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Generative Artificial Intelligence

A Simple Introduction to GPT – A Guide for Businesses

Generative AI (GPT) is identified as the top priority for CEOs in 2024 according to the latest trend report by McKinsey[1].

Following the significant hype around ChatGPT, concrete use cases for businesses are now emerging. But how does the foundation of this new technology actually work?

The aim of this article is to explain the concept of GPT (Generative Pre-trained Transformer) in an understandable way, debunk myths, and highlight the real limitations and possibilities of GPT.

[1] https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/what-matters-most-eight-ceo-priorities-for-2024

CEO -Leftshift One - Patrick Ratheiser

Patrick Ratheiser

CEO & Founder

Karin Schnedlitz

Content Managerin

What is GPT?

GPT stands for “Generative Pre-trained Transformer.” Developed by OpenAI, GPT models are based on the transformer architecture and are designed to generate text by predicting successive words in a sequence. The development of GPT models began with GPT-1 in 2018 and has significantly evolved up to GPT-4 in 2023, with each version becoming more powerful.

Development of GPT

The development of GPT models represents a turning point in natural language processing. With the emergence of transformer-based architectures, they surpassed the limitations of traditional neural network models. The GPT models learn in a semi-supervised manner, initially through unsupervised pre-training on large text datasets and then through fine-tuning for specific tasks.

Applications and Examples

GGPT models find applications in a variety of areas:

  • They can create original content
  • write code
  • summarize texts
  • extract data
  • assist in generating content for social media
  • convert text into different styles
  • analyze data
  • create educational materials
  • also enable the development of interactive voice assistants

Myths and Realities

There are numerous myths surrounding GPT, such as the assumption that it is omniscient. In reality, GPT models are based on the analysis and reconstruction of language patterns learned from large datasets. They are powerful tools, but they have limitations in their accuracy and in the ethical implications of their application.

Functionality of GPT

How GPT Works

To understand how GPT works, let’s take a detailed look at all the components and then go through a simple example step by step.

1.     Embedding: This concept is like a dictionary that translates each word into a numerical vector to capture its meaning and context. These vectors not only represent the words but also their positions in the sentence. It is a way to compress information from the internet by transforming text data into a compact form through the model parameters.

2.     Layer Norm: Layer normalization is akin to balancing a plate on a stick, where the values in each vector are normalized so that their average is zero and their standard deviation is one. This process contributes to the model’s stability and helps minimize fluctuations during training.

3.     Self-Attention: In this step, the vectors “talk” to each other. Each vector in the model looks at the others and decides how relevant they are to its context. It’s like a team meeting where everyone shares their opinion, and the others determine how important it is for the current discussion.

4.     Projection: Here, the results of self-attention are combined, similar to putting together puzzle pieces to create a larger picture. Each piece that carries specific information is combined to form a more comprehensive understanding.

5.     MLP (Multi-Layer Perceptron): The MLP functions as a filtering process that extracts useful information from raw data. It takes the combined vectors and transforms them through several layers to recognize new, meaningful patterns.

6.     Transformer: The transformer block is the core of the model, where all previous steps converge and are refined. Each element in the transformer contributes to the overall picture, much like an orchestra producing a harmonious sound.

7.     Softmax: This function acts as a probability calculator, determining how important individual parts of the information are. It converts numbers into a probability distribution, with higher values indicating greater significance.

8.     Output: At the end of the process, a prediction is made based on all the collected and processed information. Similar to an expert weighing all available data to reach a conclusion, this step represents the model’s final decision, indicating which word or term should follow next.

A Simple Example

Imagine the word “cat.” In a large language model (LLM), “cat” is first converted into a token, let’s say the number 5. This token is transformed into a vector through embedding, representing “cat” in a multidimensional space, similar to a point on a map. This vector is then normalized through layer normalization to make the data more uniform and manageable.

In the self-attention step, “cat” interacts with other words in the sentence, with the model weighing the relevance of each word. It’s as if “cat” is asking the other words how they fit together. After projection, where this information is combined, “cat” goes through the MLP, which acts as a filter and refines the information. Finally, the word reaches the transformer block, where all these steps come together to shape the overall picture.

The softmax function takes all this information and calculates the probabilities to predict the most likely continuation of the sentence. In the end, a decision is made, and the model predicts the continuation of “cat,” based on the entire process.

Zukunftsausblick

The future of GPT looks promising. Each new version brings improvements in performance and application areas. Future developments could lead to even more precise adaptations for specific tasks and potentially new, innovative applications across various fields.

With Leftshift One, you are perfectly positioned in generative AI. With MyGPT, you can leverage the benefits of ChatGPT using your internal company data. Schedule a free initial consultation here now!

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