Jan Schweiger
Hybrid AI: Next Generation Natural Language Processing
#1about 1 minute
Why 90% of AI projects fail in production
Most AI projects fail to reach production due to challenges with accuracy, data quality, and robustness in real-world scenarios.
#2about 5 minutes
How modern NLP uses Transformer models for search
Transformer models understand the full context of a sentence, enabling semantic search by converting text into vectors for comparison.
#3about 1 minute
Why pure Transformer models fail in the real world
Transformer-only models often struggle in production due to inefficiency, reliance on domain-specific training data, and a lack of robustness.
#4about 2 minutes
The strengths of classical NLP and keyword search
Classical NLP methods like BM25 keyword search are computationally efficient, require no training data, and are highly robust across different domains.
#5about 1 minute
Combining models with the hybrid AI approach
Hybrid AI combines the high accuracy of modern NLP with the efficiency and robustness of classical methods to create superior production models.
#6about 3 minutes
How to build a hybrid search engine with Vespa
Vespa is an open-source tool that simplifies building hybrid systems by allowing you to define parallel search pipelines for Transformers and BM25.
#7about 2 minutes
Analyzing the performance of a hybrid search model
The hybrid AI approach was four times faster than a pure Transformer model while maintaining high accuracy and robustness.
#8about 2 minutes
Exploring other real-world use cases for hybrid AI
Hybrid AI can be used for expert identification by building correctable knowledge graphs and for safety-critical systems like train controls.
#9about 3 minutes
Recap and recommended tools for building NLP models
A summary of how hybrid AI balances deep learning's accuracy with rule-based systems' robustness, plus recommended libraries to get started.
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