Picking up steam in 1997 with Intel opening of a microchip factory and an $800 million USD investment, Costa Rica has since blossomed into a key tech hub in Latin America, according to an article from Nearshore Americas. But leaving the landmark Intel investment aside (the factory is now closed), the country is fostering growth through government spending in the space, high public funds devoted to education and tax-friendly technology parks that attract investors and talent from around the globe. With a population of five million inhabitants and 51,000 square kilometers, the number of companies in the country has reached over 546 IT companies, 3,447 manufacturing (including medical components), and performed 12,281 various commercial activities by 2018. This activity generated over 300,000 jobs, according to the National Institute of Statistics and Census. One of the major fields in the current technological revolution is artificial intelligence (AI), of which a high amount of development is occurring in Costa Rica.

From Mexican immigrant to MIT, from Girl Power in Latin America to robotics entrepreneurs in Africa and India, the 2019 annual "women in robotics you need to know about" list is here! We've featured 150 women so far, from 2013 to 2018, and this time we're not stopping at 25. We're featuring 30 badass #womeninrobotics because robotics is growing and there are many new stories to be told. So, without further ado, here are the 30 Women In Robotics you need to know about – 2019 edition! There are 150 more stories on our 2013 to 2018 lists. Why not nominate someone for inclusion next year!

We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research. We show that this new approach requires less training data and is more generalizable as it shows greater resilience to random attacks. We investigate the shape of the discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not necessary to achieve results similar to those obtained using differentiable programming approaches such as deep learning. In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that (i) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; (ii) that parameter solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; (iii) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; (iv) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges.

We design a classifier for transactional datasets with application in malware detection. We build the classifier based on the minimum description length (MDL) principle. This involves selecting a model that best compresses the training dataset for each class considering the MDL criterion. To select a model for a dataset, we first use clustering followed by closed frequent pattern mining to extract a subset of closed frequent patterns (CFPs). We show that this method acts as a pattern summarization method to avoid pattern explosion; this is done by giving priority to longer CFPs, and without requiring to extract all CFPs. We then use the MDL criterion to further summarize extracted patterns, and construct a code table of patterns. This code table is considered as the selected model for the compression of the dataset. We evaluate our classifier for the problem of static malware detection in portable executable (PE) files. We consider API calls of PE files as their distinguishing features. The presence-absence of API calls forms a transactional dataset. Using our proposed method, we construct two code tables, one for the benign training dataset, and one for the malware training dataset. Our dataset consists of 19696 benign, and 19696 malware samples, each a binary sequence of size 22761. We compare our classifier with deep neural networks providing us with the state-of-the-art performance. The comparison shows that our classifier performs very close to deep neural networks. We also discuss that our classifier is an interpretable classifier. This provides the motivation to use this type of classifiers where some degree of explanation is required as to why a sample is classified under one class rather than the other class.

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.