Fox News Washington-based correspondent Mark Meredith breaks down which jobs are most at risk during the AI revolution on'Special Report.' Artificial intelligence (AI) is set to completely disrupt the American education system and experts say the new technology could push forth a new model that produces more efficient and relevant students within the workforce. While many critics have argued ChatGPT and other bots will exacerbate cheating or hinder critical thinking, others have claimed it is necessary to train students on the tool in order to set them up for future success. David Espindola, a digital technology entrepreneur and the author of "Soulful: You in the Future of Artificial Intelligence," told Fox News Digital the current educational system is "broken" and needs a new model. "I think education is ready to be disrupted big time," he said.

Recently, many improved naive Bayes methods have been developed with enhanced discrimination capabilities. Among them, regularized naive Bayes (RNB) produces excellent performance by balancing the discrimination power and generalization capability. Data discretization is important in naive Bayes. By grouping similar values into one interval, the data distribution could be better estimated. However, existing methods including RNB often discretize the data into too few intervals, which may result in a significant information loss. To address this problem, we propose a semi-supervised adaptive discriminative discretization framework for naive Bayes, which could better estimate the data distribution by utilizing both labeled data and unlabeled data through pseudo-labeling techniques. The proposed method also significantly reduces the information loss during discretization by utilizing an adaptive discriminative discretization scheme, and hence greatly improves the discrimination power of classifiers. The proposed RNB+, i.e., regularized naive Bayes utilizing the proposed discretization framework, is systematically evaluated on a wide range of machine-learning datasets. It significantly and consistently outperforms state-of-the-art NB classifiers.

Although artificial intelligence has been present in our lives for years, 2022 served as a major proving ground for the technology. Between ChatGPT, AI art generation and Hollywood embracing AI, AI found a new kind of foothold––and hype––with the general public. But it also came with a fresh wave of concerns about privacy and ethics. With all that 2022 did to raise the profile of the technology, AI experts at Northeastern University say 2023 will be an equally major year for the future of AI––but it will also face its fair share of challenges. Usama Fayyad, executive director for the Institute for Experiential AI at Northeastern, says the hype around AI wasn't the only thing that defined the technology's trajectory last year. As the public profile of AI grew in 2022, so did the misunderstandings and misinterpretations around it.

Data science tools now automate various pieces of the analytics process, from data preparation to model selection. And automation will only broaden the future scope of data science. According to most analytics and artificial intelligence experts, trends like augmented analytics will only increase the efficiency and reach of data science within the enterprise. Even with accelerating analytics automation, data scientists will be sitting pretty with job security for a long time. "I think what is happening with AI and a lot of these technologies is they are making our jobs easier," said data science expert Usama Fayyad, co-founder of the Initiative for Analytics and Data Science Standards.

For large collections of images, such as those resulting from astronomy sky surveys, the typical useful product is an online database cataloging entries of interest. We focus on the automation of the cataloging effort of a major sky survey and the availability of digital libraries in general. For the primary scientific analysis of these data, it is necessary to detect, measure, and classify every sky object. The learning algorithms are trained to classify the detected objects and can classify objects too faint for visual classification with an accuracy level exceeding 90 percent. This accuracy level increases the number of classified objects in the final catalog threefold relative to the best results from digitized photographic sky surveys to date.

We are pleased to introduce the space application issue articles in this issue of AI Magazine. The exploration of space is a testament to human curiosity and the desire to understand the universe that we inhabit. As many space agencies around the world design and deploy missions, it is apparent that there is a need for intelligent, exploring systems that can make decisions on their own in remote, potentially hostile environments. At the same time, the monetary cost of operating missions, combined with the growing complexity of the instruments and vehicles being deployed, make it apparent that substantial improvements can be made by the judicious use of automation in mission operations.

The National Aeronautics and Space Administration (NASA) is being challenged to perform more frequent and intensive space-exploration missions at greatly reduced cost. Nowhere is this challenge more acute than among robotic planetary exploration missions that the Jet Propulsion Laboratory (JPL) conducts for NASA. This article describes recent and ongoing work on spacecraft autonomy and ground systems that builds on a legacy of existing success at JPL applying AI techniques to challenging computational problems in planning and scheduling, real-time monitoring and control, scientific data analysis, and design automation.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.

The value of scientific digital-image libraries seldom lies in the pixels of images. For large collections of images, such as those resulting from astronomy sky surveys, the typical useful product is an online database cataloging entries of interest. We focus on the automation of the cataloging effort of a major sky survey and the availability of digital libraries in general. The SKICAT system automates the reduction and analysis of the three terabytes worth of images, expected to contain on the order of 2 billion sky objects. For the primary scientific analysis of these data, it is necessary to detect, measure, and classify every sky object. SKICAT integrates techniques for image processing, classification learning, database management, and visualization. The learning algorithms are trained to classify the detected objects and can classify objects too faint for visual classification with an accuracy level exceeding 90 percent. This accuracy level increases the number of classified objects in the final catalog threefold relative to the best results from digitized photographic sky surveys to date. Hence, learning algorithms played a powerful and enabling role and solved a difficult, scientifically significant problem, enabling the consistent, accurate classification and the ease of access and analysis of an otherwise unfathomable data set.

Instead of "ground truth" one may only have the subjective opinion(s) of one or more experts. For example, medical data or image data may be collected off-line and some time later a set of experts analyze the data and produce a set of class labels. The central problem is that of trying to infer the "ground truth" given the noisy subjective estimates of the experts. When one wishes to apply a supervised learning algorithm to the data, the problem is primarily twofold: (i) how to evaluate the relative performance of experts and algorithms, and (ii) how to train a pattern recognition system in the absence of absolute ground truth. In this paper we focus on problem (i), namely the performance evaluation issue, and in particular we discuss the application of a particular modelling technique to the problem of counting volcanoes on the surface of Venus.