By 2020, 30% of all website sessions will be conducted without a screen. Now, you may be asking yourself, how is that possible? It turns out that voice-only search allows users to browse the web the Internet and consumer information without actually having to scroll through sites on desktops and mobile devices. And this new technology may be the key to successful brands in the future. Voice search essentially allows users to speak into a device as opposed to typing keywords into a search query to generate results.

Do you want to learn how you can accelerate your AI strategy or get ahead of the latest AI trends? Or are you more curious to learn what results businesses are achieving by adopting AI? Either way, make sure you attend Think 2019 and experience Watson AI technology first-hand. Here's a sneak peek at five sessions you can't miss: Being able to explain the decisions your AI makes and have trust in them is crucial to accelerating adoption of AI in your business. In these sessions, you'll learn how AI OpenScale provides businesses with confidence in AI decisions and infuses AI throughout its full lifecycle with trust and transparency, explains outcomes, and automatically mitigates bias. However, there are still a variety of hurdles businesses need to overcome to scale and automate their AI.

Exploring the future of artificial intelligence (AI) in our day to day lives, computer whiz kid Tanmay Bakshi said at an event in Greater Noida that instances of fake news, hate speech and harassment on social media can be dealt with the use of AI. Fifteen-year-old Bakshi, the world's youngest IBM Watson programmer, was at Sharda University in Greater Noida on Friday for a TED Talk with students on computer programming and the future of artificial intelligence. He said AI can be monumental in curbing fake news and hate speech. "Fake news is huge and I myself have been a victim of it where one of my TED Talk videos was uploaded on Facebook with the caption that I work for Google and I make billions of dollars a year. I believe social media giants have started using AI to clamp down on fake news and hate speech. For example, Facebook is using machine learning (alternatively known as AI) to understand the content being put up, match it with trusted sources, understand the different point of views which people can have, and when they are absolutely sure that it is fake news then it will be automatically flagged for deletion," Bakshi said.

IBM has launched a unit designed for human resources to better find talent and recruit using artificial intelligence. The company's HR effort, dubbed IBM Talent & Transformation, includes select Watson AI-based services that can help HR become a growth engine to enable digital transformation. AI can be used to revamp workflow, employee engagement, recruitment and retention while providing a more diverse workforce, the company says. The Watson Talent Suite rolls up behavioral science, AI, and psychology and applies it to HR. Components include Watson Career Coach, a virtual coach that provides advice for career paths, and Watson Candidate Assistant, which looks through the history of job seekers and matches them with openings. These services were developed for IBM's internal HR team and the company claims it drove $107 billion in benefits in 2017 with better employee satisfaction.

Content provided by IBM with Insider Studios. In the summer of 1956, 10 scientists and mathematicians gathered at New Hampshire's Dartmouth College to brainstorm a new concept Assistant Professor John McCarthy called "artificial intelligence." According to the original proposal for the research project, McCarthy--along with fellow organizers from Harvard, Bell Labs and IBM--wanted to explore the idea of programming machines to use language and solve problems for humans while improving over time. It would be years before these lofty objectives were met, but the summer workshop is credited with launching the field of artificial intelligence (AI). Sixty years later, cognitive scientists, data analysts, UX designers and countless others are doing everything those pioneering scientists hoped for--and more.

We introduce the task of acoustic question answering (AQA) in the area of acoustic reasoning. In this task an agent learns to answer questions on the basis of acoustic context. In order to promote research in this area, we propose a data generation paradigm adapted from CLEVR (Johnson et al. 2017). We generate acoustic scenes by leveraging a bank elementary sounds. We also provide a number of functional programs that can be used to compose questions and answers that exploit the relationships between the attributes of the elementary sounds in each scene. We provide AQA datasets of various sizes as well as the data generation code. As a preliminary experiment to validate our data, we report the accuracy of current state of the art visual question answering models when they are applied to the AQA task without modifications. Although there is a plethora of question answering tasks based on text, image or video data, to our knowledge, we are the first to propose answering questions directly on audio streams. We hope this contribution will facilitate the development of research in the area.

Computers aren't going to replace creative pros -- but machine learning and artificial intelligence can be powerful tools in the storytelling process. The 60-second spot was directed by Oscar-winner Kevin Macdonald, working from a script that was developed by IBM's Watson AI system. To produce the spot for the Lexus ES executive sedan launching in Europe, the automaker enlisted its creative agency, The&Partnership London, along with technical partner Visual Voice. The agencies collaborated with the IBM Watson team to use AI to analyze 15 years' worth of footage, text and audio for car and luxury brand campaigns that have won Cannes Lions awards for creativity, as well as a range of other external data. Watson identified elements common to award-worthy commercials that were "both emotionally intelligent and entertaining," according to IBM.

The elite team of engineers and medical specialists assembled by IBM's Watson Health division had the innocuous code name "Project Josephine," but its mission could not have been more urgent: to fix the artificial intelligence software at the core of the company's campaign to tackle the $7 trillion global health care market. The predicament faced by IBM officials, STAT has found, was that it could not get its software to reliably understand and analyze language in patient medical records. That was critical for the company to deliver on multimillion-dollar contracts with hospitals and drug companies. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is a premium subscription that delivers daily market-moving biopharma coverage and in-depth science reporting from a team with decades of industry experience.

Despite the great success of word embedding, sentence embedding remains a not-well-solved problem. In this paper, we present a supervised learning framework to exploit sentence embedding for the medical question answering task. The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module. The former is developed with contextual self-attention and multi-scale techniques to encode a sentence into an embedding tensor. This module is shortly called Contextual self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association Scoring (SAS). SMS measures similarity while SAS captures association between sentence pairs: a medical question concatenated with a candidate choice, and a piece of corresponding supportive evidence. The proposed framework is examined by two Medical Question Answering(MedicalQA) datasets which are collected from real-world applications: medical exam and clinical diagnosis based on electronic medical records (EMR). The comparison results show that our proposed framework achieved significant improvements compared to competitive baseline approaches. Additionally, a series of controlled experiments are also conducted to illustrate that the multi-scale strategy and the contextual self-attention layer play important roles for producing effective sentence embedding, and the two kinds of scoring strategies are highly complementary to each other for question answering problems.

In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs. We experiment with six different ranking models and propose a novel self-attention based slot matching model which exploits the inherent structure of query graphs, our logical form of choice. Our proposed model generally outperforms the other models on two QA datasets over the DBpedia knowledge graph, evaluated in different settings. In addition, we show that transfer learning from the larger of those QA datasets to the smaller dataset yields substantial improvements, effectively offsetting the general lack of training data.