Owkin, a medical research machine learning startup, has announced a new investor in the form of GV (formerly Google Ventures), which has now joined as a late entrant to Owkin's series A round. Founded in 2016, Owkin's platform leverages deep learning algorithms to help clinical researchers across academia, medicine, and the pharmaceutical industries develop predictive models and expedite drug development throughout the whole process. The platform, dubbed Socrates, integrates genomics, clinical data, and biomedical images to identify characteristics, or "biomarkers," associated with diseases. "There is a constant race happening between the data at hand and the knowledge we gain from it," noted Owkin CEO and cofounder Thomas Clozel. "At Owkin, our goal is to augment doctors' and researchers' capabilities in transforming data into knowledge and prediction, and achieve breakthrough medical moments such as the discovery of a new biomarker or target that could transform how patients are treated."

It's a Saturday morning in February, and Chloe, a curious 3-year-old in a striped shirt and leggings, is exploring the possibilities of a new toy. Her father, Gary Marcus, a developmental cognitive scientist at New York University (NYU) in New York City, has brought home some strips of tape designed to adhere Lego bricks to surfaces. Chloe, well-versed in Lego, is intrigued. But she has always built upward. Could she use the tape to build sideways or upside down? Marcus suggests building out from the side of a table. Ten minutes later, Chloe starts sticking the tape to the wall.

There is another way to categorize the machine learning models above, which is supervised learning (where machine learns from past data that humans have already labeled as good or bad, attack or false positive, fraud or normal data), unsupervised learning (where no past labeled data exist) or reinforcement learning (where machine learns from feedback from its longer-term results). Supervised learning will include classification, regression and deep learning. Unsupervised learning includes clustering, association rules and pattern matching. Diagram 2 will now become diagram 3 below.

Three-dimensional (3D) reconstruction from multiple images obtained from different viewpoints has been actively examined. However, it was difficult to reconstruct the structure of objects which have hidden portions, such as plants with branch structures hidden under their leaves. By combining the original image-to-image translation approach in a Bayesian deep learning framework and 3D reconstruction, a group of researchers led by Fumio Okura estimated the existence probability of branches that are hidden under leaves in images obtained. Using these estimated branch positions, they achieved 3D reconstruction of plant structure, i.e., accurate reconstruction of branch structures, including those hidden under leaves. Specifically, they converted images of leafy plants to images showing branch existence probability, thereby achieving 3D reconstruction. The results of this study will be presented at the EEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) to be held from June 18 through June 22, 2018.

Over the past decade, advances in deep learning have transformed the fortunes of the artificial intelligence (AI) community. The neural network approach that researchers had largely written off by the end of the 1990s now seems likely to become the most widespread technology in machine learning. However, protagonists find it difficult to explain why deep learning often works well, but is prone to seemingly bizarre failures. The success of deep learning came with rapid improvements in computational power that came through the development of highly parallelized microprocessors and the discovery of ways to train networks with enormous numbers of virtual neurons assembled into tens of linked layers. Before these advances, neural networks were limited to simple structures that were easily outclassed in image and audio classification tasks by other machine-learning architectures such as support vector machines.

New Delhi: Artificial intelligence (AI) and machine learning (ML) are witnessing increasing adoption in the Indian healthcare setting. With a surge in non-communicable diseases and the increasing number of aging population in the country, the overall burden of disease management has been increasing year-on-year and to manage that, the government, the healthcare professionals and the healthcare institutions are looking for innovative ways. Studies have shown that deep learning algorithms have given better insights to clinicians in predicting prognosis and future events in patients. Also, advanced digital technologies like AI and ML can help in prevention as well as early detection of diseases by capturing and analysing various vitals of patients. Artificial intelligence makes it possible to access the learning and data from hundreds of thousands of patient cases.

Designing a building, developing a constructible model from a design or working out how to go about constructing a complicated model are all tasks that already contain some degree of automation. So when researchers and others in the architectural, engineering and construction world start talking about bringing artificial intelligence into the mix, many say it's already here. But recent advances in generative design, safety analysis and 5D scheduling are only the first hints of what sophisticated algorithms and deep-learning AI can bring to construction. Getting smart algorithms and other AI-derived technologies onto the project team may not be as far-fetched an idea as it once was. But rather than having a computer that takes over the existing job duties of an architect or engineer, those professions may soon have some form of AI-based assistant offering options and providing clarifications all along the way.

Machine learning (ML) has recently undergone huge progress in research and development. Importantly, the emergence of deep learning (DL) and the computing power enhancement of accelerators like GPUs have together enabled a tremendous adoption of machine learning applications. This has caused machine learning to have a broader and deeper impact on our lives in many areas like health science, finance, security, data center monitoring and intelligent systems. Hence, machine learning and deep learning workloads are also growing in the datacenters and cloud environments. To support customers with deploying ML / DL workloads on VMware vSphere, we conducted a series of performance studies on ML-based workloads using GPUs.

Convolutional Neural Networks (CNNs) are the current state-of-art architecture for image classification task. Whether it is facial recognition, self driving cars or object detection, CNNs are being used everywhere. In this post, a simple 2-D Convolutional Neural Network (CNN) model is designed using keras with tensorflow backend for the well known MNIST digit recognition task. The data set used here is MNIST dataset as mentioned above. The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits (0 to 9).

A USC-led study of violent protest has found that moral rhetoric on Twitter may signal whether a protest will turn violent. The researchers also found that people are more likely to endorse violence when they moralize the issue that they are protesting--and when they believe that others in their social network moralize that issue, too. "Extreme movements can emerge through social networks," said the study's corresponding author, Morteza Dehghani, a researcher at the Brain and Creativity Institute at USC. "We have seen several examples in recent years, such as the protests in Baltimore and Charlottesville, where people's perceptions are influenced by the activity in their social networks. People identify others who share their beliefs and interpret this as consensus. In these studies, we show that this can have potentially dangerous consequences."