Innovation Pipeline with Prof. Steve Masiclat

Innovation Pipeline with Prof. Steve Masiclat

 
 
00:00 / 00:34:36
 
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On the show today we welcome Professor Steve Masiclat (Newhouse School, Syracuse University) to explain his ideas around the innovation pipeline and the issues he sees arising when it comes to reaching the next level of disruptive innovations. Buckle up for this episode, as we are diving deep into AI and big data. In our discussion we cycle through many examples from the technology world, in particular AI, and use these to explore what Steve argues is a lack of sufficient expertise to further the fields of research study that are currently available to us. We talk about deep neural nets, uneven distribution of technology and expertise, and the slowing effect this may have in the tech fields. We also discuss the different arenas in which research takes place, contrasting the strengths and weakness of academia, industry and startups. Lastly, Steve shares some perspective on his years of teaching and things that have changed over time. He contrasts student attitudes, technologies and social settings. For all this and a whole lot more, be sure to join us today on the For Your Innovation podcast!

 

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Key Points From This Episode:

  • The issue that Steve is identifying in the innovation pipeline.
  • The example of signal degradation in deep neural nets
  • Misclassification and the care that’s needed for variables
  • The lack of real experience wielding necessary mathematical tools
  • A slowing down in technological research advancements
  • The uneven distribution of futuristic tech advancement
  • Disparity between interest and necessary qualification in the data field
  • Steve’s thoughts on the industry driven research initiatives
  • Academia, startups and pushing the frontiers of knowledge
  • Contrasting attitudes towards tech over the last ten years
  • Do we still need universities?

Tweetables:

“One of the things I noticed is there are many calls for building and extending our educational capacity. Especially in advanced science, advanced data science and mathematics.” —  @masiclat [0:04:19]

“I’m just looking at a capacity problem. Who is going to do the research?” — @masiclat [0:16:39]

“While there may be a pipeline issue in my industry in education, there is no issue in human ingenuity.” —  @masiclat [0:34:03.0]


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