We rely on bleeding edge technologies to drive companies forward with innovative solutions. That's why, in discussions with customers and partners or in our webinars, we are always keen to explain the benefits and possibilities of modern technologies to companies. But we also use AI ourselves: by automating tendering processes, we have been able to save valuable resources and increase efficiency.
The technology must be embedded in the process and adapted to the process and not float next to it unconnected. This also applies to large language models - although their "humanity" gives the impression that they can be treated like a colleague rather than a tool. But how many business areas benefit from a colleague with the intellectual capacity of a twelve-year-old?
Language models are notoriously struggling to recall facts reliably. Unfortunately, they also almost never answer "I don't know". The burden of distinguishing between hallucination and truth is therefore entirely on the user. This effectively means that this user must verify the information from the language model - by simultaneously obtaining the fact they are looking for from another, reliable source. LLMs are therefore more than useless as knowledge repositories.
Digital technology has overloaded people with information, but technology can also help them to turn this flood into a source of knowledge. Large language models can - if used correctly - be a building block for this. Our "rundify" tool shows what something like this could look like.
Like with deep learning before, data remains important in the context of large language models. But this time around, since somebody else trained the foundation model, it is impossible to tell what data is really in there. Since lack of data causes hallucinations etc. this ignorance has pretty severe consequences.
We take a look at major German media portals and how they deal with the issue of search and autocomplete. A spoiler in advance: The situation remains sad.
How can companies innovate using AI? How do you go beyond wrapping ChatGPT in a pretty UI?
What ticket sellers and Instagrammers have in common, and how AI might cost them both their jobs.
The demo of our new product "Quaestio" pits AI and paperwork against each other. It looks like the AI is winning.
At the end of last year, Johannes wrote a blog post about how online magazines can improve user experience by implementing an autocomplete feature. „Proxima“ is our suggested solution in order to provide the best results possible.
The release of GPT-4 is renewing the buzz around Large Language Models. Are these tools ready for prime time now?
If an AI sometimes lies to me, is it of any use at all? We examine the areas of application for which lying AI is suitable and where it has no place.
What is ChatGPT capable of? Instead of uncritical applause or doomsday scenarios, a level-headed look at the underlying technologies and the possibilities that arise from them.
We prefer to discuss Text AI and Natural Language Processing, but in this video we'll go over the basics. We talk about scaling, the serverless trend, and cloud-native services - all things you might be interested in.
We develop products. Whether for ourselves or for customers, we invest the same enthusiasm and have the same quality standards. How we deliver the maximum added value, we describe here.
ChatGPT may help software developers in some cases, but it won't replace them. It can be helpful to a certain degree, but its eagerness to give wrong answers can be counterproductive. (Automatically generated summary)
Web portals should go beyond offering a search function and implement Suggest features to make searching easier, more relevant and more enjoyable. This is possible by providing suggestions in different categories. (Summary generated by NEOMO summarizer)
We are still not done with search – although we are at it since 2001.
Some may claim that my co-founder Florian and I simply enjoy rebranding companies on a regular basis.
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