The graph represents a network of 3,989 Twitter users whose tweets in the requested range contained "futureofwork ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 27 November 2019 at 03:02 UTC. The requested start date was Monday, 25 November 2019 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 3-day, 1-hour, 59-minute period from Thursday, 21 November 2019 at 23:00 UTC to Monday, 25 November 2019 at 01:00 UTC.

Machine learning systems now play a much bigger role in many of our social institutions, from education to healthcare to criminal justice. But many scholars have shown the way these systems are built on data that result in the reproduction of structural bias and discrimination. In this talk, Professor Crawford opens the substrates of training data to uncover the historical origins, labor practices, infrastructures, and epistemological assumptions that go into the production of artificial intelligence. Rather than a focus on technically correcting biases, she argues for a recentering of justice and the enforcement of limits on centralized power. Kate Crawford, Co-Founder of the AI Now Institute, is a Distinguished Research Professor at NYU and a Principal Researcher at Microsoft Research, and she is a leading scholar of the social implications of data systems, machine learning, and artificial intelligence.

Variational inference offers scalable and flexible tools to tackle intractable Bayesian inference of modern statistical models like Bayesian neural networks and Gaussian processes. For largely over-parameterized models, however, the over-regularization property of the variational objective makes the application of variational inference challenging. Inspired by the literature on kernel methods, and in particular on structured approximations of distributions of random matrices, this paper proposes Walsh-Hadamard Variational Inference, which uses Walsh-Hadamard-based factorization strategies to reduce model parameterization, accelerate computations, and increase the expressiveness of the approximate posterior beyond fully factorized ones.

ABSTRACT Speech emotion recognition is a challenging task and an important step towards more natural human-machine interaction. We show that pre-trained language models can be fine-tuned for text emotion recognition, achieving an accuracy of 69 .5 % on Task 4A of SemEval 2017, improving upon the previous state of the art by over 3 % absolute. We combine these language models with speech emotion recognition, achieving results of 73. 5 % accuracy when using provided transcriptions and speech data on a subset of four classes of the IEMOCAP dataset. For our experiments, we created IEmoNet, a modular and adaptable bimodal framework for speech emotion recognition based on pre-trained language models. Lastly, we discuss the idea of using an emotional classifier as a reward for reinforcement learning as a step towards more successful and convenient human-machine interaction. Index T erms-- Speech Emotion Recognition, Text Emotion Recognition, Bimodal Emotion Recognition, IEMOCAP, Self Attention, Pre-trained Language Models 1. INTRODUCTION Emotions are an important aspect of human behavior. They do not only influence the reaction to our environment [1, 2], but also actively change our perception of it [3] and sometimes even contribute to how well we remember specific events [4]. As such, they influence both human-human and human-machine interaction. However, in human-machine interaction, emotions are often not at all or only scarcely considered.

The performance of deep neural networks is often attributed to their automated, task-related feature construction. It remains an open question, though, why this leads to solutions with good generalization, even in cases where the number of parameters is larger than the number of samples. Back in the 90s, Hochreiter and Schmidhuber observed that flatness of the loss surface around a local minimum correlates with low generalization error. For several flatness measures, this correlation has been empirically validated. However, it has recently been shown that existing measures of flatness cannot theoretically be related to generalization due to a lack of invariance with respect to reparameterizations. We propose a natural modification of existing flatness measures that results in invariance to reparameterization.

Such behavior is desirable in multivariate structured prediction tasks, where handcrafted per-pixel loss-based methods inadequately capture strong correlations between output dimensions. CNFs are efficient in sampling and inference, they can be trained with a likelihood-based objective, and CNFs, being generative flows, do not suffer from mode collapse or training instabilities. We provide an effective method to train continuous CNFs for binary problems and in particular, we apply these CNFs to super-resolution and vessel segmentation tasks demonstrating competitive performance on standard benchmark datasets in terms of likelihood and conventional metrics. When the output y is high-dimensional this is a particularly challenging task, and the practitioner is left with many design choices. Do we factorize the conditional? If not, do we model correlations with, say, a conditional random field (Prince, 2012)? Do we use a unimodal distribution? How fat should the tails be? Do we use an explicit likelihood at all, or use implicit methods (Mohamed & Rezende, 2015) such as a GAN (Goodfellow et al., 2014)? Do we quantize the output?

In many collective decision making situations, agents vote to choose an alternative that best represents the preferences of the group. Agents may manipulate the vote to achieve a better outcome by voting in a way that does not reflect their true preferences. In real world voting scenarios, people often do not have complete information about other voter preferences and it can be computationally complex to identify a strategy that will maximize their expected utility. In such situations, it is often assumed that voters will vote truthfully rather than expending the effort to strategize. However, being truthful is just one possible heuristic that may be used. In this paper, we examine the effectiveness of heuristics in single winner and multi-winner approval voting scenarios with missing votes. In particular, we look at heuristics where a voter ignores information about other voting profiles and makes their decisions based solely on how much they like each candidate. In a behavioral experiment, we show that people vote truthfully in some situations and prioritize high utility candidates in others. We examine when these behaviors maximize expected utility and show how the structure of the voting environment affects both how well each heuristic performs and how humans employ these heuristics.

By Country (United States; Canada; Mexico and Rest of North America) 6.3.

The company's app and platform lets wineries and wine producers create, manage, and market an augmented reality experience for their own labels via a smartphone app. Now, says the company, 524 wineries from across the world are participating in the trial and accessing the marketing potential of a marriage of artificial intelligence and augmented reality. "I don't believe anything like this has been done to this level before – bringing together artificial intelligence, augmented reality, clever technology, 500 different wineries with different visuals and branding, as well as different languages," says app creator and Winerytale founder Dave Chaffey. "This platform is purpose-built for mass adoption and accessibility to any winery wanting to take advantage of a brand marketing and sales future that will undeniably involve augmented reality." The technology is designed to work on any wine label, using artificial intelligence to scan and recognize labels, and augmented reality to showcase the wine's backstory by beaming it from an imaginary space inside and outside of the bottle.

One of the anxieties plaguing the work force as we enter the next decade is the fear of automation replacing blue collar jobs. At the forefront of these anxieties is artificial intelligence (AI). The discussion is even such a hot-pressed issue that American Democratic candidate Andrew Yang's argument for a universal basic income policy is riding on its ability to speak to middle-American manual labour workers. However, for a lot of consumers around the world, the thought of automation can be exciting -- such as letting a Tesla drive you hands free down the freeway on a long trip. But this doesn't create an exception for those that are worried their jobs will be replaced by an automated crane or self-driven truck.