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.

Detection of unwanted (`foreign') objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images), enabling automated X-ray based foreign object detection. However, these methods require a large number of training examples and manual annotation of these examples is a subjective and laborious task. In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labour requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that have been acquired as part of the CT-scan data serve as input for the machine learning method. High-quality ground truth locations of the foreign objects are obtained through accurate 3D reconstructions and segmentations. Using these segmented volumes, corresponding 2D segmentations are obtained by creating virtual projections. We outline the benefits of objectively and reproducibly generating training data in this way compared to conventional radiograph annotation. In addition, we show how the accuracy depends on the number of objects used for the CT reconstructions. The results show that in this workflow generally only a relatively small number of representative objects (i.e., fewer than 10) are needed to achieve adequate detection performance in an industrial setting. Moreover, for real experimental data we show that the workflow leads to higher foreign object detection accuracies than with standard radiograph annotation.

You can check the schedule in your city, here. Description This session aims to reveal the infrastructure and energy resources that are needed to support digitization, massive data processing and most Artificial Intelligence developments, emphasizing the ecological footprint of its processes. Every week you will receive the links for the sessions, book it on your calendar!

Many astrophysical analyses depend on estimates of redshifts (a proxy for distance) determined from photometric (i.e., imaging) data alone. Inaccurate estimates of photometric redshift uncertainties can result in large systematic errors. However, probability distribution outputs from many photometric redshift methods do not follow the frequentist definition of a Probability Density Function (PDF) for redshift -- i.e., the fraction of times the true redshift falls between two limits $z_{1}$ and $z_{2}$ should be equal to the integral of the PDF between these limits. Previous works have used the global distribution of Probability Integral Transform (PIT) values to re-calibrate PDFs, but offsetting inaccuracies in different regions of feature space can conspire to limit the efficacy of the method. We leverage a recently developed regression technique that characterizes the local PIT distribution at any location in feature space to perform a local re-calibration of photometric redshift PDFs. Though we focus on an example from astrophysics, our method can produce PDFs which are calibrated at all locations in feature space for any use case.