We derive a new Bayesian Information Criterion (BIC) from first principles by formulating the problem of estimating the number of clusters in an observed data set as maximization of the posterior probability of the candidate models. Given that some mild assumptions are satisfied, we provide a general BIC expression for a broad class of data distributions. This serves as an important milestone when deriving the BIC for specific data distributions. Along this line, we provide a closed-form BIC expression for multivariate Gaussian distributed observations. We show that incorporating data structure of the clustering problem into the derivation of the BIC results in an expression whose penalty term is different from that of the original BIC. We propose a two-step cluster enumeration algorithm. First, a model-based unsupervised learning algorithm partitions the data according to a given set of candidate models. Subsequently, the optimal cluster number is determined as the one associated to the model for which the proposed BIC is maximal. The performance of the proposed criterion is tested using synthetic and real data sets. Despite the fact that the original BIC is a generic criterion which does not include information about the specific model selection problem at hand, it has been widely used in the literature to estimate the number of clusters in an observed data set. We, therefore, consider it as a benchmark comparison. Simulation results show that our proposed criterion outperforms the existing cluster enumeration methods that are based on the original BIC.

Abstract--Recently, there is increasing interest and research on the interpretability of machine learning models, for example how they transform and internally represent EEG signals in Brain-Computer Interface (BCI) applications. This can help to understand the limits of the model and how it may be improved, in addition to possibly provide insight about the data itself. Schirrmeister et al. (2017) have recently reported promising results for EEG decoding with deep convolutional neural networks (ConvNets) trained in an end-to-end manner and, with a causal visualization approach, showed that they learn to use spectral amplitude changes in the input. In this study, we investigate how ConvNets represent spectral features through the sequence of intermediate stages of the network. We show higher sensitivity to EEG phase features at earlier stages and higher sensitivity to EEG amplitude features at later stages. Intriguingly, we observed a specialization of individual stages of the network to the classical EEG frequency bands alpha, beta, and high gamma. Furthermore, we find first evidence that particularly in the last convolutional layer, the network learns to detect more complex oscillatory patterns beyond spectral phase and amplitude, reminiscent of the representation of complex visual features in later layers of ConvNets in computer vision tasks. Our findings thus provide insights into how ConvNets hierarchically represent spectral EEG features in their intermediate layers and suggest that ConvNets can exploit and might help to better understand the compositional structure of EEG time series.

But one telehealth company – Now Healthcare Group – believes it can apply AI to its existing apps to relieve some of that strain.

AI also raises the prospect of affordable healthcare for all. According to the World Health Organization (WHO), 400 million people do not have access to one or more essential health services, and 6% of those in low and middle-income countries are pushed further into extreme poverty because of health spending. In the future, we will see physicians working in partnership with AI – enabling technology to free up their time to concentrate on treatment of the disease as opposed to the diagnosis. Here we look at areas where AI promises to have a real impact on chronic and infectious diseases, from diagnosis and treatment plans to containing the global outbreaks of the likes of SARs and Ebola. Nearly 18 million people die each year from cardiovascular disease, according to WHO.

WebRTC services have already permeated corporate communications in the form of videoconferencing solutions. However, WebRTC has the potential of going beyond and catalyzing a new class of services providing more than calls with capabilities such as mass-scale real-time media broadcasting, enriched and augmented video, person-to-machine and machine-to-machine communications. In his session at @ThingsExpo, Luis Lopez, CEO of Kurento, introduced the technologies required for implementing these ideas and some early experiments performed in the Kurento open source software community in areas such as entertainment, video surveillance, interactive media broadcasting, gaming or advertising. He concluded with a discussion of their potential business applications beyond plain call models. Speaker Bio Dr. Luis Lopez is associate professor at Universidad Rey Juan Carlos in Madrid, where he works in the creation of advanced multimedia communication technologies.

Artificial Intelligence (AI) is starting to change how many businesses operate. The ability to accurately process and deliver data faster than any human could is already transforming how we do everything from studying diseases and understanding road traffic behaviour to managing finances and predicting weather patterns. For business leaders, AI's potential could be fundamental for future growth. With so much on offer and at stake, the question is no longer simply what AI is capable of, but where AI can best be used to deliver immediate business benefits. According to Forrester, 70% of enterprises will be implementing AI in some way over the next year. Additionally our recent Evolution report revealed that 40% of IT decision makers in the UK are planning on increasing IT budget for AI and machine learning projects in the next financial year.

During a 2016 simulation exercise, researchers evaluated the ability of 32 different deep learning algorithms to detect lymph node metastases in patients with breast cancer. Each algorithm's performance was then compared to that of a panel of 11 pathologists with time constraint (WTC). Overall, the team found that seven of the algorithms outperformed the panel of pathologists, publishing an in-depth analysis in JAMA. "To our knowledge, this is the first study that shows that interpretation of pathology images can be performed by deep learning algorithms at an accuracy level that rivals human performance," wrote lead author Babak Ehteshami Bejnordi, MS, Radboud University Medical Center in Nijmegen, the Netherlands, and colleagues. The simulation took place during the Cancer Metastases in Lymph Nodes Challenge 2016 (CAMELYON16) in the Netherlands.

The age of highly accessible, open source machine learning tools is upon us. No longer niche, everyone -- from data scientists to Japanese cucumber farmers -- is using machine-learning technologies. But what is machine learning? Machine learning is exactly what it sounds like -- software that can learn to solve a problem. Using large sets of data, an algorithm can be trained to understand that data.

Professor of Artificial Intelligence Wolfgang Faber comments on Google announcing that its AlphaGo Zero artificial intelligence program has triumphed at chess against world-leading specialist software within hours of teaching itself the game from scratch and considers where humans will start losing their jobs to intelligent computers and machines. "'Google's'superhuman' DeepMind AI claims chess crown' has been a headline on the BBC recently. What does it mean, and are our jobs, or even our lives in danger? First, let us have a look at what caused this headline: A few days ago, a manuscript by a group around David Silver, Thomas Hubert, and Julian Schrittwieser of London-based, Google (or rather Alphabet)-owned DeepMind was uploaded to arXiv, in which the system AlphaZero is described and very impressive results in learning how to play three traditional board games (chess, shogi, Go) well are reported. The setup allowed for learning very successful (superhuman) strategies in a few ...