Dr. Peter Schentler
People tend to automatically classify AI as human-like AI, which is an egocentric view. They assume human intelligence signifies 100% on the intelligence scale, which conveniently hides the true AI potential. Machines which feature multi-faculty intelligence, called General AIs, are certainly not nearly the end of the yardstick. Machine AI will be much more different and much more complex as it covers extra-human sensor perception and can resort to several complementary processing substrates or architectures. With new technologies such as neuromorphic chips or quantum compute architectures on the horizon AIs will definitely be able to think beyond human scales.
Super-human AI has already been achieved in many aspects today. However, these AIs are hyper-specialized and their capability spectrum is very narrow and mimic or outperform but single human faculties
Right from its inception the Steering Lab has taken a new path to develop a more comprehensive mind architecture and created its own AI framework, COGITAAURUM. On the one hand we have taken a closer look at human learning and on the other, we have incorporated more von Neumann features into our models. This has opened up new AI faculties and enables hyper-efficient learning, which is key to edge intelligence and very important to autonomous systems in general including energy efficient, confined bandwidth and space learning as well as distributed hive-learning.
We work together with universities and the R&D units of our clients to embed state-of-the-art AI capabilities to spaces and products alike.
We also work on advanced hierarchical intelligence concepts which will rapidly evolve into complex AI organizations and form part of hybrid human-bot organizations called HybOrgs. This again reflects that intelligent machines will not simply replace the human work force over-night but rather work in a very immersive way with humans for many decades to come. Algorithmic governance as part of the Digital Governance is
For businesses, deploying AI in their processes most certainly will prove much more challenging than Predictive Analytics or Optimization Analytics solutions. The knowledge gap between data science and business processes and the missing operationalization concepts of AI are two of the main obstacles for I-Spaces and I-Products to evolve and flourish. Setting AIs to work requires a lot of expectation management and psychological finesse. we have understood this holistic challenge of AI and developed a more subtle and more natural way of endowing processes and products with the breath of intelligence.