Trends in AI and machine learning

Trends in AI and Machine Learning

Artificial intelligence (AI) and machine learning (ML) are currently the dominant topics in society, politics and business. Experts are of the opinion that only those economies that make the greatest efforts in these areas now will be able to maintain their competitiveness. We at have also been dealing with the topics of ML and AI since the company was founded. For our customers, this means that they benefit from intelligent, automated and data-driven processes.

AI and ML affect a wide range of topics in IT and have an impact on very different areas. For this reason, there are currently very different trends on these topics. We will try to bring you a little closer to the current trends.

  1. Generative AI: Let’s start with Generative AI, an area that has gained tremendous importance in recent years. Generative AI is a form of artificial intelligence that is able to generate new content in various media such as text, images, audio, synthetic data and, more recently, video from text input. The latest breakthrough technology driving progress in this area is large language models (LLMs). These models have enabled the development of renowned texting tools such as ChatGPT, which are specifically designed to have natural language conversations with users based on their requests.
  2. Prompt Engineering: Prompt engineering has garnered increased attention with the emergence of generative AI. It revolves around the concept of prompts, which involve instructions and contextual cues provided to a language model to accomplish specific tasks. Prompt engineering refers to the practice of formulating and refining prompts to effectively leverage language models across diverse applications.
  3. RAGs (Retrieval Augmented Generation): RAGs represent a further development of Generative AI, which combines the generation of content with the ability to retrieve relevant information from large databases. This technology is particularly useful when creating texts, as generated content can be enriched with facts and information from reliable sources.
  4. Multimodal KI: Multimodal AI systems integrate information from different media such as text, images and speech to develop a more comprehensive understanding of the world. This technology has the potential to revolutionize the interaction between humans and computers by enabling more natural and complex forms of communication.
  5. ModelOps vs. MLOps: While MLOps, short for Machine Learning (ML) Operations, applies software engineering and DevOps best practices to the entire lifecycle of ML models from development to deployment, ModelOps focuses specifically on the operational aspects of managing models in the production and post-deployment phases. It focuses on the continuous monitoring, performance tracking, control and maintenance of deployed models to ensure their effectiveness, reliability and compliance with business requirements and regulations.
  6. Artificial General Intelligence (AGI): AGI is the ultimate goal of many researchers in the field of machine learning. Unlike today’s specialized AI systems, AGI strives to achieve a wide range of cognitive abilities and solve complex problems – much like a human mind.
  7. Responsible AI: With the increasing use of AI systems in various areas, the question of responsibility and ethics has become a central issue. Responsible AI deals with issues of fairness, transparency, security and accountability in the development and provision of AI technologies. In future, it will be crucial that AI systems are designed in such a way that they reflect the needs and values of society and do not cause any harm.


The trends in machine learning and AI are diverse and exciting. From Generative AI to Responsible AI, there is a wealth of innovations that could fundamentally change the way we use technology in the future. The world of machine learning will continue to evolve and impact our daily lives in new and exciting ways. By using Generative AI and Multimodal AI, for example, companies can offer personalized services and products that are better tailored to the individual needs and preferences of consumers.

Communication between humans and computers will also be more efficient and natural. For example, we could use voice assistants that not only understand our voice commands, but can also interpret images and provide contextualized responses.

For companies, this progress means that they can make better decisions thanks to the ever-improving processing of large volumes of data. For example, doctors could receive support in the diagnosis of diseases or companies could make better predictions about future trends. Our way of working will also be influenced by these trends. With the advent of ModelOps and increasing automation, some tasks can be taken over by machines, while human workers can focus on creative or strategic tasks.