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IBM Watson and the future of Artificial Intelligence

#artificialintelligence

Watson, a supercomputer by IBM, shot to fame in 2011 as the'brain' that beat two of the best contestants of Jeopardy! to win a million dollars. This system that combines artificial intelligence (AI) and sophisticated analytical software to answer questions was widely deployed in many industries. The supercomputer was developed in IBM's DeepQA project and was named after IBM's founder Thomas J. Watson. "You can be discouraged by failure, or you can learn from it. So go ahead and make mistakes, make all you can. Because, remember that's where you'll find success – on the far side of failure."



@Radiology_AI

#artificialintelligence

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To automatically identify a cohort of patients with pancreatic cystic lesions (PCLs) and to extract PCL measurements from historical computed tomographic (CT) and magnetic resonance (MR) imaging reports using natural language processing (NLP) and a question answering system. Institutional review board approval was obtained for this retrospective HIPAA-compliant study and the requirement to obtain informed consent was waived.


Integrate IBM Watson with Whatsapp

#artificialintelligence

IBM Watson Assistant is a chatbot that employs artificial intelligence. It comprehends customers queries and responds quickly, consistently, and accurately across any application, device, or channel. And mainly Watson Assistant is a service that allows you to integrate conversational interfaces into any website or app. In this tutorial, I will show how to use Kommunicate to link a Watson Assistant chatbot to WhatsApp, extending its capabilities. Assuming you're familiar with Watson Assistant and how it works.


IBM Watson Health Introduces New Opportunities for Imaging AI Adoption

#artificialintelligence

Orchestration--of AI and of workflow--offers a new way to help imaging organizations improve radiologists' reading experience while significantly reducing the impact on IT IBM (NYSE: IBM) Watson Health is introducing a new AI orchestration offering to help imaging organizations experience the benefits of having AI applications work seamlessly together. IBM Watson Health will officially launch IBM Imaging AI Orchestrator at the Radiological Society of North America (RSNA) 2021 Annual Meeting in Chicago this week. In addition, IBM is announcing IBM Imaging Workflow Orchestrator with Watson, a new solution that modernizes the radiologist's reading experience while reducing the demands on IT and imaging system administrators. "We recognize that when it comes to applying AI in imaging, it's hard to go it alone," said David Gruen, MD, MBA, FACR, Chief Medical Officer, Imaging, Watson Health. "Because each AI application is developed in a unique way with a specific purpose, it can be challenging for organizations to review and assess each one, and then to deploy them in a way that's beneficial to radiologists and their patients. That's why, with the rapid proliferation of approved algorithms, staffing shortages, and complexity of disease, the IBM Imaging AI Orchestrator could not come at a better time."


Deep Learning: Advanced Natural Language Processing and RNNs

#artificialintelligence

It's hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing). A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you. So what is this course all about, and how have things changed since then? In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.


IBM Watson is AI for business - Drive Real Business Transformation

#artificialintelligence

For AI to thrive and for businesses to reap its benefits, it needs to be built on principles of trust. Watson is AI that you can understand and feel confident about because it provides the tools to help explain and manage AI-led decisions in your business. At IBM, your data and insights belong to you. That's the confidence you can pass onto your team and your customers.


IBM Watson and the future of Artificial Intelligence

#artificialintelligence

Watson, a supercomputer by IBM, shot to fame in 2011 as the'brain' that beat two of the best contestants of Jeopardy! to win a million dollars. This system that combines artificial intelligence (AI) and sophisticated analytical software to answer questions was widely deployed in many industries. The supercomputer was developed in IBM's DeepQA project and was named after IBM's founder Thomas J. Watson. "You can be discouraged by failure, or you can learn from it. So go ahead and make mistakes, make all you can. Because, remember that's where you'll find success -- on the far side of failure."


SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning

arXiv.org Artificial Intelligence

State-of-the-art approaches to reasoning and question answering over knowledge graphs (KGs) usually scale with the number of edges and can only be applied effectively on small instance-dependent subgraphs. In this paper, we address this issue by showing that multi-hop and more complex logical reasoning can be accomplished separately without losing expressive power. Motivated by this insight, we propose an approach to multi-hop reasoning that scales linearly with the number of relation types in the graph, which is usually significantly smaller than the number of edges or nodes. This produces a set of candidate solutions that can be provably refined to recover the solution to the original problem. Our experiments on knowledge-based question answering show that our approach solves the multi-hop MetaQA dataset, achieves a new state-of-the-art on the more challenging WebQuestionsSP, is orders of magnitude more scalable than competitive approaches, and can achieve compositional generalization out of the training distribution.


IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning

arXiv.org Artificial Intelligence

Current visual question answering (VQA) tasks mainly consider answering human-annotated questions for natural images. However, aside from natural images, abstract diagrams with semantic richness are still understudied in visual understanding and reasoning research. In this work, we introduce a new challenge of Icon Question Answering (IconQA) with the goal of answering a question in an icon image context. We release IconQA, a large-scale dataset that consists of 107,439 questions and three sub-tasks: multi-image-choice, multi-text-choice, and filling-in-the-blank. The IconQA dataset is inspired by real-world diagram word problems that highlight the importance of abstract diagram understanding and comprehensive cognitive reasoning. Thus, IconQA requires not only perception skills like object recognition and text understanding, but also diverse cognitive reasoning skills, such as geometric reasoning, commonsense reasoning, and arithmetic reasoning. To facilitate potential IconQA models to learn semantic representations for icon images, we further release an icon dataset Icon645 which contains 645,687 colored icons on 377 classes. We conduct extensive user studies and blind experiments and reproduce a wide range of advanced VQA methods to benchmark the IconQA task. Also, we develop a strong IconQA baseline Patch-TRM that applies a pyramid cross-modal Transformer with input diagram embeddings pre-trained on the icon dataset. IconQA and Icon645 are available at https://iconqa.github.io.