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Filene Fill-In Ep. 50: Talking Algorithms and Algorithmic Systems with Dr. Nick Seaver

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Manage episode 233072146 series 1206040
Inhalt bereitgestellt von Filene Research Institute. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von Filene Research Institute oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.
What really is an algorithm? This episode dives a bit deeper into this topic that we’ve talked about here before and relates to much of our research on emerging technology. I wanted to know more, in ordinary terms, not only what an algorithm is, but how they work, and what the prevalence of the use of artificial intelligence and algorithms mean for consumer and cooperative finance. Filene’s Research Director, Taylor Nelms brings us his friend Dr. Nick Seaver, Assistant Professor of Anthropology at Tufts University, who has written a book about the making of algorithmic music recommendations, to provide us some clarity on this cloudy topic. In this episode, we explore how algorithms can be a series of steps to follow to turn an input into an output – that’s an algorithm, or it can be more complex machine learning software process that learns from the source data and makes use of it to provide a future prediction – that’s an algorithmic system. Nick also teaches us why you can’t just “set it and forget it” with these types of algorithmic systems, and what can go wrong if you do that. The coolest part is when we explore how amazing it would be for credit unions to be the ones to make data portable for their members, let them decide to participate in the data’s use and have power over how they are represented in the data… Just don’t be caught calling an algorithmic system an algorithm… Visit filene.org/trusttech for more information on the "Future of Trust" event mentioned in this episode.
  continue reading

82 Episoden

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iconTeilen
 
Manage episode 233072146 series 1206040
Inhalt bereitgestellt von Filene Research Institute. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von Filene Research Institute oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.
What really is an algorithm? This episode dives a bit deeper into this topic that we’ve talked about here before and relates to much of our research on emerging technology. I wanted to know more, in ordinary terms, not only what an algorithm is, but how they work, and what the prevalence of the use of artificial intelligence and algorithms mean for consumer and cooperative finance. Filene’s Research Director, Taylor Nelms brings us his friend Dr. Nick Seaver, Assistant Professor of Anthropology at Tufts University, who has written a book about the making of algorithmic music recommendations, to provide us some clarity on this cloudy topic. In this episode, we explore how algorithms can be a series of steps to follow to turn an input into an output – that’s an algorithm, or it can be more complex machine learning software process that learns from the source data and makes use of it to provide a future prediction – that’s an algorithmic system. Nick also teaches us why you can’t just “set it and forget it” with these types of algorithmic systems, and what can go wrong if you do that. The coolest part is when we explore how amazing it would be for credit unions to be the ones to make data portable for their members, let them decide to participate in the data’s use and have power over how they are represented in the data… Just don’t be caught calling an algorithmic system an algorithm… Visit filene.org/trusttech for more information on the "Future of Trust" event mentioned in this episode.
  continue reading

82 Episoden

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