The use of artificial intelligence (AI), cognitive technologies, and robotics to automate and augment work is on the rise, prompting the redesign of jobs in a growing number of domains. The jobs of today are more machine-powered and data-driven than in the past, and they also require more human skills in problem-solving, communication, interpretation, and design. As machines take over repeatable tasks and the work people do becomes less routine, many jobs will rapidly evolve into what we call "superjobs"--the newest job category that changes the landscape of how organizations think about work. During the last few years, many have been alarmed by studies predicting that AI and robotics will do away with jobs. In 2019, this topic remains very much a concern among our Global Human Capital Trends survey respondents.

Artificial Intelligence in Practice examines 50 use cases of companies that are applying AI to solve industry specific problems. In that sense it provides interesting insights how AI is used in real life as opposed to the more theoretical discussions focusing on its potential. The fact that the examples exclusively focuses on large corporations also cements the progress AI has already made and the impact it has on our daily life. Examples range from the big tech companies Apple, Google, Facebook and alike to Retail, Consumer Goods and Food and Beverage Companies to Media, Entertainment and Telecom Companies to Services, Financial and Healthcare Companies to Manufacturing, Automotive, Aerospace and Industry 4.0 Companies. Each case gives a brief overview of the company, discusses what problem AI is solving in the specific example, how it is used in practice, what technology, tools and data were used to do so, showcases the results and concludes with a summary on the key challenges, learning points and takeaways.

While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniques are still susceptible to the longstanding problem of reward sparsity. This is especially true for tasks such as training an agent to play StarCraft II, a real-time strategy game where reward is only given at the end of a game which is usually very long. While this problem can be addressed through reward shaping, such approaches typically require a human expert with specialized knowledge. Inspired by the vision of enabling reward shaping through the more-accessible paradigm of natural-language narration, we develop a technique that can provide the benefits of reward shaping using natural language commands. Our narration-guided RL agent projects sequences of natural-language commands into the same high-dimensional representation space as corresponding goal states. We show that we can get improved performance with our method compared to traditional reward-shaping approaches. Additionally, we demonstrate the ability of our method to generalize to unseen natural-language commands.

One of the reasons for the success of convolutional networks is their equivariance/invariance under translations. However, rotatable data such as molecules, living cells, everyday objects, or galaxies require processing with equivariance/invariance under rotations in cases where the rotation of the coordinate system does not affect the meaning of the data (e.g. object classification). On the other hand, estimation/processing of rotations is necessary in cases where rotations are important (e.g. motion estimation). There has been recent progress in methods and theory in all these regards. Here we provide an overview of existing methods, both for 2D and 3D rotations (and translations), and identify commonalities and links between them, in the hope that our insights will be useful for choosing and perfecting the methods.

Automatic machine learning (AutoML) is an area of research aimed at automating machine learning (ML) activities that currently require human experts. One of the most challenging tasks in this field is the automatic generation of end-to- end ML pipelines: combining multiple types of ML algorithms into a single architecture used for end-to-end analysis of previously-unseen data. This task has two challenging aspects: the first is the need to explore a large search space of algorithms and pipeline architectures. The second challenge is the computational cost of training and evaluating multiple pipelines. In this study we present DeepLine, a reinforcement learning based approach for automatic pipeline generation. Our proposed approach utilizes an efficient representation of the search space and leverages past knowledge gained from previously-analyzed datasets to make the problem more tractable. Additionally, we propose a novel hierarchical-actions algorithm that serves as a plugin, mediating the environment-agent interaction in deep reinforcement learning problems. The plugin significantly speeds up the training process of our model. Evaluation on 56 datasets shows that DeepLine outperforms state-of-the-art approaches both in accuracy and in computational cost.

Debra Fahey will be joining the company as Senior Vice President, Global Head of Delivery & Operations, and Michael Fox will be joining as Senior Vice President of Product Management. Together, these leaders bring over 40 years of experience in the fields of technology, business analytics, innovation strategy, and software development to ElectrifAi's growing team of skilled professionals. "I am proud to welcome Debra and Michael as the newest additions to ElectrifAi's deep executive leadership team," said CEO, Edward Scott. "Their wealth of knowledge and invaluable experience in the fields of delivery, operations, and product management will be vital in their new roles advancing ElectrifAi's industry-leading Ai and ML products. I am confident Ms. Fahey and Mr. Fox will add value to our expanding global leadership team and continue to strengthen our expertise within the Ai and ML technology, innovation, and delivery of solutions."

We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence. The proposed contrastive attention mechanism accommodates two categories of attention: one is the conventional attention that attends to relevant parts of the source sentence, the other is the opponent attention that attends to irrelevant or less relevant parts of the source sentence. Both attentions are trained in an opposite way so that the contribution from the conventional attention is encouraged and the contribution from the opponent attention is discouraged through a novel softmax and softmin functionality. Experiments on benchmark datasets show that, the proposed contrastive attention mechanism is more focused on the relevant parts for the summary than the conventional attention mechanism, and greatly advances the state-of-the-art performance on the abstractive sentence summarization task. We release the code at https://github.com/travel-go/

The SaaS company will explore how it can leverage AI to improve its software's lead conversion capabilities Is IIT-M becoming India's new-age technology hub? New-age technology like artificial intelligence (AI) has come a long way in bringing unique solutions for companies working across multiple sectors. The revolutionary technology has also become a go-to innovation tool for software-as-a-service (SAAS) companies as well. This time SaaS unicorn Freshworks has partnered with Robert Bosch Centre for Data Science and Artificial Intelligence in Indian Institute of Technology, Madras (IIT Madras) to improve the predictive capability of its customer relationship management (CRM) software. Freshworks will initially work with the centre exploring how it can leverage AI to improve its software's lead-conversion capabilities, helping its clients to increase their business.

Science fiction is quickly becoming everyday reality. Chatbots, robots, digital assistants, automated vehicles, virtual assistants, and much more... are the products of artificial intelligence (AI), which is already transforming entire industries. An infographic by TechJury, provider of one-step tech guides and product reviews, provides a detailed overview of AI. The infographic begins with a timeline of AI, starting in the mid-20th century with the "father of theoretical computer science and artificial intelligence," Alan Turing, who developed the "Turing test" for determining what qualifies as artificial intelligence. The infographic goes on to outline various classifications of AI, provides examples of AI technology, highlights statistics about the AI market, and lists the companies and countries at the forefront of the AI race.

Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce Copter - an intelligent travel assistant that evaluates multi-modal travel alternatives to find a plan that is acceptable to a person given their context and preferences. We propose a formulation for acceptable planning that brings together ideas from AI, machine learning, and economics. This formulation has been incorporated in Copter that produces acceptable plans in real-time. We adopt a novel empirical evaluation framework that combines human decision data with a high fidelity multi-modal transportation simulation to demonstrate a 4% energy reduction and 20% delay reduction in a realistic deployment scenario in Los Angeles, California, USA. This article is part of the special track on AI and Society.