03.01.2024 | News These five trends will shape the AI year 2024

2023 was the year of generative AI. The large language models behind popular applications such as ChatGPT have taken the performance of artificial intelligence in text comprehension and text creation to a whole new level. In 2024, with all the knowledge and first experiences that they have gathered, companies will increasingly focus on implementing concrete solutions. From IntraFind’s point of view, the following trends are emerging:

1. Companies create secure frameworks for the use of AI. In addition to the benefits of generative AI, the risks of this technology come more and more into focus. In 2024, companies will provide generative AI with a secure framework that mitigates these risks by managing internal access rights, minimizing hallucinations, and preventing unauthorized data outflows. Accordingly, security will play a decisive role in the selection of AI solutions and service providers.

2. The use of open-source models increases. Not all AI is the same. In addition to proprietary language models such as GPT from Open AI or Luminous from Aleph Alpha, powerful open-source models from the open-source community are also available. They are the better choice for many applications and will therefore be increasingly used. This applies particularly to applications with especially high requirements for data sovereignty and transparency.

3. Retrieval Augmented Generation (RAG) optimizes generative AI. Large language models have their limitations regarding up-to-dateness and subject-specific domain knowledge. This is why companies are increasingly integrating retrieval systems into the models. With these, additionally to the language model data, other sources such as the organization's own documents or knowledge databases are queried. In combining the results from both, answers are more up-to-date and accurate. User permissions are also considered during retrieval.

4. Vector databases become increasingly important for generative AI. Special language models, known as representation learning models, can identify similarities between terms when indexing documents and store these relationships in the form of word vectors. This technology is increasingly being used in enterprise search, as it optimally supports semantic search. The system identifies and finds what the user really means instead of considering only the search term they type into the search field. Thus, the output of generative AI improves.

5. Companies improve their data quality. For generative AI to deliver optimal results, companies will work on optimizing their data quality, especially as they are dealing more and more with unstructured data. Here, too, AI provides support. With the help of intelligent solutions, data can be automatically enriched with metadata and classified by topic. Multimodal language models that process layout information as well as text, offer an optimal solution for extracting all relevant information from non-linear documents such as delivery bills, invoices, or forms.

"Companies can definitely increase their productivity with generative AI and large language models. The task now is to generate real benefits from the AI euphoria. However, to achieve this, organizations need a plan and advice from experts to find and define meaningful use cases for themselves," explains IntraFind CEO Franz Kögl. "They need to ask themselves what their goal is, which processes they want to speed up and which framework conditions are required for safe use."