Digital systems offer a great opportunity to significantly reduce carbon emissions and can contribute to the efficient use of energy. However, all systems also need energy. This area of tension is addressed in the course: Sustainability in the digital age - Energy-Efficient Software Development. To effectively reduce the carbon footprint of digitalization, it is necessary to apply algorithmic efficiency and sustainability by design as guiding principles in digital engineering. We will introduce strategies to develop software that prioritizes minimized energy consumption through optimal coding and green testing practices. We will look at how the CO2 emissions of operating software applications can be measured and calculated. How to measure the performance and energy consumption of Large Language Models will be covered as well. Further we share approaches how to use advances in hardware technology and operate digital systems efficiently in data centers based on eco-friendly and cost-effective capacity management strategies.

This course is part of the Sustainability in the Digital Age series, a collaborative project between colleagues from Stanford University, SAP and the Hasso Plattner Institute.

Practical Computer Vision in PyTorch is a comprehensive, hands-on course designed for developers and practitioners eager to explore computer vision using PyTorch. The course covers a wide spectrum of tasks, ranging from image classification and object detection to segmentation and generative modeling. With a strong emphasis on hands-on implementation, the participants will engage in coding demos and build projects using industry-standard tools and libraries. By the end of the course, the participants will be equipped with the skills to build, fine-tune, and deploy computer vision models for real-world applications.

In an era where digital technologies are reshaping industries and daily life, the environmental impact of AI systems has become a growing concern. This course explores efficient AI methodologies to address these challenges. From deep learning model compression to low-bit quantization and collaborative inference, we delve into techniques that enhance computational efficiency and reduce energy consumption. In Week 2, we focus on low-bit quantization specifically for large language models (LLMs), showcasing cutting-edge open-source tools and models. Join us to learn how to build sustainable AI systems while pushing the boundaries of innovation.

This course is part of the Sustainability in the Digital Age series, a collaborative project between colleagues from Stanford University, SAP and the Hasso Plattner Institute.

This course explores the critical  intersection of AI and sustainability, highlighting how technological advancement can align with environmental responsibility. It offers insights into the dual nature of AI, noting its potential for increasing efficiency and driving sustainable innovation, while also acknowledging the significant energy consumption involved in training and applying AI models. In this course, we are not only examining the paradox of AI and sustainability but also provides actionable recommendations for incorporating sustainability into AI application development. By examining AI's transformative role and its environmental impact, the course offers a comprehensive understanding of how AI can be harnessed to support global sustainability goals effectively.

This course is part of the Sustainability in the Digital Age series, a collaborative project between colleagues from Stanford University, SAP and the Hasso Plattner Institute.

Artificial Intelligence (AI) offers transformative potential across industries, but its development and deployment come with environmental costs. The course covers topics such as the carbon footprint of AI models, methods for measuring and reporting environmental impacts, and challenges in estimating the sustainability of AI technologies. Students will gain insights into energy and carbon accounting, along with case studies demonstrating how AI’s environmental footprint is assessed. The course aims to provide a comprehensive understanding of the relationship between AI and the environment, equipping learners with knowledge to contribute to more sustainable AI practices.

This course is part of the Sustainability in the Digital Age series, a collaborative project between colleagues from Stanford University, SAP and the Hasso Plattner Institute.

This course explores the hype and realities surrounding AI, the challenges companies face in using AI profitably, and how Germany is performing in the AI landscape. It offers insights into how AI can power business strategies, be successfully integrated into operations, and scaled for long-term profitable growth. Created for managers and data experts looking to maximize AI’s potential, the course requires no prior experience, though a basic understanding of business strategy, data analytics, and AI concepts is beneficial.

Gain a basic understanding of how numerical representations transform language! Explore the world of text embeddings in this online course, covering essential topics such as tokenization, historical models, modern techniques, and practical applications.

It's free of charge and no prior AI experience is necessary.

Despite the fact that it affects our lives on a daily basis, most of us are unfamiliar with the concept of a knowledge graph. When we ask Alexa about tomorrow's weather or use Google to look up the latest news on climate change, knowledge graphs serve as the foundation of today's cutting-edge information systems. In addition, knowledge graphs have the potential to elucidate, assess, and substantiate information produced by Deep Learning models, such as Chat-GPT and other large language models. Knowledge graphs have a wide range of applications, including improving search results, answering questions, providing recommendations, and developing explainable AI systems. In essence, the purpose of this course is to provide a comprehensive overview of knowledge graphs, their underlying technologies, and their significance in today's digital world.

On 11th July, 2023, the first KISZ workshop on "Pre-trained AI Models: The speech-to-summary example" takes place. The contents of the workshop will be prepared in this Background Talk format, which is open to all interested parties.

The ultimate goal of the bootcamp is to cultivate strong data science skills with an emphasis on machine learning techniques to satisfactorily meet and exceed the requests of the Data science world. In the process, we will develop good habits for operating independently as data scientists and for operating as members of productive data science teams.

Bayesian data analysis is increasingly becoming the tool of choice for many data-analysis problems.

This free course on Bayesian data analysis will teach you basic ideas about random variables and probability distributions, Bayes' rule, and its application in simple data analysis problems. You will learn to use the R package brms (which is a front-end for the probabilistic programming language Stan). The focus will be on regression modeling, culminating in a brief introduction to hierarchical models (otherwise known as mixed or multilevel models).

This course is appropriate for anyone familiar with the programming language R and for anyone who has done some frequentist data analysis (e.g., linear modeling and/or linear mixed modeling) in the past.

Whether we stream our favorite series, develop new drugs or have us being chauffeured by a self-driving car -- machine learning is an essential part of our modern life, and of our future. But the growing amount of data and our increasing demands pose difficulties for today's classical computers. Can quantum computing overcome these challenges? What potentials does the emerging field of quantum machine learning have?

In this course, we will not only learn about quantum machine learning and its prospects, but we will also solve concrete tasks with both classical and quantum models. This course is aimed at students, experts and enthusiasts of quantum computing or machine learning. Prior knowledge about quantum computing or quantum information are strongly recommended.

In this course you will learn how to use Qiskit for working with quantum computers. Qiskit is an SDK for working at the level of pulses, circuits, algorithms and application modules. During the first week you will explore the available tools to run your experiments on IBM Quantum computers in the cloud, write your first lines of Qiskit code, do a recap of the fundamentals of quantum computing and understand how to run experiments both on simulators and on quantum devices. During the second week you will use everything you have learnt to implement two of the first quantum computing algorithms.

Digitalization is a game changer in the pursuit of a sustainable future. The latest digital technologies and applications like cloud, AI, and mobile devices enable us to achieve the Sustainable Development Goals and reduce carbon emissions in many sectors. Yet computer systems themselves have an immense energy requirement for their countless devices, data centers, applications and global networks. To effectively reduce the carbon footprint of digitalization, it is necessary to apply algorithmic efficiency and sustainability by design as guiding principles in digital engineering. The clean-IT Forum is the international platform to exchange ideas, recent research findings and applications to make digital technologies more energy-efficient.