Filter by categories
Telecom sector: How AI is Changing the Game
Artificial intelligence optimises networks, improves customer service through personalised chatbots, and enables new data-driven business models. However, challenges such as data privacy must be addressed. AI offers great potential for the industry.
Developing responsible AI systems: An introduction
In our latest article, we highlight the central role of compliance and quality management in the development of AI systems. We show how innovative approaches can be reconciled with regulatory requirements — an important step for companies that want to drive sustainable innovations in the fast-moving world of technology.
The next big leap in retail
Discover the impact of generative AI in retail. This technology unites departments, personalises content, and transforms customer experiences. Leading brands such as Coca-Cola and Walmart are already using their potential to optimise operations and drive innovation. Explore the future of retail with generative AI...
How do you use LLMs with internal company data?
This article explores how large language models such as ChatGPT can be integrated with proprietary data. The recent advent of large language models opens up numerous opportunities for companies, but connecting with proprietary data is a challenge in such a transformation. This article discusses the various approaches to solve this problem, such as fine-tuning and contextual learning. In addition, the associated challenges and risks are addressed.
Superhuman Assistant - LLMs as a Strategic Priority
In this article, our CEO Sebastian highlights the transformative power of large language models (LLMs) in a corporate context and provides guidance for their implementation. This is not just a dream of the future, but a strategic necessity that future-oriented companies are taking on today to drive groundbreaking innovations.
A look behind the scenes of “No-code”
In an era of digitalization, no-code and low-code platforms enable rapid business process optimization without in-depth programming knowledge. These tools promote the role of “citizen developers” who independently create IT solutions and relieve IT departments of more complex tasks. At the same time, this autonomy requires a critical look at platform security to prevent data leaks and cyber attacks, with careful selection of trustworthy software providers being essential.
Why IT is not the same as programming
IT doesn't always mean code. Citizen developers can contribute even without extensive programming knowledge using low-code and no-code platforms. These platforms allow applications to be built through visual interfaces or with customization options and coding support. Generative AI makes code creation easier through...
In-context learning techniques
Most scenarios to use LLMs require that these models know business data and business documents. One promising approach is the concept of in-context learning, in which the relevant data is handed over to the LLM during the query. Since the model is not fine-tuned, there is no cost to adjust the model weights and the model remains flexible with regard to the context of the inquiries. In this article, we will exemplify the basic approaches to in-context learning and their disadvantages using externally provided and self-hosted LLMs.
Business intelligence
Business intelligence (BI) is a critical aspect of modern decision-making in organizations. It refers to the use of data, technology, and analytical methods to transform raw data into actionable insights that support effective decision making. BI helps companies use their data assets to gain a competitive advantage, improve operational efficiency, and make better decisions. Whether it's identifying trends in sales data, predicting future demand, or optimizing supply chains, business intelligence provides valuable insights that drive business success...
Computer Vision
Computer vision is everywhere these days. It is an exciting area of research that involves teaching machines to “see” and interpret the world around them using digital images or videos. In the past, computer vision was a separate area of science and technology. One of the key differentiators between modern computer vision and its previous versions is the shift from rule-based to data-driven approaches. With the advent of deep learning, computer vision has moved away from hand-crafted features and towards end-to-end models that can learn directly from raw data.
Machine learning and the cloud
The increasing level of automation in manufacturing also requires the automation of material and plant testing with as little human intervention as possible. In order to remain competitive while meeting industry standards, companies strive to achieve both quantity and quality in production without having to make compromises. However, manual quality testing of workpieces usually only allows the analysis of individual samples from a specific product series...
