The interaction between our changing behavior, our society, and the new landscape of Software Services.
How will the digital transformation and emerging technologies like Artificial Intelligence (AI) and Machine Learning change society? And how will our changing behavior and our increasing demands lead the development of these technologies? We explore the interaction between these complementary views.
Cost effective and vast computing capacity in combination with new ways of managing information enable the implementation of these techniques in an increasing number of applications. It puts new requirements on technology areas such as data analytics, algorithm design, AI, AR/VR, machine learning, sensor control, robotics/mechatronics and complex architecture management. In combination with Internet of Things (IoT) it creates a base for a completely new business landscape, enabling an automated learning society:
- A patient’s health condition is measured via the hospital’s connected instruments. If the patient’s condition changes, it is compared with historical data and measures. Improvements in treatment methods are instantly shared with other hospitals and patients.
- A vehicle with autonomous driving capabilities makes a mistake and learns locally, but the behavior is transferred to all other self-driving cars.
- All components in an urban energy system will be connected, from rectifier to every lamp. If a part of the power net fails measures will be taken and reported to all other utilities.
- Gaming technologies are used in automotive and educational applications.
New possibilities follow new responsibilities; ethical, safety as well as moral aspects, regarding data- collection, exchange and ownership as well as interaction of software systems with humans. We have the data and the computational capacity but do we have the development and monitoring capabilities to handle all aspects End To End?
- Will development-tools in the cloud enable ecosystems of collaborators - while protecting IP of contributors?
- How do we do Verification/Validation/Quality Assurance for AI-intensive/learning systems?
- We will do more monitoring of the behavior of running systems to obtain feedback on the design and operation. How do we instrument, collect and analyze systems? While preserving user privacy?