A longheld theory that animals raised in captivity perform better in cognitive testing may need to be rethought. A new study organized by the University of Veterinary Medicine in Vienna found evidence that wild animals perform just as well at intelligence tests as their lab-raised counterparts. To test the theory, researchers compared two groups of Goffin's cockatoos, a species often found in the tropical jungles of Singapore, Indonesia, and Puerto Rico. The team compared a lab-raised'colony' of 11 cockatoos at their lab in Vienna to eight wild cockatoos recently taken into captivity at a field laboratory in Indonesia. The researchers compared the performance of both groups in a series of simple problem solving tests and found the wild cockatoos were just as clever as the lab-raised ones.

This paper provides a graphical characterization of Markov blankets in chain graphs (CGs) under the Lauritzen-Wermuth-Frydenberg (LWF) interpretation. The characterization is different from the well-known one for Bayesian networks and generalizes it. We provide a novel scalable and sound algorithm for Markov blanket discovery in LWF CGs and prove that the Grow-Shrink algorithm, the IAMB algorithm, and its variants are still correct for Markov blanket discovery in LWF CGs under the same assumptions as for Bayesian networks. We provide a sound and scalable constraint-based framework for learning the structure of LWF CGs from faithful causally sufficient data and prove its correctness when the Markov blanket discovery algorithms in this paper are used. Our proposed algorithms compare positively/competitively against the state-of-the-art LCD (Learn Chain graphs via Decomposition) algorithm, depending on the algorithm that is used for Markov blanket discovery. Our proposed algorithms make a broad range of inference/learning problems computationally tractable and more reliable because they exploit locality.

Framed in positive terms, this report examines how technical AI research might be steered in a manner that is more attentive to humanity's long-term prospects for survival as a species. In negative terms, we ask what existential risks humanity might face from AI development in the next century, and by what principles contemporary technical research might be directed to address those risks. A key property of hypothetical AI technologies is introduced, called \emph{prepotence}, which is useful for delineating a variety of potential existential risks from artificial intelligence, even as AI paradigms might shift. A set of \auxref{dirtot} contemporary research \directions are then examined for their potential benefit to existential safety. Each research direction is explained with a scenario-driven motivation, and examples of existing work from which to build. The research directions present their own risks and benefits to society that could occur at various scales of impact, and in particular are not guaranteed to benefit existential safety if major developments in them are deployed without adequate forethought and oversight. As such, each direction is accompanied by a consideration of potentially negative side effects.

Effective peer assessment requires students to be attentive to the deficiencies in the work they rate. Thus, their reviews should identify problems. But what ways are there to check that they do? We attempt to automate the process of deciding whether a review comment detects a problem. We use over 18,000 review comments that were labeled by the reviewees as either detecting or not detecting a problem with the work. We deploy several traditional machine-learning models, as well as neural-network models using GloVe and BERT embeddings. We find that the best performer is the Hierarchical Attention Network classifier, followed by the Bidirectional Gated Recurrent Units (GRU) Attention and Capsule model with scores of 93.1% and 90.5% respectively. The best non-neural network model was the support vector machine with a score of 89.71%. This is followed by the Stochastic Gradient Descent model and the Logistic Regression model with 89.70% and 88.98%.

An emerging number of learning scenarios involve a set of learners/analysts each equipped with a unique dataset and algorithm, who may collaborate with each other to enhance their learning performance. From the perspective of a particular learner, a careless collaboration with task-irrelevant other learners is likely to incur modeling error. A crucial problem is to search for the most appropriate collaborators so that their data and modeling resources can be effectively leveraged. Motivated by this, we propose to study the problem of'meta clustering', where the goal is to identify subsets of relevant learners whose collaboration will improve the performance of each individual learner. In particular, we study the scenario where each learner is performing a supervised regression, and the meta clustering aims to categorize the underlying supervised relations (between responses and predictors) instead of the raw data. We propose a general method named as Select-Exchange-Cluster (SEC) for performing such a clustering. Our method is computationally efficient as it does not require each learner to exchange their raw data. We prove that the SEC method can accurately cluster the learners into appropriate collaboration sets according to their underlying regression functions. Synthetic and real data examples show the desired performance and wide applicability of SEC to a variety of learning tasks. Index Terms Distributed computing; Fairness; Meta clustering; Regression.

