The adoption of Artificial Intelligence in Education (AIED) holds the promise of revolutionizing educational practices by offering personalized learning experiences, automating administrative and pedagogical tasks, and reducing the cost of content creation. However, the lack of standardized practices in the development and deployment of AIED solutions has led to fragmented ecosystems, which presents challenges in interoperability, scalability, and ethical governance. This article aims to address the critical need to develop and implement industry standards in AIED, offering a comprehensive analysis of the current landscape, challenges, and strategic approaches to overcome these obstacles. We begin by examining the various applications of AIED in various educational settings and identify key areas lacking in standardization, including system interoperability, ontology mapping, data integration, evaluation, and ethical governance. Then, we propose a multi-tiered framework for establishing robust industry standards for AIED. In addition, we discuss methodologies for the iterative development and deployment of standards, incorporating feedback loops from real-world applications to refine and adapt standards over time. The paper also highlights the role of emerging technologies and pedagogical theories in shaping future standards for AIED. Finally, we outline a strategic roadmap for stakeholders to implement these standards, fostering a cohesive and ethical AIED ecosystem. By establishing comprehensive industry standards, such as those by IEEE Artificial Intelligence Standards Committee (AISC) and International Organization for Standardization (ISO), we can accelerate and scale AIED solutions to improve educational outcomes, ensuring that technological advances align with the principles of inclusivity, fairness, and educational excellence.

In the age of Industry 4.0 and Cyber-Physical Production Systems (CPPSs) vast amounts of potentially valuable data are being generated. Methods from Machine Learning (ML) and Data Mining (DM) have proven to be promising in extracting complex and hidden patterns from the data collected. The knowledge obtained can in turn be used to improve tasks like diagnostics or maintenance planning. However, such data-driven projects, usually performed with the Cross-Industry Standard Process for Data Mining (CRISP-DM), often fail due to the disproportionate amount of time needed for understanding and preparing the data. The application of domain-specific ontologies has demonstrated its advantageousness in a wide variety of Industry 4.0 application scenarios regarding the aforementioned challenges. However, workflows and artifacts from ontology design for CPPSs have not yet been systematically integrated into the CRISP-DM. Accordingly, this contribution intends to present an integrated approach so that data scientists are able to more quickly and reliably gain insights into the CPPS. The result is exemplarily applied to an anomaly detection use case.

Federated learning (FL) is a distributed machine learning strategy that enables participants to collaborate and train a shared model without sharing their individual datasets. Privacy and fairness are crucial considerations in FL. While FL promotes privacy by minimizing the amount of user data stored on central servers, it still poses privacy risks that need to be addressed. Industry standards such as differential privacy, secure multi-party computation, homomorphic encryption, and secure aggregation protocols are followed to ensure privacy in FL. Fairness is also a critical issue in FL, as models can inherit biases present in local datasets, leading to unfair predictions. Balancing privacy and fairness in FL is a challenge, as privacy requires protecting user data while fairness requires representative training data. This paper presents a "Fair Differentially Private Federated Learning Framework" that addresses the challenges of generating a fair global model without validation data and creating a globally private differential model. The framework employs clipping techniques for biased model updates and Gaussian mechanisms for differential privacy. The paper also reviews related works on privacy and fairness in FL, highlighting recent advancements and approaches to mitigate bias and ensure privacy. Achieving privacy and fairness in FL requires careful consideration of specific contexts and requirements, taking into account the latest developments in industry standards and techniques.

Fox News contributor Joe Concha joins "Fox & Friends First" to discuss Elon Musk's warning that AI could threaten elections and his concerns on the declining birth rate. The top lawyer for OpenAI, the company that developed ChatGPT, argued that the best way to regulate artificial intelligence is not to start with government mandated rules and regulations but to allow the companies themselves to set standards that ensure AI is used safely and responsibly. OpenAI General Counsel Jason Kwon made that argument during a Tuesday panel discussion in Washington, D.C., which was hosted by BSA/The Software Alliance, even as he acknowledged that AI is developing so quickly that it can often lead to unexpected results that companies quickly need to rein in. Still, when asked what his message to policymakers was, Kwon recommended voluntary, industry-led standards for AI, calling for a tactic that many companies in most industries tend to favor over government mandates. The top lawyer at OpenAI, run by CEO Sam Altman, above, said this week that the company recommends voluntary industry standards to regulate AI, not government mandates.

