Is It Machine Learning?

Meade: The way that unstructured data has been solved and that youv created business value withis is by foxusing in on some narrow part of that question. We actually use these sort of, next generation, i don't know, like newton x man language models, i might call them, right? But the idea is that when you look at a document and a, this is not only true of documents. Documents just happen to be an amazingly valuable use case. There's no amount of language modelling that will account for the differences in read order and anesting that you got there, right? Not not even to mention things like rich text and imbedded images,

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

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// 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 that 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 multifaceted, 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

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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

Timestamps:

[00:00] Introduction to Slater Victoroff

[03:52] Slater's journey into ML

[06:52] Birth of Indico

[07:04] "Unstructured data"

[09:47] Historical perspective journey of Indico

[11:13] Adoption of unstructured technologies

[16:05] Technology techniques

[21:40] Unstructured data challenges

[26:35] Changing Supervision

[28:35] Synthetic data and its role