Joint models are a common and important tool in the intersect ion of machine learning and the physical sciences, particularly in contex ts where real-world measurements are scarce. Recent developments in rainfall-run off modeling, one of the prime challenges in hydrology, show the value of a joint m odel with shared representation in this important context. However, curren t state-of-the-art models depend on detailed and reliable attributes characteriz ing each site to help the model differentiate correctly between the behavior of diff erent sites. This dependency can present a challenge in data-poor regions. In this p aper, we show that we can replace the need for such location-specific attributes w ith a completely data-driven learned embedding, and match previous state-of-the -art results with less information.

We survey a burgeoning and promising new research area that considers the online nature of many practical fair division problems. We identify wide variety of such online fair division problems, as well as discuss new mechanisms and normative properties that apply to this online setting. The online nature of such fair division problems provides both opportunities and challenges such as the possibility to develop new online mechanisms as well as the difficulty of dealing with an uncertain future. Introduction Fair division (Brams and Taylor 1996) is an important problem facing society today as increasing economical, environmental, and other pressures require us to try to do more with limited resources. Much previous work in fair division assumes the problem is offline and fixed. That is, we suppose that the agents being allocated resources, and the resources being allocated to these agents are all known and fixed. But practical reality is often quite different (Walsh 2014a; 2015). Fair division problems are often online, with either the agents, or the resources to be allocated, or both not being fixed and potentially changing over time.

A new international study commissioned by WP Engine and conducted by researchers at The University of London and Vanson Bourne explored the present and near future of artificial intelligence (AI)-driven human digital experiences on the web, and the often tenuous but also potentially rewarding relationship between consumers, brands and AI. The study, which surveyed consumers and enterprise companies (1,000 employees or more) in the US, UK and Australia, found that in an era of purpose-driven consumption, values--such as transparency, trust and humanness--are key drivers that unlock value in AI. According to IDC, worldwide spending on artificial intelligence (AI) systems is forecast to reach $35.8 billion in 2019, an increase of 44% over the amount spent in 2018. Much of that growth will come from the application of AI online because there is a natural, evolutionary symbiosis between AI and the internet. However, it was a sudden burst of activity starting in 2013 that marks the beginning of what we might term the modern AI period, especially for digital and digital experiences, characterised predominantly by automated content creation, programmatic ad buying in 2014, and intelligent search.

The government must take strategic action to ensure the nation's AI workforce will meet future demands because current supply is falling short, a new report warns. The report from CSIRO's data sciences arm Data61 focuses on how the nation can capture the full potential of artificial intelligence technology, which is already being used in a wide range of fields. The Artificial Intelligence: Solving problems, growing the economy and improving our quality of life report found that Australia currently has 6,600 AI specialist workers, which is up from 650 AI workers in 2014 and is predicted to grow. However it is well short of the up to 160,000 workers that may be required in the next ten years. "We estimate that by 2030 Australian industry will require a workforce of between 32,000 to 161,000 employees in computer vision, robotics, human language technologies, data science and other areas of AI expertise," the report says.

Often the development of novel materials is not amenable to high-throughput or purely computational screening methods. Instead, materials must be synthesized one at a time in a process that does not generate significant amounts of data. One way this method can be improved is by ensuring that each experiment provides the best improvement in both material properties and predictive modeling accuracy. In this work, we study the effectiveness of active learning, which optimizes the order of experiments, and meta learning, which transfers knowledge from one context to another, to reduce the number of experiments necessary to build a predictive model. We present a novel multi-task benchmark database of peptides designed to advance active, few-shot, and meta-learning methods for experimental design. Each task is binary classification of peptides represented as a sequence string. We show results of standard active learning and meta-learning methods across these datasets to assess their ability to improve predictive models with the fewest number of experiments. We find the ensemble query by committee active learning method to be effective. The meta-learning method Reptile was found to improve accuracy. The robustness of these conclusions were tested across multiple model choices.

