In the space of a single year, South African agritech enterprise Aerobotics has won numerous awards and made strategic inroads into the massively competitive US agriculture industry. Propelled by world-leading technology, the South African success story is poised to mushroom into a truly global data and analytics software company serving the entire agriculture value chain. Aerobotics, which as little as a year ago was nominated as one of South Africa's most exciting startups, turns imagery into actionable data so that any issues on the farm, or elsewhere in the value chain, can be identified and resolved before they become problems. In essence, Aerobotics exposes what the naked eye cannot see in order to solve problems and make accurate projections, translating into improved yields and profitability. The company's CEO, James Paterson, says the business is ready to build on its highly successful launch in the US and strategically drop further roots and extend services in numerous regions around the world.

Artificial Intelligence (AI) in healthcare is expected to revolutionise the sector. Expectations are high and since 2015 the number of medical algorithms approved by the FDA has grown exponentially. We can see it in this interesting infographic designed by Dr. Bertalan Mesko (@Berci), known as the Medical Futurist, which shows that in 2014 only AliveCor's algorithm for the detection of atrial fibrillation was approved, and then in recent years dozens of algorithms have burst onto the scene with the go-ahead of the FDA, among them, products from Apple and Verily. However, the hype in which AI has been involved has left us some disappointments. But in recent years, a great number of startups and healthcare organisations are getting tangible results in AI applied to different medical fields.

An underreported aspect of the climate crisis is that archaeological sites, cultural landscapes, biodiversity, and distributions of flora and fauna--much of which modern people will never even know about--are disappearing at an alarming rate. I'm an archaeologist, and while I don't know how to solve the climate crisis, I do know what I want to contribute to our shared legacy: a comprehensive digital map of the surface of the planet and everything on it. Such a project will serve both as a record of the state of the planet as it exists now, to help scientists better understand how it is changing, and as a "virtual planet" that can serve as a precious gift for future generations. In June, I and other like-minded scientists launched the Earth Archive: a massive scientific effort aiming to scan the entire solid surface of the planet, starting with the areas most threatened, at a resolution smaller than a meter. This effort aims to use lidar technology, or light detection and ranging technology, which can map both the vegetation and the ground beneath it in three dimensions from the vantage point of a plane, helicopter, or drone.

Every day, global trends are reshaping society and the business landscape. Today's infographic from McKinsey Global Institute (MGI) presents a snapshot of 10 insights into how the world is changing, based on its research work from 2019. How did we get here, and where are we going? Globalization is making the world "shrink" every day, as humans and trade become increasingly connected. However, there are signs that point to a new phase of globalization that is leading to different outcomes than prior years.

Data imbalance is a major problem that affects several machine learning algorithms. Such problems are troublesome because most of the learning algorithms attempts to optimize a loss function based on error measures that do not take into account the data imbalance. Accordingly, the learning algorithm simply generates a trivial model that is biased toward predicting the most frequent class in the training data. Data augmentation techniques have been used to mitigate the data imbalance problem. However, in the case of histopathologic images (HIs), low-level as well as high-level data augmentation techniques still present performance issues when applied in the presence of inter-patient variability; whence the model tends to learn color representations, which are in fact related to the stain process. In this paper, we propose an approach capable of not only augmenting HIs database but also distributing the inter-patient variability by means of image blending using Gaussian-Laplacian pyramid. The proposed approach consists in finding the Gaussian pyramids of two images of different patients and finding the Laplacian pyramids thereof. Afterwards, the left half of one image and the right half of another are joined in each level of Laplacian pyramid, and from the joint pyramids, the original image is reconstructed. This composition, resulting from the blending process, combines stain variation of two patients, avoiding that color misleads the learning process. Experimental results on the BreakHis dataset have shown promising gains vis-\`a-vis the majority of traditional techniques presented in the literature.

Loss function optimization for neural networks has recently emerged as a new direction for metalearning, with Genetic Loss Optimization (GLO) providing a general approach for the discovery and optimization of such functions. GLO represents loss functions as trees that are evolved and further optimized using evolutionary strategies. However, searching in this space is difficult because most candidates are not valid loss functions. In this paper, a new technique, Multivariate Taylor expansion-based genetic loss-function optimization (TaylorGLO), is introduced to solve this problem. It represents functions using a novel parameterization based on Taylor expansions, making the search more effective. TaylorGLO is able to find new loss functions that outperform those found by GLO in many fewer generations, demonstrating that loss function optimization is a productive avenue for metalearning.

--One problem found when working with satellite images is the radiometric variations across the image and different images. Intending to improve remote sensing models for the classification of burnt areas, we set two objectives. The first is to understand the relationship between feature spaces and the predictive ability of the models, allowing us to explain the differences between learning and generalization when training and testing in different datasets. We find that training on datasets built from more than one image provides models that generalize better . These results are explained by visualizing the dispersion of values on the feature space. The second objective is to evolve hyper-features that improve the performance of different classifiers on a variety of test sets. We find the hyper-features to be beneficial, and obtain the best models with XGBoost, even if the hyper-features are optimized for a different method. Deforestation has serious implications on biodiversity, on rural communities that depend on forests for survival, and on greenhouse gas emissions that drive the global climate. The machine learning (ML) community can help by providing predictive models that, after learning from a small sample of an image, can automatically classify the whole image. Although previous ML work in forest monitoring has shown good results, the predictive models are often applied on the same location where they were learnt, i.e., the models are trained and tested in samples from the same dataset (e.g., [1]) or time series from the same area (e.g., [2]).

Recurrent correlation neural networks (RCNNs), introduced by Chiueh and Goodman as an improved version of the bipolar correlation-based Hopfield neural network, can be used to implement high-capacity associative memories. In this paper, we extend the bipolar RCNNs for processing hypercomplex-valued data. Precisely, we present the mathematical background for a broad class of hypercomplex-valued RCNNs. Then, we provide the necessary conditions which ensure that a hypercomplex-valued RCNN always settles at an equilibrium using either synchronous or asynchronous update modes. Examples with bipolar, complex, hyperbolic, quaternion, and octonion-valued RCNNs are given to illustrate the theoretical results. Finally, computational experiments confirm the potential application of hypercomplex-valued RCNNs as associative memories designed for the storage and recall of gray-scale images.

The 2016 United States presidential election was marked by the abuse of targeted advertising on Facebook. Concerned with the risk of the same kind of abuse to happen in the 2018 Brazilian elections, we designed and deployed an independent auditing system to monitor political ads on Facebook in Brazil. To do that we first adapted a browser plugin to gather ads from the timeline of volunteers using Facebook. We managed to convince more than 2000 volunteers to help our project and install our tool. Then, we use a Convolution Neural Network (CNN) to detect political Facebook ads using word embeddings. To evaluate our approach, we manually label a data collection of 10k ads as political or non-political and then we provide an in-depth evaluation of proposed approach for identifying political ads by comparing it with classic supervised machine learning methods. Finally, we deployed a real system that shows the ads identified as related to politics. We noticed that not all political ads we detected were present in the Facebook Ad Library for political ads. Our results emphasize the importance of enforcement mechanisms for declaring political ads and the need for independent auditing platforms.

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