Over the past three years we have built a practice-oriented, bachelor level, educational programme for software engineers to specialize as AI engineers. The experience with this programme and the practical assignments our students execute in industry has given us valuable insights on the profession of AI engineer. In this paper we discuss our programme and the lessons learned for industry and research.

In today's digital age, artificial intelligence (AI) has found its applications in almost all spheres of life and business. In a time when organizations find it increasingly challenging to upskill and reskill their workforce, they are adopting AI to deliver effective corporate e-learning solutions. These solutions offer a personalized way for employees to learn and grow as per their needs. There are many ways AI helps to facilitate the overall corporate training and development experience. These days, the one-size-fits-all strategy doesn't work in regards to training the millennial work force.

A team of Carnegie Mellon University learning scientists are developing a tool that could change the way high school teachers and students approach their computer science classes. This month, Schmidt Futures announced that the team is one of the winners of their Futures Forum on Learning: Tools Competition. This award will fund tools to aid recovery from pandemic learning loss and advance the field of learning engineering. The tool, RecapCS, was created by Ember Liu and Neil Thawani with support from John Stamper, an assistant professor in the Human-Computer Interaction Institute. Liu and Thawani both graduated from the HCII's Master of Educational Technology and Applied Learning Science (METALS) program, which trains graduate students to become learning engineers and learning experience designers.

Hoy traemos a este espacio esta conferencia Neuroscience, AI and the Future of Education Scott Bolland TEDxSouthBank, que nos presentan así: Currently around 63% of students are disengaged at school, meaning that they withdrawal either physically or mentally before they have mastered the skills that are required to flourish in later life. In this talk Scott Bolland explores the science of learning, the mismatch between how we teach and how the brain natural learns, and the important role that artificial intelligence could take in addressing the limitations in our current education system. Dr Scott Bolland is the founder of New Dawn Technologies, a high-tech software company aiming to revolutionise education through the use of artificial intelligence. He has spent the last 20 years actively researching and teaching in the field of cognitive science – the scientific study of how the mind works – which spans disciplines such as psychology, philosophy, neuroscience, artificial intelligence and computer science. He holds a PhD in this field, as well as a university medal for outstanding academic scholarship.

Whether you're running an online marketplace, classifieds site, or sharing economy platform you'll want to optimize so you can manage User Generated Content (UGC) quickly and effectively. Unfortunately, the larger you scale, and the more users you gain, the harder this becomes. There are always going to be users who abuse your trust – whether they're posting poor-quality photographs or uploading content with malicious or fraudulent intent. Moderating content is a complex and constantly evolving process that requires a carefully blended package of tech and human expertise to protect your platform's trust, content quality, and user experience. Creating that takes time, effort, and know-how.

Artificial Intelligence plays a major role in most sectors worldwide. Its early incorporation has benefited various businesses and organization to smoothly run different processes and systems. AI in education sector is transforming the conventional ways of performing different activities. AI-enabled grading software is making the process of assessing and grading assignments easier for educational organizations. Here, we will discuss in brief the benefits of AI grader.

At Pearson, we're committed to a world that's always learning and to our talented team who make it all possible. By embracing a massive digital transformation that includes highly experiential and personalized learning, we are always re-examining and continuously improving the way people learn best, whether it's one child in our own backyard or an education community across the globe. We are bold thinkers and standout innovators who are mission-driven and motivate each other to explore new frontiers in an environment that supports and inspires us to always be better. We are currently looking for a hands-on Senior Manager to lead our Machine Learning Engineering team to work with our new and existing learning product platforms. The Senior Manager, Machine Learning Engineering will report to the Director of Adaptivity and Machine Learning Engineering and work with cross product and engineering teams to build real-time adaptive and personalized learning services that optimize learner interactions and behavior to allow more efficient and effective study and engagement.

Data science is an interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from data. The field of data science has several subdivisions such as data mining, data transformation, data visualization, machine learning, deep learning, etc. This is the stage where data is collected, analyzed to unravel patterns and relationships in the data, and questions are asked to be answered using the data. This is where mathematical skills come into play. In this stage, mathematical tools are used to build models (predictive models) for quantifying patterns or studying the relationships between features in the dataset.

We consider a novel data driven approach for designing learning algorithms that can effectively learn with only a small number of labeled examples. This is crucial for modern machine learning applications where labels are scarce or expensive to obtain. We focus on graph-based techniques, where the unlabeled examples are connected in a graph under the implicit assumption that similar nodes likely have similar labels. Over the past decades, several elegant graph-based semi-supervised and active learning algorithms for how to infer the labels of the unlabeled examples given the graph and a few labeled examples have been proposed. However, the problem of how to create the graph (which impacts the practical usefulness of these methods significantly) has been relegated to domain-specific art and heuristics and no general principles have been proposed. In this work we present a novel data driven approach for learning the graph and provide strong formal guarantees in both the distributional and online learning formalizations. We show how to leverage problem instances coming from an underlying problem domain to learn the graph hyperparameters from commonly used parametric families of graphs that perform well on new instances coming from the same domain. We obtain low regret and efficient algorithms in the online setting, and generalization guarantees in the distributional setting. We also show how to combine several very different similarity metrics and learn multiple hyperparameters, providing general techniques to apply to large classes of problems. We expect some of the tools and techniques we develop along the way to be of interest beyond semi-supervised and active learning, for data driven algorithms for combinatorial problems more generally.

While pretrained language models ("LM") have driven impressive gains over morpho-syntactic and semantic tasks, their ability to model discourse and pragmatic phenomena is less clear. As a step towards a better understanding of their discourse modelling capabilities, we propose a sentence intrusion detection task. We examine the performance of a broad range of pretrained LMs on this detection task for English. Lacking a dataset for the task, we introduce INSteD, a novel intruder sentence detection dataset, containing 170,000+ documents constructed from English Wikipedia and CNN news articles. Our experiments show that pretrained LMs perform impressively in in-domain evaluation, but experience a substantial drop in the cross-domain setting, indicating limited generalisation capacity. Further results over a novel linguistic probe dataset show that there is substantial room for improvement, especially in the cross-domain setting.