In our research at the University of Amsterdam we have married two types of models into a single comprehensive framework which we have called "Variational Auto Encoders". The two types of models are: 1) generative models where the data generation process is modelled, and 2) discriminative models, such as deep learning, where measurements are directly mapped to class labels. Deep learning is particularly successful in learning powerful (e.g., predictive/ discriminative) features from raw, unstructured sensor data. Deep neural networks can effectively turn raw data streams into new representations that represent abstract, disentangled and semantically meaningful concepts. Based on these, a simple linear classifier can achieve the state of the art.

Over the last decades, we have witnessed the importance of medical imaging, e.g., computed tomography (CT), magnetic resonance (MR), positron emission tomography (PET), mammography, ultrasound, X-ray, and so on, for the early detection, diagnosis, and treatment of diseases (1). In the clinic, the medical image interpretation has mostly been performed by human experts such as radiologists and physicians. However, due to large variations in pathology and potential fatigue of human experts, researchers and doctors have recently begun to benefit from computer-assisted interventions. While, compared to the advances in medical imaging technologies, it is belated for the advances in computational medical image analysis, it has recently been improving with the help of machine learning techniques. In the stream of applying machine learning for data analysis, meaningful feature extraction or feature representation lies at the heart of its success to accomplish target tasks.

A pathologist's report after reviewing a patient's biological tissue samples is often the gold standard in the diagnosis of many diseases. For cancer in particular, a pathologist's diagnosis has a profound impact on a patient's therapy. The reviewing of pathology slides is a very complex task, requiring years of training to gain the expertise and experience to do well. Even with this extensive training, there can be substantial variability in the diagnoses given by different pathologists for the same patient, which can lead to misdiagnoses. For example, agreement in diagnosis for some forms of breast cancer can be as low as 48 per cent, and similarly low for prostate cancer.

Early I brought up Howard Gardner's theory of multiple intelligences. That is, humans exhibit strengths in different kinds of intelligences. Specifically these are interpersonal, intrapersonal, verbal, logical, spatial, rhythmic, naturalistic and kinaesthetic intelligence. Clearly there are many kinds of ways of thinking, each with their own strengths. Therefore, one may ask if we can use this notion of multiple intelligences to explore the different ways that AGI research may evolve.

At HyperScience we bring AI to the office. Our products help enterprises and government institutions function by automating certain kinds of office work and reducing bureaucratic burden both on businesses and their customers. We take a heterogeneous approach to AI, using a blend of what are traditionally considered different fields of ML: deep learning, computer vision, and NLP among others. We believe that AI is destined to be the biggest event in the history of human labor since the Industrial Revolution, and we want to be a part of it. ML is at the core of what we do. We productize ML lab experiments into enterprise-ready AI solutions - and we're looking for continuous learners to lead these efforts. This is an opportunity to both research cutting edge ML techniques and to implement them at a fast-growth AI startup.

Recently, analyst Trip Chowdhry of Global Equities Research wrote in an investor note that Wal-Mart Stores (NYSE:WMT) will ramp up its focus on deep neural networks for its OneOps cloud business and that the retailer will tap NVIDIA's (NASDAQ:NVDA) graphics processing units (GPUs) to make this happen. Deep neural networks are used in artificial intelligence processing to allow computers to understand the relationships between pieces of information without having to be specifically programmed to understand that the information is related. Deep neural networks, and the broader deep learning segment, are part of a growing artificial intelligence market. Chowdhry thinks the ramp-up of Wal-Mart's cloud will happen over the next six months and will be "incrementally positive" to NVIDIA's GPU business. These rumors come after reports surfaced in June that Walmart was asking some of its technology customers to move off of Amazon's Web Service (AWS) cloud business.

The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attentions. Recent progress in the design and applications of deep learning methods has shown promising results and is forcing massive changes in healthcare academia and industry, but most of these methods rely on massive labeled data. In this work, we propose a general deep learning framework which is able to boost risk prediction performance with limited EHR data. Our model takes a modified generative adversarial network namely ehrGAN, which can provide plausible labeled EHR data by mimicking real patient records, to augment the training dataset in a semi-supervised learning manner. We use this generative model together with a convolutional neural network (CNN) based prediction model to improve the onset prediction performance. Experiments on two real healthcare datasets demonstrate that our proposed framework produces realistic data samples and achieves significant improvements on classification tasks with the generated data over several stat-of-the-art baselines.

Despite recent advances, the remaining bottlenecks in deep generative models are necessity of extensive training and difficulties with generalization from small number of training examples. We develop a new generative model called Generative Matching Network which is inspired by the recently proposed matching networks for one-shot learning in discriminative tasks. By conditioning on the additional input dataset, our model can instantly learn new concepts that were not available in the training data but conform to a similar generative process. The proposed framework does not explicitly restrict diversity of the conditioning data and also does not require an extensive inference procedure for training or adaptation. Our experiments on the Omniglot dataset demonstrate that Generative Matching Networks significantly improve predictive performance on the fly as more additional data is available and outperform existing state of the art conditional generative models.

Maybe you were wondering how you can place funny objects on faces in real-time video chats or detect emotions? I'll show you one possible approach here utilizing deep learning as well as skim over one older approach. A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. This task can be now "magically" solved by deep learning and any talented teenager can do it in a few hours. I will show you such an approach in this post.

We asked Sebastian Raschka, applied machine learning and deep learning researcher and the author of Packt's best-selling book Python Machine …