Advances in artificial intelligence (AI), machine learning and natural language processing are impacting the way we build and interact with clinical information systems and document patient care. Nuance Dragon Medical Advisor, Computer-Assisted Physician Documentation (CAPD) and Computer-Assisted Clinical Documentation Improvement (CACDI) process very complicated cases and extract facts and evidence from those cases to provide physicians with real-time feedback at the point of care. Powered by deep learning and AI, Nuance healthcare solutions amplify human intelligence and help clinicians turn their chairs back to focusing on the patient instead of technology.

"AI could play a big role in supporting prevention, diagnosis, treatment plans, medication management, precision medicine and drug creation" – Bruce Lang Artificial intelligence, the capability of a machine to reflect human behavior in an intelligent manor, has the opportunity to revolutionize healthcare. These algorithms and deep learning processes evaluate and study data, utilizing their findings to apply to problems. When the data is inputed into the algorithm, the information is more understood throughout time and the AI is able to provide exponentially better feedback. Artificial intelligence can, for example, exam a patient medical history and predict the likelihood of that individual having a certain disease. Such uses are for hospital management, drug discovery, lifestyle monitoring, patient risk analysis, diagnostics, and more.

Emerging technologies and their rapid advancements will impact every player--big or small--across geographies, and regardless of the industry a company operates in. According to Convergence Catalyst's June 2017 report on artificial intelligence (AI), it took between 19 and 40 million pictures of cats for an average unsupervised (unaided) deep learning algorithm to recognize a picture of a cat. A follow-up research on the same topic earlier in February this year pegged the number down to less than one million--thanks to the significant advancements in low-data techniques, clustering algorithms and subject vector machines in the AI/ML (machine learning) space. Moreover, computing power used in the largest AI training runs has been doubling every 3.5 months. Since 2012, this metric has grown by more than 300,000x.

Click here to skip the intro and dive right in! When I started the residency program in the summer of 2017, I had a lot of experience programming, and a good understanding of machine learning, but I had never used Tensorflow before. I figured that given my background I'd be able to pick it up quickly. To my surprise, the learning curve was fairly steep, and even months into the residency, I would occasionally find myself confused about how to turn ideas into Tensorflow code. I'm writing this blog post as a message-in-a-bottle to my former self: it's the introduction that I wish I had been given before starting on my journey. Hopefully, it will also be a helpful resource for others. In the three years since its release, Tensorflow has cemented itself as a cornerstone of the deep learning ecosystem.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Since the inception of artificial intelligence in the 1950s, we've been trying to find ways to measure progress in the field of AI. For many, the golden criteria for AI the Turing Test, an evaluation of whether a computer can exhibit human behavior. But the Turing Test only defines whether AI can fool humans, not compete with them, and it's very hard to say how deep the Test goes. A much better arena to test the extent of AI's intelligence, many scientists believe, are games, domains where contestants can measure and compare their success and clearly determine which one performs better.

The digital world is full of insecurities like identity fraud, cloning and many other vulnerabilities causing hurdle in achieving a secure environment. According to a study conducted by Javelin, it is recognised that 16.7 million people face identity fraud in the year 2017 and this figure is 8% above than a year before. The figure of data theft, illegal transactions and many other fraudulent larcenies increased rapidly in the past few years. Hackers with their new moves always try to breach in others system. Artificial Intelligence with its basic approaches of the machine and deep learning creates an accurate and efficient process solution to eliminate obstacles involved in the organisational processes.

TechCrunch has reported that Facebook is acquiring London-based startup, Bloomsbury AI, as part of its efforts to fight against fake news on the world's largest social network. Bloomsbury's product is an NLP engine that helps machines answer questions on information derived from documents. TechCrunch's sources report that Facebook plans to use the firm's team and technology in policing the platform, and combating the scourge of bullshit fake news stories that have proliferated since the 2016 US general election. This is easily the biggest UK AI acquisition this year, and one of the most interesting since Google sucked up the machine learning powerhouse DeepMind in 2014. The deal is believed to be valued between $23 and $30 million, which is a far cry from DeepMind's $500 million asking price.

Have you started working with the Qualcomm Snapdragon Neural Processing Engine (NPE) SDK yet? I posted last time about deep learning at the edge on IoT devices and described how you can use the SDK to execute artificial intelligence (AI) workloads on GPU, DSP and CPU. Since then, we've added to the number and strength of deep neural networks (DNN) we support in the SNPE SDK. To help get the wheels of innovation turning for you, in this post I'll describe ways you can apply AI using those DNNs to various IoT devices and use cases, including context-aware AI. How You Can Use AI in the Internet of Things First, we've found that although many developers want to use AI in IoT, there is no comprehensive resource telling them how they can use it.

"A Neural Algorithm of Artistic Style" is very intuitive to understand and not terribly difficult to get going. Plus you don't need crazy hardware as you work with pre-trained models. "Human Level Control Through Deep Reinforcement Learning" is much more complicated, but very rewarding when you get it right as you can watch a machine learn to play your favorite childhood games. And, you'll get a strong grasp of your framework of choice, good debugging techniques, and how to effectively leverage training time on a back-end.

This paper proposes an approach for rapid bounding box annotation for object detection datasets. The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step proposes annotations for the remaining samples using a model trained with the first stage annotations. We experimentally study which first/second stage split minimizes to total workload. In addition, we introduce a new fully labeled object detection dataset collected from indoor scenes. Compared to other indoor datasets, our collection has more class categories, different backgrounds, lighting conditions, occlusion and high intra-class differences. We train deep learning based object detectors with a number of state-of-the-art models and compare them in terms of speed and accuracy. The fully annotated dataset is released freely available for the research community.