AI: Infinite possibilities with full independence from third parties

In-house, high-performance AI systems can be set up quickly and cost-effectively. Companies and public authorities can particularly benefit from this. An in-house AI server, whether rented or purchased, can produce millions of new results. Examples are given in the article.

Introduction

In-house AI systems have so many decisive advantages that the reluctance of German companies is somewhat surprising. Now is a better time than ever to get started with AI: more powerful, cheaper and simpler than ever. You just have to have the right advisors who don't advise ChatGPT or similar, because simple is simple, but not exactly intelligent either.

Many only get news about artificial intelligence when it comes from OpenAI, Apple or Google. What sounds charming for private users who don't value data sovereignty is disadvantageous for companies from several reasons. The talk is of dependence on third parties, which are especially American corporations.

AI has never been as easy to implement as it is now. Top quality results at low cost and without ChatGPT or Azure. But with full optimizability and data control.

By the way: Also economical in 24/7 operation and for millions of documents.

Companies and authorities suffer several serious disadvantages if they become dependent on ChatGPT, Gemini, Claude or similar offers.

Advantages of Offline-AI

An Offline-AI is a standalone AI system for your company.

Some of the most important reasons why companies and public authorities should invest in their own AI solution are:

• Independence: With your own AI solution, you are independent from third-party providers and can control your processes and results yourself.
• Cost Efficiency: A proprietary AI solution can be more cost-efficient compared to third-party providers, especially when processing large data sets.
• Data Security: With your own AI solution, you have control over your data and can ensure that it is processed securely and in accordance with applicable data protection regulations.
• Adaptability: A custom AI solution can be tailored to your company's specific needs, whereas third-party providers often offer standardized solutions.
• Scalability: A custom AI solution can be easily scaled to meet the growing demands of your business.
• Innovation: With your own AI solution you can develop new and innovative applications that give your company a competitive advantage.

These advantages also eliminate the following disadvantages that your company or organization suffers when ChatGPT is used.

Disadvantages of using ChatGPT

The disadvantages of using ChatGPT (or similar) in organizations include:

• Functional Dependence: The AI services of providers are often not interchangeable, among other things because they differ or are differently priced. In addition come laborious and error-prone fine-tuning, which is necessary with each provider change.
• Price Dependence: As an example, two concrete cases from current times are mentioned here. Spotify and Netflix have simply raised their prices. If the customer does not accept this, they must endure ads at Spotify and will be cancelled by Netflix. Analogously, it is with ChatGPT and others. The price must be accepted or the provider cancels you.
• Knowledge Drain: All data loaded into Microsoft's (ChatGPT, Bing Bot) systems, Google's (Gemini, Google Bot), or Apple's (analog) are potentially used for training AI. Thankfully, some providers offer an opt-out option. Whether this is respected can be doubted. After all, data is the gold of our time and sucked-in data that lies in a machine learning model or is output by it again are mostly not recognizable from a copyright perspective.
• Lack of knowledge acquisition: Data that flows elsewhere makes the data sender quickly comfortable. He relies on the provider and wants nothing more to do with this "AI-Dings". The main thing is for the electronic brain to deliver good answers.
• Lack of Flexibility: A Black Box is a Black Box. Those who have never developed anything themselves can't or won't do it. The dependency keeps growing, while innovation power drops massively instead.
• Lack of Innovation: Own AI systems that can run permanently offer unprecedented possibilities that third-party systems could never provide. Simply because of the costs of third-party services.

Companies that still use Microsoft Azure seem to have a good sponsor. These often immense costs don't have to be there when it can be much cheaper and often better.

Some examples of the unimagined possibilities offered by in-house AI systems are illustrated below.

Unimagined opportunities for companies

The prerequisites for unimagined possibilities are in particular:

• Low-Cost Flat Rate instead of high costs or unpredictable costs.
• High Flexibility: Thanks to extremely powerful open-source AI frameworks and language models, possible today without significant effort.
• Own AI Server, rented or bought. Results from the previous points.

Opportunities become particularly unimaginable when an AI system is able to improve itself.

