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The topic will be introduced by Jessica Groopman (Founder & Industry Analyst at Kaleido Insights):
Current machine learning is only as "intelligent" as the data we feed it. It produces an output based on the data we expose it to, and the parameters we define. But just because the machine says X is 90% likely, does not mean it is so. The machine infers statistical likelihood based on data, which itself is typically limited to explicit (codifiable) knowledge.
Yet when humans receive that machine-generated output– be it a recommendation, diagnostic, worthiness score, or otherwise– from an AI-powered application, little regard is given to this grey area. By grey area, we refer to the fact that an AI output is an inference, but the use of that output becomes a reference.
In the context of all the layers of context collected about us by the Internet of Everything, inferencing is not just about the reliability of recommendations, but about inferencing for missing context, which blurs the lines of Truth itself. Consider the image above. That outer layer is rich with context used for inferencing eye tracking data. When inferences become references, we assume the individual's [personality traits, mental health, (pink labels) etc.] are what the algorithm tells us they are.
Discerning between inferences and what we humans reference is a critical tension for our time; the more we rely on big data, the more we rely on AI to reduce dimensionality to make sense of it. To learn more about impact areas, and how to apply this thinking to your AI initiatives, we have invited:
- Mick Jongeling (Lead & Ambassador at Speculative Futures Amsterdam)
- Rob van Kranenburg (Founder of Iot Council & iotday; working on disposable identities for NGI.eu)
- Lorna Goulden (Digital Experience Principal; initiator of IoT Eindhoven)
- Iskander Smit (innovation @info.nl, organising Thingscon Amsterdam)
for discussion and hope you join in as well!!
Image from: https://rd.springer.com/chapter/10.1007/978-3-030-42504-3_15#enumeration
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