Innovation and progress: top AI trends 2024

from | 29 January 2024 | Basics

The world of artificial intelligence (AI) is developing rapidly and producing innovative breakthroughs every year. The year 2024 promises not only technological revolutions, but also new challenges and opportunities for companies in the DACH region. In this article, we take a look at the latest AI trends that have the potential to fundamentally change the landscape of industry and technology. From Quantum Machine Learning to Neurosymbolic AI and AI regulation, we look at how these trends will shape the way we work, communicate and innovate.

Generative AI: from disruption to competitive advantage

Advanced AI-driven natural language generation systems and generative AI models, including the latest developments in multimodality, are revolutionising the way content is created and interactions are designed. Technologies such as Text-to-Image, text-to-video and image-to-text make it possible, Generate complex, multimedia contentthat seamlessly integrate text, image and video. These multimodal AI models have significant potential for maturity and performance enhancement, which can be utilised for extended and effective communication across different media and platforms.

Generative AI models like ChatGPT and Google Bard, which are trained on large amounts of data, offer improved capabilities in natural language processing and content creation. However, these models also encounter Legal challengesespecially in the area of copyright. As these technologies mature, the issue of ethical use and copyright compliance will become increasingly relevant. It is expected that the debate surrounding the use of generative AI models will continue to develop and deepen, with the balance between creative freedom and the protection of intellectual property taking centre stage.

In addition, the use of multimodal AI models leads to an increased Integration of digital assistants into everyday lifewhich increases the demand for interactive and personalised services. Organisations that adopt these technologies can benefit from increased efficiencies in their operations while creating innovative customer experiences. However, the challenge lies in managing the impact of these technologies on the workforce as automated systems increasingly take over traditional labour roles.

It is important that companies in the DACH region not only see these disruptive technologies as a tool for increasing efficiency, but also consider the ethical and social implications. The ability to recognise these responsibly integrate new technologies and at the same time develop innovative business modelswill be crucial in order to survive in an increasingly Artificial intelligence to be successful in an economic landscape characterised by

Learn more about Generative AI, how it works and what it means, as well as its ability to create and process innovative content through large neural networks.

Generative AI - An Overview

Regulation of AI systems

The regulation of AI technologies is becoming increasingly important. The EU recently adopted the AI Act adoptedwhich provides for new restrictions on AI applications and demands transparency from companies regarding data use. This act is expected to come into force at the beginning of 2024. In the USA, the focus is on the enforcement of data protection, securities and antitrust laws in relation to generative AI. Globally, AI regulation is increasingly seen as necessary to combat algorithmic discrimination and promote the responsible use of AI (Source: Foley & Lardner LLP,

AI regulation also includes growing concerns about the impact of AI on privacy and data security. Companies must prepare for a Stronger monitoring and stricter reporting requirements especially when using personal data in their AI systems. In addition to the EU and the US, other countries, including China, Japan and Canada, are taking a more active role in shaping AI regulations to balance technological advancement with ethical and legal concerns (Source: Skadden Foundation,​​​​.

The increasing globalisation of the AI industry requires a harmonised approach to regulation to ensure consistency and a level playing field. This emphasises the need for companies in the DACH region to actively engage with international standards and best practices in AI regulationto remain competitive while building trust and credibility in their AI-driven solutions.

AI hardware to the edge

Edge computingwhich moves data processing to the edge of the network, is significantly improved by AI technologies. In the DACH region, where Industry 4.0 and the Internet of Things (IoT) are becoming increasingly importantAI in edge computing offers numerous advantages.

Moving data processing closer to where it is generated can reduce latency and enable real-time analyses in various applications, from manufacturing to smart cities. This is particularly relevant for countries such as Germany, Austria and Switzerland, which are leading the way in the automation and digital transformation of their industries. AI-driven Edge computing enables companies to react more quickly to data, organise processes more efficiently and open up new opportunities for data usage. It also supports the development of intelligent systems in areas such as transport, energy supply and healthcare by applying the power of AI models directly to the locations where they are most needed.

