DeepMind has joined the ranks of those using AI to save fragile wildlife populations, and it's doing that on a grand scale. The company is partnering with conservationists and ecologists on a project that uses machine learning to speedily detect and count animals in "millions" of photos taken over the past nine years in Tanzania's Serengeti National Park. Where it normally takes up to a year for volunteers to return labeled photos, DeepMind has developed a model that can label most animals at least as well as humans while shortening the process by up to nine months That's no small challenge when animals seldom cooperate with motion-sensitive cameras -- the AI can recognize out-of-focus cheetahs or fast-moving ostriches. The technology should also be viable in the wild. DeepMind is developing a pre-trained version of its AI model that would need only "modest" hardware and little internet connectivity -- important when a powerful computer and fast internet access could be disruptive to wildlife and expensive to deploy.

Developing a reliable parametric cost model at the conceptual stage of the project is crucial for projects managers and decision-makers. Existing methods, such as probabilistic and statistical algorithms have been developed for project cost prediction. However, these methods are unable to produce accurate results for conceptual cost prediction due to small and unstable data samples. Artificial intelligence (AI) and machine learning (ML) algorithms include numerous models and algorithms for supervised regression applications. Therefore, a comparison analysis for AI models is required to guide practitioners to the appropriate model. The study focuses on investigating twenty artificial intelligence (AI) techniques which are conducted for cost modeling such as fuzzy logic (FL) model, artificial neural networks (ANNs), multiple regression analysis (MRA), case-based reasoning (CBR), hybrid models, and ensemble methods such as scalable boosting trees (XGBoost). Field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the applied ML models. Out of 20 AI techniques, the results showed that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on Mean Absolute Percentage Error (MAPE) and adjusted R2. Nonlinear adaptability, handling missing values and outliers, model interpretation and uncertainty have been discussed for the twenty developed AI models. Keywords: Artificial intelligence, Machine learning, ensemble methods, XGBoost, evolutionary fuzzy rules generation, Conceptual cost, and parametric cost model.

As data breaches, misuse of personal information and the spread of disinformation erode the public's trust in Silicon Valley, it can be all too easy to become cynical about technology's impact on the world. But there are still plenty of reasons to be optimistic about tech's role in society moving forward. Below, TIME speaks to 10 innovators, founders, investors and even athletes who remain upbeat about technology's influence despite the many challenges facing the industry today. Moustapha Cisse left Senegal a decade ago to study artificial intelligence, and now he believes the technology can change Africa for the better. Cisse, 34, is leading Google's AI research center in Accra, Ghana, the company's first such venture in Africa. "I built my team here around people who are really committed to make a difference in people's lives," Cisse tells TIME. "[They] bring a fresh perspective in the field by looking at the problems that we have in Africa." Growing up, no one would have expected Cisse to be heading up a multi-billion dollar corporation's research initiative.

JOHANNESBURG – If data is the new oil, artificial intelligence (AI) can arguably be its best drill, able to uncover insights and mine real business value from the huge and complex data sets that typify modern organisations. Enterprises are not blind to the massive opportunities that can be extracted: according to the latest Gartner data, enterprise adoption of AI has grown 270% over the past four years. In the last year alone, AI adoption has essentially tripled within enterprises of all sizes. That's not surprising considering 85% of global CEOs believe AI will fundamentally change the way they conduct business within the next five years. Until recently, the majority of business decision-making was predominantly driven by human centric capabilities.

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods.

Concretely, that the proposed algorithm does in fact recover the correct super-paramagnetic cluster configurations that are near the entropy maxima. Previous cases studies include data clustering of stocks [15] and gene data in [4], temporal states of financial markets [8], and state-detection for adaptive machine learning in trading [5]. There is an endless variety of potential use-cases for this type of fast big-data clustering technology. Building on prior work we propose and demonstrate an alternative to fast Super-Paramagnetic Clustering (f-SPC) [15] using a modern and streamlined implementation of the "Merging Algorithm" first suggested by Gi-ada [4], one that can recover the same cluster configurations for a variety of test-cases, but with significantly reduced compute times. We again use the Noh Ansatz [11] and the Maximum Likelihood Estimation approach introduced by Giada and Marsili [4]. We call the new algorithm Agglomerative Super-Paramagnetic Clustering (ASPC) and it has the benefit of being less computationally expensive than the PGAs implemented in [5, 6, 15].

