It is only natural that businesses look within their own territory or market to learn about new technology. But chatbots are a global phenomenon, adopted far faster outside the west that most others, so taking a look at their growth, use and adoption globally provides some valuable insights. Both countries have populations of over 1.3 billion people, or over a third of the world's population. That makes them the global focus for customer service automation to battle the sheer volume of calls that even a small public business will receive. Banks, airlines and hotels are adopting chatbots at high speed to cope with the demand, with the likes of WeChat dominating platform usage with its 700 million users across Asia.

Recent innovations in agile aerospace have created unique offerings in high cadence satellite imagery. While this is of immense interest to imagery analysts, a significant portion of GIS professionals and geo-data scientists work less with raster data (AKA imagery) and more with point and vector data. Planet operates the world's largest constellation of earth observation satellites providing near-daily coverage of the entirety of Earth's landmass. Over the past couple of years, we have been working on bringing computer vision and spatiotemporal analysis to market to enable access and data transformations on this rich imagery archive. We recently announced the general availability of our analytic feeds and the launch of our building change detection analytics in private beta (sign up for info here).

The term'Artificial Intelligence' (AI) is currently trending as a revolutionary new technology that will change the face of business forever. The reality though is that AI is actually not new, and the phrase is being misused to cover a broad spectrum of different concepts. One of those is deep machine learning, which actually is new and fairly revolutionary. It's already being applied by organisations like Amazon and Google to perform complex analytics that previously was impossible. The trouble is, for the majority of businesses, it is just too expensive and too complicated to be of much use.

As the world enters the age of the fourth industrial revolution, marked by accelerating innovation and the adoption of automation, the future of work is a fundamental question for the Middle East. While some jobs will be lost and others will be created, nearly all jobs will be transformed. The new reality is one in which 45 per cent of jobs will be automatable by 2030. The automation potential will vary across sectors: jobs requiring repetitive routine work such as manufacturing, warehousing and transportation will see more than 50 per cent of its work done by smart devices. Jobs that require emotional intelligence and creativity such as the arts, health care and entertainment will only see a 29 to 37 per cent automation rate.

Pocket-size ultrasound devices that cost 50 times less than the machines in hospitals (and connect to your phone). These are just some of the innovations now transforming medicine at a remarkable pace. No one can predict the future, but it can at least be glimpsed in the dozen inventions and concepts below. Like the people behind them, they stand at the vanguard of health care. Neither exhaustive nor exclusive, the list is, rather, representative of the recasting of public health and medical science likely to come in the 2020s.

Tuberculosis (TB) can now be identified in less than an hour thanks to a new blood test. The test procedure -- developed by The University of Queensland's Emeritus Professor Ian Riley in collaboration with researchers in Tanzania, India, Mexico and the Philippines -- is hoped to positively impact TB diagnosis in adults living in remote areas. "TB has been difficult to control because its symptoms are similar to many other diseases," Prof Riley said. "Other challenges include drug resistance to the disease and the high burden of HIV-positive cases in developing countries." Prof Riley explained that the discovery of the testing procedure came from using machine learning techniques to study three groups of adults who had a persistent cough for more than three weeks.

A proper understanding of the striking generalization abilities of deep neural networks presents an enduring puzzle. Recently, there has been a growing body of numerically-grounded theoretical work that has contributed important insights to the theory of learning in deep neural nets. There has also been a recent interest in extending these analyses to understanding how multitask learning can further improve the generalization capacity of deep neural nets. These studies deal almost exclusively with regression tasks which are amenable to existing analytical techniques. We develop an analytic theory of the nonlinear dynamics of generalization of deep neural networks trained to solve classification tasks using softmax outputs and cross-entropy loss, addressing both single task and multitask settings. We do so by adapting techniques from the statistical physics of disordered systems, accounting for both finite size datasets and correlated outputs induced by the training dynamics. We discuss the validity of our theoretical results in comparison to a comprehensive suite of numerical experiments. Our analysis provides theoretical support for the intuition that the performance of multitask learning is determined by the noisiness of the tasks and how well their input features align with each other. Highly related, clean tasks benefit each other, whereas unrelated, clean tasks can be detrimental to individual task performance.

-- Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. M achine learning prediction as an alternative method has shown promising results. This paper present s a method based on a mul tilayer fuzzy expert system for the detection of breast cancer using an e xtreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM - RBF, considering the Wisconsin dataset . The performance of the propose d model is further compared with a l inear - SVM model. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false - negative rate (FNR). The ELM - RBF model for these criteria presents better performance compared to the SVM model . Breast cancer is among the most common disease of young women over the world [1 - 3]. Approximately 29.9% of mortality from can cer in women is due to breast cancer. The incidence of this disease is lower in developing countries than in developed countries, about 10% of women with breast cancer in Western countries.

Recent advances in computer vision and deep learning have led to breakthroughs in the development of automated skin image analysis. In particular, skin cancer classification models have achieved performance higher than trained expert dermatologists. However, no attempt has been made to evaluate the consistency in performance of machine learning models across populations with varying skin tones. In this paper, we present an approach to estimate skin tone in benchmark skin disease datasets, and investigate whether model performance is dependent on this measure. Specifically, we use individual typology angle (ITA) to approximate skin tone in dermatology datasets. We look at the distribution of ITA values to better understand skin color representation in two benchmark datasets: 1) the ISIC 2018 Challenge dataset, a collection of dermoscopic images of skin lesions for the detection of skin cancer, and 2) the SD-198 dataset, a collection of clinical images capturing a wide variety of skin diseases. To estimate ITA, we first develop segmentation models to isolate non-diseased areas of skin. We find that the majority of the data in the the two datasets have ITA values between 34.5{\deg} and 48{\deg}, which are associated with lighter skin, and is consistent with under-representation of darker skinned populations in these datasets. We also find no measurable correlation between performance of machine learning model and ITA values, though more comprehensive data is needed for further validation.

Artificial Intelligence (AI) will impact every single industry in the next ten years, according to Peter DeBenedictus, CMO - Middle East and Africa, Microsoft. DeBenedictus was part of a panel discussion at the Arabian Business forum Success 2020 entitled: 'Embracing new technological innovations to deliver success'. He said: "There's not one single job or industry that will not be impacted by AI." He revealed that the jobs under threat include lawyers and radiologists as well as the more typical example of call centre operators. "Any job or task that is routine, repeatable and learnable by a machine, will disappear in the next five to ten years. "Think about accounting and audit.