Due to limited computational power, performing uncertainty quantification analyses with complex computational models can be a challenging task. This is exacerbated in the context of stochastic simulators, the response of which to a given set of input parameters, rather than being a deterministic value, is a random variable with unknown probability density function (PDF). Of interest in this paper is the construction of a surrogate that can accurately predict this response PDF for any input parameters. We suggest using a flexible distribution family -- the generalized lambda distribution -- to approximate the response PDF. The associated distribution parameters are cast as functions of input parameters and represented by sparse polynomial chaos expansions. To build such a surrogate model, we propose an approach based on a local inference of the response PDF at each point of the experimental design based on replicated model evaluations. Two versions of this framework are proposed and compared on analytical examples and case studies.

It is a particularly well-suited approach to better understand the underlying structure of data when scientific understanding of the data is at an early stage. BN modelling is designed to sort out directly from indirectly related variables and offers a far richer modelling framework than classical approaches in epidemiology like, e.g., regression techniques or extensions thereof. In contrast to structural equation modelling (Hair, Black, Babin, Anderson, Tatham et al. 1998), which requires expert knowledge to design the model, the Additive Bayesian Network (ABN) method is a data-driven approach (Lewis and Ward 2013; Kratzer, Pittavino, Lewis, and Furrer 2019b). It does not rely on expert knowledge, but it can possiarXiv:1911.09006v1

Uplift models support decision-making in marketing campaign planning. Estimating the causal effect of a marketing treatment, an uplift model facilitates targeting communication to responsive customers and efficient allocation of marketing budgets. Research into uplift models focuses on conversion models to maximize incremental sales. The paper introduces uplift modeling strategies for maximizing incremental revenues. If customers differ in their spending behavior, revenue maximization is a more plausible business objective compared to maximizing conversions. The proposed methodology entails a transformation of the prediction target, customer-level revenues, that facilitates implementing a causal uplift model using standard machine learning algorithms. The distribution of campaign revenues is typically zero-inflated because of many non-buyers. Remedies to this modeling challenge are incorporated in the proposed revenue uplift strategies in the form of two-stage models. Empirical experiments using real-world e-commerce data confirm the merits of the proposed revenue uplift strategy over relevant alternatives including uplift models for conver-sion and recently developed causal machine learning algorithms. To quantify the degree to which improved targeting decisions raise return on marketing, the paper develops a decomposition of campaign profit. Applying the decomposition to a digital coupon targeting campaign, the paper provides evidence that revenue uplift modeling, as well as causal machine learning, can improve cam-paign profit substantially.

An important challenge for the automatic analysis of English written text is the abundance of noun compounds: sequences of nouns acting as a single noun. In our view, their semantics is best characterized by the set of all possible paraphrasing verbs, with associated weights, e.g., malaria mosquito is carry (23), spread (16), cause (12), transmit (9), etc. Using Amazon's Mechanical Turk, we collect paraphrasing verbs for 250 noun-noun compounds previously proposed in the linguistic literature, thus creating a valuable resource for noun compound interpretation. Using these verbs, we further construct a dataset of pairs of sentences representing a special kind of textual entailment task, where a binary decision is to be made about whether an expression involving a verb and two nouns can be transformed into a noun compound, while preserving the sentence meaning.

One of the most anticipated books of the fall! - Adam Grant, Ars Technica, Philadelphia Inquirer, Next Big Idea Club, BookPage "If you're terrified that artificial intelligence is going to take over the world, you clearly haven't asked a computer to write pick-up lines, name pets, or do anything else social or creative. Janelle Shane has, and she's the perfect tour guide to explain what machine learning can and can't do--and why it's already affecting your life. I can't think of a better way to learn about artificial intelligence, and I've never had so much fun along the way."―Adam Grant, New York Times bestselling author of Originals "While everyone else is making questionable predictions about the future of AI, Janelle Shane cuts through the fog by telling you how AI actually works. And even better: she makes it fun!"―Zach Weinersmith, creator of Saturday Morning Breakfast Cereal and New York Times bestselling author of Soonish "An incredibly accessible, informative, and hilarious look at how the AIs deciding things around us operate."―Ryan