Chat GPT has triggered an AI boom that has led to a multitude of new applications and discourses in business and society relating to artificial intelligence. As a provider of highly specialised cloud services for companies, it is crucial for us to show and provide customers with concrete ways in which they can use technological trends such as AI to their advantage. But how do you manage to stay 'state of the art' in such a fast-moving industry? At CONCETO, we succeed by attracting new talent who enrich our opportunities with fresh ideas and up-to-date knowledge from universities, start-ups or other environments. One such example is our colleague Anny, who has a Master's degree in computer science specialising in AI from the University of Bonn, one of 10 German universities of excellence. She is significantly involved in the development and implementation of the latest solutions with a focus on AI. Today she took the time to answer our questions.
Hello Anny, please could you start by telling us about your role at CONCETO and what it involves?
I am a developer for our web applications and currently lead the technical project management of our AI-Extractor. So I am the technical side of the project. There are also other sides, such as marketing, with whom I regularly discuss how the project is developing, what requirements there are and how these can be implemented. Such interdisciplinary meetings are very helpful when evaluating a new feature. We clarify issues ranging from customer requirements to user guidance. Together, we don't lose sight of the big picture and end up with a solution that really helps the customer.
So you're currently working on the AI-Extractor. What exactly does this software?
Let's start with the problem: Many companies still send their invoices, delivery notes or other business documents as PDFs. This causes a lot of work for the recipient and is a source of errors, because someone spends the whole day manually transferring the data from the PDFs to the ERP. We come in at this non-digitised point. The AI-Extractor reads the relevant data, checks it and sends it. The AI shows its full strength when the PDFs are unstructured, which occurs due to different formats and individual company designs. The total amount is sometimes displayed at the bottom right, sometimes at the top right, and sometimes only on page 3. The AI recognises the relevant values and formats them into standardised artefacts that are automatically fed into the ERP. In this context, you have probably heard of OCR, or Optical Character Recognition. This process has been around for a long time and works quite reliably, but is limited to character recognition. For example, an OCR can read the value of €5.30 on an invoice, but whether this is the net amount or the gross amount is beyond its capabilities. And this is where we come in with AI tools.
Were there any particular challenges in developing the AI-Extractor?
This project was the first time we used artificial intelligence in a solution for our customers. Although we had already worked with AI before, it presented us with new challenges in addition to normal project management. AI is currently a hype topic and much is still under development. This puts a lot of pressure on the market, as interest is enormous, expectations are high and the competition is large. However, we don't want to chase the trend or raise unrealistic expectations, but rather build optimal solutions with the most innovative technologies.
An important aspect of this is the safe handling of AI. Word has got around, but many private individuals and customers are still unaware that large language models such as ChatGPT can lie. This is called hallucinating. The programme does not think like a human and cannot understand things logically. Instead, it says to itself: 'If I were human, what would the most likely next sentence be?'. This usually works really well and it's especially great for coding because code is also a language. The most probable next sentence is just logically not always the right one. An answer to the simple question of how many "m" there are in the word "always" can produce an incorrect result because the programme cannot actually count or logically process the letters. These are challenges that you naturally have to face and that you should always keep in mind.
Although we don't primarily work with LLMs, it is essential to handle every AI model responsibly. Nobody should be blinded by the hype and unrealistic promises. As the technical project manager, I am proud of how confidently the team overcame the hurdles of the project and how successfully the foray into the field of AI at business level got off to a good start.
"What research shows again and again is that every finding has a value. (...) This is in the nature of experimentation, because you never know exactly what these experiences can be useful for."
What does the use of AI look like in your and your colleagues' day-to-day work and what impact does it have?
We use AI to support programming. Contrary to what some people think and what is sometimes reported in the news, programmers will not become redundant, and ChatGPT cannot write everything. That's good, because, as I mentioned earlier with regard to hallucinations, you can't completely rely on the output of artificial intelligence.
But AI is a great help, especially for simpler tasks, because it simply saves time. Of course, I can code a loop myself, but it's much faster this way. Nevertheless, the result is always checked to ensure functionality. In addition to coding, they also help with brainstorming or when you are looking for suggestions on how to implement something. The generated suggestions provide a good basis for further development.
It can be dangerous to use these programmes unsupervised. This is particularly relevant for those starting out in their careers, as they may pick up incorrect information or not learn anything at all if they let ChatGPT write everything. However, we want them to use such tools to support them because it is important for their day-to-day work. That's why we make sure to teach basic skills so that they understand what exactly happens in the code. This allows the result to be optimised afterwards and leads to a better product. At the same time, we teach the responsible use of such ubiquitous tools.
To what extent did the research-based approach from your Master's programme influence you?
In academia, you work with bleeding edge technology and, in the best-case scenario, you are involved in its development. These are technologies that have only recently been published, for which there is usually only one paper. Nothing has been implemented yet; there is no library, let alone anything on the market. That challenges you to develop the ideas yourself and always keep up with the latest technology. This combines well with what companies need: constant improvement, stability and future-proofing.
What research shows time and again is that every insight has a value. If I tackle a problem and try out five solutions and none of them work, this is initially a setback. However, it is still an important realisation, as I now know that I don't need to pursue these five approaches any further here. These findings could be valuable for other applications.That's the nature of experimentation, because you never know exactly what these experiences can be useful for. Through research I have also learnt that there is actually always a workaround for problems. There are only a few things that cannot be solved. You might have to go in a different direction and get creative, but somewhere along the line there is a path that leads to the goal.
Apart from artificial intelligence, are there any software trends or developments that you think will have a significant impact on the way companies operate in the coming years?
We will see how trends continue, even if the hype surrounding AI will gradually stabilise. At the moment, we are mainly seeing large language models, but I believe that it won't just be generative technologies that are pushed in the future. For example, everyone knows Amazon's "Frequently bought together" - that's also AI, an area called machine learning. It's been around for a while, it's just not yet as effective on a mass scale. However, development in this area is not standing still and I believe that we will see a few more things in this direction.
This area is of great interest to us and includes unsupervised learning, for example.This allows insightful information to be extracted from large volumes of unstructured data. A simple example: Let's take my personal calendar as a data basis. As a viewer, I can see the appointments, but machine learning can recognise the underlying relationships. It can tell me how likely it is that I will go on holiday in the next quarter, how long this holiday will last and where it will probably take me. If you add other values, such as weather data, the predictions become even more precise. These interconnections are something that humans can only recognise with great effort, if at all, and this is where machine learning comes into its own.
Finally, could you give us an insight into a current project or a new idea that you are currently working on or that you are planning for the future?
At the moment, I am fully occupied with the AI-Extractor. In the long term, it opens up many possibilities that we can and want to utilise. For example, what I just mentioned with machine learning: If invoices, orders or any other documents are sent to the AI-Extractor, it extracts the data and forwards it to the ERP. As an intermediate step, this data could be used for business intelligence. If you know which customer buys what, when and at what price, you can draw well-founded conclusions that provide significant support for business decisions. This is the kind of direction we're keen to go in.
Thank you for taking the time to give us an insight into the innovative side of CONCETO. We wish you continued success and enjoyment in developing new solutions!
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