In case you missed it, insurtech -- technology developed to improve and transform the insurance industry -- is having a bit of a moment. Forrester recently reported record-breaking funding for insurtechs, closing Q3 at $15 billion - more funding than in 2019 and 2020 combined - with more deals anticipated by the end-of-year. Crunchbase reports "In the world of venture capitalism, you'd be hard-pressed to find a sector with more buzz than insurtech," and Deloitte contends a revolution in insurance has been sparked. There's no denying it: insurtech is booming. There are a few trends that I think executives should anticipate next.

Thousands of working adults will soon benefit from free courses that will help them to rapidly upskill or retrain, as part of the government's drive to plug skills gaps and boost access to more high-quality training alternatives. Sixty-five short and modular courses will start to roll out from later this month at 10 Institutes of Technology (IoTs) across England in sought-after STEM subjects. This will include courses such as Artificial Intelligence, Digitisation of Manufacturing, Digital Construction, Agricultural Robotics, and Cyber Security. The courses will be a blend of classroom and remote online study, and will vary in length from 50 to 138 hours – giving more adults greater flexibility in how and when they learn, so they can fit it around their lives. Swindon and Wiltshire IoT, for instance, will offer five short 50 hour courses across eight weeks.

While demand for robotic process automation continues to skyrocket, it is increasingly accompanied by frustration over RPA's failure to deliver on promised improvements in productivity and cost savings. As might be expected, there's plenty of blame to go around for this situation, with industry insiders pointing to everything from a lack of proper RPA governance to organizations selecting the wrong processes to automate. Casting perhaps the largest shadow, however, is the absence of an industry-wide RPA framework that provides a standard way for describing what each process to be automated does in a way that all automation tools can understand. At present, RPA programs have little choice but to make do with the different ways process automations are described and detailed, with every RPA platform and the complementary tools that occupy different points along the automation value chain speaking a different language. Look, for example, at the inability of RPA users to open, read, and act on automation files in any RPA platform.

The self-driving technology industry is in a strange state right now. A number of companies have been pouring millions of dollars into self-driving technology for years, and many of them have prototype self-driving vehicles that seem to work. Yet I know of only one company--Waymo--that has launched a fully driverless commercial taxi service. And I only know of one company--Nuro--that's running a driverless commercial delivery service on public roads. You'd expect these companies to be capitalizing on their early leads by expanding rapidly, but neither seems to be doing that.

Every once in awhile an idea comes along that's so good it makes you wonder why it took so long for someone to think of it. IBM's AI Fact Sheets is one of those ideas. AI Fact Sheets are a lot like packaged food nutrition labels. They contain information about an AI model's development, capabilities, benchmark performance, and more. Big Blue today announced its plans to "commercialize key automated documentation capabilities from IBM Research's AI Factsheets methodology into Watson Studio in Cloud Pak for Data throughout 2021." In other words: businesses and developers using Watson Studio in Cloud Pak for Data will soon have access to an automated AI Fact Sheets tool to create transparency and info reports.

A new report reveals that robots working in Amazon fulfillment centers are leading to more injuries among human employees - although the e-commerce giant claims the technology reduces incidents. Based on internal records from 150 warehouses, serious injuries were 50 percent higher at facilities with robots than those without, according to the Center for Investigative Reporting's news site, Reveal. There were 14,000 serious injuries in 2019 - a spike of nearly 33 percent from 2015, and nearly double the industry average. The overall injury rate for the 150 facilities was also almost double the industry standard, according to Reveal. Amazon insisted its numbers are inflated because it encourages workers to report even minor incidents.