Artificial Intelligence (AI) is being much discussed, with visions of the future between apocalypse and utopia. AI is often equalized with statistical machine learning, but it has much more to offer: optimization and search algorithms plan complex processes, sensory networks collect data and supervise environmental factors and production processes, agent architectures abstracts from single tasks towards interactive systems. In spite of all successes many basic concepts of intelligence and cognition are still unknown.
I assist my clients in evaluating the opportunities and limits of artificial intelligence in the context of their busniess objectives.
Data Science is a young discipline that employs methods of machine learning to extract information from an ever growing mass of data. Beyond single learning algorithms, the results depend mostly on the modelling and preprocessing of the data. Since the algorithms are based on statistics, data science also requires new ways of interpreting the results. In contrast to typical software the results are less reliable and explainable.
Since my PhD thesis I have employed machine learning for processing and evaluating data in application contexts.
User Experience Design
User Experience Design takes the viewpoint of users. Press releases about autonomous cars, natural language assistants and household robots convey a picture that any task could and should be performed by machines. However, such reports overlook the gap between a research result or an early phase product and a reliable application for anyone to use. When half-baked technology is spread, both users and deployers suffer, feeling like a slave of some gadget.
Good design and usability are just as important as state-of-the-art AI methods.
- joint identification of potential use cases of AI techniques in your company: examples of applications, joint brainstorming, elaboration of use cases;
- assessment of the level of maturity of AI techniques: research of similar application cases, prototyping for specific tasks;
- development of solution options for specific problems: pros and cons, cost assessment, necessary know-how and other resources.
- Direct application of up-to-date scientific results
- Assessment of techniques in specific contexts, including usability
- Experience from projects in science and business
- for decision makers
- High-level introduction to machine learning and artificial intelligence
- Design thinking for management
- The business case for usability engineering
- for implementers
- AI programming: Basics of Python or Clojure/LISP
- Machine learning in Python or Clojure/Java
- Agile software development and design thinking
- Usability engineering
- Maximum quality of long-standing experience in university teaching and international courses
- Individual adaptation of each training to your specific situation (e.g. planned project, prior knowledge of participants)
- After-training support: call back service for questions arising afterwards; option for extended coaching on the job in a project
- Coaching on the job: Coaching in the course of a project, ideally following a training
- Project prototyping: Testing of AI or usability techniques in a specific project context
- Experience with well-established and lesser-known AI programming languages: Clojure, Lisp, Java, Python, Prolog
- Maintaining own open source project of a decision architecture (Heuristic Problem Solver)