The possibility of self-improvement is best achieved when the AI system runs for as long as possible.

What does "the AI system is running" mean? This does not mean that the AI server is switched on. Rather, it means that we (can) keep the AI system constantly busy. A child learns more when it learns for longer. At some point, learning comes to an end. But if you only invest a little time, you will never reach the point where you have finished learning.

An AI system potentially never stops learning. Because it can always improve itself. At any rate, this applies to many use cases.

The following processes, for example, can be continuously improved, almost 100 % automatically:

• Summarize documents and texts.
• Simplify text.
• Search for documents.
• Identifying certain relevant text parts in texts. Examples:
  • Keywords,
  • positive terms for marketing,
  • positively or negatively charged terms or phrases.
• Answering difficult knowledge questions, also with the help of world knowledge (automated internet search)

Through so-called Agents, some complex tasks can already be automated processed today, also in your company and with your own AI system. Complex tasks are those that require several processing steps including research and/or the use of specific tools. Such tools can for example be calculators, weather forecasts, news robots or stock analyses.

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With agents alone, no miracles happen, at least not big ones (but potentially small!). Approaches like RAG help shape the AI system on its own documents and company knowledge. RAG means that small information snippets from company knowledge are pre-selected for a question to be given to the AI and passed on to the language model for answering the question.

What is now standard has already been exhausted, at least when it comes to ChatGPT. Anyone who needs reliable answers and may want to identify unreliable ones will not get around having their own AI system. Then reliability of AI answers can be established!

The special charm of having your own AI server running 24/7 is the incredible range of possibilities. This results from the fact that the AI language model can criticize and improve itself. Theoretically, this is also possible with ChatGPT, but fails due to the following points:

1. Costs: Far too high for usage-based fees if 100,000 or even 10 million work steps (including learning steps) are to be carried out.
2. Manual is no fun: prompt tuning or not. But who creates a document with 10 different prompts, which then have to be entered 500 times for 100 cases with 5 steps per case? Optimization only works with a few dozen or hundreds of cases. This cannot be done manually.

The situation is different if you have your own server. My company's AI server here in Idstein has been doing the following without interruption for weeks:

1. Documents are presented to the AI. These can be newspaper articles, blog posts, legal texts or other texts. For some applications, the texts are automatically retrieved from the Internet. Automated internet searches and scraping processes are also used.
2. Our language model analyzes the texts according to the task. Tasks could be: Summarizing a text, simplifying texts in different language styles, extracting important terms, extracting knowledge, translating into other languages and much more. Real processes were mentioned, not imaginary ones.
3. For many use cases, it makes sense to solve the language model only one task at a time. By task here, we do not mean "term extraction", but a more granular activity. All of the AI's answers from the previous step are now refined in the detailed step and in some cases already improved.
4. Depending on the use case, repeat step 3 with other detailed activities zero to x times. For certain use cases, five detailed steps per data record make perfect sense.
5. Let the AI criticize itself for every AI response it has previously received.
6. Use the result from the previous step to get a better AI result using the initial inputs.
7. Save the results.
8. Use the results to fine-tune the AI language model.
9. Start at 1. with a better AI.

Where has the human being gone here? They are essentially no longer needed. Human intervention is necessary or useful in these processes:

• Setting up the AI mechanism technically
• Monitor the results of the AI (samples)
• Optimize the AI mechanism until it works as well as possible.

Here is a list of specific AI solutions that have already been implemented and described on Dr. GDPR:

• Knowledge assistant
• Simplification of legal texts
• Simplify communication (applications, questions from citizens or customers, support tickets, …)
• Generate images (as fast as an arrow, on a laptop)

Further applications that have been realized but only roughly described under DR. GDPR are the transcription of videos and podcasts and the recognition of objects in images. 3sat has conducted an interview with the author of this article, titled "New Ways" (from minute 33:18 in the 3sat documentary).

Thanks to own AI system, any optimizations are possible, multiple iterations no problem, data fully under control and costs not an issue! The ideal soil for innovations has been created.

When do you use a AI server for your company or authority and start into the future? For quick entry, there are now pre-installed and directly usable AI servers for rent.