If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Nvidia reported revenues of $5.0 billion for its fourth fiscal quarter ended January 31, up 61% from a year earlier. The revenues and non-GAAP earnings per share of $3.10 beat expectations as new gaming hardware and AI products generated strong demand. A year ago, Nvidia reported non-GAAP earnings per share of $1.89 on revenues of $3.1 billion. The Santa Clara, California-based company makes graphics processing units (GPUs) that can be used for games, AI, and datacenter computing. While many businesses have been hit hard by the pandemic, Nvidia has seen a boost in those areas.
Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work. At the same time, it is a time-consuming process and increasing interest in emerging fields often results in a high review workload, especially for senior researchers in this area. How to cope with this problem is an open question and it is vividly discussed across all major conferences. In this work, we propose an Argument Mining based approach for the assistance of editors, meta-reviewers, and reviewers. We demonstrate that the decision process in the field of scientific publications is driven by arguments and automatic argument identification is helpful in various use-cases. One of our findings is that arguments used in the peer-review process differ from arguments in other domains making the transfer of pre-trained models difficult. Therefore, we provide the community with a new peer-review dataset from different computer science conferences with annotated arguments. In our extensive empirical evaluation, we show that Argument Mining can be used to efficiently extract the most relevant parts from reviews, which are paramount for the publication decision. The process remains interpretable since the extracted arguments can be highlighted in a review without detaching them from their context.
Beijing – China livestreamed footage of its new manned submersible parked at the bottom of the Mariana Trench on Friday, part of a historic mission into the deepest underwater valley on the planet. The "Fendouzhe," or "Striver," descended more than 10,000 meters (about 33,000 feet) into the submarine trench in the western Pacific Ocean with three researchers on board, state broadcaster CCTV said. Only a handful of people have ever visited the bottom of the Mariana Trench, a crescent-shaped depression in the Earth's crust that is deeper than Mount Everest is high and more than 2,550 km (1,600 miles) long. The first explorers visited the trench in 1960 on a brief expedition, after which there had been no missions until Hollywood director James Cameron made the first solo trip to the bottom in 2012. Cameron described a "desolate" and "alien" environment.
While Artificial Intelligence (AI) is a much touted technology in mining, it would seem that the sector is yet to fully embrace this advance technology. Why is this and how can we insure that AI can be beneficial to mining in Africa. According to Prof. Frederick Cawood, Director of Wits Mining Institute at the University of the Witwatersrand, it will take a policy change to ensure that it can benefit mining in Africa. Cawood was a panellist on a recent Mining Review Africa webinar titled Mining 2025: A 5-year vision for AI in mining. Cawood was joined on the panel by Eric Croeser, MD for Africa at Accenture Industry X and Jean-Jacques Verhaeghe, programme manager for real-time information management systems at Mandela Mining Precinct.
The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets. However, as with many other NLU tasks, the dominant language is English, with resources in other languages being few and far between. In this work, we explore the potential of transfer learning using the multilingual BERT model to address argument mining tasks in non-English languages, based on English datasets and the use of machine translation. We show that such methods are well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments, presumably because quality is harder to preserve under translation. In addition, focusing on the translate-train approach, we show how the choice of languages for translation, and the relations among them, affect the accuracy of the resultant model. Finally, to facilitate evaluation of transfer learning on argument mining tasks, we provide a human-generated dataset with more than 10k arguments in multiple languages, as well as machine translation of the English datasets.
Pre-sales customer service is of importance to E-commerce platforms as it contributes to optimizing customers' buying process. To better serve users, we propose AliMe KG, a domain knowledge graph in E-commerce that captures user problems, points of interests (POI), item information and relations thereof. It helps to understand user needs, answer pre-sales questions and generate explanation texts. We applied AliMe KG to several online business scenarios such as shopping guide, question answering over properties and recommendation reason generation, and gained positive results. In the paper, we systematically introduce how we construct domain knowledge graph from free text, and demonstrate its business value with several applications. Our experience shows that mining structured knowledge from free text in vertical domain is practicable, and can be of substantial value in industrial settings.
The deployment of artificial intelligence (AI) in mining is enabling companies to improve their efficiency and productivity, which is crucial to their profitability. The mining industry is pivotal to the world's economy. The mining industry's top companies had a total revenue of approximately 683 billion U.S. dollars in 2018. Implementation of AI in mining activities can help push the industry even further forward by reducing the operating costs and simplifying the mining processes. A majority of mining companies still depend on traditional mining practices.
We consider the problem of visually explaining similarity models, i.e., explaining why a model predicts two images to be similar in addition to producing a scalar score. While much recent work in visual model interpretability has focused on gradient-based attention, these methods rely on a classification module to generate visual explanations. Consequently, they cannot readily explain other kinds of models that do not use or need classification-like loss functions (e.g., similarity models trained with a metric learning loss). In this work, we bridge this crucial gap, presenting the first method to generate gradient-based visual explanations for image similarity predictors. By relying solely on the learned feature embedding, we show that our approach can be applied to any kind of CNN-based similarity architecture, an important step towards generic visual explainability. We show that our resulting visual explanations serve more than just interpretability; they can be infused into the model learning process itself with new trainable constraints based on our similarity explanations. We show that the resulting similarity models perform, and can be visually explained, better than the corresponding baseline models trained without our explanation constraints. We demonstrate our approach using extensive experiments on three different kinds of tasks: generic image retrieval, person re-identification, and low-shot semantic segmentation.
Becoming a data scientist is a relatively new career trajectory that merges statistics, business logic, and programming knowledge. Especially a data scientist and not just a machine learning engineer needs a comprehensive understanding of algebra, statistics, machine learning, and Deep Learning algorithms. I want to suggest a path which you can take in 3 months to prepare for a data scientist interview . This path starts with simple steps and is completed with a crucial part of the field. This article is written in 2020.
Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark datasets and standardized evaluation procedures is lagging, consequently hindering advancements in this area. To address this, we introduce the TUDataset for graph classification and regression. The collection consists of over 120 datasets of varying sizes from a wide range of applications. We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools. Here, we give an overview of the datasets, standardized evaluation procedures, and provide baseline experiments. All datasets are available at www.graphlearning.io. The experiments are fully reproducible from the code available at www.github.com/chrsmrrs/tudataset.