In this manuscript, we offer a gentle review of submodularity and supermodularity and their properties. We offer a plethora of submodular definitions; a full description of a number of example submodular functions and their generalizations; example discrete constraints; a discussion of basic algorithms for maximization, minimization, and other operations; a brief overview of continuous submodular extensions; and some historical applications. We then turn to how submodularity is useful in machine learning and artificial intelligence. This includes summarization, and we offer a complete account of the differences between and commonalities amongst sketching, coresets, extractive and abstractive summarization in NLP, data distillation and condensation, and data subset selection and feature selection. We discuss a variety of ways to produce a submodular function useful for machine learning, including heuristic hand-crafting, learning or approximately learning a submodular function or aspects thereof, and some advantages of the use of a submodular function as a coreset producer. We discuss submodular combinatorial information functions, and how submodularity is useful for clustering, data partitioning, parallel machine learning, active and semi-supervised learning, probabilistic modeling, and structured norms and loss functions.

AlphaGo, AlphaGo Zero, and all of their derivatives can play with superhuman strength because they are able to predict the win-lose outcome with great accuracy. However, Go as a game is decided by a final score difference, and in final positions AlphaGo plays suboptimal moves: this is not surprising, since AlphaGo is completely unaware of the final score difference, all winning final positions being equivalent from the winrate perspective. This can be an issue, for instance when trying to learn the "best" move or to play with an initial handicap. Moreover, there is the theoretical quest of the "perfect game", that is, the minimax solution. Thus, a natural question arises: is it possible to train a successful Reinforcement Learning agent to predict score differences instead of winrates? No empirical or theoretical evidence can be found in the literature to support the folklore statement that "this does not work". In this paper we present Leela Zero Score, a software designed to support or disprove the "does not work" statement. Leela Zero Score is designed on the open-source solution known as Leela Zero, and is trained on a 9x9 board to predict score differences instead of winrates. We find that the training produces a rational player, and we analyze its style against a strong amateur human player, to find that it is prone to some mistakes when the outcome is close. We compare its strength against SAI, an AlphaGo Zero-like software working on the 9x9 board, and find that the training of Leela Zero Score has reached a premature convergence to a player weaker than SAI.

Kernel mean embeddings are a powerful tool to represent probability distributions over arbitrary spaces as single points in a Hilbert space. Yet, the cost of computing and storing such embeddings prohibits their direct use in large-scale settings. We propose an efficient approximation procedure based on the Nystr\"om method, which exploits a small random subset of the dataset. Our main result is an upper bound on the approximation error of this procedure. It yields sufficient conditions on the subsample size to obtain the standard $n^{-1/2}$ rate while reducing computational costs. We discuss applications of this result for the approximation of the maximum mean discrepancy and quadrature rules, and illustrate our theoretical findings with numerical experiments.

AI influences nearly 8 billion people and human & earth diverse ecosystems on an unprecedented scale. Startups accelerate to incorporate AI innovation as AI tools proliferate. UNESCO is the United Nations Educational, Scientific and Cultural Organization. The UNESCO recommendations on the ethics of AI recently adopted by member states provides a foundational global agreement on AI Ethics. The objectives ultimately drive emerging AI driven technologies that are trustworthy, safe, human-centered for the benefit of people and humanity.

Did you miss a session from the Future of Work Summit? This article was contributed by Wilson Pang, CTO at Appen. The new foundation of the artificial intelligence (AI) economy is flexible, remote work. Thanks to advances in technology that enable remote work at an unimaginable scale, organizations developing AI can now collaborate with people from almost anywhere, including previously inaccessible areas. People across the globe can now contribute to building AI in meaningful ways, particularly through data preparation and annotation work.

Nowadays, most methods in end-to-end contextual speech recognition bias the recognition process towards contextual knowledge. Since all-neural contextual biasing methods rely on phrase-level contextual modeling and attention-based relevance modeling, they may encounter confusion between similar context-specific phrases, which hurts predictions at the token level. In this work, we focus on mitigating confusion problems with fine-grained contextual knowledge selection (FineCoS). In FineCoS, we introduce fine-grained knowledge to reduce the uncertainty of token predictions. Specifically, we first apply phrase selection to narrow the range of phrase candidates, and then conduct token attention on the tokens in the selected phrase candidates. Moreover, we re-normalize the attention weights of most relevant phrases in inference to obtain more focused phrase-level contextual representations, and inject position information to better discriminate phrases or tokens. On LibriSpeech and an in-house 160,000-hour dataset, we explore the proposed methods based on a controllable all-neural biasing method, collaborative decoding (ColDec). The proposed methods provide at most 6.1% relative word error rate reduction on LibriSpeech and 16.4% relative character error rate reduction on the in-house dataset over ColDec.

Frost & Sullivan, a leading management consultancy supporting clients on their journey to visionary innovation and transformational growth, announced that it has honored Startek a global provider of customer experience (CX) management solutions, with the 2021 Americas New Product Innovation Award for customer experience outsourcing services. "Companies must move forward with centralized data and automated tasks and interactions through self-service tools," said Sebastian Menutti, industry principal, Frost & Sullivan. "But, human agents must still be available when intuition, emotional intelligence, and empathy are required to deliver positive customer outcomes. This award recognizes the Startek approach that seamlessly combines live agent interactions with digital solutions to deliver added benefit for their customers." Startek enables its partners to integrate digital technologies and live interactions to deliver best-in-class CX.

Graviti, a New York based modern data infrastructure startup that has been in stealth mode for the past three years, is launching its first product Graviti Data Platform. Graviti Data Platform is designed to eliminate one of the costliest and confounding problems faced by developers of artificial intelligence (AI) applications worldwide: working with large volumes of unstructured data. This product not only removes much of the hassle faced when managing and processing such data, but also radically enables the scalability and complexity of unstructured data those developers work with. Enterprises can acquire, manage, and query data faster on the cloud with more ease, identify the actionable insights from the data collected and create pioneering AI applications. The launch of the data platform couldn't come at a more critical time for enterprises and AI developers.

A 2019 study conducted by DataChildFutures found that 46% of participating Italian households had AI-powered speakers, while 40% of toys were connected to the internet. More recent research suggests that by 2023 more than 275 million intelligent voice assistants, such as Amazon Echo or Google Home, will be installed in homes worldwide. As younger generations grow up interacting with AI-enabled devices, more consideration should be given to the impact of this technology on children, their rights and wellbeing. AI-powered learning tools and approaches are often regarded as critical drivers of innovation in the education sector. Often recognized for its ability to improve the quality of learning and teaching, AI is being used to monitor students' level of knowledge and learning habits, such as rereading and task prioritization, and ultimately to provide a personalized approach to learning. Knewton is one example of AI-enabled learning software that identifies knowledge gaps and curates education content in line with user needs.

Fredrik Nilsson is Vice President of the Americas for Axis Communications, overseeing the company's operations in North and South America. What is the general public's impression of artificial intelligence (AI)? It isn't always easy to gauge. AI-powered voice assistants like Siri and Alexa are in our phones, our homes and even our cars, making them a part of everyday life. Yet most people don't expect them to be particularly accurate.