There is little argument about whether or not machine learning models are useful for applying to social systems. But if we take seriously George Box's dictum, or indeed the even older one that "the map is not the territory' (Korzybski, 1933), then there has been comparatively less systematic attention paid within the field to how machine learning models are wrong (Selbst et al., 2019) and seeing possible harms in that light. By "wrong" I do not mean in terms of making misclassifications, or even fitting over the'wrong' class of functions, but more fundamental mathematical/statistical assumptions, philosophical (in the sense used by Abbott, 1988) commitments about how we represent the world, and sociological processes of how models interact with target phenomena. This paper takes a particular model of machine learning research or application: one that its creators and deployers think provides a reliable way of interacting with the social world (whether that is through understanding, or in making predictions) without any intent to cause harm (McQuillan, 2018) and, in fact, a desire to not cause harm and instead improve the world, 1 for example as most explicitly in the various "{Data [Science], Machine Learning, Artificial Intelligence} for [Social] Good" initiatives, and more widely in framings around "fairness" or "ethics." I focus on the almost entirely statistical modern version of machine learning, rather than eclipsed older visions (see section 3). While many of the limitations I discuss apply to the use of machine learning in any domain, I focus on applications to the social world in order to explore the domain where limitations are strongest and stickiest.

Exploration in sparse reward environments remains one of the key challenges of model-free reinforcement learning. Instead of solely relying on extrinsic rewards provided by the environment, many state-of-the-art methods use intrinsic rewards to encourage exploration. However, we show that existing methods fall short in procedurally-generated environments where an agent is unlikely to visit a state more than once. We propose a novel type of intrinsic reward which encourages the agent to take actions that lead to significant changes in its learned state representation. We evaluate our method on multiple challenging procedurally-generated tasks in MiniGrid, as well as on tasks with high-dimensional observations used in prior work. Our experiments demonstrate that this approach is more sample efficient than existing exploration methods, particularly for procedurally-generated MiniGrid environments. Furthermore, we analyze the learned behavior as well as the intrinsic reward received by our agent. In contrast to previous approaches, our intrinsic reward does not diminish during the course of training and it rewards the agent substantially more for interacting with objects that it can control.

We believe graph machine learning is at the intersection of art and science. We use cutting-edge engineering and data science to help reveal insight from data, and find innovative ways to enable our users to get the most from the experience. The StellarGraph team consists of engineers, data scientists, researchers, devops, product managers, and UX designers all driven to build amazing technology. Get in touch to meet the team and learn how we can partner.

Our skilled project managers use multiple quality control methods and mechanisms to meet and exceed quality standards for training data. Quality assurance is built into both the platform and processes at Appen. With a crowd of over 1 million skilled contractors operating in 130 countries and 180 languages and dialects, Appen can collect and label high volumes of image, text, speech, audio, and video data used to build and improve artificial intelligence systems. Our platform and solutions are purpose-built to handle large-scale data collection and annotation projects, on demand. With deep expertise planning and recruiting to meet a variety of uses cases for our clients, we can quickly ramp up new projects in new markets.

People's Google searches, social media posts and even chatbot questions are being used by artificial intelligence to try and predict where the novel coronavirus is going to pop up next. The technology, which has been fine-tuned over the last 15 years, is already feeding information to major health agencies like the World Health Organisation to help them decide where they should focus their efforts. One system, called HealthMap, uses publicly available data from across the internet as well as user-submitted information, according to one of its developers, John Brownstein, a professor at Harvard Medical School. "We work in this hybrid of data mining as well as crowdsourcing," he told the ABC's news podcast The Signal. "What's really phenomenal here is we're seeing incredible international collaboration and a huge amount of data sharing."

Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such multidimensional (aka tensor) datasets. Finding a low dimensional representation of the data, that is, its inherent structure, is one of the approaches that can serve to understand the dynamics of low dimensional latent features hidden in the data. Nonnegative RESCAL is one such technique, particularly well suited to analyze self-relational data, such as dynamic networks found in international trade flows. Nonnegative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities. Estimating the dimensionality of this latent space is crucial for extracting meaningful latent features. Here, to determine the dimensionality of the latent space with nonnegative RESCAL, we propose a latent dimension determination method which is based on clustering of the solutions of multiple realizations of nonnegative RESCAL decompositions. We demonstrate the performance of our model selection method on synthetic data and then we apply our method to decompose a network of international trade flows data from International Monetary Fund and validate the resulting features against empirical facts from economic literature.

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. The review shows that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exists in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption of BNs; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review empowers researchers and clinicians with an analytical framework and findings that will enable understanding of the need to address the problems of restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.

This paper investigates how accurately the prediction of being an introvert vs. extrovert can be made with less than ten predictors. The study is based on a previous data collection of 7161 respondents of a survey on 91 personality and 3 demographic items. The results show that it is possible to effectively reduce the size of this measurement instrument from 94 to 10 features with a performance loss of only 1%, achieving an accuracy of 73.81% on unseen data. Class imbalance correction methods like SMOTE or ADASYN showed considerable improvement on the validation set but only minor performance improvement on the testing set.

This paper presents a novel differentiable method for unstructured weight pruning of deep neural networks. Our learned-threshold pruning (LTP) method enjoys a number of important advantages. First, it learns per-layer thresholds via gradient descent, unlike conventional methods where they are set as input. Making thresholds trainable also makes LTP computationally efficient, hence scalable to deeper networks. For example, it takes less than $30$ epochs for LTP to prune most networks on ImageNet. This is in contrast to other methods that search for per-layer thresholds via a computationally intensive iterative pruning and fine-tuning process. Additionally, with a novel differentiable $L_0$ regularization, LTP is able to operate effectively on architectures with batch-normalization. This is important since $L_1$ and $L_2$ penalties lose their regularizing effect in networks with batch-normalization. Finally, LTP generates a trail of progressively sparser networks from which the desired pruned network can be picked based on sparsity and performance requirements. These features allow LTP to achieve state-of-the-art compression rates on ImageNet networks such as AlexNet ($26.4\times$ compression with $79.1\%$ Top-5 accuracy) and ResNet50 ($9.1\times$ compression with $92.0\%$ Top-5 accuracy). We also show that LTP effectively prunes newer architectures, such as EfficientNet, MobileNetV2 and MixNet.

Machine learning algorithms typically perform optimization over a class of non-convex functions. In this work, we provide bounds on the fundamental hardness of identifying the global minimizer of a non convex function. Specifically, we design a family of parametrized non-convex functions and employ statistical lower bounds for parameter estimation. We show that the parameter estimation problem is equivalent to the problem of function identification in the given family. We then claim that non convex optimization is at least as hard as function identification. Jointly, we prove that any first order method can take exponential time to converge to a global minimizer.