Alexander Thamm [at]

The introduction and scaling of agile project management presents many companies with considerable challenges. Traditional methods quickly reach their limits when it comes to implementing agile principles at scale.  The Scaled Agile Framework (SAFe) offers a solution to overcome these hurdles and achieve sustainable improvements.🚀 In our upcoming free webinar, our [at] expert Tobias Seidl will give an introduction to SAFe 6.0 and show how you can take full advantage of agile methodologies in your organization. He will shed light on the basic principles of agile project management – from Scrum to SAFe. To do this, he explains the different levels and innovations of the framework and discusses its specific advantages and disadvantages. Look forward to interesting discussions and valuable insights into the world of agile project management with SAFe 6.0.  Together, we will find out how you can successfully scale agile methodologies and make your projects more efficient.📈💪

Do you know Tom and Jerry? And do you also know which of the two wins?🐈🐁 Then you will also understand how Generative Adversarial Networks (GANs) work. Generative Adversarial Networks (GANs) are a technique from the field of unsupervised machine learning. You can generate new data (mostly images) that resemble existing training data. The special thing about GANs is that they can not only generate new data, but also identify differences between real and fake content. GANs resemble a never-ending game of cat and mouse. Because they consist of two parts: On the one hand, the generator G (comparable to the mouse Jerry): This has the task of generating new images.🖼️ And on the other hand, a discriminator D (comparable to the cat Tom): This has the task of recognizing whether the entered image is real or fake (i.e. created by the generator).🕵️♂️ This topic plays the main role in our latest blog post. We will address the following points: ✅What are Generative Adversarial Networks? ✅Model architecture and training ✅Generative Adversarial Networks Model ✅Application examples for Generative Adversarial Networks Generative Adversarial Networks opens up new groundbreaking possibilities for us. So, if you want to learn more about this technical cat and mouse game, be sure to check out our latest blog post. 🔗https://lnkd.in/dE5_FaNG

The enforcement countdown starts today! Welcome Regulation 2024/1689, better known as the European AI Act. Mark your calendars with the following deadlines: ➡️August 1st, 2024 – The AI Act will enter into force ➡️February 2nd 2025 – Ban on prohibited AI Systems applies ➡️August 2nd 2025 – Rules on General Purpose AI apply ➡️August 2nd 2026 – Obligations on stand-alone high-risk AI systems listed in Annex III apply (biometrics, critical infrastructure, education, employment, access to essential public services, law enforcement, immigration and administration of justice) ➡️August 2nd 2027 – Obligations high-risk AI systems listed in Annex I apply (AI systems embedded in products) Download our EU AI Act Whitepaper now and get ahead of the curve! https://lnkd.in/ey8k7sBS It’s crucial to understand the requirements and prepare your organization for compliance. The Whitepaper breaks down respective obligations and lays out [at]’s unique holistic approach to assist companies in achieving compliance with the AI Act. Don’t miss out on this essential guide!

Grüezi aus Zürich 🇨🇭 von der Fachkonferenz "Künstliche Intelligenz mit Fokus auf Robotik, Autonome Systeme und Automatiserungslösungen" Zuallererst ein großes Dankeschön an die Organisatorinnen der Handelskammer Deutschland-Schweiz Verena Stübner Nicole Suter Verena Miller 💐 für die wirklich reibungslose Koordination und Organisation des Events. Es war von Anfang bis Ende eine großartige Konferenz, die uns nicht nur viele Einblicke zur Schweiz als Markt gegeben hat, sondern auch sehr viel Spaß gemacht hat, insbesondere die Stadtführung durch Zürich mit anschließendem Abendessen und Speeddating am Tisch. Auch ein großes Lob an die tolle Moderatorin Nadine Brönnimann, die uns sehr charmant durch den Tag der Fachkonferenz begleitet hat. Besonders #inspirierend für mich war zum Einen die Eröffnungsrede von Daniel Abbou - Geschäftsführer des KI Bundesverband und auch der Fachvortrag von Dominik Rohrmus - CTO LNI 4.0 (Labs Network Industrie 4.0 e.V.). Beide aus der Sicht von Deutschland und wie dort #KI bereits genutzt wird bzw. vor welchen #Herausforderungen wir stehen. Vor allem spannend die Aussage: "Die Frage nach der Definition von KI ist eine philosophische Frage". Natürlich auch René Brugger, der das Ganze aus der Sicht des Schweizer Marktes beleuchtet hat und am Ende des Tages ein überaus unterhaltsames Wrap up der Konferenz gegeben hat. Einen kleinen Einblick zu der Zusammenfassung von Manuel Kesseler & meinem Vortrag zur [at]: "Netflix zum Kochen. Das ist bei mir hängengeblieben. Aber natürlich ging es bei ihnen nicht rein ums Kochen, sondern sie kochen die #Transformation. Sie sind die Köche einer hervorragenden Transformation, weil sie können die #Umsetzung." Besser hätte man das nicht zusammenfassen können! Vielen Dank an alle Beteiligten für die Konferenz. #Merci und bis zum nächste Mal!

