Technology has increasingly become an integral part of the Bible translation process. Over time, both the translation process and relevant technology have evolved greatly. More recently, the field of Natural Language Processing (NLP) has made great progress in solving some problems previously thought impenetrable. Through this study we endeavor to better understand and communicate about a segment of the current landscape of the Bible translation process as it relates to technology and identify pertinent issues. We conduct several interviews with individuals working in different levels of the Bible translation process from multiple organizations to identify gaps and bottlenecks where technology (including recent advances in AI) could potentially play a pivotal role in reducing translation time and improving overall quality.

Machine learning (ML) tasks are one of the major workloads in today's edge computing networks. Existing edge-cloud schedulers allocate the requested amounts of resources to each task, falling short of best utilizing the limited edge resources for ML tasks. This paper proposes TapFinger, a distributed scheduler for edge clusters that minimizes the total completion time of ML tasks through co-optimizing task placement and fine-grained multi-resource allocation. To learn the tasks' uncertain resource sensitivity and enable distributed scheduling, we adopt multi-agent reinforcement learning (MARL) and propose several techniques to make it efficient, including a heterogeneous graph attention network as the MARL backbone, a tailored task selection phase in the actor network, and the integration of Bayes' theorem and masking schemes. We first implement a single-task scheduling version, which schedules at most one task each time. Then we generalize to the multi-task scheduling case, in which a sequence of tasks is scheduled simultaneously. Our design can mitigate the expanded decision space and yield fast convergence to optimal scheduling solutions. Extensive experiments using synthetic and test-bed ML task traces show that TapFinger can achieve up to 54.9% reduction in the average task completion time and improve resource efficiency as compared to state-of-the-art schedulers.

Expressing attitude or stance toward entities and concepts is an integral part of human behavior and personality. Recently, evaluative language data has become more accessible with social media's rapid growth, enabling large-scale opinion analysis. However, surprisingly little research examines the relationship between personality and evaluative language. To bridge this gap, we introduce the notion of evaluative topics, obtained by applying topic models to pre-filtered evaluative text from social media. We then link evaluative topics to individual text authors to build their evaluative profiles. We apply evaluative profiling to Reddit comments labeled with personality scores and conduct an exploratory study on the relationship between evaluative topics and Big Five personality facets, aiming for a more interpretable, facet-level analysis. Finally, we validate our approach by observing correlations consistent with prior research in personality psychology.

On Jan. 18, the Association for Computing Machinery (ACM) announced its 2022 fellows, those it recognizes "for significant contributions in areas including cybersecurity, human-computer interaction, mobile computing, and recommender systems among many other areas." Included in the crop of new fellows were six distinguished scientists with ties to MIT. Constantinos Daskalakis, the Armen Avanessians (1982) Professor in the Department of Electrical Engineering and Computer Science (EECS), was honored "for contributions to the foundations of algorithmic game theory, mechanism design, sublinear algorithms, and theoretical machine learning." Daskalakis is a theoretical computer scientist who works at the interface of game theory, economics, probability theory, statistics, and machine learning. His current work focuses on multi-agent learning, learning from biased and dependent data, causal inference and econometrics.

Robocorp CMO Dave Dabbah is a full-stack marketing leader with a proven record of success driving growth and brand recognition. The ChatGPT revolution has officially gone into full unicorn mode, which theoretically should mean that marketers across the globe can now significantly increase their ability to produce high-quality content at scale. I'll let the cat out of the bag early in this article to say that I believe the underlying technology that makes ChatGPT so good will revolutionize how marketing teams produce content in the future. Although "revolutionize" is an overused term, the potentially far-reaching effects of an AI-powered service delivering high-quality content are among the most exciting developments in the tech industry that I have seen, reminiscent of the game-changing impact of Napster (in which I may have downloaded the entire Nirvana collection in under 60 seconds). I believe that the introduction of ChatGPT, which utilizes AI to deliver information, will be as impactful on content producers as the birth of the internet.

He is a professor emeritus of computer science and engineering at the University of Washington and the author of The Master Algorithm. Pedro is a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. He is the author or co-author of over 200 technical publications in machine learning, data mining, and other areas; a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. Pedro has written for the Wall Street Journal, Spectator, Scientific American, Wired, and others. Our speaker helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks.

Context and motivation: The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that behaviour. Question / problem: We see major uncertainty in how to specify training data and runtime monitoring for critical ML models and by this specifying the final functionality of the system. In this interview-based study we investigate the underlying challenges for these difficulties. Principal ideas/results: Based on ten interviews with practitioners who develop ML models for critical applications in the automotive and telecommunication sector, we identified 17 underlying challenges in 6 challenge groups that relate to the challenge of specifying training data and runtime monitoring. Contribution: The article provides a list of the identified underlying challenges related to the difficulties practitioners experience when specifying training data and runtime monitoring for ML models. Furthermore, interconnection between the challenges were found and based on these connections recommendation proposed to overcome the root causes for the challenges.

What can marketers bring to the mix when AI is so powerful? Don't miss a MarketingProfs podcast, subscribe to our free newsletter! Passion, for one thing, says Peter Prodromou of Boathouse. "If you're in the upper right-hand corner with passion, chances are people are going to want to work with you or buy your product," he says on the latest episode of Marketing Smarts. "Think about Apple and Tesla; those are two brands that are very much about passion. Your ability to convey that is critically important." AI is just an algorithm, after all. "Everybody is going to shop at Amazon because they have the best algorithm, and there may or may not be passion for it," Peter says.

The world faces a significant risk of passing a crucial global warming threshold earlier than scientists had suggested, possibly as soon as 2050, a paper published Monday found. The threshold is the point at which Earth's overall temperature has increased by 2.0 degrees Celsius, or 3.6 degrees Fahrenheit. If greenhouse gas emissions remain at high levels, there's a 50% probability the world could reach that catastrophic milestone before 2050, said co-author Noah Diffenbaugh, a climate scientist at Stanford University. The chance it could reach 2.0 degrees before 2058 is 84 in 100. Earlier estimates had put it closer to the end of the century.

A Northumbria University physicist has been awarded more than half a million pounds to develop artificial intelligence which will protect the Earth from devastating space storms. Activity from the Sun such as solar eruptions, known as Coronal Mass Ejections, results in plasma being fired towards Earth at supersonic speeds, which can result in serious disruption to power and communication systems. With our increasing reliance on technology, solar storms pose a serious threat to our everyday lives, leading to severe space weather being added to the UK National Risk Assessment for the first time in 2011. Northumbria's Dr Andy Smith has recently been awarded a Research Fellowship from the Natural Environment Research Council (NERC) to explore how physics-inspired machine learning could be used to forecast space weather more accurately and predict serious space storms. During the Next Generation, Physics-Inspired AI for Space Weather Forecasting project, Dr Smith and his team will analyse huge amounts of data from satellites and space missions over the last 20 years to gain a better understanding of the conditions under which storms are likely to occur.