Governments across the world came together in Marrakesh this past December to ratify a pact to improve cooperation on international migration. Among other objectives, the Global Compact for Migration seeks to use "accurate and disaggregated data as a basis for evidence-based policies." How can machine learning technologies help with deeply polarizing societal issues like migration? In early 2018, with support from IBM Corporate Citizenship and the Danish Ministry for Foreign Affairs, IBM and the Danish Refugee Council (DRC) embarked on a partnership aimed squarely at the need to better understand migration drivers and evidence-based policy guidance for a range of stakeholders. At the recent THINK Copenhagen keynote, the Secretary General of the DRC, Christian Friis Bach, presented the first results of this effort.

Community detection refers to the task of discovering groups of vertices sharing similar properties or functions so as to understand the network data. With the recent development of deep learning, graph representation learning techniques are also utilized for community detection. However, the communities can only be inferred by applying clustering algorithms based on learned vertex embeddings. These general cluster algorithms like K-means and Gaussian Mixture Model cannot output much overlapped communities, which have been proved to be very common in many real-world networks. In this paper, we propose CommunityGAN, a novel community detection framework that jointly solves overlapping community detection and graph representation learning. First, unlike the embedding of conventional graph representation learning algorithms where the vector entry values have no specific meanings, the embedding of CommunityGAN indicates the membership strength of vertices to communities. Second, a specifically designed Generative Adversarial Net (GAN) is adopted to optimize such embedding. Through the minimax competition between the motif-level generator and discriminator, both of them can alternatively and iteratively boost their performance and finally output a better community structure. Extensive experiments on synthetic data and real-world tasks demonstrate that CommunityGAN achieves substantial community detection performance gains over the state-of-the-art methods.

Rapid identification of bacteria is essential to prevent the spread of infectious disease, help combat antimicrobial resistance, and improve patient outcomes. Raman optical spectroscopy promises to combine bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to the weak Raman signal from bacterial cells and the large number of bacterial species and phenotypes. By amassing the largest known dataset of bacterial Raman spectra, we are able to apply state-of-the-art deep learning approaches to identify 30 of the most common bacterial pathogens from noisy Raman spectra, achieving antibiotic treatment identification accuracies of 99.0$\pm$0.1%. This novel approach distinguishes between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) as well as a pair of isogenic MRSA and MSSA that are genetically identical apart from deletion of the mecA resistance gene, indicating the potential for culture-free detection of antibiotic resistance. Results from initial clinical validation are promising: using just 10 bacterial spectra from each of 25 isolates, we achieve 99.0$\pm$1.9% species identification accuracy. Our combined Raman-deep learning system represents an important proof-of-concept for rapid, culture-free identification of bacterial isolates and antibiotic resistance and could be readily extended for diagnostics on blood, urine, and sputum.

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.

Previous work on predicting or generating 3D human pose sequences regresses either joint rotations or joint positions. The former strategy is prone to error accumulation along the kinematic chain, as well as discontinuities when using Euler angles or exponential maps as parameterizations. The latter requires re-projection onto skeleton constraints to avoid bone stretching and invalid configurations. This work addresses both limitations. QuaterNet represents rotations with quaternions and our loss function performs forward kinematics on a skeleton to penalize absolute position errors instead of angle errors. We investigate both recurrent and convolutional architectures and evaluate on short-term prediction and long-term generation. For the latter, our approach is qualitatively judged as realistic as recent neural strategies from the graphics literature. Our experiments compare quaternions to Euler angles as well as exponential maps and show that only a very short context is required to make reliable future predictions. Finally, we show that the standard evaluation protocol for Human3.6M produces high variance results and we propose a simple solution.

