What makes Artificial Intelligence become a driver of innovation
Generative Artificial Intelligence (AI) like the Large Language Models (LLM) represent novel technology that can be disruptive as well as highly innovative. It is worthwhile therefore to think about the innovation potential of AI in general and observe its evolution carefully. A suitable starting point is the simulative nature of LLM’s. They are models of the use of language based on empirical data taken from existing pools of e.g. text from manifold sources. They typically are applied to generate new text the validity of which is not sure without further acknowledgement. Basic feature of AI so is simulation for the purpose of structural or functional investigation. Not only in generative AI, but also in e.g. the application of digital twins for AI based manufacturing, the evaluation of achieved results is a must. There is good reason to accept that for AI systems of any kind, in order to make them enablers of sustainable innovation. Is that all? Or, putting it differently: how can we best use AI to support human intelligence, and vice versa? And what are promising fields of application?
The rationale of combining human and artificial intelligence builds upon basic limitations and strengths:
Human intelligence is lacking the capacity of utilizing massive data:
- Measurement and collection of large amounts of parameters are not practicable.
- The analysis of large data sets is inefficient.
- Algorithmic thinking is slow.
Artificial intelligence systems have principle limits:
- The formal systems they are based on cannot completely cope with reality.
- Mental capabilities like understanding, meaning, and insight are not available.
- Parameters and metrics depend on ex ante definition and selection.
The architecture of combining human and artificial intelligence rests upon the utilization of powerful data technology and the creative mind of humans:
Functions on the AI side would be:
- Collection and analysis of massive data.
- Pattern recognition, reasoning, and generalization.
- Machine learning procedures.
Human functions would be:
- Supervision and control of digitization and model design activities.
- Formalization and validation of assumptions and hypotheses.
- Formulation and proof of propositions.
The resulting concept of hybrid intelligence is presented and illustrated in our recent publication[1] . Further to the two examples described in this paper, from a general perspective there are diverse potential fields of application in science and engineering, including
- pro-active risk governance in the health sector,
- lifecycle enhancement of operation domain design in autonomous vehicles engineering,
- simulation using digital twins in manufacturing,
- smart electric grid management in the public utilities sector.
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Norbert JASTROCH
eMail norbert.jastroch@metcommunications.de
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[1] Jastroch, N.: Sustainable Artificial Intelligence: In Search of Technological Resilience. Springer 2023. https://doi.org/10.1007/978-3-031-25182-5_31 . PLM2022, Grenoble/France
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Commenti
Thank you for this very clear and insightful overview.
The distinction you make between the simulation capabilities of AI and the meaning-oriented nature of human intelligence is fundamental, especially now that generative models are increasingly used outside controlled environments.
From the work I am doing on hybrid human-AI knowledge systems, I see the same structural gap you describe:
AI excels at scale, pattern extraction and generalization, while humans remain indispensable for validation, conceptual framing, and grounding assumptions in real-world meaning.
One point that might complement your analysis is the need for verified human-generated micro-knowledge as a stabilising element.
As AI systems continue to train on vast quantities of uncurated data, they risk amplifying errors and drifting away from reality.
Introducing mechanisms for expert-reviewed inputs and traceable human contributions can significantly increase the resilience and reliability of hybrid intelligence.
This aligns well with the architecture you outline:
• AI handles data collection, modelling, simulation and pattern discovery.
• Humans formalize hypotheses, validate results, supervise assumptions and ensure semantic correctness.
But for this interaction to work at scale, the underlying knowledge must be:
• attributable (so that contributions can be validated),
• reviewable (so that errors can be corrected),
• and trustworthy (so that AI systems do not degrade).
In this context, hybrid intelligence becomes not only a conceptual model, but a practical framework for sustainable innovation in areas such as health governance, autonomous systems, manufacturing twins, and critical infrastructure management.
If the community or the Commission is interested, I would be glad to contribute further on validation mechanisms and human-verified knowledge architectures, which I believe are becoming essential for any long-term hybrid intelligence strategy.
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