In our automated lives, we generate and interact with unprecedented amounts of data. This sea of information is constantly searched, catalogued, analyzed and referenced by machines with the ability to uncover patterns unseen by their human creators. These new insights have far reaching implications for our society. From our everyday presence online, to scientists sequencing billions of genes or cataloging billions of stars, to cars that drive themselves – this series of six lectures will explore how the confluence of humans, data and machines extends beyond science – raising new philosophical and ethical questions.
All content for Humans, Data and Machines is the property of University of Arizona and is served directly from their servers
with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
In our automated lives, we generate and interact with unprecedented amounts of data. This sea of information is constantly searched, catalogued, analyzed and referenced by machines with the ability to uncover patterns unseen by their human creators. These new insights have far reaching implications for our society. From our everyday presence online, to scientists sequencing billions of genes or cataloging billions of stars, to cars that drive themselves – this series of six lectures will explore how the confluence of humans, data and machines extends beyond science – raising new philosophical and ethical questions.
Nirav Merchant, Director Data Science Institute, Data7, University of Arizona
Machine learning (ML) based systems are rapidly becoming pervasive, powering many applications from recommending music, movies and merchandise to driving our cars to assisting in medical diagnoses. Our daily interactions, behavior, and choices, whether we are aware of them or not, are the sources of data for training these systems. But how are these ML based platforms built and utilized ?. While ML based platforms create amazing opportunities, especially when coupled with advances in cloud computing, reliance on these platforms comes with ethical, security, and technical concerns. How do we strike a balance for enabling pragmatic and productive use of these capabilities? ML powered platforms are gaining proficiency and becoming deeply integrated into existing and emerging automation across many domains of science and society, causing a shift in opportunities impacting many professions. What are the new learning and training opportunities that allow us to stay relevant and lead the way for future innovations
Humans, Data and Machines
In our automated lives, we generate and interact with unprecedented amounts of data. This sea of information is constantly searched, catalogued, analyzed and referenced by machines with the ability to uncover patterns unseen by their human creators. These new insights have far reaching implications for our society. From our everyday presence online, to scientists sequencing billions of genes or cataloging billions of stars, to cars that drive themselves – this series of six lectures will explore how the confluence of humans, data and machines extends beyond science – raising new philosophical and ethical questions.
