This chapter focuses on the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in science assessments. The paper begins with a discussion of the Framework for K-12 Science Education, which calls for a shift from conceptual learning to knowledge-in-use. This shift necessitates the development of new types of assessments that align with the Framework's three dimensions: science and engineering practices, disciplinary core ideas, and crosscutting concepts. The paper further highlights the limitations of traditional assessment methods like multiple-choice questions, which often fail to capture the complexities of scientific thinking and three-dimensional learning in science. It emphasizes the need for performance-based assessments that require students to engage in scientific practices like modeling, explanation, and argumentation. The paper achieves three major goals: reviewing the current state of ML-based assessments in science education, introducing a framework for scoring accuracy in ML-based automatic assessments, and discussing future directions and challenges. It delves into the evolution of ML-based automatic scoring systems, discussing various types of ML, like supervised, unsupervised, and semi-supervised learning. These systems can provide timely and objective feedback, thus alleviating the burden on teachers. The paper concludes by exploring pre-trained models like BERT and finetuned ChatGPT, which have shown promise in assessing students' written responses effectively.

Retired Navy Adm. William McRaven's nearly 40-year career in the U.S. military has spanned everything from deployments as a Navy SEAL, hunting down high-value targets overseas, commanding U.S Special Operations forces in Iraq and Afghanistan, and advising Presidents George W. Bush and Barack Obama. But McRaven is best known for planning and overseeing the 2011 raid that ended with the death of Osama bin Laden. In December that year, McRaven was named as a runner-up for TIME's Person of the Year for his role in the operation. "There is nobody in the U.S. government that thinks we can kill our way to victory, certainly not the special-operations guys," he told TIME in 2011, "but what happens is, by capturing and killing some of these high-value targets, we buy space and time for the rest of the government to work." After retiring from the U.S. military in 2014, McRaven served as the chancellor of the University of Texas System and has written several books on leadership.

Machine Learning has been one of the most discussed topics in the world of technology in recent years. It is a subset of Artificial Intelligence (AI) that allows machines to learn and improve their performance without being explicitly programmed. Machine Learning involves the use of algorithms that can learn from data and make predictions or decisions based on that learning. In this article, we will explore what Machine Learning is, how it works, what it is used for, and some examples of it in action. At its core, Machine Learning is a technique that enables machines to learn from data and improve their performance on a specific task.

Machine learning (ML) and Artificial Intelligence (AI) have been receiving a lot of public interest in recent years, with both terms being practically common in the IT language. Despite their similarities, there are some important differences between ML and AI that are frequently neglected. Thus we will cover the key differences between ML and AI in this blog so that you can understand how these two technologies vary and how they may be utilized together. Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that automates data analysis and prediction using algorithms and statistical models. It allows systems to recognize patterns and correlations in vast amounts of data and can be applied to a range of applications like image recognition, natural language processing, and others.

As the field of AI and machine learning continues to evolve, pre-trained language models like ChatGPT/GPT-4 have emerged as powerful tools for natural language processing tasks. In a recent tweet, Daniel Bourke posed the question, "Why bother building your own custom ML models when ChatGPT/GPT-4 will be better?" It's a valid question, given the impressive capabilities of pre-trained language models like ChatGPT/GPT-4. However, there are still several compelling reasons to build custom ML models, even in the face of such impressive technology. If you are interested in learning more about AI and machine learning, you may want to check out Daniel Bourke's Twitter and Medium accounts, as well as his YouTube channel.

A partnership between Cubic Transportation Systems (CTS) and McMaster University in Hamilton, Ontario, is exploring new technologies around traffic prediction and a heightened understanding of vulnerable road users. Cubic, a transportation and transit technology provider, has entered into a five-year partnership with the university to further develop technology products that include artificial intelligence and machine learning that takes into consideration the wide variety of users of transportation systems. "At the center of everything we do is DEI -- diversity, equity and inclusion," said Ali Emadi, professor and research chair at McMaster University. "That's what makes us different from a lot of artificial intelligence centers in academia or in industry," he added. The project, known as the Centre of Excellence for Artificial Intelligence and Smart Mobility, is also exploring the notion of "human-centered design," and populating design teams with researchers and technicians from across multiple backgrounds and expertise.

ERP (Enterprise Resource Planning) is the brain of your organization's technology apparatus. The brain coordinates the activities of your body. It is responsible for telling the body what it should do. A well-planned Enterprise Resource Planning system is essential for any organization to function. But things will change over time. Digital transformation is an important driving force in today's business world. Businesses that want to make the most of Industry 4.0's technological advances will need them. Enterprise services that are efficient and error-free make it possible to use machine learning and artificial Intelligence technologies in real time and automate operations. This is a significant influence on digital transformation. One of the significant impacts of ML is the potential enhancement of Enterprise resource plan (ERP) applications.

It's easy to mix up artificial intelligence (AI) and machine learning (ML). Despite the fact that machine learning is an element of artificial intelligence, these two phrases refer to two separate concepts that can be difficult to distinguish between. Put simply, machine learning is a subset of artificial intelligence, which is a large field of study. Machine learning is an application of AI that allows machines to learn from data without being explicitly programmed. AI is a larger idea that aims to produce intelligent machines that can replicate human thinking capabilities and behavior.

Looking back at 2022, it was obvious that Artificial Intelligence made incredible strides. These breakthroughs came in the form of NLP and Computer Vision to Generative AI and Explainable AI. We caught up with AI/ML experts from JP Morgan & Chase, UBS, University of Greenwich, Cornell University, and Fidelity Investments to find out about the most popular trends in Artificial Intelligence and Machine Learning in finance in 2023. Here's what they had to say: These leading experts will be joining us at the AI in Finance Summit New York on April 20-21, 2023, where they will be discussing the challenges of AI in Finance in more detail and how to overcome them. Standard Rate ticket sale for AI in Finance Summit New York ends on Friday, April 7, so secure your place today to save $200.

Data generation (DG) refers to creating or producing new data. This can be done through various means, such as collecting data from sources, conducting surveys, performing experiments, or generating data through algorithms and simulations. The generated data can be used for various purposes, such as research, analysis, modeling, and decision-making. In machine learning, DG also consists of creating synthetic data (SD) that can be used to train and evaluate machine learning models. This process involves using various methods and algorithms to generate new data sets similar to existing ones but with some variation.