"Questions are asked and answered every day. Question answering (QA) technology aims to deliver the same facility online. It goes further than the more familiar search based on keywords (as in Google, Yahoo, and other search engines), in attempting to recognize what a question expresses and to respond with an actual answer. This simplifies things for users in two ways. First, questions do not often translate into a simple list of keywords. ...Second, QA takes responsibility for providing answers, rather than a searchable list of links to potentially relevant documents (web pages), highlighted by snippets of text that show how the query matched the documents."
– from Bonnie Webber & Nick Webb. Question Answering. In The Handbook of Computational Linguistics and Natural Language Processing. Alexander Clark, Chris Fox, Shalom Lappin (Eds.). Wiley, 2010.
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. To do well on SQuAD2.0, SQuAD2.0 is a challenging natural language understanding task for existing models, and we release SQuAD2.0 to the community as the successor to SQuAD1.1. We are optimistic that this new dataset will encourage the development of reading comprehension systems that know what they don't know. SQuAD 1.1, the previous version of the SQuAD dataset, contains 100,000 question-answer pairs on 500 articles.
Customer experience management (CXM) programs are necessarily a quantitative endeavor, requiring CX professionals to decipher insights from a sea of customer data. In this post, I will illustrate how you can use IBM Watson Studio to analyze one source of customer data, customer survey responses, to answer two important questions about the health of your customer relationship: 1) what is the current level of satisfaction across the CX touch points and 2) which of these touch points is responsible for ensuring customers are loyal? Customer Experience Management (CXM) programs rely on different types of data that come from a variety of sources. The most popular source of customer feedback is surveys. These two questions will help you understand how well you are meeting the needs of your customers and, more importantly, understand what you need to do to improve customer loyalty.
Artificial intelligence (AI) works as the driver of exponential economic value creation by making its way into our lives. You can assume the development of AI through Amazon's Alexa and Facebook's M to Google's Now and Apple's Siri and thus you can track your future growth, incredible opportunities and expected problems. Today, you can take the example of IBM's Watson. Watson is a question-answering computer system which can give answers to questions created in natural language, hypothesis generation and evaluation; and dynamic learning that is developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO, industrialist Thomas J. Watson.
Question Answering (QnA) model is one of the very basic systems of Natural Language Processing. In QnA, the Machin Learning based system generates answers from the knowledge base or text paragraphs for the questions posed as input. Various machine learning methods can be implemented to build Question Answering systems. Create a Question Answering Machine Learning model system which will take comprehension and questions as input, process the comprehension and prepare answers from it. With the Concept of Natural Language Processing, we can achieve this objective.
"Alexa, what to watch on Roku?" Streaming device Roku now has three ways to use voice control to get programming, the Google Assistant, Roku's own voice search and now Amazon Alexa. Roku makes the most popular brand of low-price streaming players to connect to TVs and bring in internet channels, and it also has a low-priced branded Roku TV from TCL that's among Amazon's best sellers in the category. Roku's chief rival is Amazon, which counts the Fire TV Stick streaming player is in its top best sellers category. The Fire TV Stick uses voice search from Alexa. More: What exactly is a'smart' device anyway?
There has been a lot of hand-wringing in certain circles that European businesses are not exploiting advanced technologies such as AI anything like as well as US or Chinese companies. It is true we haven't (yet) spawned global giants like Google or Baidu. But O think there's a more nuanced reality. Back in November 2018, I was delighted to be invited by IBM to be a judge at its European IBM Watson Challenge event. This was a "Dragon's Den" style event where 32 IBM business partners (from an initial submission of 155 prototypes) were each invited to present an innovative AI-based business solution and associated business plan to a panel of judges (the Dragons!) over two, exhausting and intensive (but exhilarating) days.
Finding the best way to get around a busy city is no easy task. At MWC 2019, Seat and IBM announced Mobility Advisor, which uses Watson artificial intelligence (AI) to work out the best way to reach your destination – whether it's a train, ride-hailing service or an electric scooter. The tool's suggestions will take into account traffic reports, weather forecasts, and any events happening in the city that day, so you won't get caught in the rain riding a hire bike, or reach a train station at the same time as a crowd of sports fans. Mobility Advisor is currently in development, and is intended to run as a mobile app on 4G and 5G networks. Over time, it will learn your preferences and make personalized recommendations based on the way you like to travel.
With Watson Machine Learning Accelerator you can drive faster time to results and accuracy, running in special AI hardware in the Cloud on On-Premises. WML Accelerator comes with SnapML library. We have developed an effi cient, scalable machine-learning library that enables very fast training of generalized linear models. We have demonstrated that our library can remove the training time as a bottleneck for machine-learning workloads, paving the way to a range of new applications. For instance, it allows more agile development, faster and more fine-grained exploration of the hyper-parameter space, enables scaling to massive datasets and makes frequent retraining of models possible in order to adapt to events as they occur.
Multimodal attentional networks are currently state-of-the-art models for Visual Question Answering (VQA) tasks involving real images. Although attention allows to focus on the visual content relevant to the question, this simple mechanism is arguably insufficient to model complex reasoning features required for VQA or other high-level tasks. In this paper, we propose MuRel, a multimodal relational network which is learned end-to-end to reason over real images. Our first contribution is the introduction of the MuRel cell, an atomic reasoning primitive representing interactions between question and image regions by a rich vectorial representation, and modeling region relations with pairwise combinations. Secondly, we incorporate the cell into a full MuRel network, which progressively refines visual and question interactions, and can be leveraged to define visualization schemes finer than mere attention maps. We validate the relevance of our approach with various ablation studies, and show its superiority to attention-based methods on three datasets: VQA 2.0, VQA-CP v2 and TDIUC. Our final MuRel network is competitive to or outperforms state-of-the-art results in this challenging context. Our code is available: https://github.com/Cadene/murel.bootstrap.pytorch