The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated machine learning (autoML) has been gaining attention. Here, we apply an autoML library called TPOT which uses a tree-based representation of machine learning pipelines and conducts a genetic-programming based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging datasets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean accuracy error (MAE): $4.612 \pm .124$ years) and a Relevance Vector Regression (MAE $5.474 \pm .140$ years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications.

Chris Moody knows a thing or two about the universe. As an astrophysicist, he built galaxy simulations, using supercomputers to model the way the universe expands and how galaxies crash into one another. One night, not long after he'd finished his PhD at UC Santa Cruz, he met up with a few other astrophysicists for beers. But that night, no one was talking about galaxies. Instead, they were talking about fashion.

The pace and scale of medical and scientific innovation is transforming the biopharma industry. The need for better patient engagement and experience is spurring new business models. Data generated, captured, analysed and used in real time by innovative medical devices is biopharma's new currency. A key differentiator for companies is the extent to which they are able to generate insights and evidence from multiple data sources. Consequently, digital transformation is a strategic imperative. This report outlines how artificial intelligence-enabled technologies will impact the biopharma value chain and accelerate biopharma's digital transformation. Although there is a high level of innovation in the industry, biopharma companies are facing a complex and challenging environment due to increased competition and R&D cycle times, shorter time in market, expiring patents, declining peak sales, pressure around reimbursement and mounting regulatory scrutiny. As we have shown in our series of reports on'Measuring the return from pharmaceutical innovation', these factors are contributing to an alarming decline in the projected return on investment that large biopharma companies might expect to achieve from their late-stage pipelines, threatening their long-term futures.1 Digital transformation could provide a lifeline to biopharma research and development (R&D) and help reverse this trend. Digital transformation will also impact beyond R&D, as companies look to improve their operational performance, productivity, efficiency and cost-effectiveness across the entire biopharma value chain (see figure 1). Digital transformation will also impact business models, the development of new products and services, and how companies engage with health care professionals, patients and other customers. Ultimately, digital transformation is the next step in the evolution of biopharma companies.

The social internet has a bot problem. Fake accounts plague Twitter and Facebook, and content designed to misinform readers has become an issue that's drawn the attention of Congress. This difficult and growing problem hasn't stopped a team of researchers from creating an algorithm that can parse news stories, then bicker with real humans in the comments section. Engineers at Beihang University and Microsoft China developed a bot that reads and comments on online news articles. They call their model "DeepCom," short for "deep commenter."

The use of "nudges" in policymaking has been a major trend since the UK launched the world's first government-embedded behavioural insights unit in 2010. But governments around the world, from Denmark to Singapore, have been using principles from behavioural science to influence citizens since at least the 1960s. That's according to a new World Bank report, Behavioural Science Around the World, which highlights 10 countries that are pioneering the use of behavioural insights: Australia, Canada, Denmark, France, Germany, the Netherlands, Peru, Singapore, the UK and the US. The World Bank report looks at how these teams are integrated into government, which projects they're working on and how they are run -- and, most importantly, which experiments have worked. It predicts that in the future, behavioural insights units will benefit from artificial intelligence, machine learning and virtual reality the same way they've gained from advancements in open data and e-government.

There is a huge whitespace waiting to be filled by the tech companies that recognize the power and potential of messaging. Roughly 63% of people prefer to share information on "dark social," or closed, private messaging environments like Facebook Messenger and WhatsApp. However, the experience on these platforms remains painfully circuitous. In order to share a single piece of content within a conversation, users typically have to leave their active chat, open a new window to locate and copy the file, then re-enter the original chat to paste and share. So there is a big opportunity in providing more intelligent ways to share content on messaging – whether that content is a funny animation, a dinner reservation, or the directions for getting somewhere.

Cell detection and cell type classification from biomedical images play an important role for high-throughput imaging and various clinical application. While classification of single cell sample can be performed with standard computer vision and machine learning methods, analysis of multi-label samples (region containing congregating cells) is more challenging, as separation of individual cells can be difficult (e.g. touching cells) or even impossible (e.g. overlapping cells). As multi-instance images are common in analyzing Red Blood Cell (RBC) for Sickle Cell Disease (SCD) diagnosis, we develop and implement a multi-instance cell detection and classification framework to address this challenge. The framework firstly trains a region proposal model based on Region-based Convolutional Network (RCNN) to obtain bounding-boxes of regions potentially containing single or multiple cells from input microscopic images, which are extracted as image patches. High-level image features are then calculated from image patches through a pre-trained Convolutional Neural Network (CNN) with ResNet-50 structure. Using these image features inputs, six networks are then trained to make multi-label prediction of whether a given patch contains cells belonging to a specific cell type. As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples. Finally, for the purpose of SCD testing, we train another machine learning classifier to predict whether the given image patch contains abnormal cell type based on outputs from the six networks. Testing result of the proposed framework shows that it can achieve good performance in automatic cell detection and classification.

This paper proposes an improved design of the perceptron unit to mitigate the vanishing gradient problem. This nuisance appears when training deep multilayer perceptron networks with bounded activation functions. The new neuron design, named auto-rotating perceptron (ARP), has a mechanism to ensure that the node always operates in the dynamic region of the activation function, by avoiding saturation of the perceptron. The proposed method does not change the inference structure learned at each neuron. We test the effect of using ARP units in some network architectures which use the sigmoid activation function. The results support our hypothesis that neural networks with ARP units can achieve better learning performance than equivalent models with classic perceptrons.

The Cloud Machine Learning Market report provides an unbiased and detailed analysis of the on-going trends, opportunities/ high growth areas, market drivers, which would help stakeholders to device and align Cloud Machine Learning market strategies according to the current and future market The Cloud Machine Learning Market report covers the Global market and regional market analysis. The Cloud Machine Learning industry report examines, keep records and presents the worldwide market size of the important players in each region around the globe. Also, the report offers information of the leading market players in the Cloud Machine Learning market. Look insights of Global Cloud Machine Learning industry market research report at https://www.pioneerreports.com/report/519414 The overviews, SWOT analysis and strategies of each vendor in the Cloud Machine Learning market provide understanding about the market forces and how those can be exploited to create future opportunities.

Just like a knife can be used to slice a fruit as well as to commit a murder, artificial intelligence can be used for improving healthcare, but also for discrimination based on facial features and complexion; 3D printing can make organs as well as guns. Technologies are creating new jobs but also leading to displacing workers. Companies complain of difficulty in finding people with requisite skills, even as millions of graduates (and, even more others) remain jobless. Narendra Jadhav, prolific author and Rajya Sabha MP, explores these conundrums in New Age Technology and Industrial Revolution 4.0 and argues for development of a rubric of conducive public policies alongside development and deployment of technology. The book starts with an overview of technologies like AI, augmented reality (AR), additive manufacturing (aka 3D printing) and blockchain. Jadhav puts these within the realm of education, healthcare, digital payments, national security and jobs to discern policy aspects pertaining to economic growth, social inequalities and yes, financial services and banking.