Unknowing and Unknowable | Last Chance for Super Early Summit Tickets
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In this episode, we talk with Barbara Tversky about spatial thinking as the foundation of abstract thought, the linearity of spaces and perception of distances, putting thought into the world, and the creative power of sketching.
In this episode, we talk with Megan Brown, the Director of Data Science for Starbucks’ Global Center of Excellence.
In this episode, we talk with Peter Sterling, the author of What is Health. Peter caught our attention with his concise and understandable description of how evolution, by optimizing for energy efficiency, has built human brains.
Making a good decision implies that we have some idea of what’s true. But we do not have infinite data inputs or processing capacity. We are limited by our lifetimes.
Caring machines may be the only way to scale empathy across our species.
Now that more machine learning-based AI has been deployed in more places, human skills are being replaced in finer slices with new automation technologies. What has been observed in traditional blue collar work is that not all AI is good enough to increase the value of the output.
In this episode, we talk with Stephen Fleming, Professor of Cognitive Neuroscience at University College London, about his book, Know Thyself.
Covid gave us a chance to read—a lot! As we prepare for the holidays (and factoring in supply chain issues and shipping delays) we thought we’d share the books that have had the most influence on our ideas about the emerging human-machine community this year.
How AI could help our reasoning when it's most flawed: aka when we're subject to cognitive biases.
Resolving a fundamental incompatibility with AI in human decision-making
Artificiality Co-founders, Helen and Dave Edwards, gave a presentation at the House of Beautiful Business titled: Mind for our Minds.
Most decisions and most deciders are hybrids. Some machine, some human. The trick is to imagine all the ways that humans figure out ways around, over, and through the machine when what they really want is to make the decision themselves even if it means sacrificing accuracy.