AI LAB
Beginner tutorials for AIMS
- Notice:
Some projects might exceed the possible number of allowed data points or images for the version you are using. Downloading and calling is possible, but transformations are limited.
AI – for every use case:
Texts, images, tables…
On this page you will find video tutorials, descriptions and ready-to-download project files for direct import into AIMS.Take a look around and find your problem solution in the shown tutorials.
Text regression
Prediction of the next letter
In this video you can see how AIMS and your own text can be used to create an AI that tries to predict the next character after 100 characters.
You can try yourself and try to predict whole words, choose a German text and understand how to combine AI and text fundamentally.
- Text Regression
- Understanding Text AI
- Text preprocessing techniques
- Tokenizer
Text classification
Sentiment analysis
This test project shows how to proceed if you want to classify your own texts into certain categories.
These can be categories like “Good | Bad”, or much more complex ones. For example, if you want to pre-filter your emails and automatically classify them into “Technical question | Product question | Spam | Customer feedback”.
There are no limits to your imagination in this respect.
- Text Classification
- Understanding Text AI
- Text preprocessing techniques
- Sentiment analysis
- Countvectorizer
- Tokenizer
- Notice:
After downloading AIMS, you can start directly with GO BASIC.
If you want to use more freedoms within the software and work faster, there is the possibility of a free registration.
Other software features, such as:
- Training with GPU
- Multi ThreadedData Processing
- AI Wizards
can be purchased within the software.
Image segmentation
Recognize objects in images
This tutorial shows how to use AIMS to solve image segmentation problems with AI.
You use your own images and draw in the objects that you want an AI algorithm to recognize later. In addition to the balloons shown here, these can also be other objects, such as defective spots on products, people, or any other form of object.
Image recognition thus becomes fully autonomous and fully automatic in no time at all.
- Image segmentation
- Understanding Image AI
- Image Annotation
- Image Augmentation
Image classification
Classify whole images
If you have less complex image recognition tasks to solve and do not want to mark the objects directly pixel by pixel in the image, you can easily classify entire image stacks into classes via AI.
This procedure is perfectly suited for IO | nIO classification in production processes or for assigning people, animals, and objects to certain groups.
- Image Classification
- Understanding Image AI
- Image Labeling
Measured value classification
Classify numerical values in a meaningful way
The following tutorial was about using a .CSV file in which measured values of orchid flowers were tabulated.
If a collection of measured values is typical for a certain class, an AI algorithm can be trained effectively and quickly that recognizes the correlations between these values and also evaluates which class it is in the future based on unseen data.
- Numerical classification
- Understanding Measurement AI
- Measurement Labeling
- Encoding
- Scaling
We also program your tailored AI solution for you!
If you don’t want to or can’t deal with the programming and configuration of your AI solution yourself, our specialized programmers are also there for you.
–
Just contact us to discuss your request!
Time series forecast
FinTech Autotrading AI for stock market trading
In this “Stock Predictor” example, we would like to show how AIMS can be used to automatically predict trading recommendations based on past market patterns.
For this purpose, we used historical data of the DJI index to elicit a time series prediction from an AI. As a result, we get a statement about how likely a buy, hold or sell scenario is.
The goal of all this is to derive statements about system behavior or make predictions based on a table of measurements or data in general. This is not only useful in the financial technology area, but also for predictive maintenance, condition monitoring and quality control.
- Time Series Analysis
- Understanding time series AI
- Sequencing
- Encoding
- Scaling
- Resampling
Anomaly detection
Condition Monitoring and Predictive Maintenance
The adjacent tutorial deals in four parts with the structure and functionality of a self-generated AI for process and/or machine monitoring.
Similar to a digital twin, it is thus possible to detect system deviations even if they have never occurred in this form before.
The so-called “autoencoder” is often the tool of choice for this purpose. This special form of neural networks also offers other possibilities. Here, we will primarily examine the use case that seems most relevant for Industry 4.0.
- Anomaly detection
- Understanding of autoencoders
- Extensive data preparation
- Scaling
- Data preparation and presentation
Quality assurance
Io | nIO detection of component defects in production
In the following tutorial we show how to use machine data to evaluate whether a process leads to scrap or not. It is based on this article in cooperation with the Competence Center Mittelstand 4.0 and the Department of Manufacturing Engineering at TU Ilmenau (link to publication).
On 30.09.2021 there will be an AI cooking show related to exactly this project, if you are interested you can find all information here:
Link to Komeptenzzentrum
- Time Series Analysis
- Understanding time series AI
- Sequencing
- Encoding
- Scaling
Wizards
AIUI opens up the field of artificial intelligence to all users. Not familiar with the field of AI? No problem! Through our wizards, even users without special prior knowledge or programming skills can train and use AI systems.
Custom Nodes
The integration of additional code is possible through Custom Nodes. These special and modifiable components of our AI solutions can be flexibly adapted to your specific requirements and individual needs.
Asset Management
AIMS provides management for all your datasets, AI models, architectures, integration flows, and workbench projects. Each of these assets can also be exported or imported. This allows you to share your solutions with others or benefit from the solutions of others.
Data-Transformation
AIMS offers you a wealth of predefined data transformations. However, if you cannot find the right one for your use case, you can write your own nodes starting with version 1.0.
Workbench
The AIMS Workbench invites you to experiment: Use it for analysis and data processing as well as model training and evaluation.
Tensorflow compatible
AIMS uses Tensorflow/Keras as Backend. Thus, all generated models can also be integrated into your own Python scripts.
Integration Flow Designer
Using our Integration Flow Designer, you can simply drag and drop an interface for your models and integrate them into your existing business environment using REST API.
Privacy
AIMS is a rich client application. This means that the entire application and all data is with you on your computer. Except for your login, we do not store any information about how and when you use AIMS.
S.T.A.M.
The Systematica and touchable architecture modeling designer helps you understand and create complex model architectures. Using transfer learning, you can integrate existing AI models into new AI models or decompose them into their individual neural layers to reuse only parts.
REST-API
AIMS uses a client/server architecture. This means that our AI services can be used independently of the UI. Send your data from an external application directly to AIMS using the REST interface.