AI Showcase: Simplify communication intelligently with your own AI model

This AI showcase demonstrates the possibilities of a self-contained AI system in companies and authorities. It shows how employees can be relieved with AI. For example, complaints, citizen inquiries, support tickets, emails or damage reports can be recognized and processed purposefully. Features: full data control, full independence, no additional costs.

Introduction

In the previous AI-Showcase, it was demonstrated how impressive images can be generated on an AI-laptop with little effort and at lightning speed.

In this AI showcase, it is shown that with almost equally low programming effort and within minutes an intelligent classifier for documents can be trained. This allows communication to be significantly simplified in companies and authorities.

Requests from citizens or customers can be sent directly to the right handler or automatically assigned to a suitable category. Even recommendations for the best answer, based on previous comparable cases, are possible. All this automatically and AI-based. Work processes supported by AI instead of replacing work processes. Because Reliability is based on a prerequisite: Responsibility. And this can only be taken over by a human.

An intelligent classifier can understand the meaning of documents or requests. The possibilities that follow are enormous. The following process shows in bullet points what is possible in your company or in a government agency:

• A customer or citizen writes you a message. This can be a damage report, a complaint, an appointment request, a termination, a question about something, or anything else.
• One of your employees should edit the message.
• With the intelligent classifier, it can be recognized how a customer's or citizen's message is to be processed.
• This leads to a series of possibilities:
  • The message will be automatically forwarded to the right employee.
  • The message is compared with earlier messages. An employee receives a recommendation for processing based on an earlier case that was very similar.

An alternative is a ticket system for support (customer support, citizen service, programmers) conceivable: :

• A new case comes in, for example, a problem report or complaint or a software error.
• With the intelligent classifier, this case can be automatically sorted into a suitable category.
• The right employee receives the case for processing.
• He optionally receives a recommendation on how a very similar case was handled in the past.
• The reporter (customer, citizen, user) receives a response that is appropriate for their request: "Dear customer, thank you for your message. Your message has been classified as an error report and will be processed with high priority. One of our employees has already been informed."

The Showcase

Work is done with an open-source AI model that understands German well. While the whole world speaks other languages besides German, at least AI language models understand our language very well.

The goal is to determine the category of a given document. A document is equivalent to a piece of text. This can also include an email, a message from a contact form, a web search, or input into a chatbot. This showcase works very well with texts that are no longer than a few hundred words. For longer texts, the approach used can be refined with conventional methods.

You determine which categories exist. For example, if there are employees who handle complaints on a specific topic, a category would be "Complaints on topic X". Another category could be "General Inquiry" or "Purchase Interest" or "Question about getting an ID card".

For these categories you have defined, example documents are now needed. Often, just a few examples per category suffice.

If there are very few examples, they can be multiplied by a AI process. One speaks here of synthetic datasets. For example, a new version can be generated from an existing document that is linguistically different but belongs to the same category. In many cases, conventional programming can already multiply the examples collected by humans.

Since your documents are your own, it's often important to have Data Control. That's why the AI process runs locally on your hardware. For this showcase, a AI laptop was used where this text is also being written. Companies would use their (own or rented) AI server. What's possible on a laptop is even more possible on a box called Server.

Then begins the exciting part, the training of a classifier. This classifier learns when a document is to be assigned to a certain category. How does it learn that? Exactly as if you were to look at hundreds or thousands of documents and learn to recognize the respective category yourself. The difference is that you neither have the time nor the desire nor the good memory nor are fast enough.

Results

The training took only a few minutes on the aforementioned AI laptop:

Training runtime: 212.2422 seconds

Training samples processed per second: 155.671

train_steps_per_second: 9.729

Total optimization steps = 5443628

One of the outputs of the AI training process

This output came out after a first test run. A refinement of the result took a bit longer than the mentioned 212 seconds, which is less than 4 minutes calculation time. In this time approximately 5.5 million optimization steps were performed. On a laptop (the fan is sometimes a bit loud).

Approximately ten thousand training steps were executed per second. Similarly, over 155,000 examples were hammered into the AI brain per second so that it learns to understand. The following image shows the statistics from another AI training. It's nice to see the staircases indicating the success of the training. The learning rate goes down with time (here intentionally) just like in humans (where it is often not intended).