifications and Classification of Global Artificial Intelligence in Platform as a Service (PaaS) market, Applications [SME & Large Enterprises], Market Segment by Types, Machine Learning Platform, Natural Language Processing Service, Visual Analysis Service, Language Processing Service & Data Insight Service; Chapter 2, objective of the study.

As was mentioned earlier in this article, Type Curves that are generated using mathematical equations are very "well-behaved" (continuous, non-linear, certain shape that changes in a similar fashion from curve to curve). Figure 16 demonstrates few more examples of Type Curves that have been generated in reservoir engineering. The question is, "what is the main characteristic of a model that is capable of generating series of well-behave Type Curves?" The immediate, simple answer to this question would be: "the model that is capable of generating a series of well-behave Type Curves is a physics-based model developed by one or more mathematical equations. The well-behave Type Curves that clearly explain the behavior of the physics-based model are generated through the solutions of the mathematical equations."

US-headquartered IQVIA is the latest health information technology and clinical research company to partner with the Saudi Data and Artificial Intelligence Authority (SDAIA), it has been announced. The multinational – described as "a leading global provider of advanced analytics, technology solutions, and clinical research services to the life sciences industry" – has signed a memorandum of understanding (MoU) with the Saudi government agency to "explore opportunities of mutual interest and support innovation in the field of health data in the Kingdom of Saudi Arabia (KSA)." According to the country's official news agency, both parties will reportedly collaborate on joint ideas and research in data and artificial intelligence (AI), build a centre for "innovation and knowledge", and develop training programmes that can make use of this data and AI in the health sector. The agreement was co-signed by Majid Mohammed Al-Tuwaijri, supervisor of the National Center for Artificial Intelligence (NCAI) at the SDAIA; and Mohamed Mostafa Elbadawy, IQVIA's General Manager for KSA and Egypt. "This MoU will contribute towards creating opportunities for development and growth in the health sector, supporting the goals of Vision 2030," said Al-Tuwaijri.

Measuring public opinion is a key focus during democratic elections, enabling candidates to gauge their popularity and alter their campaign strategies accordingly. Traditional survey polling remains the most popular estimation technique, despite its cost and time intensity, measurement errors, lack of real-time capabilities and lagged representation of public opinion. In recent years, Twitter opinion mining has attempted to combat these issues. Despite achieving promising results, it experiences its own set of shortcomings such as an unrepresentative sample population and a lack of long term stability. This paper aims to merge data from both these techniques using Bayesian data assimilation to arrive at a more accurate estimate of true public opinion for the Brexit referendum. This paper demonstrates the effectiveness of the proposed approach using Twitter opinion data and survey data from trusted pollsters. Firstly, the possible existence of a time gap of 16 days between the two data sets is identified. This gap is subsequently incorporated into a proposed assimilation architecture. This method was found to adequately incorporate information from both sources and measure a strong upward trend in Leave support leading up to the Brexit referendum. The proposed technique provides useful estimates of true opinion, which is essential to future opinion measurement and forecasting research.