Stay in control of your AI models and data

Besides high performance computers AI needs vasts amounts of data to feed advanced and complex algorithms that identify patterns and relationships. But do bear in mind that any AI model is only as good as the data provided during training.

Yes, absolutely. Like in any other business areas, high quality data leads to accurate predictions and results, improved efficiency, reduced bias and waste of resources. In the end there’s a clear correlation between excellent data quality and better business outcomes as well as lower operational costs. Even more importantly, clean, balanced, and representative datasets help prevent AI models from creating bubbles full of unfair or discriminatory results.

There’s Narrow AI used for specific task, Generative AI used to create new content which can be text, images or even code. The last type is General AI which is human-like intelligence which is not there yet. Some experts believe that Artificial General Intelligence will be achieved in 2026, others say that it’s decades away and some do even believe that it’ll never happen.

AI agents are specialised digital assistants that perform tasks indepentently. They use AI to understand information, make decisions and automate processes. Unlike traditional software solutions, they work flexibly, learn from data and can be individually adapted to your requirements.

Generative AI is not just analysing data. It’s the type of artificial intelligence that creates new content, no matter it it’s text, imagery, audio or video. Gen AI models are also trained on loads of existing data and learn the underlying patterns, structures and relationships within that data in order to create new output in response to user prompts.

ML is a subset of AI and uses algorithms to process vast amounts of historical data. Unlike traditional software or applications, ML learns and gains experience from even more datasets by itself and without explicit programming. Within the huge amounts of data the algorithms can identify patterns, trends and relationships. Common use cases for ML iare fraud detection or image recognition.

LLM stands for large language model. LLMs are trained with huge amounts of text enabling them to understand language, generate text and code, answer questions, handle enquiries, translage text from one language to another, condense large documents into short and crisp summaries, or analyse tons of text-based datasets for insights and trends.

Deep Learning is a subset of Machine Learning and inspired by human brains. Deep Learning uses multi-layered neural networks to recognise complex patterns in images, text, and sounds,which can lead to highly accurate insights and predictions. The term ‘deep’ refers to the number of layers in the networks which can easily sum up to more than hundreds. One example of Deep Learning is an app on your smartphone giving you an instant translation of a foreign street sign.