The development and adoption of advanced technologies including smart automation and artificial intelligence has the potential not only to raise productivity and GDP growth but also to improve well-being more broadly, including through healthier life and longevity and more leisure. Alongside such benefits, these technologies also have the potential to reduce disruption and the potentially destabilizing effects on society arising from their adoption. Tech for Good: Smoothing disruption, improving well-being (PDF–1MB) examines the factors that can help society achieve such benefits and makes a first attempt to calculate the impact of technology adoption on welfare growth beyond GDP. Our modeling suggests that good outcomes for the economy overall and for individual well-being come about when technology adoption is focused on innovation-led growth rather than purely on labor reduction and cost savings through automation. This needs to be accompanied by proactive transition management that increases labor market fluidity and equips workers with new skills. Technology for centuries has both excited the human imagination and prompted fears about its effects. Today's technology cycle is no different, provoking a broad spectrum of hopes and fears.

The paper copes with the task of automatic assessment of second language proficiency from the language learners' spoken responses to test prompts. The task has significant relevance to the field of computer assisted language learning. The approach presented in the paper relies on two separate modules: (1) an automatic speech recognition system that yields text transcripts of the spoken interactions involved, and (2) a multiple classifier system based on deep learners that ranks the transcripts into proficiency classes. Different deep neural network architectures (both feed-forward and recurrent) are specialized over diverse representations of the texts in terms of: a reference grammar, the outcome of probabilistic language models, several word embeddings, and two bag-of-word models. Combination of the individual classifiers is realized either via a probabilistic pseudo-joint model, or via a neural mixture of experts. Using the data of the third Spoken CALL Shared Task challenge, the highest values to date were obtained in terms of three popular evaluation metrics.

This paper studies regret minimization in multi-armed bandits, a classical online learning problem. To develop more statistically-efficient algorithms, we propose to use the assumption of a random-effect model. In this model, the mean rewards of arms are drawn independently from an unknown distribution, whose parameters we estimate. We provide an estimator of the arm means in this model and also analyze its uncertainty. Based on these results, we design a UCB algorithm, which we call ReUCB. We analyze ReUCB and prove a Bayes regret bound on its $n$-round regret, which matches an existing lower bound. Our experiments show that ReUCB can outperform Thompson sampling in various scenarios, without assuming that the prior distribution of arm means is known.

This volume contains revised versions of the papers selected for the second volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

India is expected to invest $1 billion in artificial intelligence by 2023, according to a recent report from Project Management Institute (PMI), a Philadelphia-based non-profit organisation. Last year, the Indian government allocated $477 million to boost the country's AI ecosystem. Further, as part of the National Education Policy (NEP), AI will be introduced in school curriculums. Inarguably, India is making bottom-up changes to become an AI superpower. Below, we take a look at the latest AI courses offered by Indian institutions.

We discuss Jason's path into machine learning, empowering doctors and scientists with weak supervision, and utilizing organizational resources in biomedical applications of Snorkel. This episode is part of the #ScienceTalks video series hosted by the Snorkel AI team. Jason: Originally, during my undergraduate days, I intended to go into medicine. However, I enjoyed engineering classes way more than biology classes, so I shifted and majored in Computer Science and English. I also worked with a research group at the University of Iowa to track infections in hospitals.

The business use cases for chatbots are nearly endless, but there are also interesting and impactful ways that chatbots can be used for social good around the world. In particular, our favourite technology is a fantastic medium for improving access to much-needed education in emerging international markets. In many emerging markets access to a smartphone is much more common than access to a laptop or desktop computer. And while there are still gaps in ownership between the women and men of some emerging markets, many people will share a smartphone in order to access the apps and information they require. In order to improve access to education and support services, the smartphone will play a crucial role.

This article is sponsored by IBM. SUMMARY: Organizations often miss the greatest opportunities that machine learning has to offer because tapping them requires real-time predictive scoring. In order to optimize the very largest-scale processes – which is a vital endeavor for your business – predictive scoring must take place right at the moment of each and every interaction. The good news is that you probably already have the hardware to handle this endeavor: the same system currently running your high-volume transactions – oftentimes a mainframe. But getting this done requires a specialized leadership practice and strong-willed change management. Heed this warning: The greatest opportunities with machine learning are exactly the ones that your business is most likely to miss. To be specific, there's massive potential for real-time predictive scoring to optimize your largest-scale operations. But with these particularly high stakes comes a tragic case of analysis paralysis.

When General Richard D. Clarke, commander of the U.S. Special Operations Command (USSOCOM), visited MIT in fall 2019, he had artificial intelligence on the mind. As the commander of a military organization tasked with advancing U.S. policy objectives as well as predicting and mitigating future security threats, he knew that the acceleration and proliferation of artificial intelligence technologies worldwide would change the landscape on which USSOCOM would have to act. Clarke met with Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science, and after touring multiple labs both agreed that MIT -- as a hub for AI innovation -- would be an ideal institution to help USSOCOM rise to the challenge. Thus, a new collaboration between the MIT School of Engineering, MIT Professional Education, and USSOCOM was born: a six-week AI and machine learning crash course designed for special operations personnel. "There has been tremendous growth in the fields of computing and artificial intelligence over the past few years," says Chandrakasan.

Uplatz provides this frequently asked list of Data Science Interview Questions and Answers to help you prepare for the Data Scientist and Machine Learning Engineer interviews. This comprehensive list of important data science interview questions and answers might play a significant role in shaping your career and helping you get your next dream job. You can get into the mainstream of the Data Science world learning from this powerful set of Data Science interview questions. Data Science can be defined as multidisciplinary blend of trends prediction, data inference, algorithm development, and technology to solve analytically complex problems. At the core of data science is nothing but data.