For effective recognition, we developed a recognition component, combining kinematic vessel streams with library of maritime patterns in close collaboration with domain contextual (geographical) knowledge for real-time vessel activity experts. We present a thorough evaluation of the system and the detection. To improve the accuracy of the system, we collaborated, patterns both in terms of predictive accuracy and computational in the context of this paper, with domain experts in order to construct efficiency, using real-world datasets of vessel position streams and effective patterns of maritime activity. Thus, we present a contextual geographical information.

Illegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife monitoring and protection in conservation areas are constrained by the limited resources of law enforcement agencies. To aid in wildlife protection, PAWS is an ML pipeline that has been developed as an end-to-end, data-driven approach to combat illegal poaching. PAWS assists park managers by identifying areas at high risk of poaching throughout protected areas based on real-world data and generating optimal patrol routes for deployment in the field. In this paper, we address significant challenges including extreme class imbalance (up to 1:200), bias, and uncertainty in wildlife poaching data to enhance PAWS and apply its methodology to several national parks with diverse characteristics. (i) We use Gaussian processes to quantify predictive uncertainty, which we exploit to increase the robustness of our prescribed patrols. We evaluate our approach on real-world historic poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia. (ii) We present the results of large-scale field tests conducted in Murchison Falls and Srepok Wildlife Sanctuary which confirm that the predictive power of PAWS extends promisingly to multiple parks. This paper is part of an effort to expand PAWS to 600 parks around the world through integration with SMART conservation software.

Brain lesion and anatomy segmentation in magnetic resonance images are fundamental tasks in neuroimaging research and clinical practice. Given enough training data, convolutional neuronal networks (CNN) proved to outperform all existent techniques in both tasks independently. However, to date, little work has been done regarding simultaneous learning of brain lesion and anatomy segmentation from disjoint datasets. In this work we focus on training a single CNN model to predict brain tissue and lesion segmentations using heterogeneous datasets labeled independently, according to only one of these tasks (a common scenario when using publicly available datasets). We show that label contradiction issues can arise in this case, and propose a novel adaptive cross entropy (ACE) loss function that makes such training possible. We provide quantitative evaluation in two different scenarios, benchmarking the proposed method in comparison with a multi-network approach. Our experiments suggest that ACE loss enables training of single models when standard cross entropy and Dice loss functions tend to fail. Moreover, we show that it is possible to achieve competitive results when comparing with multiple networks trained for independent tasks.

My newest Ask-A-Data-Scientist post was inspired by a computer science student who wrote in asking for advice on how to pursue a career in policy making related to the societal impacts of AI. I realized that there are many great resources out there, and I wanted to compile a list of links all in one place. You can find my previous Ask-A-Data-Scientist advice columns here. Everyone in tech should be concerned about the ethical implications of our work and actively engaging with such questions. The humanities and social sciences are incredibly relevant and important in addressing ethics questions.

I will be the first to admit that certain questions have no right or wrong answers. An example is, will artificial intelligence or even technology in general make us more or less intelligent? Looking at it from a broader perspective, one cannot deny the obvious that technology has impacted the society for good, but at the same time, it has some negative sides that we have to deal with. Crop improvement, genetics, three dimensional technology, blockchain and many more are some of the positives that we have gained from the advancement of technology but some activities from the processes that gave us these technological breakthroughs, such as pollution which creates environmental hazards, has given technology some negatives in the view of many of its sceptics. Today, AI, (one of the daring outcomes of continuous technological advancement) is arguably one of the most discussed trends in the world of technology, mainly on how it is helping to improve different aspects of society evolvement.

Could a program detect potential terrorists by reading their facial expressions and behavior? This was the hypothesis put to the test by the US Transportation Security Administration (TSA) in 2003, as it began testing a new surveillance program called the Screening of Passengers by Observation Techniques program, or Spot for short. While developing the program, they consulted Paul Ekman, emeritus professor of psychology at the University of California, San Francisco. Decades earlier, Ekman had developed a method to identify minute facial expressions and map them on to corresponding emotions. This method was used to train "behavior detection officers" to scan faces for signs of deception.

There's a good chance your smartphone contains tainted cobalt. The metal is a crucial ingredient in most of the lithium-ion batteries that power our devices, and 70% of it is mined in war-torn Democratic Republic of Congo (DRC), where children are often deployed to work in toxic environments. Though global brands like Apple and Samsung are keen to clean up their supply chain, DRC's dominance of the cobalt market makes the task difficult. These brands are also pressured by growing demand for cobalt, which Citigroup estimates will outstrip supply by 2023. That's because lithium-ion batteries also power electric cars, and every car battery needs as much as 1,000 times the amount of cobalt of a smartphone battery.

Last month, the U.S. Army put out a call to private companies for ideas about how to improve its planned semi-autonomous, AI-driven targeting system for tanks. In its request, the Army asked for help enabling the Advanced Targeting and Lethality Automated System (ATLAS) to "acquire, identify, and engage targets at least 3X faster than the current manual process." But that language apparently scared some people who are worried about the rise of AI-powered killing machines. In response, the U.S. Army added a disclaimer to the call for white papers in a move first spotted by news website Defense One. Without modifying any of the original wording, the Army simply added a note that explains Defense Department policy hasn't changed.

Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. Novelty detection is one of the classic challenges in Machine Learning and a core component of several research areas such as fraud detection, intrusion detection, medical diagnosis, data cleaning, and fault prevention. While numerous algorithms were designed to address this problem, most methods are only suitable to model continuous numerical data. Tackling datasets composed of mixed-type features, such as numerical and categorical data, or temporal datasets describing discrete event sequences is a challenging task. In addition to the supported data types, the key criteria for efficient novelty detection methods are the ability to accurately dissociate novelties from nominal samples, the interpretability, the scalability and the robustness to anomalies located in the training data. In this thesis, we investigate novel ways to tackle these issues. In particular, we propose (i) an experimental comparison of novelty detection methods for mixed-type data (ii) an experimental comparison of novelty detection methods for sequence data, (iii) a probabilistic nonparametric novelty detection method for mixed-type data based on Dirichlet process mixtures and exponential-family distributions and (iv) an autoencoder-based novelty detection model with encoder/decoder modelled as deep Gaussian processes.

In this paper, we present some theoretical work to explain why simple gradient descent methods are so successful in solving non-convex optimization problems in learning large-scale neural networks (NN). After introducing a mathematical tool called canonical space, we have proved that the objective functions in learning NNs are convex in the canonical model space. We further elucidate that the gradients between the original NN model space and the canonical space are related by a pointwise linear transformation, which is represented by the so-called disparity matrix. Furthermore, we have proved that gradient descent methods surely converge to a global minimum of zero loss provided that the disparity matrices maintain full rank. If this full-rank condition holds, the learning of NNs behaves in the same way as normal convex optimization. At last, we have shown that the chance to have singular disparity matrices is extremely slim in large NNs. In particular, when over-parameterized NNs are randomly initialized, the gradient decent algorithms converge to a global minimum of zero loss in probability.