Three introductions to generative AI

The digital landscape is constantly changing and at the forefront of this digital transformation is the Generative AI. In this article from the start-up Schopf Meta Consult founder Peter Schopf describes the origins and meaning of generative Artificial intelligence and presents three Entry scenarios for companies that can be implemented quickly and easily. Along with his Meta Business Twin (MBT) he presents the practical application.

Contents

• Basics of Generative Artificial Intelligence
  • Specialized AI systems are being replaced
  • Transformer architecture drives development
• Archetypal entry-level variants into generative AI
  • The strategic approach
  • The exploratory approach
  • The use case approach
• The Meta Business Twin – more than a chatbot
  • The concept of the Meta Business Twin
  • Possible use cases – video
• Conclusion
• Ki know-how for download
• FAQ

Basics of Generative Artificial Intelligence

Why is there currently such a hype about artificial intelligence (AI) and is it even justified when it has been around for many years? The reason is that the playing field is the AI since 2017 has fundamentally changed. That year, the Transformer architecture was introduced in a groundbreaking paper “Attention is all you need” (Vaswani, Ashish, et al.).

This Transformer architecture, which has now gained global prominence, is the backbone of many modern AI systems. Among other things, it offers the enormous advantage that pre-trained models (pre-trained transformers) can be used, which contain almost all of the publicly accessible knowledge on the Internet.

Specialized AI systems are being replaced

This is a big difference previous AI-Systems in which the availability of large amounts of data often represents a major obstacle to implementation.

The pre-trained Transformers have now opened the door to new forms of text, code and image generation. AI systems that once handled simple tasks like sorting data or answering specific questions can now create self-contained works of art, compose complex text, write code, and interact with users in real time. These multimodal capabilities will replace many specialized AI systems.

Transformer architecture drives development

ResearchAreas that have previously worked in isolation with their own approaches, algorithms and sometimes very different language usage are now focusing together on the further development and application of these multimodal Foundation models. This focus of financial and human resources, coupled with the almost exponential growth in computing power of modern processors (usually GPUs or Graphics Processing Units), leads to a breathtaking speed of technological development.

This could unhinge the foundations of our society(s). So it's worth understanding this area in order to ride the wave instead of getting buried under it. Even if the current rapid development slows down again, a lot can still be expected based on current announcements alone. Companys should use the possibilities of AI that already exist. In summary, the relatively new Transformer architecture is the driving force behind these impressive developments.

Archetypal entry-level variants into generative AI

In order to fully exploit the advantages of Transformer-based generative AI, there are various approaches that companies can pursue depending on their objectives and resource availability. Three archetypal entry-level variants are described below:

The strategic approach

Anyone who takes the topic of AI seriously and has the appropriate resources available chooses this approach. It requires a deep understanding of the current level of maturity of the company and a clear vision for the future.

First is the Status Quo to record: Which processes already exist and how could they be improved by AI? A target image must then be developed that reflects the company's ambitions in the context of an AI-supported future.

A dedicated one Data Strategy is essential here as it lays the foundation for every AI application. Equally important is effective change management to ensure that the workforce is prepared for change and that new technologies can be used optimally.

For example, a medium-sized company in the... Manufacturing Industry Define generative AI as part of its digital transformation to drive process optimization and product innovation.

While a strategic approach is comprehensive and also for example Talent and competency development taken into account, the data strategy in particular should be briefly explained. Having a good data strategy is common practice among manufacturing innovation leaders. Part of it is a well thought out one Data Catalog and robust data governance (data regulation). A data catalog is used to record and catalog all of the company's data sources and determine responsibilities. Data sources in our example could include machine data, sensor data from production, quality management data, logistics and supply chain information, and market research data. 

The Data governance (Data regulation) determines how data is handled within the company. It includes rules and processes for the lifecycle management of data, including its collection, storage, access, processing and deletion. 

By combining these elements, the manufacturing company in the example can create a clear framework to use data as a strategic resource for the use of artificial intelligence, which enables it to increase productivity and produce innovative solutions.

The exploratory approach

With this approach, employees are given the opportunity to work with AI tools experiment. This allows them to develop an intuitive understanding of their potential. Ideally paired with training, events and idea competitions Companys here on the decentralized innovative strength of the workforce.

A pioneer in this area is Siemens (author's last employer). The technology leader provided its employees with a secure AI environment for experimentation at the beginning of 2023. This enabled the entire company to get on the AI ​​path. However, at Siemens, AI is so relevant that all three approaches are addressed in parallel.

If you visit Siemens at one of the leading industry trade fairs like the SPS in Nuremberg, then you can see that almost all exhibition areas are already demonstrating some kind of AI solution. There is a little more detail here big differences and 'artificial intelligence' is interpreted liberally. What is worth noting, however, is the company's general attitude towards the topic.

Siemens does not focus on one or two use cases, but rather enables all employees and corporate units to become active and integrate AI into their solutions. However, the topic of AI is so relevant in the company that all three approaches are addressed in parallel. AI is viewed strategically, opportunities are offered for employees and company departments to exploratively explore the topic and there are dedicated reference applications such as Siemens Industrial Copilot, an AI-powered assistance for industry developed in collaboration with Microsoft.

The use case approach

Here the company focuses on one or a few specific use cases that have already demonstrated success across industries. A good example of this is the use of RAG pipelines (Retrieval Augmented Generation). It is possible to ask complex questions about documents such as contracts or process documentation, which revolutionizes the accessibility and processing of information. This is called chat-with-your-document. 

Combinations of existing offerings with AI are also becoming increasingly popular. For example, the Italian company combines 40factory Your established IoT solutions with a generative AI assistant, the chat bot Wilson. After successfully introducing reference projects with existing customers, 40Factory is now expanding its offering.