Challenge 23 Whitepaper Cover

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Quantum Machine Learning

Quantum Machine Learning (QML) is an innovative discipline that combines the principles of quantum computing with the methods of machine learning. While traditional Machine Learning based on classical computers, QML utilises the unique characteristics of Quantum computerssuch as superposition and entanglement, to process data and recognise patterns. This enables QML models to recognise complex Perform calculations and data analyses with a speed and accuracy that cannot be achieved with conventional computers. The ability of quantum computers to consider multiple states simultaneously opens up new possibilities for data processing and machine learning, especially when processing large and complex data sets.

Quantum Machine Learning has enormous potential in the DACH region, which is characterised by an advanced research landscape and strong technological infrastructures. The region is home to some of the world's leading research institutions and companies in the field of quantum technology and artificial intelligence, which provides an ideal basis for the development and application of QML. The use of QML could be particularly useful in areas such as Automation, data analysis and optimisation of processes lead to significant progress. QML also offers the opportunity to solve complex problems more efficiently in areas such as logistics, energy management and pharmaceutical research. The combination of strong academic institutions and a dynamic industrial landscape positions the DACH region as a leading player in the development and application of QML technologies. (Source: Fraunhofer Big Data and Artificial Intelligence Alliance,

Neurosymbolic AI

Neurosymbolic AI, a Fusion of neural networks and symbolic AIis at the centre of a major development in the field of AI (Source: The Alan Turing Institute, This technology aims to create machines that not only process data, but also have the ability to understand and reason like humans.

For the DACH region, with its strong tradition in research and development, Neurosymbolic AI offers enormous opportunities. It not only enables Improved decision-making and automationbut also opens up avenues for innovative applications in critical areas such as healthcare and intelligent manufacturing. The integration of symbolic AI, which is based on rules and logic, with the adaptive and adaptive properties neuronal networkscan lead to more advanced and reliable AI systems that are capable of solving complex problems that previous AI models could not handle.

Neurosymbolic AI has the potential to produce ground-breaking applications in a variety of areas. A concrete example from practice could be the development of intelligent diagnostic systems in the healthcare sector be. These systems could not only analyse medical data, but also understand the underlying biological and chemical processes and draw conclusions from them. This would significantly improve the diagnosis and treatment of diseases by providing doctors with deeper insights into complex medical issues that go beyond the capabilities of conventional, data-driven AI models.

Another area of application could be in intelligent manufacturing, where neurosymbolic AI could help to optimise Optimise production processes. Machines equipped with this technology could not only record and process sensory data, but also draw logical conclusions from this data in order to improve production processes, reduce errors and increase efficiency.

Ethical and Explainable AI

Especially in safety-critical applications for AI, it is enormously important for legal and ethical reasons alone to be able to understand algorithmic decisions. One only has to think of autonomous driving, credit scoring or medical applications: which factors lead to which result - and is that the desired result? Even in the case of less critical decisions, the Interpretability of the model Whether it is to create confidence in the model's predictions or simply to find out the reasons for the value of a forecast.

Methods and tools such as LIME, SHAP, CXPlain or Global Surrogate Models make it possible to make ML models more explainable. This data science trend will play a decisive role in the years to come and will replace the current Significantly change the way we deal with AI. In many areas plays XAI already play an important role today - for example, in AI-based claims processing for insurance companies, in the autonomous drivingThe use of AI in health care applications or simply to build trust in a certain AI model.

Our blog post sheds light on the importance and methods of Explainable AI (XAI) in the light of the EU AI Act and its practical applications.

Explainable AI - Methods for explaining AI models

Outlook: AI in 2024 and beyond

The AI trends of 2024 emphasise the continuous evolution of the technology and its increasing integration into various economic sectors and everyday applications. While Quantum Machine Learning and Neurosymbolic AI open up new possibilities in data processing and problem solving, AI regulation ensures that these developments are in line with ethical standards and legal frameworks. This presents a unique opportunity for organisations to be at the forefront of this innovative wave and take full advantage of AI while responding responsibly to the challenges and opportunities that these technologies bring. The key to success lies in adaptability, continuous education and proactive engagement with the rapidly evolving AI trends and their impact on the global economy and society.


Luke Lux

Lukas Lux is a working student in the Customer & Strategy department at Alexander Thamm GmbH. In addition to his studies in Sales Engineering & Product Management with a focus on IT Engineering, he is concerned with the latest trends and technologies in the field of Data & AI and compiles them for you in cooperation with our [at]experts.

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