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

British Airways (BA) has introduced artificial intelligence (AI) technology to parts of its "airside operation" at Heathrow Airport. In an announcement Monday, the airline said that AI would be used to tackle challenges faced when an aircraft is being prepared for departure after passengers from its previous flight have disembarked. It's during this time that staff on the ground undertake manual checks related to 18 different things, making records of what they are doing, British Airways said. These checks have to be done before the plane takes to the air again and are, among other things, related to refueling, the unloading and reloading of luggage, and cleaning of the aircraft's inside. BA said that if one of these tasks encounters an issue, it could potentially disrupt the whole process, resulting in a delayed departure.

Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like $\epsilon$-greedy. This contributes to the problem of high sample complexity, as the algorithm wastes effort by repeatedly visiting parts of the state space that have already been explored. We introduce a novel method based on Bayesian linear regression and latent space embedding to generate an intrinsic reward signal that encourages the learning agent to seek out unexplored parts of the state space. This method is computationally efficient, simple to implement, and can extend any state-of-the-art reinforcement learning algorithm. We evaluate the method on a range of algorithms and challenging control tasks, on both simulated and physical robots, demonstrating how the proposed method can significantly improve sample complexity.

This paper presents abstract art created by neural networks and broadly recognizable across various computer vision systems. The existence of abstract forms that trigger specific labels independent of neural architecture or training set suggests convolutional neural networks build shared visual representations for the categories they understand. Computer vision classifiers encountering these drawings often respond with strong responses for specific labels - in extreme cases stronger than all examples from the validation set. By surveying human subjects we confirm that these abstract artworks are also broadly recognizable by people, suggesting visual representations triggered by these drawings are shared across human and computer vision systems.