Rapid advancements in the field of artificial intelligence (AI) are uniquely poised to transform entire occupations and industries, changing the way work will be done in the future. It is imperative to understand the extent and nature of the changes so that we can prepare today for the jobs of tomorrow. New empirical work from the MIT-IBM Watson AI Lab uncovers how jobs will transform as AI and new technologies continue to scale across business and industries. We created a novel dataset using machine learning techniques on 170 million U.S. job postings. The dataset and research, The Future of Work: How New Technologies Are Transforming Tasks, allow us to extract key insights into how AI is shaping the future of work.

We propose a novel text generation task, namely Curiosity-driven Question Generation. We start from the observation that the Question Generation task has traditionally been considered as the dual problem of Question Answering, hence tackling the problem of generating a question given the text that contains its answer. Such questions can be used to evaluate machine reading comprehension. However, in real life, and especially in conversational settings, humans tend to ask questions with the goal of enriching their knowledge and/or clarifying aspects of previously gathered information. We refer to these inquisitive questions as Curiosity-driven: these questions are generated with the goal of obtaining new information (the answer) which is not present in the input text. In this work, we experiment on this new task using a conversational Question Answering (QA) dataset; further, since the majority of QA dataset are not built in a conversational manner, we describe a methodology to derive data for this novel task from non-conversational QA data. We investigate several automated metrics to measure the different properties of Curious Questions, and experiment different approaches on the Curiosity-driven Question Generation task, including model pre-training and reinforcement learning. Finally, we report a qualitative evaluation of the generated outputs.

Multi-domain dialogue state tracking (DST) is a critical component for conversational AI systems. The domain ontology (i.e., specification of domains, slots, and values) of a conversational AI system is generally incomplete, making the capability for DST models to generalize to new slots, values, and domains during inference imperative. In this paper, we propose to model multi-domain DST as a question answering problem, referred to as Dialogue State Tracking via Question Answering (DSTQA). Within DSTQA, each turn generates a question asking for the value of a (domain, slot) pair, thus making it naturally extensible to unseen domains, slots, and values. Additionally, we use a dynamically-evolving knowledge graph to explicitly learn relationships between (domain, slot) pairs. Our model has a 5.80% and 12.21% relative improvement over the current state-of-the-art model on MultiWOZ 2.0 and MultiWOZ 2.1 datasets, respectively. Additionally, our model consistently outperforms the state-of-the-art model in domain adaptation settings.

Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. To this end, we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT -2 (Radford et al., 2019) model with Transformer encoder BERT (De-vlin et al., 2018) for the downstream task for question answering. The model is trained in an end-to-end fashion, where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset (Rajpurkar et al., 2016) suggests that our method can produce semantically correct and diverse questions. Additionally, we assessed the performance of our proposed method for the downstream task of question answering. The analysis shows that our proposed generation & answering collaboration framework relatively improves both tasks and is particularly powerful in the semi-supervised setup. The results further suggest a robust and comparably lean pipeline facilitating question generation in the small-data regime.

Knowledge graph embedding, which projects symbolic entities and relations into continuous vector spaces, is gaining increasing attention. Previous methods allow a single static embedding for each entity or relation, ignoring their intrinsic contextual nature, i.e., entities and relations may appear in different graph contexts, and accordingly, exhibit different properties. This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings. Two types of graph contexts are studied: edges and paths, both formulated as sequences of entities and relations. CoKE takes a sequence as input and uses a Transformer encoder to obtain contextualized representations. These representations are hence naturally adaptive to the input, capturing contextual meanings of entities and relations therein. Evaluation on a wide variety of public benchmarks verifies the superiority of CoKE in link prediction and path query answering. It performs consistently better than, or at least equally well as current state-of-the-art in almost every case, in particular offering an absolute improvement of 19.7% in H@10 on path query answering. Our code is available at \url{https://github.com/paddlepaddle/models/tree/develop/PaddleKG/CoKE}.

IBM Watson is considered to be the first-ever commercialized cognitive computing platform, designed specifically for underpinning the development of various enterprise solutions. IBM Watson services continue to tap immense opportunity in the rapidly evolving cognitive computing field, which has been reshaping the nature of business operations, thereby determining their growth. Fact.MR's recent study projects the IBM Watson services market to record a spectacular rise in the period of forecast (2018-2028). Over US$ 20,000 Mn worth of IBM Watson services are estimated to be sold globally by 2028-end. Although cognitive computing is yet at its nascent phase, the technology is expected to have a significant influence on transformation of various businesses and industrial sectors.

Multi-channel is the new norm – consumers are not completely abandoning brick-and-mortar stores. Instead, they expect seamless shopping experiences across online, mobile and offline stores. They might first browse and research online, then purchase or pick-up in-store--or the other way around. Successful retailers who can gain customer loyalty are those who can deliver a superior seamless experience across all channels. Data is the new gold – The additional touchpoints mean retailers have greater opportunity and more data to identify their customers and discern their preferences. However, without a proper data and analytics infrastructure, many retailers struggle to mine and analyze huge volumes of data generated daily to gain valuable insights that can help them innovate.

Businesses today are increasingly certain that AI will be a driving force in the evolution of their industries over the next few years. To successfully infuse AI into your product or solution, there are many factors that challenge its widespread adoption in the business–and to achieving your expected outcomes. Building trust – Organizations and businesses tend to be skeptical about AI because of its "black box" nature, resulting in many promising models not going into production. Algorithm bias – Another inherent problem with AI systems is they're only as good–or as bad–as the data they're trained on. If the input data is filled with racial, gender, communal or ethnic biases, your model's accuracy is going to eventually drift away.

In addition to the traditional task of getting machines to answer questions, a major research question in question answering is to create interesting, challenging questions that can help systems learn how to answer questions and also reveal which systems are the best at answering questions. We argue that creating a question answering dataset---and the ubiquitous leaderboard that goes with it---closely resembles running a trivia tournament: you write questions, have agents (either humans or machines) answer the questions, and declare a winner. However, the research community has ignored the decades of hard-learned lessons from decades of the trivia community creating vibrant, fair, and effective question answering competitions. After detailing problems with existing QA datasets, we outline the key lessons---removing ambiguity, discriminating skill, and adjudicating disputes---that can transfer to QA research and how they might be implemented for the QA community.

There are many reasons why you would want to leverage Watson Assistant to make your Einstein Bot "better". In a previous blog, I spoke to just some of the key reasons why you would need to do so. I will provide additional detail here but first, let's look at how you integrate Watson into your Einstein Bot. The obvious table stakes, you need a Watson Assistant service to integrate with Bots. If you don't already have one, you can get a free IBM Cloud account to deploy a Watson Assistant service, which you can do in about a minute, also for free.