IBM and Unity are launching the IBM Watson Unity SDK on the Unity Asset Store, enabling developers to easily integrate Watson cloud services into their Unity applications such as visual recognition, speech to text, and language classification. The SDK makes it easy for developers to take advantage of modern AI techniques through a set of cloud-based services. Today we are thrilled to announce a partnership with IBM to launch the IBM Watson Unity SDK on the Unity Asset Store. This SDK is the first asset of its kind to bring scalable AI services to Unity, enabling developers to easily integrate Watson services into their Unity applications. Millions of Unity developers globally will now have access to the powerful cloud-based AI services of Watson directly within the Unity environment.

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.

This weekend's 60th Annual Grammy Awards will feature big names like Beyoncé, Rihanna, and Watson. The latter is IBM's famous artificial intelligence platform, which the Grammys are enlisting to curate the videos and photos being released to music fans following along with this year's awards show on social media in real time. IBM is partnering with Grammys organizer the Recording Academy to provide Watson's AI services to populate the event's social media feeds with automatically-generated content during the Grammy Awards ceremony, which airs this Sunday, Jan. 28, on CBS. IBM's Watson will get to work before the ceremony even starts, analyzing and sorting "hours of video and close to 125,000 photographs" taken during the Grammys' hours-long red carpet show ahead of Sunday's event, IBM said in its announcement. The platform will use features such as facial recognition, even analyzing stars' "facial emotion," to pick out the best images and videos to post for fans online.

Traditional analysis methods rely on structured data to provide intelligence - Watson is a breakthrough computing system, reading and analysing unstructured natural human language, to provide answers to complex questions. IBM developed the Watson AI supercomputer to intelligently simplify the rising flood of data and transform how computers and devices help people accomplish tasks in business, communities and their personal lives. Watson first made headlines in 2001 when it won the US quiz show Jeopardy! This was the first time that AI really came into the public consciousness when Watson beat humans in a head-to-head competition on the TV quiz show! Traditional analysis methods rely on structured data to provide intelligence - Watson is a breakthrough computing system, reading and analysing unstructured natural human language, to provide answers to complex questions.

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Cancer is responsible for one in six deaths around the world, and each year there are more than 14 million new cancer cases worldwide, according to the World Health Organization. As healthcare providers seek to enable data-driven, evidence-based cancer care, an explosion of medical information has created both challenges and opportunities to help improve quality of care. Some 50,000 oncology research papers are published each year, according to PubMed, and by 2020 medical information is projected to double every 73 days – outpacing the ability of human beings to keep up with the proliferation of medical knowledge. In this environment, Swedish oncology IT vendor Elekta is collaborating with artificial intelligence kingpin IBM Watson Health to offer Watson for Oncology as part of Elekta's cancer care systems. Elekta will market Watson for Oncology as an AI-based clinical decision support system paired within Elekta's digital cancer care systems, including its MOSAIQ Oncology Information System.

In this paper, we present Agent Assist, a virtual assistant which helps IT support staff to resolve tickets faster. It is essentially a conversation system which provides procedural and often complex answers to queries. This system can ingest knowledge from various sources like application documentation, ticket management systems and knowledge transfer video recordings. It uses an ensemble of techniques like question classification, knowledge graph based disambiguation, information retrieval, etc., to provide quick and relevant solutions to problems from various technical domains and is currently being used in more than 650 projects within IBM.

Movies provide us with a mass of visual content as well as attracting stories. Existing methods have illustrated that understanding movie stories through only visual content is still a hard problem. In this paper, for answering questions about movies, we put forward a Layered Memory Network (LMN) that represents frame-level and clip-level movie content by the Static Word Memory module and the Dynamic Subtitle Memory module, respectively. Particularly, we firstly extract words and sentences from the training movie subtitles. Then the hierarchically formed movie representations, which are learned from LMN, not only encode the correspondence between words and visual content inside frames, but also encode the temporal alignment between sentences and frames inside movie clips. We also extend our LMN model into three variant frameworks to illustrate the good extendable capabilities. We conduct extensive experiments on the MovieQA dataset. With only visual content as inputs, LMN with frame-level representation obtains a large performance improvement. When incorporating subtitles into LMN to form the clip-level representation, we achieve the state-of-the-art performance on the online evaluation task of 'Video+Subtitles'. The good performance successfully demonstrates that the proposed framework of LMN is effective and the hierarchically formed movie representations have good potential for the applications of movie question answering.

Recently, the Visual Question Answering (VQA) task has gained increasing attention in artificial intelligence. Existing VQA methods mainly adopt the visual attention mechanism to associate the input question with corresponding image regions for effective question answering. The free-form region based and the detection-based visual attention mechanisms are mostly investigated, with the former ones attending free-form image regions and the latter ones attending pre-specified detection-box regions. We argue that the two attention mechanisms are able to provide complementary information and should be effectively integrated to better solve the VQA problem. In this paper, we propose a novel deep neural network for VQA that integrates both attention mechanisms. Our proposed framework effectively fuses features from free-form image regions, detection boxes, and question representations via a multi-modal multiplicative feature embedding scheme to jointly attend question-related free-form image regions and detection boxes for more accurate question answering. The proposed method is extensively evaluated on two publicly available datasets, COCO-QA and VQA, and outperforms state-of-the-art approaches. Source code is available at https://github.com/lupantech/dual-mfa-vqa.

We present assertion based question answering (ABQA), an open domain question answering task that takes a question and a passage as inputs, and outputs a semi-structured assertion consisting of a subject, a predicate and a list of arguments. An assertion conveys more evidences than a short answer span in reading comprehension, and it is more concise than a tedious passage in passage-based QA. These advantages make ABQA more suitable for human-computer interaction scenarios such as voice-controlled speakers. Further progress towards improving ABQA requires richer supervised dataset and powerful models of text understanding. To remedy this, we introduce a new dataset called WebAssertions, which includes hand-annotated QA labels for 358,427 assertions in 55,960 web passages. To address ABQA, we develop both generative and extractive approaches. The backbone of our generative approach is sequence to sequence learning. In order to capture the structure of the output assertion, we introduce a hierarchical decoder that first generates the structure of the assertion and then generates the words of each field. The extractive approach is based on learning to rank. Features at different levels of granularity are designed to measure the semantic relevance between a question and an assertion. Experimental results show that our approaches have the ability to infer question-aware assertions from a passage. We further evaluate our approaches by incorporating the ABQA results as additional features in passage-based QA. Results on two datasets show that ABQA features significantly improve the accuracy on passage-based QA.