We are losing biodiversity at an unprecedented scale and in many cases, we do not even know the basic data for the species. Traditional methods for wildlife monitoring are inadequate. Development of new computer vision tools enables the use of images as the source of information about wildlife. Social media is the rich source of wildlife images, which come with a huge bias, thus thwarting traditional population size estimate approaches. Here, we present a new framework to take into account the social media bias when using this data source to provide wildlife population size estimates. We show that, surprisingly, this is a learnable and potentially solvable problem.

TRIPOLI – A drone airstrike by eastern Libyan forces on the southern Libyan town of Murzuq has killed at least 43 people, a local official said on Monday. The attack is the second major airstrike blamed on the eastern Libyan National Army (LNA) forces loyal to Khalifa Hifter after at least 44 migrants were killed in June when a detention center in a suburb of the capital Tripoli was hit. The LNA confirmed a strike late on Sunday on Murzuq, but denied it had targeted any civilians. The LNA had also denied it had hit the detention center but acknowledged increased air strikes on the capital. The internationally recognized government based in Tripoli opposing Hifter said dozens were killed and wounded in Murzuq. Reached by telephone, Murzuq municipal council member Mohamed Omar told Reuters: "The airstrike resulted in 43 killed and 51 wounded.

--Malaria is a life-threatening disease affecting millions. Microscopy-based assessment of thin blood films is a standard method to (i) determine malaria species and (ii) quanti-tate high-parasitemia infections. Full automation of malaria microscopy by machine learning (ML) is a challenging task because field-prepared slides vary widely in quality and presentation, and artifacts often heavily outnumber relatively rare parasites. In this work, we describe a complete, fully-automated framework for thin film malaria analysis that applies ML methods, including convolutional neural nets (CNNs), trained on a large and diverse dataset of field-prepared thin blood films. Quanti-tation and species identification results are close to sufficiently accurate for the concrete needs of drug resistance monitoring and clinical use-cases on field-prepared samples. We focus our methods and our performance metrics on the field use-case requirements. We discuss key issues and important metrics for the application of ML methods to malaria microscopy. Index T erms --malaria, automated microscopy, deep neural networks, gradient boosted trees I. I NTRODUCTION Malaria is a mosquito-borne disease caused by Plasmodium species ( P . Manual microscopy examination of Giemsa-stained blood films is a widespread malaria diagnosis method. Key use-cases include diagnosis; species identification (ID) to guide treatment [2]; and quantitation of parasites for drug resistance studies, to track how fast a drug clears parasites from the blood. However, a lack of training, high inter-sample variability in preparation and presentation, and difficult field conditions can result in poor accuracy [3], [4]. Also, lack of trained personnel limits the number of drug resistance sentinel sites. Malaria microscopy is a difficult task for automated image-processing and machine learning (ML) systems for two reasons: Field-prepared blood films vary widely in quality and presentation; and parasites are small (with feature size close to optical limits of resolution), rare, highly variable, and easily confused with non-parasite objects (artifacts). But it is also a high-value target, due to the potential benefit for so many people, and also because automated systems have some concrete advantages: They can be widely deployed, solving the expert-training bottleneck; they can examine more blood volume per patient, reducing variability in quantitation caused by Poisson statistics; and their results are reproducible.

We know of the impact that Robotic Process Automation (RPA) has made on multiple industries. However, RPA in itself is undergoing a transformation that is set to bring a wave of unprecedented change. This transformation is known as Intelligent Process Automation (IPA). Intelligent Process Automation is the coming together of Artificial Intelligence with new age technologies such as Natural Language Processing, Machine Learning, Computer Vision, Robotic Process Automation, Cognitive Automation and more. IPA is set to become the core of next generation operating models.