One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
Over the last 10 years, we have come to see robots perform and execute jobs that were once exclusive to humans – be it, manufacturing cars or filling warehouse orders. As of today, we are no strangers to the fact that there are multiple industries that AI/ML have significantly impacted over the last couple years. However, the integration of Artificial Intelligence in healthcare with a chatbot as your doctor is set to witness a significant paradigm shift. We are already seeing image recognition algorithms assist in detecting diseases at an astounding rate and are only beginning to scratch the surface. Chatbots are slowly being adopted within healthcare, albeit being in their nascent stage.
Developing an AI use case that lays out what the project will cost, the value it will provide and the potential risks it will bring can be a head scratcher for CIOs. AI in the enterprise is uncharted territory for many companies. What exactly is digital transformation? You may hear the term often, but everyone seems to have a different definition. See how our experts define digitization, and how you can get started in this free guide.
Developing an AI use case that lays out what the project will cost, the value it will provide and the potential risks it will bring can be a head scratcher for CIOs. AI in the enterprise is uncharted territory for many companies. This complimentary document comprehensively details the elements of a strategic IT plan that are common across the board – from identifying technology gaps and risks to allocating IT resources and capabilities. You forgot to provide an Email Address. This email address doesn't appear to be valid.
Since a few years, chatbots are here, and they will not go away any time soon. Facebook popularised the chatbot with Facebook Messenger Bots, but the first chatbot was already developed in the 1960s. The chatbot was developed to demonstrate the superficiality of communication between humans and machines, and it used very simple natural language processing. Of course, since then we have progressed a lot and, nowadays, it is possible to have lengthy conversations with a chatbot. For an overview of the history of chatbots, you can read this article.
This paper builds on the recent ASPIC+ formalism, to develop a general framework for argumentation with preferences. We motivate a revised definition of conflict free sets of arguments, adapt ASPIC+ to accommodate a broader range of instantiating logics, and show that under some assumptions, the resulting framework satisfies key properties and rationality postulates. We then show that the generalised framework accommodates Tarskian logic instantiations extended with preferences, and then study instantiations of the framework by classical logic approaches to argumentation. We conclude by arguing that ASPIC+'s modelling of defeasible inference rules further testifies to the generality of the framework, and then examine and counter recent critiques of Dung's framework and its extensions to accommodate preferences.
We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform. Previous work has been confined to simulation experiments, whereas in this paper we work with real logged feedback for offline bandit learning of NMT parameters. We conduct a thorough analysis of the available explicit user judgments---five-star ratings of translation quality---and show that they are not reliable enough to yield significant improvements in bandit learning. In contrast, we successfully utilize implicit task-based feedback collected in a cross-lingual search task to improve task-specific and machine translation quality metrics.
The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DOM trees of semi-structured webpages can achieve high precision and recall only when manual annotations for each website are available. Although there have been efforts to learn extractors from automatically-generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites. In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a web page and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotation-based techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%.
This is the final installment of a three-part series exploring the impact of artificial intelligence (AI) on investment management. I want to thank the speakers at the AI and the Future of Financial Services Forum, hosted by CFA Institute and CFA Society Beijing, for inspiring this series. The initial articles offered a primer on the AI technologies that are relevant to investment professionals and explored the potential threat AI posed to human portfolio managers. Not all is lost, investment professionals. Despite artificial intelligence (AI)'s significant and rapidly increasing "brain" power, the investment management business is not going away tomorrow.
After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance. Every day we can read about the latest AI advances and applications from startups and large companies. AI was the star of the 2018 Consumer Electronic Show earlier this year in Las Vegas. But, despite its market acceptance, a recent McKinsey report found that AI adoption is still at an early, experimental stage, especially outside the tech sector. Based on a survey of over 3,000 AI-aware C-level executives across 10 countries and 14 sectors, the report found that 20 percent of respondents had adopted AI at scale in a core part of their business, 40 percent were partial adopters or experimenters, while another 40 percent were still waiting to take their first steps.