"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.

Drones have a fundamental design problem. The kind of drone that can carry large payloads at high speeds over long distances is fundamentally different from the kind of drone that can take off and land from a small area. In very simple terms, for the former, you want fixed wings, and for the latter, you want rotors. This problem has resulted in a bunch of weird drones that try to do both of these things at once, usually by combining desired features from fixed-wing drones and rotorcraft. We've seen tail-sitter drones that can transition from vertical take off to horizontal flight; we've seen drones with propeller systems that swivel; and we've seen a variety of airframes that are essentially quadrotors stapled to fixed-wing aircraft to give them vertical take-off and landing capability.

PwC has highlighted six important factors for healthcare organisations to effectively incorporate AI in a new report. The report, 'From Virtual to Reality: Six imperatives for becoming an AI-ready healthcare business', outlines use cases and learnings from six main areas, including Leadership & Culture, Workforce Transformation, Clinical Effectiveness, Commercial Investment, Public Readiness and Regulation, Ethics and Confidentiality. PwC outlines the importance of several factors for enabling AI-powered healthcare businesses, including creating the right leadership and culture; improving clinical effectiveness; investing in and funding businesses and technology; ensuring appropriate regulation and addressing concerns about ethics and confidentiality. The company also says that the Middle East is in a unique position to lead the development of international standards and become a hub for AI research and development. Factors such as the proactive development of governance and regulatory frameworks will facilitate the implementation of AI in healthcare.

Without doubt, artificial intelligence and machine learning are major areas of innovation for the greater tech community. If you are a tech freelancer and eager to stay in touch with future directions, you will want to know what companies like Google are investing in, the new technologies they are advancing and the research priorities they are supporting or sponsoring. And, you are in luck! A post yesterday by Jeff Dean, senior fellow and Google AI lead, on behalf of the Google Research Community, reviews how Google has focused its research talent and dollars. I've provided a thumbnail summary of the priorities, quoting descriptions from the blog post.

Modern human DNA computational analysis suggests that the extinct species was a hybrid of Neanderthals and Denisovans and cross bred with Out of Africa modern humans in Asia. This finding would explain that the hybrid found this summer in the caves of Denisova -- the offspring of a Neanderthal mother and a Denisovan father -- was not an isolated case, but rather was part of a more general introgression process. The study, published in Nature Communications, uses deep learning for the first time ever to account for human evolution, paving the way for the application of this technology in other questions in biology, genomics and evolution. One of the ways of distinguishing between two species is that while both of them may cross breed, they do not generally produce fertile descendants. However, this concept is much more complex when extinct species are involved.

The gradient noise (GN) in the stochastic gradient descent (SGD) algorithm is often considered to be Gaussian in the large data regime by assuming that the classical central limit theorem (CLT) kicks in. This assumption is often made for mathematical convenience, since it enables SGD to be analyzed as a stochastic differential equation (SDE) driven by a Brownian motion. We argue that the Gaussianity assumption might fail to hold in deep learning settings and hence render the Brownian motion-based analyses inappropriate. Inspired by non-Gaussian natural phenomena, we consider the GN in a more general context and invoke the generalized CLT (GCLT), which suggests that the GN converges to a heavy-tailed $\alpha$-stable random variable. Accordingly, we propose to analyze SGD as an SDE driven by a L\'{e}vy motion. Such SDEs can incur `jumps', which force the SDE transition from narrow minima to wider minima, as proven by existing metastability theory. To validate the $\alpha$-stable assumption, we conduct extensive experiments on common deep learning architectures and show that in all settings, the GN is highly non-Gaussian and admits heavy-tails. We further investigate the tail behavior in varying network architectures and sizes, loss functions, and datasets. Our results open up a different perspective and shed more light on the belief that SGD prefers wide minima.

A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes. Using Gromov-Wasserstein discrepancy, we measure the dissimilarity between two graphs and find their correspondence, according to the learned optimal transport. The node embeddings associated with the two graphs are learned under the guidance of the optimal transport, the distance of which not only reflects the topological structure of each graph but also yields the correspondence across the graphs. These two learning steps are mutually-beneficial, and are unified here by minimizing the Gromov-Wasserstein discrepancy with structural regularizers. This framework leads to an optimization problem that is solved by a proximal point method. We apply the proposed method to matching problems in real-world networks, and demonstrate its superior performance compared to alternative approaches.