Another popular use case is chat bots in customer service. For example, this approach can be used with the Meta Business Twin be implemented.

The Meta Business Twin – more than a chatbot

While chatbots are primarily designed to respond to customer inquiries and provide answers from a limited set of information, companies can achieve much more with little effort. But be careful! For this use case it is very helpful to understand the meaning of Corporate Culture to understand. Here in Germany we often still have considerable potential for improvement.

Many companies continue to fail digital transformation, because the focus is too much on the technological components and the effects on processes and employees are not taken into account enough. With an understanding of how important a company's principles and culture are for efficient and effective decision-making, the added value and differentiation of the application described here becomes much clearer.

The concept of the Meta Business Twin

One application of generative AI is the Meta Business Twin (MBT) from the startup Schopf Meta Consult. The concept of the digital twin is ideal for companies or departments getting started. The MTB is one virtual representation of an individual, e.g. B. in the form of a department head or an organization represented by the deceased founder, the CEO or an artificial person.

The purpose of the MBT is to explicit knowledge such as process descriptions, regulations and company data with implicit knowledge that includes the principles and cultural norms the kombinier. Through simple and intuitive interaction, the MBT is anchored in the company as a reliable source of knowledge and improved step by step.

He acts like a coach and mentor for new employees and helps to retain the knowledge of departing specialists. In addition, the MBT can be used as a benchmark in its assessments and recommendations Best practices incorporate into company processes.

Supporting principle-based decisions enables agile response to new challenges and opportunities through increased speed. This is another advantage of the MBT, which not only acts as a tool, but also as a dynamic knowledge storage is that embodies and continues the corporate culture in the digital space.

In the expansion stage, the Meta Business Twin is not only used for internal use and organizational development, but can also be used externally to communicate the corporate philosophy to external stakeholders.

Possible use cases

Conclusion

In summary, this article aims to show that generative AI is more than just that next level of chatbots is. It is a transformative force that fundamentally changes the way companies operate, innovate and communicate. The Meta Business Twin is a manageable example of how far generative AI applications can go - beyond automating tasks to creating value through deep understanding and improving human interactions.

Ki know-how for download

We at Schopf Meta Consult are convinced that the German-speaking region can and must keep up with the global wave of digitalization. With the Meta Bunsiness Twin and other AI innovations in our toolbox, we are ready to walk this journey with you.

Download our executive presentation on Generative AI in one Entry-level version which provides you with an interesting insight into the possibilities of AI.

Download now!

If we've made you curious for more and you want to understand the full scope of Generative AI and its impact on your business, we offer one more Expert presentation at. Inquiry at: redaktion@developmentscout.com

further information: https://smc.meta-twin-peter.business

FAQ

What is generative AI?

Generative AI, too Generative AI, stands for generative artificial intelligence. It is a type of AI for generating new content, such as text, images or music. In contrast to other forms in which AI functions are designed to perform specific tasks or solve problems, generative artificial intelligence specializes in being creative on the one hand and developing new ideas on the other.

Basically, generative AI works on the basis of so-called neural networks, which are trained on large amounts of data. These networks learn to recognize patterns and connections in the data and can then independently generate new content that is similar to these patterns (deep learning).

An example of generative AI is a Text generator, who is able to write articles, stories or even entire books. The generator analyzes a large amount of text with a large vocabulary and learns from it how to create meaningful and well-formulated sentences and paragraphs. On this basis, he can then independently generate new texts that are similar to the style and structure of the analyzed texts.

Generative AI has the potential to impact many areas of life and work. For example, it could be used in art to generate new pieces of music or paintings. In medicine, it could help develop new drugs or analyze medical data. And in business, generative artificial intelligence could be used in development new business ideas or support the optimization of processes.

Which generative AI models are there?

There are different types of generative AI models, each aimed at specific AI applications. Some prominent examples of a Generative AI model are:

• GPT-3 and GPT-4 (OpenAI) are advanced language models that are used for text generation and text understanding.
• GIVE HER (OpenAI) specializes in generating images from text descriptions.
• Deepfakes is a technology used to create convincing fake videos and audio files by mimicking faces and voices.
• Wavennet (Deepmind) is a model for generating natural speech output.
• BERT (Google) is a language processing model used in search and other applications.

Is ChatGPT generative AI?

Ja, ChatGPT is a form of generative AI. It is a language model based on the GPT (Generative Pre-trained Transformer) and is capable of generating human-like text. ChatGPT was trained with large amounts of text data and can now answer questions or generate texts independently.

What are the 4 types of artificial intelligence?

There are many types of artificial intelligence (AI), the classification of which depends on how you define the distinguishing criteria. However, four stages can be roughly distinguished:

1. Reactive AI

This type of AI is based on predefined rules and algorithms. She can react to certain situations, but she has no memory or understanding of the context. Reactive artificial intelligence can perform specific tasks well, but it cannot learn new information or remember past experiences.

2. Limited cognitive AI

This type of AI can go beyond predefined rules and based on Experiences learn. She can recognize patterns and make decisions, but she still has her limitations. Limited cognitive AI can be used in certain areas such as speech recognition or image recognition, but it cannot achieve comprehensive human-like intelligence.

3. Artificial General Intelligence (AGI)

AGI is a form of AI capable of performing a wide range of tasks and human-like intelligence to reach. AGI can learn to remember past experiences, process new information, and solve complex problems. However, this type of artificial intelligence is not yet fully developed and remains a challenge for research.

4. Superintelligence

Superintelligence is one hypothetical form of AI that far exceeds the intelligence of a human brain. This type of AI would be able to solve complex problems, generate new insights and further develop itself. However, superintelligence is still a long way off and remains a topic of speculation and debate.