AI in the here and now
Even if it still looks like hype at the moment: The focus on artificial intelligence is here to stay. Why? Because some companies are using AI in a very targeted, meaningful and therefore successful way and therefore have the potential to transform the world. They will lead the way and artificial intelligence will be the key to success. Companies can become faster and, to a large extent, more efficient in some activities. The money saved will increase profits and therefore the opportunities to invest in new projects on the one hand and payouts and bonuses for the staff involved on the other. Artificial intelligence is primarily a tool for increasing efficiency in many different areas. Depending on how well the company is already positioned, these increases in efficiency are accompanied by staff reductions or support the existing staff to significantly increased performance.
In the following, I would like to take a closer look at individual points in order to provide a different perspective away from the hype and towards a planned approach:
AI is a tool, not a magic remedy
First of all: artificial intelligence is a tool. It can be used to perfection, or it may not. AI does not always mean success and, above all, not always efficiency. Because AI is software. And that means that AI applications, like other software, need to be regularly and reliably maintained in order to function as intended. This is often not taken into account in current deployment scenarios, but must be an integral part of any project planning.
Nothing works without clean data
The use of AI requires data. Sometimes there is more, sometimes there is a lot. On the one hand, this data must be available and, on the other, it must be processed in such a way that it makes sense to use it so that it serves the purpose of using artificial intelligence. The data must be tagged (marked) and a distinction must be made as to which data is training data and which is test data. What do you want to achieve with the data? What should the AI deliver based on this data? In what form? Should it generate something complex?
Different types of AI make the difference
Depending on the objective and the amount of data available, it is important to find the right AI basis that should be selected for the procedure.
1. NLP (Natural Language Processing)
NLP is the abbreviation for Natural Language Processing and stands for the interaction of computers and human ("natural") language, which they should be able to understand and interpret. Typical areas of application include text translations, text summaries or sentiment analysis based on spoken audio sequences.
2. machine learning
The aim of machine learning is to develop systems that learn from data. These systems are not explicitly programmed for a specific task, but are trained to recognize patterns and make decisions based on the data with which they have been trained. Machine learning includes various algorithms and models in which predictions are to be made taking into account many influencing factors, making recommendations based on previous behavior, in image recognition or, for example, for medical diagnoses.
3. deep learning
Deep learning is a sub-area of machine learning in which artificial neural networks with many layers (hence "deep") are used to model complex patterns in data. Deep learning techniques are particularly powerful when processing large volumes of unstructured data such as images, sounds and text. This technology is behind many modern AI applications such as voice-controlled assistants, facial recognition systems and self-driving automobiles.
4 LLM (Large Language Models)
LLM stands for Large Language Model, which is a type of deep learning model developed specifically for understanding and generating natural language. These models are trained on large collections of text data and can generate coherent and in-context texts based on the input. They can take on more demanding tasks such as conducting a dialog in a specific context, generating code according to specific requirements or answering complex questions.
Start small, achieve big results
The point at which companies can successfully use AI applications is entirely individual. The best way for teams to get started is to select a specific area for implementation and carefully consider where they have a high level of overview knowledge and can narrow down the scope of what the AI should be able to achieve. The more defined the goal is, the more successful you will be with AI. A general solution for all possible tasks may seem promising at first, but will then be very unsatisfactory and have many shortcomings. Solutions that specifically address a problem and have enough data for training and can be thoroughly tested from the very beginning will provide support for employees and make companies more successful and efficient. Some applications will certainly be that efficient that they can indeed replace many jobs, but not all of them. After all, the aim is to increase efficiency. And artificial intelligence, contrary to its name, has no intelligence - it makes decisions mathematically. As soon as decisions have to be made with intelligence, humans are needed. This will not change in the future. AI models are only as good as the data and how they are trained and tested.
Generative artificial intelligence will completely change the way we work
Companies will and must experiment. What works and what doesn't? What works but doesn't make sense? What works, makes sense and ensures the company's success? In the long run? Companies will have to develop their own customized AI solutions. This means that they will operate with their own data - taking into account data protection, security and, most importantly, company expertise on their own servers in order to make their processes, working methods and, finally, their core business more efficient. Securing the future of many companies will depend on this. This is because all market participants will try to do everything possible to gain a competitive advantage. Companies will offer their services and products with less effort and consequently in less time. We are moving towards a rapid development in competition. Those who are too late will be left behind.
If you want to have a chat about AI implementation in your company or are looking for specific services, write to us at ai@trendig.com or check out our engineering page.
Do you want to develop your skills in this area? Then take a look at our practical AiU GenAI-Assisted Test Engineer course here.
Or if you are interested in testing with and from AI, then the ISTQB® Certified Tester AI-Testing could be right for you.
