Near Term Future of Neural Networks

Hello,

I’m happy to provide more info on the BI team’s our work!  We’re in the midst of writing a reporting SQL VIEW layer in the impresario database that will denormalize (demystify) the impresario schema.  These data reporting views will be the foundation of our future BI tools.   We’re also working on the integration of a reporting and analytics suite from Sisense into Tessitura.  This is extremely exciting, Sisense’s tools are world-class!  Lastly we’re working on the next generation web-based List Manager and Output sets UX.  There’s a lot of exciting work going on.  I’ll be providing much more information in a webinar on October 26th.  Sign-up here - http://www.tessituranetwork.com/network/Learning/Webinars/Upcoming%20Webinars.aspx

Flavia,

You are not alone.  I know that we spend some time here at BAM doing the same kinds of thing.

I also know that John Jakovich and the network team are working on standardizing part of the question of making data easier to get at from Tessitura. 

John I think you are on the list here with us, can you give us some further insights on some of the things you and the team are working on, around making getting to data easier.  I know you retweeted one of my comments last week.  https://twitter.com/John4NpoTech/status/783759790308589568 related to this subject.

Yes, really interesting. But at the end of the day, capturing the right data in the right format with the right labels/tags, is still so difficult, and the tools all need that.

From: Self-service Business Intelligence [mailto:groups-selfservicebi@tessituranetwork.com] On Behalf Of Samantha Erenberger
Sent: Wednesday, October 12, 2016 10:10 AM
To: Flavia Barros <fbarros@amnh.org>
Subject: Re: [Self-service Business Intelligence] Near Term Future of Neural Networks

It's really interesting how data processing seems to be a cornerstone but also a limitation of AI. SNN seems to have the capacity to see more of the forest rather than the trees, but CNNs are a more useful tool at least in terms of aggregation and classification. 

Hello,

I’m happy to provide more info on the BI team’s our work!  We’re in the midst of writing a reporting SQL VIEW layer in the impresario database that will denormalize (demystify) the impresario schema.  These data reporting views will be the foundation of our future BI tools.   We’re also working on the integration of a reporting and analytics suite from Sisense into Tessitura.  This is extremely exciting, Sisense’s tools are world-class!  Lastly we’re working on the next generation web-based List Manager and Output sets UX.  There’s a lot of exciting work going on.  I’ll be providing much more information in a webinar on October 26th.  Sign-up here - http://www.tessituranetwork.com/network/Learning/Webinars/Upcoming%20Webinars.aspx

Flavia,

You are not alone.  I know that we spend some time here at BAM doing the same kinds of thing.

I also know that John Jakovich and the network team are working on standardizing part of the question of making data easier to get at from Tessitura. 

John I think you are on the list here with us, can you give us some further insights on some of the things you and the team are working on, around making getting to data easier.  I know you retweeted one of my comments last week.  https://twitter.com/John4NpoTech/status/783759790308589568 related to this subject.

Yes, really interesting. But at the end of the day, capturing the right data in the right format with the right labels/tags, is still so difficult, and the tools all need that.

From: Self-service Business Intelligence [mailto:groups-selfservicebi@tessituranetwork.com] On Behalf Of Samantha Erenberger
Sent: Wednesday, October 12, 2016 10:10 AM
To: Flavia Barros <fbarros@amnh.org>
Subject: Re: [Self-service Business Intelligence] Near Term Future of Neural Networks

It's really interesting how data processing seems to be a cornerstone but also a limitation of AI. SNN seems to have the capacity to see more of the forest rather than the trees, but CNNs are a more useful tool at least in terms of aggregation and classification.