Introducing AI in products and services is a challenging process in many respects. From applications in medicine to autonomous driving, AI requires huge amounts of data to learn to perform its tasks. Data acquisition and labeling are both time consuming and demanding in terms of human resources. Still, it is crucial for developing reliable AI-based solutions.
The development of AI-based solutions involves computer simulation throughout the process, from the first proof of concept to the final deployment. Computer simulation can help to quickly try the first data-set to see if an idea is feasible, or to enrich data of complex systems. Using computer simulations requires understanding physical processes and their mathematical formulation, as well as the level of complexity required to be able to efficiently turn ideas into business.
In medical applications simulation can be a way to deal with the challenges of modelling complex biological systems or a lack of large-scale clinical data. Similar issues arise when developing autonomous vehicles. It has been estimated that an autonomous vehicle must travel 4 million km to encounter all possible risk situations. Computer simulation offers the possibility to teach autonomous vehicles how to tackle risky situations before they drive in the real world.
Missing or biased data influence the way an AI model can make decisions outside historical data, limiting the capability of AI to act in unknown situations. Simulation has the potential to help AI predict behavior that has not been seen before in collected data.
Guiding companies to AI-based solutions is much more then training a machine learning model, however complex this task is. It is also a question of selecting a working data strategy, which, by balancing expensive data collection and data simulation, can speed up the introduction of new products to investors, as well as boost the reliability of the final products. Selecting the right data strategy means, for example, to explore which data are required and available, how much data is needed and possible to collect, how it is possible to avoid collecting corrupted and biased data and how simulation and augmentation can help mitigate the problem of biased or scarce data.
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