At the front lines of business communications lies the customer service team, a vital link between customers and your organization. With rising customer expectations and multiple communications channels available, connecting with customers in their preferred method is essential to a positive experience. Customer experience (CX) improvements are driven by new technology, and each customer interaction impacts the user's relationship with your organization. AI is one of these technologies that can improve your customer experience and contact center. For example, AI can connect the dots between the maze of data in your contact center and change the way your teams interact with your customers.

We tackle the task of question generation over knowledge bases. Conventional methods for this task neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to be definitive. In this paper, we strive toward the above two issues via incorporating diversified contexts and answer-aware loss. Specifically, we propose a neural encoder-decoder model with multilevel copy mechanisms to generate such questions. Furthermore, the answer aware loss is introduced to make generated questions corresponding to more definitive answers. Experiments demonstrate that our model achieves state-of-the-art performance. Meanwhile, such generated question can express the given predicate and correspond to a definitive answer. 1 Introduction Question Generation over Knowledge Bases (KBQG) aims at generating natural language questions for the corresponding facts on KBs, and it can benefit some real applications. Secondly, the generated questions and answers will be able to augment the training data for QA systems. More importantly, KBQG can improve the ability of machines to actively ask questions on human-machine conversations (Duan et al., 2017; Sun et al., 2018).

Apple introduced CoreML in WWDC 2017, and it is a great deal. CoreML is a machine learning framework used in many Apple products, like Siri, Camera, Keyboard Dictation, etc. The cool stuff about CoreML is that it can use a pre-trained model to work offline. Apple has provided lots of pre-trained models like MobileNet, SqueezeNet, Inception v3, VGG16 to help us with image recognition tasks, especially detecting dominant objects in a scene. The job of CoreML is simply predicting data based on the models.

Semantic Question Answering The use of Semantic Web technologies led to an increasing number of structured data published on the Web. Despite the advances on question answering systems retrieving the desired information from structured sources is still a substantial challenge.

Natural language question answering over knowledge graphs is an important and interesting task as it enables common users to gain accurate answers in an easy and intuitive manner. However, it remains a challenge to bridge the gap between unstructured questions and structured knowledge graphs. To address the problem, a natural discipline is building a structured query to represent the input question. Searching the structured query over the knowledge graph can produce answers to the question. Distinct from the existing methods that are based on semantic parsing or templates, we propose an effective approach powered by a novel notion, structural query pattern, in this paper. Given an input question, we first generate its query sketch that is compatible with the underlying structure of the knowledge graph. Then, we complete the query graph by labeling the nodes and edges under the guidance of the structural query pattern. Finally, answers can be retrieved by executing the constructed query graph over the knowledge graph. Evaluations on three question answering benchmarks show that our proposed approach outperforms state-of-the-art methods significantly.

Machine reading comprehension(MRC) has attracted significant amounts of research attention recently, due to an increase of challenging reading comprehension datasets. In this paper, we aim to improve a MRC model's ability to determine whether a question has an answer in a given context (e.g. the recently proposed SQuAD 2.0 task). Our solution is a relation module that is adaptable to any MRC model. The relation module consists of both semantic extraction and relational information. We first extract high level semantics as objects from both question and context with multi-head self-attentive pooling. These semantic objects are then passed to a relation network, which generates relationship scores for each object pair in a sentence. These scores are used to determine whether a question is non-answerable. We test the relation module on the SQuAD 2.0 dataset using both BiDAF and BERT models as baseline readers. We obtain 1.8% gain of F1 on top of the BiDAF reader, and 1.0% on top of the BERT base model. These results show the effectiveness of our relation module on MRC

Data science is a required practice for organizations accelerating their journeys to AI. Businesses are keen on hiring the right talent, acquiring the right tools and evolving the discipline. Solving the lack of data scientists' problems requires investment in our employees in terms of time and training. We can't expect these people to just keep on learning for a year before they can be productive. We need to reach a stage where people know enough to start contributing immediately while continuing to improve their skills. As far as the second problem is concerned, taking too much time getting to a usable and tuned model, we need tools to help us optimize our data scientists' productivity.

The exponential growth in data has proven to be a gamechanger in marketing, especially with the introduction of cognitive computing and AI. IBM Watson is breaking new ground in this area and speaking more on this is Marta McMichael, global director of performance marketing at IBM Watson IoT. With an extensive background in the high-tech industry, Marta has worked in varied roles, including working as a programmer, a consultant and managing large account sales at IBM. It is here at IBM that she discovered her passion for marketing and transitioned into it. In the interview, Marta shares her big career epiphany that helped her refocus on creating value for her clients.

David Ferrucci led the team that built Watson, the IBM question-answering system that beat the top humans in the world at the game of Jeopardy. He is also the Founder, CEO, and Chief Scientist of Elemental Cognition, a company working engineer AI systems that understand the world the way people do. This conversation is part of the Artificial Intelligence podcast.