Dr Jacques Ludik is a smart technology entrepreneur, AI expert, investor & ecosystem builder and currently Founder & President of Machine Intelligence Institute of Africa (MIIA), Founder & CEO of Cortex Logic, Founder of Bennit AI, Founder of SynerG, The Talent Index, & aiTRADE Systems, and investor in The Student Hub (ERAOnline). He holds a Ph.D. in Computer Science (AI) with many publications and has 25 years' experience in Machine / Artificial Intelligence (AI) & Data Science and its applications. MIIA is an innovative community & accelerator for Machine Intelligence & Data Science Research & Applications to help transform Africa, whereas Cortex Logic is an AI company that provides an AI Engine for Business, advances AI and builds end-to-end AI solutions for a range of industries. Bennit A.I. is an intelligent virtual production assistant/advisor for manufacturing. For businesses to thrive in the smart technology era, they need to be agile, innovative and adapt quickly to stay relevant, given the swift pace of change and disruption to business and society.

"They were trying to run their operation from that physical board," says Ted Schmitt, principal business development manager for conservation technology at Vulcan, the Seattle-based philanthropic tech company founded by Microsoft cofounder Paul Allen (who died on October 15), which partnered with the park to help it move to the company's digital system, called EarthRanger, in April. "They all know that poaching goes up during a full moon, for obvious reasons," says Schmitt. "But what they don't know, and they all expect, is that there are patterns like that latent in the data that they just can't pull out. That's the promise of machine learning…it's going to let them be proactive." The machine learning is still in early stages of development, but some analytic tools are already in use. A new heat map feature, for example, first tested at Grumeti Game Reserve in Tanzania and Liwonde National Park in Malawi, showed that most incidents were happening near the borders of each park, so rangers could focus on those areas with the highest risk.

The new research comes from Institute of Evolutionary Biology (IBE), the Centro Nacional de Análisis Genómico (CNAG-CRG) of the Centre for Genomic Regulation (CRG) and the Institute of Genomics at the University of Tartu. In studies researchers have applied deep learning algorithms and statistical methods to establish the footprint of a new hominid. The application of human DNA computational analysis indicates that the extinct species was a hybrid of Neanderthals and Denisovans. At some stage this hominid cross bred with'Out of Africa' modern humans within the region of the world that is now Asia. The scientific theory of recent African origin of modern humans is the most widely accepted model of the geographic origin and early migration of anatomically modern humans (Homo sapiens).

Access Now is a growing international human rights organisation dedicated to defending and extending the digital rights of users at risk around the world, including issues of privacy, security, freedom of expression, and transparency. Our policy, advocacy, technology, and operations teams have staff presences in Europe, Latin America, the Middle East/North Africa (MENA), North America, and South/Southeast Asia, to provide global support to our mission. Access Now's Policy team works globally and supports our mission by developing and promoting rights-respecting practices and policies. The Policy team seek to advance laws and global norms to affect long-term systemic change in the area of digital rights and online security, developing insightful, rights-based, and well-researched policy guidance to governments, corporations, and civil society. The need to hold both the public and private sectors accountable leads the Policy team to use diverse fora, including domestic and regional courts, intergovernmental bodies, and expert offices to promote norms and best practices.

Assisted by the availability of data and high performance computing, deep learning techniques have achieved breakthroughs and surpassed human performance empirically in difficult tasks, including object recognition, speech recognition, and natural language processing. As they are being used in critical applications, understanding underlying mechanisms for their successes and limitations is imperative. In this paper, we show that overfitting, one of the fundamental issues in deep neural networks, is due to continuous gradient updating and scale sensitiveness of cross entropy loss. By separating samples into correctly and incorrectly classified ones, we show that they behave very differently, where the loss decreases in the correct ones and increases in the incorrect ones. Furthermore, by analyzing dynamics during training, we propose a consensus-based classification algorithm that enables us to avoid overfitting and significantly improve the classification accuracy especially when the number of training samples is limited. As each trained neural network depends on extrinsic factors such as initial values as well as training data, requiring consensus among multiple models reduces extrinsic factors substantially; for statistically independent models, the reduction is exponential. Compared to ensemble algorithms, the proposed algorithm avoids overgeneralization by not classifying ambiguous inputs. Systematic experimental results demonstrate the effectiveness of the proposed algorithm. For example, using only 1000 training samples from MNIST dataset, the proposed algorithm achieves 95% accuracy, significantly higher than any of the individual models, with 90% of the test samples classified.