The availability of a vast array of research papers in any area of study, necessitates the need of automated summarisation systems that can present the key research conducted and their corresponding findings. Scientific paper summarisation is a challenging task for various reasons including token length limits in modern transformer models and corresponding memory and compute requirements for long text. A significant amount of work has been conducted in this area, with approaches that modify the attention mechanisms of existing transformer models and others that utilise discourse information to capture long range dependencies in research papers. In this paper, we propose a hybrid methodology for research paper summarisation which incorporates an extractive and abstractive approach. We use the extractive approach to capture the key findings of research, and pair it with the introduction of the paper which captures the motivation for research. We use two models based on unsupervised learning for the extraction stage and two transformer language models, resulting in four combinations for our hybrid approach. The performances of the models are evaluated on three metrics and we present our findings in this paper. We find that using certain combinations of hyper parameters, it is possible for automated summarisation systems to exceed the abstractiveness of summaries written by humans. Finally, we state our future scope of research in extending this methodology to summarisation of generalised long documents.

This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We consider three main perspectives in which ontologies can contribute significantly, namely reference modelling, common-sense reasoning, and knowledge refinement and complexity management. We overview some of the existing approaches in the literature, and we position them according to these three proposed perspectives. The paper concludes by discussing what challenges still need to be addressed to enable ontology-based approaches to explanation and to evaluate their human-understandability and effectiveness.

A machine learning (ML) project requires collaboration across multiple roles in a business. We'll introduce the high level steps of what the end-to-end ML lifecycle looks like and how different roles can collaborate to complete the ML project. Machine learning is a powerful tool to help solve different problems in your business. The article "Building your first machine learning model" gives you basic ideas of what it takes to build a machine learning model. In this article, we'll talk about what the end-to-end machine learning project lifecycle looks like in a real business.

A machine learning (ML) project requires collaboration across multiple roles in a business. We'll introduce the high level steps of what the end-to-end ML lifecycle looks like and how different roles can collaborate to complete the ML project. Machine learning is a powerful tool to help solve different problems in your business. The article "Building your first machine learning model" gives you basic ideas of what it takes to build a machine learning model. In this article, we'll talk about what the end-to-end machine learning project lifecycle looks like in a real business.

Salary is £80,000 – £150,000, however, this is up to £200,000 for absolute superstars… we are talking about multiple 1st author publications at NIPS, TPAMI, CVPR, ICML et al. and/or multiple SOTA results across relevant data sets. We are looking for several world-class Senior R&D Scientist's to apply deep learning on a range of bleeding-edge projects that will make AI accessible to the world. This global company's platform has been labelled the most accurate on the planet which has secured them a spot on the big table as a market leader in not only the design but the development of next-gen AI and Computer Vision technology. Working within this environment would allow you to join forces with a team that has 20 years' experience and is recognised as world leading talent when it comes to deep learning & computer vision research The research team is central to making this organisation the best AI company on the planet and work on the core underpinning AI, they continuously aim to push the bar and work with bleeding edge technology & techniques to achieve the impossible. This team look beyond the latest techniques in Deep Learning to what is next.

A century and a half ago, French physician Pierre Paul Broca found that patients with damage to part of the brain's frontal lobe were unable to speak more than a few words. Later dubbed Broca's area, this region is believed to be critical for speech production and some aspects of language comprehension. However, in recent years neuroscientists have observed activity in Broca's area when people perform cognitive tasks that have nothing to do with language, such as solving math problems or holding information in working memory. Those findings have stimulated debate over whether Broca's area is specific to language or plays a more general role in cognition. A new study from MIT may help resolve this longstanding question.