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Study mode:
on
1
Intro
2
What is a Mode
3
Why Everyone Gathers Data
4
Outliers
5
Assumption of Distribution
6
NonLinearities
7
Statistics vs AI
8
Vision vs Text
9
Netnet
10
Demo
11
Analog Gauges
12
Conversational Systems
13
Architecture
14
Example
15
How did they get the yes
16
Where did they get the data
17
Training data set
18
Mechanical Turk
19
Catastrophic forgetting
20
repeatability crisis
21
why I love this space
22
eigenvalue decomposition
23
Visual inspection
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore multimodal generative AI in this comprehensive 1-hour 38-minute conference talk from Microsoft Reactor Bengaluru. Delve into the technical aspects of training generative AI systems that handle multiple input types simultaneously, including text, image, and audio. Learn about business applications, limitations, and associated costs of these advanced systems. Gain insights into the open-source LLaVA (Large Language-and-Vision Assistant) multimodal system. Discover key concepts such as data gathering, outliers, nonlinearities, and the differences between statistics and AI. Examine practical examples like analog gauges and conversational systems. Understand the architecture, training data sets, and challenges like catastrophic forgetting and repeatability crisis. Investigate advanced topics including eigenvalue decomposition and visual inspection techniques. Access the accompanying presentation slides for a deeper understanding of the material covered.

Multimodal Generative AI: Technology Overview and Business Implications

Applied Singularity
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