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.

Experience-based planning domains (EBPDs) have been recently proposed to improve problem solving by learning from experience. EBPDs provide important concepts for long-term learning and planning in robotics. They rely on acquiring and using task knowledge, i.e., activity schemata, for generating concrete solutions to problem instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we extend previous work to generate a set of conditions as the scope of applicability for an activity schema. The inferred scope is a bounded representation of a set of problems of potentially unbounded size, in the form of a 3-valued logical structure, which allows an EBPD system to automatically find an applicable activity schema for solving task problems. We demonstrate the utility of our approach in a set of classes of problems in a simulated domain and a class of real world tasks in a fully physically simulated PR2 robot in Gazebo.

Bipolar Argumentation Frameworks (BAFs) admit several interpretations of the support relation and diverging definitions of semantics. Recently, several classes of BAFs have been captured as instances of bipolar Assumption-Based Argumentation, a class of Assumption-Based Argumentation (ABA). In this paper, we establish the complexity of bipolar ABA, and consequently of several classes of BAFs. In addition to the standard five complexity problems, we analyse the rarely-addressed extension enumeration problem too. We also advance backtracking-driven algorithms for enumerating extensions of bipolar ABA frameworks, and consequently of BAFs under several interpretations. We prove soundness and completeness of our algorithms, describe their implementation and provide a scalability evaluation. We thus contribute to the study of the as yet uninvestigated complexity problems of (variously interpreted) BAFs as well as of bipolar ABA, and provide the lacking implementations thereof.

Despite what Donald Trump would have you believe, the biggest factor when it comes to American employment is automation, not job theft by Mexico or China or other foreign countries that the president says "you've never even heard of." Although as John Oliver points out, Trump is the same person who reportedly pronounced Nepal and Bhutan as nipple and button, so the list of countries he's never heard of might be higher than average. Elsewhere in the segment, Oliver stopped listing fake countries long enough to explain in detail how machines are replacing jobs in some fields and how that can actually a good thing (unless you want to kill a lumberjack). He also broke the news to some kids who will probably grow up to do jobs that don't already exist, like "crypto-baker" or "snail rehydrater." Good thing that unlike "mermaid doctor," the job of "culture blogger" will never be replaced by BEEP BOOP ERROR 404.