GNW The global artificial intelligence (AI) in retail market attained $720.0 million in 2018 and is predicted to witness a CAGR of 35.4% during 2019–2024 (forecast period). The factors contributing to the growth of the market include the increasing investments in AI by retail companies and expanding e-retail industry. With AI, retailers have been able to automate their work processes, study consumer behavior, and capture relevant data through the adoption of numerous advanced technologies, such as machine learning, natural language processing (NLP), and computer vision. When technology is considered, the AI in retail market is divided into computer vision, NLP, machine learning, and others (which include gesture recognition and analytics). Machine learning generated the highest revenue during the historical period (2014–2018) and is expected to dominate the market during the forecast period as well.

I cover AI and future trends for a living. One overreaching theme I've noticed is how AI is being used not to augment people, but to weaponize their data against them. Businesses across almost every industry deploy artificial intelligence to make jobs simpler for staff and tasks easier for consumers. But that doesn't tell the whole story. Indeed unbridled adoption of AI won't just result in job losses, but data purgatory, and an internet that feels more like slavery than education and freedom.

I cover AI and future trends for a living. One overreaching theme I've noticed is how AI is being used not to augment people, but to weaponize their data against them. This is going to be a long read, and it's because I feel a bit passionate about this topic. The debate over free-speech this week related to the Hong Kong protests signals no trade war resolution will take place since a cold tech war also is about information wars and basic freedom of speech. As companies with billion-dollar market caps like Microsoft, Apple and Google cave into creating censorship products, we all lose and begin to enter a potential data-based Neo-Fascism era of control and surveillance.

GNW The global artificial intelligence (AI) in retail market attained $720.0 million in 2018 and is predicted to witness a CAGR of 35.4% during 2019–2024 (forecast period). The factors contributing to the growth of the market include the increasing investments in AI by retail companies and expanding e-retail industry. With AI, retailers have been able to automate their work processes, study consumer behavior, and capture relevant data through the adoption of numerous advanced technologies, such as machine learning, natural language processing (NLP), and computer vision. When technology is considered, the AI in retail market is divided into computer vision, NLP, machine learning, and others (which include gesture recognition and analytics). Machine learning generated the highest revenue during the historical period (2014–2018) and is expected to dominate the market during the forecast period as well.

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 inspiring #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!

I've been asked many times recently to comment on how the rise of AI will impact jobs and the economy, particularly in customer service and contact centers. I've seen wildly differing forecasts, from the dire predictions of Elon Musk to the optimistic predictions of Accenture. According to Forrester's'The Future of Jobs' report, robots will take 24.7 million jobs by 2027, but create 14.9 million new jobs in the same period. There is no doubt that AI will impact jobs globally more than any other technology in our lifetime. The key question is - what should we do about it?

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.