BMT Business meets Technology Consulting AG

Provision of intelligence services that were previously reserved for humans - artificial intelligence

Artificial Intelligence (AI)

Artificial intelligence, machine learning and data analysis are among the pioneering driving forces of the digital revolution in the 21st century.

Data is the raw material of the future, which increasingly determines competitive advantages and, unlike traditional raw materials, is not used up.

In the data age, a large part of the world's population is networked and communicates with machines, cars, houses and other devices on the Internet of Things (IoT).

Unfortunately, about 90% of currently available data is still unstructured or partially structured, which prevents or reduces its usability for machine learning.

Therefore, it is all the more important for companies to adjust data acquisition to the collection of useful semantic data in good time in order to avoid wasting valuable capital and to save the high costs of subsequent manual semantification.



Artificial Intelligence (AI)
Artificial intelligence is the generic term for intelligence services that were previously reserved for humans.

"Machine Learning", "Deep Learning", "Natural Language Processing" (NLP) and "Neural Networks" are sub-areas of artificial intelligence or "Artificial Intelligence (AI)".


Machine Learning (ML)
Machine learning describes mathematical techniques that enable a system (machine) to independently generate knowledge from experience.


Deep learning and artificial neural networks
"Deep Learning" is a sub-area of machine learning. Sometimes the terms "deep learning" and artificial neural networks are used synonymously.


"Deep Learning" works with particularly deep artificial neural networks in order to achieve very effective learning success with the help of large amounts of data. Using neural networks, the machine enables itself to recognize structures, to evaluate this recognition and to improve itself independently in several forward and backward passes of the adaptation of links.

The neural networks are divided into several layers.
"Deep Learning" relies on statistical data analysis and not on a deterministic algorithm. Statistical data analysis is required whenever there are no clear rules for linking data, such as in image recognition (classification of image data) or analysis of audio and video data.

AI & Data Science Workshop

The application possibilities and areas of artificial intelligence range from prognosis, image recognition, face recognition, anomaly recognition, pattern recognition, recommendation services, etc. up to the optimization of almost all conceivable processes.

Our workshop is intended to give your employees a comprehensible insight into the potential of data, its analysis and forecasting with the help of machine learning.

Don't miss out on the latest developments in digitization. Learn everything about data acquisition, processing, analysis, visualization, reduction, modeling, and forecasting.

After a general overview and insight into essential theoretical basics, we will examine your personal data situation with you and point out possibilities for optimising acquisition and exploitation so that you can fully exploit your potential.



We offer workshops in the following areas:

Data Science, ML and AI
- Overview and History

- Data potential
- knowledge discovery
- Big Data (Mining and Visualization)
- KI applications (robots, sensors, inference)

ML applications
- Machine vision (image recognition, video analysis, etc.)
- Speech and text (speech analysis and synthesis)
- computational linguistics
- recommendation services
- time series analysis

ML Methods
- characteristic analysis
- Observed learning (regression, classification, etc.)
- In-depth learning (neural networks, transfer learning, etc.)
- Unobserved learning Cluster analysis, dimension reduction, etc.)
- Partially supervised learning (active learning, feedback, etc.)
- Encouraging learning (game theory, Markow decision problem, etc.)

AI and society
- Opportunities and risks
- Autonomous driving
- AI in health care
- AI in the legal system
- AI in education

The composition and depth of the workshop can be individually arranged.


Example workshop:

Active Learning for semantification of unstructured data:
How to use your unstructured data for machine learning and AI applications
- ML and KI applications
- Unstructured data
- Semantification through annotation and labeling
- Active Learning


Machine Learning is not possible with unstructured data! We will show you how to structure and semantify your data and make it usable for ML applications.



BMT Business meets Technology
Consulting AG

+41 71 688 87 87