Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Unfortunately, when there are several source domains available, it is difficult to select the right source domains for transfer. The right source domain means that it has the most similar properties with the target domain, thus their similarity is higher, which can facilitate transfer learning. Choosing the right source domain helps the algorithm perform well and prevents the negative transfer. In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR). USSAR is able to select the most similar $K$ source domains from a list of available domains. After this, we propose an effective Transfer Neural Network to perform knowledge transfer for Activity Recognition (TNNAR). TNNAR could capture both the time and spatial relationship between activities while transferring knowledge. Experiments on three public activity recognition datasets demonstrate that: 1) The USSAR algorithm is effective in selecting the best source domains. 2) The TNNAR method can reach high accuracy when performing activity knowledge transfer.

Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine translation. Most of existing RNN models have been designed for sequences assumed to be identically and independently distributed (i.i.d). However, in many real-world applications, sequences are naturally linked. For example, web documents are connected by hyperlinks; and genes interact with each other. On the one hand, linked sequences are inherently not i.i.d., which poses tremendous challenges to existing RNN models. On the other hand, linked sequences offer link information in addition to the sequential information, which enables unprecedented opportunities to build advanced RNN models. In this paper, we study the problem of RNN for linked sequences. In particular, we introduce a principled approach to capture link information and propose a linked Recurrent Neural Network (LinkedRNN), which models sequential and link information coherently. We conduct experiments on real-world datasets from multiple domains and the experimental results validate the effectiveness of the proposed framework.

Natural Language Generation is an AI sub-discipline that converts data into text, enabling computers to communicate ideas with perfect accuracy. It is used in customer service to generate reports and market summaries and is offered by companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, and Yseop. Siri is just one of the systems that can understand you. Every day, more and more systems are created that can transcribe human language, reaching hundreds of thousands through voice-response interactive systems and mobile apps. Companies offering speech recognition services include NICE, Nuance Communications, OpenText and Verint Systems.

Deep learning might be an overhyped term in Artificial Intelligence (AI) among the modern technologies today, but there is a good possibility that it will drive the business processes of tomorrow. For newbies entering the workforce--or strategizing to position their careers in the long term--this will be an appropriate time to understand its implications effectively. The term "deep learning" involves the application of artificial neural networks to carry out advanced pattern recognition. Deep learning algorithms are trained on large amounts of data. Once trained, these algorithms are applied on to fresh data to draw insights.

WIRE)--New technology using deep learning and advanced algorithms to evaluate blood flow to the heart is now being used in English hospitals to fight against coronary heart disease. Coronary heart disease (CHD) is one of the leading causes of death in the UK. It is responsible for more than 66,000 deaths each year and it is estimated that 2.3 million people in the UK are currently living with the diseasei. CHD develops when the arteries leading to the heart narrow or become blocked, which can reduce blood flow, and cause chest pain and heart attacksii. The HeartFlow FFRct Analysis is being supported by NHS England as part of the Innovation and Technology Payment (ITP) programme to help physicians better diagnose coronary heart disease.

Intel has acquired a small artificial intelligence startup called Vertex.Ai that will give the company a boost in developing deep learning software tools. News of the deal, first reported by TechCrunch on Thursday, comes after the Santa Clara, Calif.-based company recently disclosed that Xeon processor sales for AI applications reached $1 billion last year. The acquisition price of the Seattle-based startup was not disclosed. The semiconductor giant has made several moves in the past few years in attempt to become a big player in AI applications. This includes acquisitions of structured ASIC maker eASIC, reprogrammable chip maker Altera, autonomous vehicle software maker Mobileye, computer vision provider Movidius, and AI chip maker Nervana Systems.

Over the past decade, machine learning techniques have made substantial advances in many domains. In health care, global interest in the potential of machine learning has increased; for example, a deep learning algorithm has shown high accuracy in detecting diabetic retinopathy.1 There have been suggestions that machine learning will drive changes in health care within a few years, specifically in medical disciplines that require more accurate prognostic models (eg, oncology) and those based on pattern recognition (eg, radiology and pathology).

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Tech industry insiders regularly herald AI as the solution to all of our problems, included those posed by health care. London-based DeepMind, owned by Google's parent company, Alphabet, focuses heavily on the specifics of using artificial intelligence in health care, and on Monday it released a study showing the progress it's made in using AI to diagnose eye conditions. Published in the science journal Nature Medicine, the study reports that DeepMind, in partnership with Moorfields Eye Hospital in London, has trained its algorithms to detect over 50 sight-threatening conditions to the same accuracy as expert clinicians. In a project that began two years ago, DeepMind trained its machine learning algorithms using thousands of historic and fully anonymized eye scans to identify diseases that could lead to sight loss. According to the study, the system can now do so with 94 percent accuracy, and the hope is that it could eventually be used to transform how eye exams are conducted around the world.

This is an eclectic collection of interesting blog posts, software announcements and data applications I've noted over the past month or so. ONNX Model Zoo is now available, providing a library of pre-trained state-of-the-art models in deep learning in the ONNX format. In the 2018 IEEE Spectrum Top Programming Language rankings, Python takes the top spot and R ranks #7. Julia 1.0 has been released, marking the stabilization of the scientific computing language and promising forwards compatibility. Google announces Cloud AutoML, a beta service to train vision, text categorization, or language translation models from provided data.