A random dot product graph (RDPG) is a generative model for networks in which vertices correspond to positions in a latent Euclidean space and edge probabilities are determined by the dot products of the latent positions. We consider RDPGs for which the latent positions are randomly sampled from an unknown $1$-dimensional submanifold of the latent space. In principle, restricted inference, i.e., procedures that exploit the structure of the submanifold, should be more effective than unrestricted inference; however, it is not clear how to conduct restricted inference when the submanifold is unknown. We submit that techniques for manifold learning can be used to learn the unknown submanifold well enough to realize benefit from restricted inference. To illustrate, we test a hypothesis about the Fr\'{e}chet mean of a small community of vertices, using the complete set of vertices to infer latent structure. We propose test statistics that deploy the Isomap procedure for manifold learning, using shortest path distances on neighborhood graphs constructed from estimated latent positions to estimate arc lengths on the unknown $1$-dimensional submanifold. Unlike conventional applications of Isomap, the estimated latent positions do not lie on the submanifold of interest. We extend existing convergence results for Isomap to this setting and use them to demonstrate that, as the number of auxiliary vertices increases, the power of our test converges to the power of the corresponding test when the submanifold is known.

A curriculum is an efficient tool for humans to progressively learn from simple concepts to hard problems. It breaks down complex knowledge by providing a sequence of learning steps of increasing difficulty. In this post, we will examine how the idea of curriculum can help reinforcement learning models learn to solve complicated tasks. It sounds like an impossible task if we want to teach integral or derivative to a 3-year-old who does not even know basic arithmetics. That's why education is important, as it provides a systematic way to break down complex knowledge and a nice curriculum for teaching concepts from simple to hard. A curriculum makes learning difficult things easier and approachable for us humans.

The Association for the Advancement of Artificial Intelligence (AAAI) and Squirrel AI Learning announced the establishment of a new $1M annual award for societal benefits of AI. The award will be sponsored by Squirrel AI Learning as part of its mission to promote the use of artificial intelligence with lasting positive effects for society. The new Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity was announced jointly by Derek Haoyang Li, Founder and Chairman of Squirrel AI Learning, and Yolanda Gil, President of AAAI, at the 2019 conference for AI for adaptive Education (AIAED) in Beijing. Established in 2014, Squirrel AI Learning Intelligent Adaptive Education by Yixue Group is the first artificial intelligence company in China to apply AI-powered adaptive learning technology to K12 education. Squirrel AI Learning products use a model that combines artificial intelligence and human coaches to provide students with access to individualized and affordable high-quality education. Although the focus of Squirrel AI Learning is on education, Li insisted that the award will recognize AI innovations across all disciplines. The establishment of this award aims to inspire the AI community and draw attention to AI that can benefit humanity. This new international award will recognize significant contributions in the field of artificial intelligence with profound societal impact that have generated otherwise unattainable value for humanity. The award nomination and selection process will be designed by a committee led by AAAI that will include representatives from international organizations with relevant expertise that will be designated by Squirrel AI Learning.

Linear algebra is to machine learning as flour to bakery: every machine learning model is based in linear algebra, as every cake is based in flour. It is not the only ingredient, of course. Machine learning models need vector calculus, probability, and optimization, as cakes need sugar, eggs, and butter. Applied machine learning, like bakery, is essentially about combining these mathematical ingredients in clever ways to create useful (tasty?) models. This document contains introductory level linear algebra notes for applied machine learning. It is meant as a reference rather than a comprehensive review. If you ever get confused by matrix multiplication, don't remember what was the $L_2$ norm, or the conditions for linear independence, this can serve as a quick reference. It also a good introduction for people that don't need a deep understanding of linear algebra, but still want to learn about the fundamentals to read about machine learning or to use pre-packaged machine learning ...

Endowed with higher levels of autonomy, robots are required to perform increasingly complex manipulation tasks. Learning from demonstration is arising as a promising paradigm for transferring skills to robots. It allows to implicitly learn task constraints from observing the motion executed by a human teacher, which can enable adaptive behavior. We present a novel Gaussian-Process-based learning from demonstration approach. This probabilistic representation allows to generalize over multiple demonstrations, and encode variability along the different phases of the task. In this paper, we address how Gaussian Processes can be used to effectively learn a policy from trajectories in task space. We also present a method to efficiently adapt the policy to fulfill new requirements, and to modulate the robot behavior as a function of task variability. This approach is illustrated through a real-world application using the TIAGo robot.