Deep Learning (DL) and Large Language Model (LLM)

Deep Learning, Natural Language Processing (NLP), and Large Language Models (LLMs) are driving current IT innovation and reshaping the way we interact with and benefit from technology. These ground-breaking technologies have become a central part of modern applications, from improving user experience to automating complex tasks.

At Fraunhofer IESE, we are working on the design and development of dependable solutions using Deep Learning, NLP, and LLMs. Our goal is to use these technologies in customized systems in such a way that safety, security, and reliability are guaranteed and that your digital solutions will benefit from them.

Deep Learning, a subfield of Artificial Intelligence (AI), refers to algorithms that are based on artificial neural networks. These networks are inspired by the structure and function of the human brain and consist of layers of nodes or “neurons”. Each layer in such a network can recognize and process complex patterns in data, which makes Deep Learning particularly effective for tasks such as image and speech recognition.

Comparison: Deep Learning vs. Machine Learning

How does Deep Learning work?

Deep Learning is a sub-area of “Machine Learning”, where a model is trained through exposure to large amounts of data. As “unsupervised learning”, this can also be done without labeled data. However, Deep Learning methods require huge amounts of data for this (20-50 times more than for simple Machine Learning). Deep Learning without labeled data requires thousands of data per feature (for comparison: Machine Learning with labeled data requires about 50-100). During the training process, the internal parameters of the model are adjusted to identify patterns and features in the data. The »depth« in Deep Learning refers to the number of layers in the neural network. More layers allow the network to recognize more complex patterns.

Deep Learning in a company

Deep Learning shows its full potential in the analysis and interpretation of large data sets in areas such as image analysis, video processing, text understanding, music, speech recognition, and financial and energy analysis. The initial step is to identify potential applications where Deep Learning can automate processes, refine customer interactions, optimize operations, or create innovative services and products.

A practical example is image recognition by deep neural networks that recognize objects or faces in images or classify images. With extensive data sets, these networks can even capture complex content and acquire “knowledge”. This can be seen in Large Language Models such as GPT-4 from OpenAI (used by ChatGPT), Gemini from Google (used by Bard), or Llama 2 from Meta (used by Meta AI), which have been trained with billions of data points and developed with considerable investment in order to independently generate new content. There are already ready-made models, which can serve as a basis for finetuning. This finetuning enables a significant increase in the quality of the use case-specific results with relatively little effort and small amounts of training data, using technologies such as LoRA.

NLP is an area of Artificial Intelligence that focuses on understanding, interpreting, and generating human (natural) language. NLP applications range from text analysis (such as sentiment analysis and keyword recognition) and speech recognition to language generation and translation. Various techniques from linguistics and computer science are used, such as grammar and syntax analysis, semantic analysis, and Machine Learning. LLMs are a specific application within NLP. They are large, highly complex models that provide a deep understanding of language and context.

Imagina a system that does not only understand your language, but is also able to perform complex tasks such as creating coherent and meaningful text paragraphs, answering questions, summarizing text, creating content, and even developing software. This is not a scene from a science fiction movie, but the reality made possible by Large Language Models (LLMs). LLMs are neural networks trained to understand and generate human language. These models, including prominent examples such as GPT-4 from OpenAI (with the AI chatbot ChatGPT), PaLM 2 from Google (with the AI chatbot Bard), or Chatbot Claude from Anthropic, can handle complex tasks such as the creation of accurate texts, the analysis and categorization of images, automated translation, or the analysis of large amounts of data.

• Automated documentation: Similar to tireless librarians who organize and catalog information, LLMs can help organize and make accessible vast amounts of technical data and documents, increasing efficiency and reducing errors.
  
  
• Semantic search: Natural Language Processing (NLP) makes it possible to understand the intention and context behind a user’s search query – for example, whether the person is looking for information, wants to make a purchase, or is searching for a specific website – and thereby deliver more intelligent and more relevant results.
  
  
• Customer support: Like a customer service agent available 24/7, AI-supported chatbots can provide efficient and accurate customer service around the clock, freeing up resources for more complex tasks.
  
  
• Research and development: Similar to innovative researchers, LLM-based solutions are able to navigate through scientific literature at lightning speed and gain valuable insights.
  
  
• Quality assurance: Similar to a meticulous quality inspector, LLMs can help analyze software codes and identify errors before they cause problems.