The development and adoption of artificial intelligence (AI) technologies in space applications is growing quickly as the consensus increases on the potential benefits introduced. As more and more aerospace engineers are becoming aware of new trends in AI, traditional approaches are revisited to consider the applications of emerging AI technologies. Already at the time of writing, the scope of AI-related activities across academia, the aerospace industry and space agencies is so wide that an in-depth review would not fit in these pages. In this chapter we focus instead on two main emerging trends we believe capture the most relevant and exciting activities in the field: differentiable intelligence and on-board machine learning. Differentiable intelligence, in a nutshell, refers to works making extensive use of automatic differentiation frameworks to learn the parameters of machine learning or related models. Onboard machine learning considers the problem of moving inference as well as learning of machine learning models onboard. Within these fields, we discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT), giving priority to advanced topics going beyond the transposition of established AI techniques and practices to the space domain, thus necessarily leaving out interesting activities with a possibly higher technology readiness level. We start with the topic of differentiable intelligence by introducing Guidance and Control Networks (G&CNets), Eclipse Networks (EclipseNETs), Neural Density Fields (geodesyNets) as well as the use of implicit representations to learn differentiable models for the shapes of asteroids and comets from LiDAR data.

Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This is the first of four courses in the Certified Artificial Intelligence Practitioner (CAIP) professional certification. This course is meant as an entry point into the world of AI/ML. You'll learn about the business problems that AI/ML can solve, as well as the specific AI/ML technologies that can solve them.

Developing and training successful machine learning (ML) fraud models requires access to large amounts of high-quality data. Sourcing this data is challenging because available datasets are sometimes not large enough or sufficiently unbiased to usefully train the ML model and may require significant cost and time. Regulation and privacy requirements further prevent data use or sharing even within an enterprise organization. The process of authorizing the use of, and access to, sensitive data often delays or derails ML projects. Alternatively, we can tackle these challenges by generating and using synthetic data.

The graph represents a network of 1,390 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 16 December 2022 at 12:05 UTC. The requested start date was Friday, 16 December 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 8-hour, 53-minute period from Tuesday, 13 December 2022 at 16:06 UTC to Friday, 16 December 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

This ChatGPT Tutorial is a Crash Course on Chat GPT for Beginners. With the increase in popularity, ChatGPT is still unknown to many people. In this video, I will provide a crash course on different ways to use ChatGPT.

Apache Spark is an open-source framework for fast and general-purpose data processing. It provides a unified engine that can run complex analytics, including Machine Learning, in a fast and distributed way. Spark NLP is an Apache Spark module that provides advanced Natural Language Processing (NLP) capabilities to Spark applications. It can be used to build complex text processing pipelines, including tokenization, sentence splitting, part of speech tagging, parsing, and named entity recognition. Although the documentation, which describes how to install Spark NLP is quite clear, sometimes you can get stuck, while installing it.

Student success models might be prone to develop weak spots, i.e., examples hard to accurately classify due to insufficient representation during model creation. This weakness is one of the main factors undermining users' trust, since model predictions could for instance lead an instructor to not intervene on a student in need. In this paper, we unveil the need of detecting and characterizing unknown unknowns in student success prediction in order to better understand when models may fail. Unknown unknowns include the students for which the model is highly confident in its predictions, but is actually wrong. Therefore, we cannot solely rely on the model's confidence when evaluating the predictions quality. We first introduce a framework for the identification and characterization of unknown unknowns. We then assess its informativeness on log data collected from flipped courses and online courses using quantitative analyses and interviews with instructors. Our results show that unknown unknowns are a critical issue in this domain and that our framework can be applied to support their detection. The source code is available at https://github.com/epfl-ml4ed/unknown-unknowns.

Are you ready to take the PCEP – Certified Entry-Level Python Programmer exam? The first two exams are in the form of practice tests and consists of 200 questions that may appear during the Certified Entry-Level Python Programmer exam. Where necessary, explanations are added to the questions. This course allows you to confirm your proficiency and give you the confidence you need to earn the PCEP – Certified Entry-Level Python Programmer certification. PCEP – Certified Entry-Level Python Programmer certification shows that the individual is familiar with universal computer programming concepts like data types, containers, functions, conditions, loops, as well as Python programming language syntax, semantics, and the runtime environment.

OpenAI has ruined my favorite assignment from when I was (briefly) a high school English teacher: Come up with a two-sentence horror story. All you really need to do the assignment is a topic and an idea of what's frightening. It seems so… human and creative, and yet, OpenAI does it better than most of my high school students ever did. What does OpenAI, which launched ChatGPT to much fanfare last week, offer developers? I fell down a rabbit hole exploring the private artificial intelligence company's playground options.

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