The greatest impact of intelligent technologies won't be from eliminating jobs but from changing what people do and driving innovation deeper into the business. Thoughtful adoption of intelligent technologies will be essential to survival for many companies. But simply implementing the newest technologies and automation tools won't be enough. Success will depend on whether organizations use them to innovate in their operations and in their products and services -- and whether they acquire and develop the human capital to do so. In a recent Deloitte survey of 250 executives familiar with how their companies are thinking about intelligent technologies, nearly three out of four said that they expected AI to substantially transform their organizations within three years.1 Of course, the workforce will be deeply affected by all this change. Yet even as AI eliminates some jobs in the coming decade (it most certainly will), it may create as many positions as it kills and open up vast new opportunities for collaborations between humans and machines.

A new smartphone tool developed for banana farmers scans plants for signs of five major diseases and one common pest. In testing in Colombia, the Democratic Republic of the Congo, India, Benin, China, and Uganda, the tool provided a 90 percent successful detection rate. This work is a step towards creating a satellite-powered, globally connected network to control disease and pest outbreaks, say the researchers who developed the technology. The findings were published this week in the journal Plant Methods. "Farmers around the world struggle to defend their crops from pests and diseases," said Michael Selvaraj, the lead author, who developed the tool with colleagues from Bioversity International in Africa.

Artificial intelligence-powered tools are rapidly becoming more accessible, including for people in the more remote corners of the globe. This is good news for smallholder farmers, who can use handheld technologies to run their farms more efficiently, linking them to markets, extension workers, satellite images, and climate information. The technology is also becoming a first line of defense against crop diseases and pests that can potentially destroy their harvests. A new smartphone tool developed for banana farmers scans plants for signs of five major diseases and one common pest. In testing in Colombia, the Democratic Republic of the Congo, India, Benin, China, and Uganda, the tool provided a 90 percent successful detection rate. This work is a step towards creating a satellite-powered, globally connected network to control disease and pest outbreaks, say the researchers who developed the technology.

Language identification ("LI") is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used in the LI literature. We describe the features and methods using a unified notation, to make the relationships between methods clearer. We discuss evaluation methods, applications of LI, as well as off-the-shelf LI systems that do not require training by the end user. Finally, we identify open issues, survey the work to date on each issue, and propose future directions for research in LI.

On a tower in the Brazilian rain forest, a sentinel scans the horizon for the first signs of fire. They don't blink or take breaks, and guided by artificial intelligence they can tell the difference between a dust cloud, an insect swarm and a plume of smoke that demands quick attention. In Brazil, the devices help keep mining giant Vale SA working, and protect trees for pulp and paper producer Suzano SA. In the future, it's a system that may be put to work in California, where deadly wildfires abound. The equipment includes optical and thermal cameras, as well as spectrometric systems that identify the chemical makeup of substances.

The app can detect Fusarium wilt, Xanthomonas wilt, bunchy top disease, black sigatoka, yellow sigatoka, and corm weevil. Fusarium Tropical race 4 fungus (TR4) has decimated banana plantations and smallholders' crops in Asia and Africa and has now spread to Latin America. Last week, Colombian officials officially confirmed the presence of TR4 in La Guajira province, declaring a state of national emergency as a result. Developed with support from Bioversity International and the International Center for Tropical Agriculture (CIAT), the AI-powered tool is built into an app called Tumaini – Swahili for'hope' – that allows farmers to take action quickly, thus preventing a widespread outbreak. The information is also uploaded to a global system that allows for large-scale monitoring.

--In recent years, the interest in interpretable classification models has grown. One of the proposed ways to improve the interpretability of a rule-based classification model is to use sets (unordered collections) of rules, instead of lists (ordered collections) of rules. One of the problems associated with sets is that multiple rules may cover a single instance, but predict different classes for it, thus requiring a conflict resolution strategy. In this work, we propose two algorithms capable of finding feature-space regions inside which any created rule would be consistent with the already existing rules, preventing inconsistencies from arising. Our algorithms do not generate classification models, but are instead meant to enhance algorithms that do so, such as Learning Classifier Systems. Both algorithms are described and analyzed exclusively from a theoretical perspective, since we have not modified a model-generating algorithm to incorporate our proposed solutions yet. This work presents the novelty of using conflict avoidance strategies instead of conflict resolution strategies.

After his tenure as chief scientist at Baidu, Andrew Ng, the founder of the Google Brain project and former CEO of Coursera, set up a number of different projects that all focus on making AI more approachable. These include the education startup Deeplearning.ai, Today, Ng announced he has opened a second office for these projects in Medellin, Colombia. At first, Medellin may seem like an odd choice. But today's Medellin is very different from the one you may have seen on Narcos (and a lot safer).

The robotic process automation (RPA) services market is booming around the world, according to a recently published Forrester Research report, titled'The RPA Services Market will Grow to Reach $12 billion by 2023'. In 2018, the market was estimated to be worth only $3.9 billion. Furthermore, Forrester's research indicates that over the past three years, annual revenue growth for the top services vendors has topped 100%, rising from around $0.6 billion in 2017 to an estimated $4.2 billion by 2023. After surveying 25 of the top RPA service providers about their customers, geographic focus, revenue and scale of implementations for the report, which was published last month, the authors – Leslie Joseph and Craig Le Clair – concluded that the massive growth in RPA services was a result not only of organisations adopting RPA as a cost mitigation strategy, but because automation was regarded as critical to the implementation of broader digital transformation efforts. However, while RPA adoption in terms of spend appears to be rising around the world, Forrester was unable to provide information about RPA adoption in Africa, or South Africa, as it was unable to source sufficient data.

We propose a Multi-Task Learning (MTL) paradigm based deep neural network architecture, called MTCNet (Multi-Task Crowd Network) for crowd density and count estimation. Crowd count estimation is challenging due to the non-uniform scale variations and the arbitrary perspective of an individual image. The proposed model has two related tasks, with Crowd Density Estimation as the main task and Crowd-Count Group Classification as the auxiliary task. The auxiliary task helps in capturing the relevant scale-related information to improve the performance of the main task. The main task model comprises two blocks: VGG-16 front-end for feature extraction and a dilated Convolutional Neural Network for density map generation. The auxiliary task model shares the same front-end as the main task, followed by a CNN classifier. Our proposed network achieves 5.8% and 14.9% lower Mean Absolute Error (MAE) than the state-of-the-art methods on ShanghaiTech dataset without using any data augmentation. Our model also outperforms with 10.5% lower MAE on UCF_CC_50 dataset.