To summarise, this use case presents a way for actuaries to automatically classify free-text claims causes data into pre-defined categories for further analyses. Ultimately, with the help of BERT, computers are able to understand human language. For this instance, computers are able to understand and compare medical terms or description of a claims event, which can be messy at times. The alternative which is manual filtering in Excel is not practical, especially for large number of claims. As mentioned previously, Excel has been the primary ETL tool for most life insurance actuaries.

Predicting human motion from historical pose sequence is crucial for a machine to succeed in intelligent interactions with humans. One aspect that has been obviated so far, is the fact that how we represent the skeletal pose has a critical impact on the prediction results. Yet there is no effort that investigates across different pose representation schemes. We conduct an indepth study on various pose representations with a focus on their effects on the motion prediction task. Moreover, recent approaches build upon off-the-shelf RNN units for motion prediction. These approaches process input pose sequence sequentially and inherently have difficulties in capturing long-term dependencies. In this paper, we propose a novel RNN architecture termed AHMR (Attentive Hierarchical Motion Recurrent network) for motion prediction which simultaneously models local motion contexts and a global context. We further explore a geodesic loss and a forward kinematics loss for the motion prediction task, which have more geometric significance than the widely employed L2 loss. Interestingly, we applied our method to a range of articulate objects including human, fish, and mouse. Empirical results show that our approach outperforms the state-of-the-art methods in short-term prediction and achieves much enhanced long-term prediction proficiency, such as retaining natural human-like motions over 50 seconds predictions. Our codes are released.

There have been significant efforts devoted to solving the longevity risk given that a continuous growth in population ageing has become a severe issue for many developed countries over the past few decades. The Cairns-Blake-Dowd (CBD) model, which incorporates cohort effects parameters in its parsimonious design, is one of the most well-known approaches for mortality modelling at higher ages and longevity risk. This article proposes a novel mixed-effects time-series approach for mortality modelling and forecasting with considerations of age groups dependence and random cohort effects parameters. The proposed model can disclose more mortality data information and provide a natural quantification of the model parameters uncertainties with no pre-specified constraint required for estimating the cohort effects parameters. The abilities of the proposed approach are demonstrated through two applications with empirical male and female mortality data. The proposed approach shows remarkable improvements in terms of forecast accuracy compared to the CBD model in the short-, mid-and long-term forecasting using mortality data of several developed countries in the numerical examples.

This work explores how to learn robust and generalizable state representation from image-based observations with deep reinforcement learning methods. Addressing the computational complexity, stringent assumptions, and representation collapse challenges in the existing work of bisimulation metric, we devise Simple State Representation (SimSR) operator, which achieves equivalent functionality while reducing the complexity by an order in comparison with bisimulation metric. SimSR enables us to design a stochastic-approximation-based method that can practically learn the mapping functions (encoders) from observations to latent representation space. Besides the theoretical analysis, we experimented and compared our work with recent state-of-the-art solutions in visual MuJoCo tasks. The results show that our model generally achieves better performance and has better robustness and good generalization.

Fine-tuning pre-trained language models for downstream tasks has become a norm for NLP. Recently it is found that intermediate training based on high-level inference tasks such as Question Answering (QA) can improve the performance of some language models for target tasks. However it is not clear if intermediate training generally benefits various language models. In this paper, using the SQuAD-2.0 QA task for intermediate training for target text classification tasks, we experimented on eight tasks for single-sequence classification and eight tasks for sequence-pair classification using two base and two compact language models. Our experiments show that QA-based intermediate training generates varying transfer performance across different language models, except for similar QA tasks.

Autoregressive (AR) and Non-autoregressive (NAR) models have their own superiority on the performance and latency, combining them into one model may take advantage of both. Current combination frameworks focus more on the integration of multiple decoding paradigms with a unified generative model, e.g. Masked Language Model. However, the generalization can be harmful to the performance due to the gap between training objective and inference. In this paper, we aim to close the gap by preserving the original objective of AR and NAR under a unified framework. Specifically, we propose the Directional Transformer (Diformer) by jointly modelling AR and NAR into three generation directions (left-to-right, right-to-left and straight) with a newly introduced direction variable, which works by controlling the prediction of each token to have specific dependencies under that direction. The unification achieved by direction successfully preserves the original dependency assumption used in AR and NAR, retaining both generalization and performance. Experiments on 4 WMT benchmarks demonstrate that Diformer outperforms current united-modelling works with more than 1.5 BLEU points for both AR and NAR decoding, and is also competitive to the state-of-the-art independent AR and NAR models.

China is deploying machinegun-carrying robots to its western desert regions amid a standoff with India because troops are struggling with the high-altitude conditions, it has been claimed. Dozens of unmanned vehicles capable of carrying both weapons and supplies are being sent to Tibet, Indian media reports, with the majority deployed in border regions where Chinese troops are locked into a standoff with Indian soldiers. Vehicles include the Sharp Claw, which is mounted with a light machinegun and can be operated wirelessly, and the Mule-200, which is designed as an unmanned supply vehicle but can also be fitted with weapons. Beijing has sent 88 Sharp Claws to Tibet, which borders India high in the Himalayas, of which 38 are deployed to the border region, Times News Now has claimed. Some 120 Mule-200s have also been sent to Tibet, News Now reports, with a majority of them deployed to the border area.

Want to seem like the smartest member of your family these holidays? Why not brag about your vast knowledge of AI? It was a big year for artificial intelligence, so here's a round-up of Cosmos' AI favourites from 2021. A team of researchers from the University of Glasgow, UK, used machine learning algorithms to find future zoonotic (originating in animals) virus threats. According to the researchers, a major stumbling block for understanding zoonotic disease has been that scientists tend to prioritise well-known zoonotic virus families based on their common features.

In May 2021, Twitter, a platform notorious for abuse and hot-headedness, rolled out a "prompts" feature that suggests users think twice before sending a tweet. The following month, Facebook announced AI "conflict alerts" for groups, so that admins can take action where there may be "contentious or unhealthy conversations taking place." Amazon's Halo, launched in 2020, is a fitness band that monitors the tone of your voice. Wellness is no longer just the tracking of a heartbeat or the counting of steps, but the way we come across to those around us. Algorithmic therapeutic tools are being developed to predict and prevent negative behavior.

Electrical utilities depend on short-term demand forecasting to proactively adjust production and distribution in anticipation of major variations. This systematic review analyzes 240 works published in scholarly journals between 2000 and 2019 that focus on applying Artificial Intelligence (AI), statistical, and hybrid models to short-term load forecasting (STLF). This work represents the most comprehensive review of works on this subject to date. A complete analysis of the literature is conducted to identify the most popular and accurate techniques as well as existing gaps. The findings show that although Artificial Neural Networks (ANN) continue to be the most commonly used standalone technique, researchers have been exceedingly opting for hybrid combinations of different techniques to leverage the combined advantages of individual methods. The review demonstrates that it is commonly possible with these hybrid combinations to achieve prediction accuracy exceeding 99%. The most successful duration for short-term forecasting has been identified as prediction for a duration of one day at an hourly interval. The review has identified a deficiency in access to datasets needed for training of the models. A significant gap has been identified in researching regions other than Asia, Europe, North America, and Australia.