In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you've not coded before.

We live in an age increasingly dominated by algorithms. As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations in the real world. Whether it's making loan decisions or re-routing traffic, machine learning models need to accurately reflect our shared values. In this course, we will explore the rise of algorithms, from the most basic to the fully-autonomous, and discuss how to make them more ethically sound.

We're excited to announce that the Microsoft Azure Connected Learning Experience (CLX) program now has three new Data and AI tracks designed for data professionals. Personalized, self-paced, and culminating in a certificate of completion, these courses help you boost your data and AI skills your way--allowing you to maximize your learning in minimal time. CLX is a four-step learning program that helps aspiring learners and IT professionals build skills on the latest topics in cloud services by providing learners with a mix of self-paced, interactive labs and virtual sessions led by Microsoft tech experts. At the start of the program, you'll take a 20-question Knowledge Assessment to test your skills. Based on your results, you'll receive customized course content that fits your experience--so you can focus only on the information that's useful for you.

As artificial intelligence systems play a bigger role in everyday life, they're changing the world of education, too. I am a literacy educator and researcher, and here are four ways I believe these kinds of systems can be used to help students learn. Teachers are taught to identify the learning goals of all students in a class and adapt instruction for the specific needs of individual students. But with 20 or more students in a classroom, fully customized lessons aren't always realistic. An AI system can observe how a student proceeds through an assigned task, how much time they take and whether they are successful.

This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

Generative AI refers to a type of artificial intelligence technology that has the ability to create new and original content, such as images, videos, text, and even music. Unlike traditional AI algorithms that are designed to perform a specific task, generative AI is designed to produce novel and unique outputs based on a given input or set of inputs. The potential of generative AI is vast and has the ability to revolutionize many industries and aspects of our daily lives. In this article, we will explore what generative AI is, how it works, and how it is already being used to benefit humans. Generative AI is a type of machine learning algorithm that uses a generative model to create new content. A generative model is a type of AI model that is capable of generating new data that is similar to the training data it has been fed.

In the tech-enhanced educational landscape, pupils, parents, educators, managers, governors and Ofsted expect instant answers to their questions. With advancements in AI, chatbots allow users to pose questions in natural language and receive immediate responses. Such a chatbot can be incorporated into MIS, allowing staff to easily access information like student enrolment numbers and staff absences. In this blog post, we will discuss the benefits of AI chatbots in education, and their integration with MIS. We will also be looking at Bromcom's role in driving forward this technology over the years and into the future.

In this latest #ChatGPT special, we are joined by the following #edtech innovators: Ceceilia Parnther, Assistant Professor of Higher Education Leadership. Research based in Student Success and Retention, Academic Integrity, Mentorship, and Community College Leadership St John's University.

Transformer-based large language models are rapidly advancing in the field of machine learning research, with applications spanning natural language, biology, chemistry, and computer programming. Extreme scaling and reinforcement learning from human feedback have significantly improved the quality of generated text, enabling these models to perform various tasks and reason about their choices. In this paper, we present an Intelligent Agent system that combines multiple large language models for autonomous design, planning, and execution of scientific experiments. We showcase the Agent's scientific research capabilities with three distinct examples, with the most complex being the successful performance of catalyzed cross-coupling reactions. Finally, we discuss the safety implications of such systems and propose measures to prevent their misuse.

In Federated Learning, a global model is learned by aggregating model updates computed at a set of independent client nodes, to reduce communication costs multiple gradient steps are performed at each node prior to aggregation. A key challenge in this setting is data heterogeneity across clients resulting in differing local objectives which can lead clients to overly minimize their own local objective, diverging from the global solution. We demonstrate that individual client models experience a catastrophic forgetting with respect to data from other clients and propose an efficient approach that modifies the cross-entropy objective on a per-client basis by re-weighting the softmax logits prior to computing the loss. This approach shields classes outside a client's label set from abrupt representation change and we empirically demonstrate it can alleviate client forgetting and provide consistent improvements to standard federated learning algorithms. Our method is particularly beneficial under the most challenging federated learning settings where data heterogeneity is high and client participation in each round is low. Federated Learning (FL) is a distributed machine learning paradigm in which a shared global model is learned from a decentralized set of data located at a number of independent client nodes (McMahan et al., 2017; Konečnỳ et al., 2016). Driven by communication constraints, FL algorithms typically perform a number of local gradient update steps before synchronizing with the global model. This reduced communication strategy is very effective under independent and identically distributed (i.i.d.) settings, but data heterogeneity across clients has direct implications on the convergence and performance of FL algorithms (Zhao et al., 2018). FL was conceptualized as a learning technique to train a shared model without sharing user sensitive data, while allowing users to benefit from data stored at other nodes, such as phones and tablets of decentralized users. Under realistic settings, client data will often have non-i.i.d.