Companies in the tech industry are often criticized for their immense negative impact on the environment. Hence, we at [at], constantly strive to use data and AI not just for innovation, but also for creating a positive impact on our world. Our corporate values reflect this: we are convinced that technologies like AI will significantly contribute to addressing humanity's challenges and actively shaping the future. For our annual Corporate Social Responsibility Report, we reassessed our respective efforts and want to share the results with you today. In fact, we are proud to contribute to seven of the UN’s Sustainable Development Goals: 3 – Good Health and Well-Being 4 – Quality Education 5 – Gender Equality 8 – Decent Work and Economic Growth 12 – Responsible Consumption and Production 13 – Climate Action 16 – Peace, Justice, and strong Institutions Our commitment to sustainability is at the core of everything we do and we are dedicated to making a difference. Join us in celebrating our achievements and exploring how data and AI can be harnessed for a more sustainable future. Read the full report here https://lnkd.in/gJHsfDYq

Large Language Models have put generative AI at the center of business interest.🤖🕵️♂️ This is because the large language models (LLMs for short) can be used for various organizational functions and use cases. These AI systems generate human-like texts by learning from large amounts of data. Companies currently use LLMs for language translation, content creation, and other applications. In this blog post, we'll explore what LLMs are, how they differ from natural language processing (NLP), what their work architecture looks like, and how they're used in companies. We will focus on the following points: ✅What are Large Language Models? ✅Large Language Models vs. Natural Language Processing ✅Architecture of a Large Language Model ✅Examples of relevant LLMs ✅Enterprise LLM use cases ✅With Large Language Models to Industry 5.0 Large Language Models are constantly evolving, improving and changing the way businesses use technology and making it an unprecedented part of business efficiency and the modern digital landscape.🚀 If you also want to learn more about the gigantic potential of LLMs, then you should read our latest blog post! 🔗https://lnkd.in/d3muJ474

Take the last opportunity today to register for tomorrow's webinar on the topic of "Ist die Zukunft agentisch? Ein Überblick über Multi-Agenten-LLM-Systeme“ 🕵♂️🤖 Places are limited, so register now and secure one of the last spots!

We recently wrapped up an insightful workshop focusing on the future of rail infrastructure operations with DB InfraGO Some of my personal highlights:   💡 Synchronising human and machine recommendations to enhance operational workflows through a single interface 💡 Strategies for managing rail infrastructure that are capacity-optimal, non-discriminatory, and secure 💡 Real-time scheduling adjustments 💡 Transparency: Combining manual inputs and automated systems across different stakeholders for fully transparent decision-making Thank you to everybody involved for such an amusing and impactful discussion! Hannah Noriko Richta, Janice Koeser, Manuela Meyer, Nick Hertel, Dr. Michael Feldmeth, Marcel Richard, Alexander Thamm [at]

Nowadays, companies are accumulating information at an unprecedented pace.🚀 According to statistics, the average data volume of a typical company is 240 backend terabytes (BETB). So how can you use this data to gain actionable insights to make strategic decisions? We addressed this question in our latest blog post and came to the conclusion that this is where the Data Mart comes into play. Data Mart is a targeted data repository that provides a targeted view of critical business information for specific departments or functions. In our blog post, we will address the following topics: ✅What is a Data Mart? ✅Types of Data Marts ✅Data Mart vs. Data Warehouse ✅Data Mart vs. Data Lake ✅Data Mart vs. OLAP/Data Cube ✅Data Mart architecture ✅Benefits of a Data Mart ✅Challenges and considerations ✅Why should companies consider Data Marts? Whether you're a business analyst or simply interested in data management, a clear understanding of Data Marts can enable smarter decision-making.🧠💪 Does that sound interesting to you? Then follow the link to our latest blog post 🔗https://lnkd.in/dMQDnNut

🏐 Big News from the Bavarian Beach Cup! Our dear musketeers got the first place with 5 out of 6 victories and are heading to the finals on July 20th! Great job to everyone who played and supported. Let’s gear up for the final round and bring our best game! Florian Wasserrab, Peyman Navidi, Simon Mayerhofer, Livia Kaiser, Arnaud Frering, Luca Maria Trautmann, Brijesh Modasara 🙌