Digitalisation in education and its influence on teaching methods is the focus of this study, which examines the use of ChatGPT in a role-playing game used in the Cloud Computing Engineering Master's programme at the University of Applied Sciences Burgenland. The aim of the study was to analyse the impact of AI-based simulations on students' learning experience. Based on Vygotsky's sociocultural theory, ChatGPT was used to give students a deeper understanding of strategic decision-making processes in simulated business scenarios. The methodological approach included role-playing and qualitative content analysis of 20 student reflections. The findings suggest that ChatGPT enhances students' engagement, critical thinking, and communication skills, in addition to contributing to the effective application of theoretical knowledge. Furthermore, simulations can contribute to the effective application of theoretical knowledge. The results underscore the significance of adaptive teaching approaches in promoting digital literacy and equipping learners for the digital workplace. The integration of AI into curricula and the need for ongoing innovation in higher education are also emphasised as a means of guaranteeing excellent, future-focused instruction. The findings highlight the potential of AI and ChatGPT in particular, as an innovative cutting-edge educational tool that can both enhance the learning experience and help achieve the Sustainable Development Goals (SDGs) through education.

Aerodynamic performance evaluation is an important part of the aircraft aerodynamic design optimization process; however, traditional methods are costly and time-consuming. Despite the fact that various machine learning methods can achieve high accuracy, their application in engineering is still difficult due to their poor generalization performance and "black box" nature. In this paper, a knowledge-embedded meta learning model, which fully integrates data with the theoretical knowledge of the lift curve, is developed to obtain the lift coefficients of an arbitrary supercritical airfoil under various angle of attacks. In the proposed model, a primary network is responsible for representing the relationship between the lift and angle of attack, while the geometry information is encoded into a hyper network to predict the unknown parameters involved in the primary network. Specifically, three models with different architectures are trained to provide various interpretations. Compared to the ordinary neural network, our proposed model can exhibit better generalization capability with competitive prediction accuracy. Afterward, interpretable analysis is performed based on the Integrated Gradients and Saliency methods. Results show that the proposed model can tend to assess the influence of airfoil geometry to the physical characteristics. Furthermore, the exceptions and shortcomings caused by the proposed model are analysed and discussed in detail.

When self learning ML or DL, I have found there are a tonne of amazing courses. However, many inevitably get bogged down in the math, and the equations, and other gibberish. Not all of us intend to do research, some of us just want to have fun, and build some badass projects along the way. So, here are some courses available on the internet that teach you the pure code you need to get started with deep learning, and hopefully build some projects along the way. They are also useful if you have gained great theoretical knowledge, and would like to supplement it with great practice.

Debdoot Mukherjee is the Chief Data Scientist and Head of AI at Meesho, the Indian origin social commerce platform at the forefront of the boundaryless workplace model that became a norm in the aftermath of the Covid-19 pandemic. Upon completing his postgraduate degree from IIT-Delhi, Mukherjee began his career in the research division at IBM, where he attained expertise in Information Retrieval and Machine Learning techniques. He then journeyed on to work in impactful roles at companies like Hike, Myntra and ShareChat before leading the AI and data science division at Meesho. In an exclusive interview with Analytics India Magazine, Debdoot Mukherjee opened up about his journey into data science, machine learning and everything AI. AIM: What attracted you to this field?

There is indeed a lot of information and academic data on the internet in the field of Machine Learning. Entropy used in decision trees, gini; Many mathematical formulas, such as the Euclidean neighbor relation used in KNN, are available on the internet on the official description page of libraries or on mathematical websites. You will not need any university or course for this. As you know, all models and algorithms used in Machine Learning are based on mathematics. As a matter of fact, Machine Learning is the autonomous form of mathematics taught to the machine. There are several advantages of using mathematics in this field.

Machine learning (ML), is the study of computer algorithms, that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms, build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. In simple words, machine learning is a subset under the broad umbrella of artificial intelligence.

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Earlier, the field of AI was more academic and was mostly researched by PhD holders. However, the area has now become approachable with software developers and other tech professionals taking advantage of the new AI paradigms to build a whole new scenario of use cases for the world. More developers are becoming interested in exploring tools and techniques such as TensorFlow and other open-source tools to explore the field. With frameworks such as TensorFlow, companies such as Google are also making it easy for a software developer who doesn't necessarily have academic expertise or a PhD in AI/ML, allowing them to ML models. Developers are using machine learning in their applications as a way to stand out in the crowd.

The curiosity in artificial intelligence (AI) is taken to a whole new level these past years. Every day new startups, new tools, new innovations are growing. This term is now always mentioned when we talk about AI. Nowadays, though, people who interested in learning more about this technology won't have time to go back to college or spend a whole year on a training course. For this reason, we decided to created this article.

A question a lot of ML practitioners get asked a frequently is: "What can I do to start being able to actually build Machine Learning projects and solutions?" There is so much information out there -- both good and bad -- that it can be hard to know where to begin. Also, people come from very different backgrounds, so the starting point can vary significantly. For example, for me, I entered the ML world by watching theoretical videos from Computer Science channels about neural networks, and as I got more and more interested I started reading articles, news, and blogs about the topic. However, by doing this I only developed a vague understanding of the most superficial part of Machine Learning, and I was nowhere near being able to tackle a project by myself.