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The Future of AI and ML in Process Automation // Slater Victoroff // MLOps Coffee Sessions #64

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Manage episode 313294415 series 3241972
Content provided by Demetrios. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Demetrios or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

MLOps Coffee Sessions #64 with Slater Victoroff, The Future of AI and ML in Process Automation.
// Abstract
The Unstructured Imperative
Recent advances in AI have dramatically advanced the state of the art around unstructured data, especially in the spaces of NLP and computer vision. Despite this, the adoption of unstructured technologies has remained low. Why do you think that is? How have the dynamics changed in the last five years?
Multimodal AI
Historic AI approaches have generally been constrained to one data modality (i.e. text or image). Recently, a wide range of papers in image captioning and document understanding have emphasized the need for more sophisticated "multimodal" techniques which can fuse information from multiple modalities. What is multimodal learning, and why is it so promising? Why are we seeing such an explosion of activity? What is Indico doing in this space?
Machine Teaching
As methods of supervision become more complex and multi-faceted, many researchers have begun investigating the inverse problem. That is how do we design supervision systems that more naturally follow human processes? What are some interesting trends in "the space", and where can we expect this field to go in the next few years?
// Bio
Slater Victoroff is the Founder and CTO of Indico, an enterprise AI solution for unstructured content that emphasizes document understanding.
Slater has been building machine learning solutions for startups, governments, and Fortune 100 companies for the past seven years and is a frequent speaker at AI conferences.
Indico’s framework requires 1000x less data than traditional machine learning techniques, and they regularly beat the likes of AWS, Google, Microsoft, and IBM in head-to-head bake-offs.
// Relevant Links
https://indico.io
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, Feature Store, Machine Learning Monitoring and Blogs: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Slater on LinkedIn: https://www.linkedin.com/in/slatervictoroff

  continue reading

441 episodes

Artwork
iconShare
 
Manage episode 313294415 series 3241972
Content provided by Demetrios. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Demetrios or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

MLOps Coffee Sessions #64 with Slater Victoroff, The Future of AI and ML in Process Automation.
// Abstract
The Unstructured Imperative
Recent advances in AI have dramatically advanced the state of the art around unstructured data, especially in the spaces of NLP and computer vision. Despite this, the adoption of unstructured technologies has remained low. Why do you think that is? How have the dynamics changed in the last five years?
Multimodal AI
Historic AI approaches have generally been constrained to one data modality (i.e. text or image). Recently, a wide range of papers in image captioning and document understanding have emphasized the need for more sophisticated "multimodal" techniques which can fuse information from multiple modalities. What is multimodal learning, and why is it so promising? Why are we seeing such an explosion of activity? What is Indico doing in this space?
Machine Teaching
As methods of supervision become more complex and multi-faceted, many researchers have begun investigating the inverse problem. That is how do we design supervision systems that more naturally follow human processes? What are some interesting trends in "the space", and where can we expect this field to go in the next few years?
// Bio
Slater Victoroff is the Founder and CTO of Indico, an enterprise AI solution for unstructured content that emphasizes document understanding.
Slater has been building machine learning solutions for startups, governments, and Fortune 100 companies for the past seven years and is a frequent speaker at AI conferences.
Indico’s framework requires 1000x less data than traditional machine learning techniques, and they regularly beat the likes of AWS, Google, Microsoft, and IBM in head-to-head bake-offs.
// Relevant Links
https://indico.io
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, Feature Store, Machine Learning Monitoring and Blogs: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Slater on LinkedIn: https://www.linkedin.com/in/slatervictoroff

  continue reading

441 episodes

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