Some say that artificial intelligence (AI) will radically change healthcare in the future. But that prediction overlooks an important detail: AI is already significantly changing healthcare. IBM (NYSE: IBM) Watson Health general manager Deborah DiSanzo spoke at the annual J. P. Morgan Healthcare Conference on Wednesday. She provided an update on the progress that IBM Watson, the AI system famous for beating Jeopardy! DiSanzo highlighted four areas where AI is making a big difference today.

Some say that artificial intelligence (AI) will radically change healthcare in the future. But that prediction overlooks an important detail: AI is already significantly changing healthcare. IBM (NYSE:IBM) Watson Health general manager Deborah DiSanzo spoke at the annual J. P. Morgan Healthcare Conference on Wednesday. She provided an update on the progress that IBM Watson, the AI system famous for beating Jeopardy! DiSanzo highlighted four areas where AI is making a big difference today.

IBM plans to make a 10-year, $240 million investment to create the MIT–IBM Watson AI Lab in partnership with MIT, where fundamental AI research will be conducted to unlock the potential of AI. Here are 5 key things to know about the new Lab.

We introduce the first system towards the novel task of answering complex multi-sentence recommendation questions in the tourism domain. Our solution uses a pipeline of two modules: question understanding and answering. For question understanding, we define an SQL-like query language that captures the semantic intent of a question; it supports operators like subset, negation, preference and similarity, which are often found in recommendation questions. We train and compare traditional CRFs as well as bidirectional LSTM-based models for converting a question to its semantic representation. We extend these models to a semi-supervised setting with partially labeled sequences gathered through crowdsourc-ing. We find that our best model performs semi-supervised training of BiDiL-STM CRF with hand-designed features and CCM(Chang et al., 2007) constraints. Finally, in an end to end QA system, our answering component converts our question representation into queries fired on underlying knowledge sources. Our experiments on two different answer corpora demonstrate that our system can significantly outperform baselines with up to 20 pt higher accuracy and 17 pt higher recall.

This special issue issue of AI Magazine presents six articles on some of the most interesting question-answering systems in development today. Included are articles on Vulcan's Project Halo, Cyc's Semantic Research Assistant, IBM's Watson, True Knowledge, and the University of Washington's TextRunner. Even though AI has diversified much beyond the notion of intelligent behavior proposed in the Turing test, QA remains a fundamental capability needed by a large class of systems. The QA problem extends beyond AI systems to many analytical tasks that involve gathering, correlating, and analyzing information in ways that can naturally be formulated as questions. Ultimately, questions are an interface to systems that provide such analytic capabilities, and the need to provide this interface has increased dramatically over the past decade with the explosion of information available in digital form.

A joint MIT-IBM team set out to build a very large-scale dataset to help AI systems recognize and understand actions in videos. The dataset contains 1 million three-seconds video clips, each annotated with the actions that occur during the clips.

The quest for algorithms that enable cognitive abilities is an important part of machine learning. A common trait in many recently investigated cognitive-like tasks is that they take into account different data modalities, such as visual and textual input. In this paper we propose a novel and generally applicable form of attention mechanism that learns high-order correlations between various data modalities. We show that high-order correlations effectively direct the appropriate attention to the relevant elements in the different data modalities that are required to solve the joint task. We demonstrate the effectiveness of our high-order attention mechanism on the task of visual question answering (VQA), where we achieve state-of-the-art performance on the standard VQA dataset.

As the growing temptation for automated processes covers the varied industrial spectrum, Machine Learning is emerging into an ocean of possibilities while IBM's Watson leads the marathon. From acting as a virtual chef of 65 recipes to embracing space programs, there's a swathe of applications being ideated, built and rolled out to the public. Pulling large volumes of data and producing the most relevant possible results to user's quest stays the flag bearer, Watson is amazing in churning higher revenues and upscaling business presence. Treatment for many of the world's deadliest diseases is entirely dependent upon reference to past records. While browsing through them all using traditional analytics applications is unvaried, IBM Watson takes a giant leap with instant derivations from years of clinical research and patient data.

This demo shows how to control a Smart Home using IBM Watson and Augmented Reality technologies.

We've all heard or read about how robots are going to take away our jobs. Saudi Arabia even went as far as granting citizenship to "Sophia the robot" back in October (See the video below). With crytocurrency at the top of daily headlines, 2017 may be remembered as the year artificial intelligence (AI, pronounced AYE-EYE) goes mainstream with more organizations adopting AI than ever. Two weeks ago, we wrote about Professor Geoffrey Hinton, known worldwide as the Godfather of AI, and how his research work in the area of Neuro Net was used in speech recognition and Android voice search. Yes, we've made a lot of progress since AI started as an academic discipline in 1956.